SENSORY AND VOLATILE ANALYSIS OF RED DRUM (SCIAENOPS OCELLATUS) GROWN IN RECIRCULATING AQUACULTURE SYSTEMS (RAS) AND INTEGRATED

MULTI TROPHIC AQUACULTURE SYSTEMS (IMTAS)

By

DANIEL W. CLARK

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2017

© 2017 Daniel W. Clark

To my Mom and Dad

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ACKNOWLEDGMENTS

First I would like to thank my advisor, Dr. George Baker, for giving me the

opportunity to pursue my master’s degree under his guidance, he has shown me how a

good leader and boss treats those he works with as he provided me an environment

that encouraged me to learn and grow as a person. I would like to thank the Food

Science and Human Nutrition Department for providing the stipend that allowed me to

focus on my research, the Yeomans group for paying for my tuition, and Dr. Baker for

providing the remainder of the funding for my graduate program. I would also like to

extend my sincere appreciation to the rest of my supervisory committee: Dr. Kevan

Main, Dr. Paul Sarnoski, and Dr. Charles Sims, for their guidance and expertise

throughout my studies. A special thanks goes to Sara Marshall, Dr. Asli Odabasi, and

the FSHN Taste Panel Staff for their assistance with sensory evaluations. I would also

like to thank my lab mates past and present for all of their help, suggestions, and

friendship which is what got me through to the end: Ruby and Caity for all of our

hilariously random, meaningful talks and adventures, Rui who always was good for a

laugh. I would also like to thank my best friend and gym partner, Zack Carter, as well as

Zach Goins, Devin Gregory, Conner Rogers, Joey Olivera and the rest of the Bromance

for being there for me through all of this and for all of the great adventures and late night

beers we’ve shared. And last but definitely not least, I would like to thank my parents,

Dale and Diane, and my brother, David, for their never ending love, patience, and

support in all of my endeavors.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

LIST OF ABBREVIATIONS ............................................................................................. 9

ABSTRACT ................................................................................................................... 11

CHAPTER

1 INTRODUCTION .................................................................................................... 13

2 LITERATURE REVIEW .......................................................................................... 16

Fish (Red Drum) ..................................................................................................... 16 Habitat and Spawning ...................................................................................... 17 Environmental Physical Tolerance ................................................................... 18

Red Drum Production ............................................................................................. 19 Types of Production Phases ............................................................................. 19

RAS at Mote Marine Aquaculture Park (MAP) ........................................... 20

Red drum market and trade ....................................................................... 20

Red drum composition ............................................................................... 21 Aquaponics ....................................................................................................... 22

Flavor Identification ................................................................................................. 24

Off-flavors in RAS ............................................................................................. 24 Gas Chromatography ....................................................................................... 25

Mass Spectrometry .......................................................................................... 27 Gas Chromatography / Mass Spectrometry (GC/MS) ...................................... 28 Solid Phase Microextraction ............................................................................. 29

Headspace aroma capture using a gas tight syringe ........................................ 34 Sensory Analysis .................................................................................................... 35

3 MATERIALS AND METHODS ................................................................................ 54

Sample Collection ................................................................................................... 54

Sample Preparation ................................................................................................ 54 Sensory Evaluation ................................................................................................. 56

Trained Panel ................................................................................................... 56 Trained Panel Testing and Data Collection ...................................................... 58 Consumer Panel ............................................................................................... 59

Volatile Analysis ...................................................................................................... 60 GC-GC/MS analysis ......................................................................................... 60

6

SPME Extraction of Headspace Volatiles .................................................. 60

Gas Tight Syringe Extraction ..................................................................... 60 Statistics ................................................................................................................. 61

4 RESULTS AND DISCUSSION ............................................................................... 64

Sensory Evaluation ................................................................................................. 64 Trained Panel ................................................................................................... 64 Consumer Panel ............................................................................................... 64

Volatile Analysis ...................................................................................................... 65

HS-SPME ......................................................................................................... 65 DI-SPME .......................................................................................................... 67 Gas Tight Syringe Headspace Extraction ......................................................... 68

5 CONCLUSIONS ..................................................................................................... 75

APPENDIX

A SAMPLE BALLOT FOR TRAINING SESSIONS FOR TRAINED TASTE PANEL .. 77

B SAMPLE BALLOT FOR TRAINED TASTE PANEL SENSORY EVALUATION ...... 78

C BALLOT FOR CONSUMER PANEL SENSORY EVALUATION ............................. 79

LIST OF REFERENCES ............................................................................................... 82

BIOGRAPHICAL SKETCH ............................................................................................ 94

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LIST OF TABLES

Table page 2-1 Typical length vs age for red drum ..................................................................... 41

2-2 Vitamins in red drum fillet per ~200g .................................................................. 42

2-3 Minerals in red drum fillet per ~200g .................................................................. 42

3-1 Attribute reference samples ................................................................................ 62

4-1 Mean aroma attribute scores for trained panels-raw samples ............................ 70

4-2 Mean aroma attribute scores for trained panels-cooked samples ...................... 70

4-3 Mean flavor attribute sensory score for trained taste panel ................................ 70

4-4 Consumer sensory data ..................................................................................... 71

4-5 Volatile compounds identified by GC-GC/MS samples extracted with HS-SPME ................................................................................................................. 72

4-6 Volatile compounds identified by GC-GC/MS samples extracted with DI-SPME ................................................................................................................. 73

4-7 Volatile compounds identified by GC-GC/MS samples extracted with a gas tight syringe ........................................................................................................ 74

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LIST OF FIGURES

Figure page 2-1 Picture of red drum fish (Sciaenops ocellatus) (FAO 2017) ................................ 42

2-2 Coastal map of red drum habitat (GBIF 2017) .................................................... 43

2-3 Map of Volusia county (World Maps 2017) ......................................................... 43

2-4 Map of Volusia county (FL Maps 2017) .............................................................. 44

2-5 Map of Indian River Lagoon (FHWA 2017) ......................................................... 44

2-6 Anatomy of fish (Fishing Target 2017) ................................................................ 45

2-7 External anatomy of a fish with soft dorsal fin and spiny dorsal fin identified (Fishing Target 2017) ......................................................................................... 45

2-8 Male fish fertilizing eggs from female (Zoology 2017) ........................................ 46

2-9 Mariculture ponds (Alloyance 2017) ................................................................... 46

2-10 Production cycle of red drum (FAO 2017) .......................................................... 47

2-11 Graph of age vs length of red drum (adapted from USM 2017) .......................... 47

2-12 Global aquaculture production for red drum (FAO 2017) .................................... 48

2-13 Diagram of a gas chromatograph (Gas chromatography 2017) ......................... 50

2-14 Inner diagram of a mass spectrometer (Overview of Mass Spectrometry 2017) .................................................................................................................. 50

2-15 Inside of a gas chromatograph/mass spectrometer (Gas chromatography–mass spectrometry 2017) ................................................................................... 51

2-16 Number of published articles in recent years related to SPME and SPME/MS applications (Vas 2004) ...................................................................................... 51

2-17 Inner design of a SPME fiber (Kataoka 2000) .................................................... 52

2-18 SPME fiber properties, retention vs polarity (Fanali 2017) ................................. 52

2-19 SPME process for headspace vs direct immersion SPME (Vas 2004) ............... 53

2-20 Extraction time and analyte absorption rate on SPME fiber (Vas 2004) ............. 53

3-1 Consumer panel screener .................................................................................. 63

9

LIST OF ABBREVIATIONS

ANOVA analysis of variance

DVB/CAR divinylbenzene/carboxen

DI direct immersion

FAO Food and Agriculture Organization

FID flame ionization detector

GC gas chromatography

GC/MS gas chromatography/mass spectrometry

GC-GC/MS multidimensional gas chromatography/mass spectrometry

GSM geosmin

HPLC high performance liquid chromatography

HS headspace

IMTAS integrated multi trophic aquaculture system

LC-MS liquid chromatography-mass spectrometry

MAP Mote marine aquaculture park

MIB 2-methylisoborneol

MD microwave distillation

MS mass spectrometry

MUFA monounsaturated fatty acids

NIST National Institute of Standards and Technology

m/z mass to charge ratio

PAH polycyclic aromatic hydrocarbons

PDMS polydimethylsiloxane

ppb parts per billion

10

ppt parts per trillion

PUFA polyunsaturated fatty acids

QDA quantitative descriptive analysis

RAS recirculating aquaculture system

SPME solid phase micro extraction

TCD thermal conductivity detector

VAS visual analog scale

μm

micro meter

11

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

SENSORY AND VOLATILE ANALYSIS OF RED DRUM (SCIAENOPS OCELLATUS)

GROWN IN RECIRCULATING AQUACULTURE SYSTEMS (RAS) AND INTEGRATED MULTI TROPHIC AQUACULTURE SYSTEMS (IMTAS)

By

Daniel W. Clark

August 2017

Chair: George Baker Major: Food Science and Human Nutrition

An increasing global demand for aquatic food products has led to the need to

grow fish using an established method like recirculating aquaculture systems (RAS) as

a means to provide safe and sustainable seafood but the presence of dirty and muddy

off-flavors like geosmin (GSM) and 2-methylisoborneol (MIB) negatively impact quality.

It is hypothesized that Integrated Multi Trophic Aquaculture (IMTA) systems may

actively suppress the production of dirty and muddy off-flavor when compared to

conventional RAS or that elimination of off-flavor compounds in aquaponics systems is

more efficient when compared to conventional RAS (Diver 2010). The objective of the

study was to compare the presence of off-flavors and sensory quality of red drum grown

in IMTAS and RAS.

Descriptive analysis and hedonic consumer panels were conducted on red drum

fillets sourced from RAS and IMTAS for sensory attribute intensity of common aromas

and flavors associated with finfish. A hedonic consumer panel tested 60 panelists using

a 9-point scale for likeability and preference. Volatile analysis was conducted using HS-

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SPME, DI-SPME and gas tight syringe extraction, headspace volatiles were then

analyzed in a GC/MS.

Trained panelists found no significant difference in aroma attributes between raw

or cooked RAS and IMTAS samples. Steamed RAS samples had a significantly higher

presence of “dirt” off-flavor compared to IMTAS (17.13 vs 10.54, respectively). A

consumer panel showed no significant difference in likeability between samples.

Preference results were evenly split (29:31 for RAS: IMTAS).

Volatile analysis detected the presence of 1, 2-Propadiene-1, 3-dione in all

samples and (S)-(+)-1-Cyclohexylethylamine in all samples except the raw IMTAS.

These compounds have an acrolein (burned fat) and amine (fishy) odor respectively. It

is assumed that the presence of these compounds, adversely affect the quality of the

fish.

Sensory results suggest that there is no significant statistical differences between

aroma of RAS and IMTAS reared red rum. However, descriptive analysis trained

panelists were able to discern a “dirty” off-flavor in RAS reared red drum when the

samples were tasted. Consumer panels could not find noticeable differences between

the samples, raw or cooked.

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CHAPTER 1 INTRODUCTION

Due to the increased need for aquatic food products globally, seafood products

grown in aquaculture have increased. Globally, China, Indonesia, the United States,

and Russia are the top producers of aquacultured products (FAO-UN, 2016).

Recirculating aquaculture systems (RAS) allow for the production of locally sourced

seafood in regions that are not near bodies of water or where there are water

restrictions. Global demand for safe, nutritious, sustainable seafood has increased due

to the projected worldwide population exceeding nine billion people by 2050 (FAO-UN,

2016). Seafood and specifically fish is recognized as an efficient source of protein

requiring 1.2 pounds of feed to attain 1.0 pound of edible flesh, compared to cattle

which require upwards of 8.7 pounds per pound of edible protein (Gerber 2007).

A problem that plagues fish grown in RAS is the development of fish off-flavors

which tend to be muddy, dirty or musty flavor (Schrader 2003). Research conducted to

identify the source of off-flavors in RAS points most commonly to the presence of 2-

methoisoborneol (MIB) and geosmin (GSM) (Yamprayoon 2003). MIB and GSM are

compounds formed by cyanobacteria and Gram-positive filamentous bacteria called

actinomycetes which produce spores and are capable of surviving multiple conditions

(Tucker 1999). The threshold of sensory detection for these compounds is low, 0.015

and 0.035 ppb for GSM and MIB, respectively (Leo 2012). At the levels present in RAS,

those compounds are not considered toxic yet negatively impact the quality of the fish.

Common techniques managing the off-flavors in RAS fish includes purging them with

approximately 0.3 m3 clean treated water/kg feed, which increases the total amount of

water, energy and chemical cost for the grower (Seginer 2008).

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A novel form of RAS incorporates the use of hydroponics to create a sustainable

recirculating system where plants being grown utilize ammonia and nutrient rich water

provided by the fish to act as fertilizer (Tisdell 1999). Research conducted has been

linked to the conclusion that these marine aquaponics systems, also known as

Integrated Multi-Trophic Aquaculture (IMTA) systems, offer fish farmers good

commercial potential due to its decreased operational cost when compared to traditional

RAS (Diver 2010). A commercial-scale, zero-discharge (whereby no water leaves the

system during the production process) aquaponics system is currently being used at

Mote Marine Aquaculture Research Park (MAP) in the production of red drum

(Sciaenops ocellatus) and sea vegetables Sea Purslane (Sesuvium portulacastrum) and

Saltwort (Batis maritimia), as well as fertilizer to be used in wetland plant nursery

farming (Boxman et al., 2016).

An informal taste panel conducted at the University of Florida, following the

lexicon and preparation procedure outlined by the Food and Agriculture Organization of

the United States (FAO) provided evidence that red drum produced in IMTA systems

have decreased levels of off-flavor compounds, while red drum grown in traditional RAS

from the same facility require fresh water purging of the fish to remove off-flavors prior

to harvesting (Davidson 2014, Schrader 2016). Informal taste panel using six panelists

who are experts in sensory description, yielded results that showed that IMTA grown

red drum had a “cleaner” flavor that had less noticeable off-flavors when compared to

the RAS samples, while the fish grown using conventional RAS had dirty, muddy,

earthy, and bitter off-flavors. IMTA systems may actively suppress the production of off-

flavor in fish muscle when compared to conventional RAS or that elimination of off-flavor

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compounds in aquaponics systems is more efficient when compared to conventional

RAS (Rimando 2003).

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CHAPTER 2 LITERATURE REVIEW

Fish (Red Drum)

Red drum (Sciaenops ocellatus) (Figure 2-1), are natural inhabitants of Florida’s

Atlantic coast, with a large population in the Indian River Lagoon as well as a habitat

that range from central Mexico and extends to Massachusetts, including the Gulf of

Mexico (Figure 2-2) (Robins 1986). The Indian River Lagoon runs from Volusia County

down through to Jupiter Inlet in Palm Beach County (Figures 2-3 and 2-4), incorporated

with Cape Canaveral, the St Lucie River, and Lake Okeechobee and connected by the

Okeechobee Waterway (Figure 2-5). Red drum are a prominently estuary dwelling fish,

living most of its life near shore (Reagan 1985). Reaching up to five feet (150 cm) and

90 lbs (41kg) in weight, the red drum is a compressed, rugged member of the Sciaenid

family (Hoese and Moore 1977). Red drum are a resilient fish and are easily raised in

aquaculture conditions due to their high tolerance of wide temperature variances (35.6°-

99.5°F), differences in salinity of the water ranging from 0.14-50 ppt, as well as

acclimating to freshwater conditions (Arnold et al 1977). Red drum are saltwater

dwelling fish that thrive when the water temperatures are warm (~72°F) and contain

both marine and estuary habitats (Sweat 2010). Since the near depletion of red drum

occurred in the late 1980s due to commercial fishing. Limitations such as size and bag

limits were implemented along with commercial fishing of red drum eliminated along the

coasts of Florida (ASFMC 2010). Red drum have a straight head, large mouth with

villiform teeth, and a blunt, cone shaped snout (Reagan 1985). The body color is

primarily coppery brown or reddish with a white underside. Red drum lack barbells

which are whisker like organs located at the base of the mouth used to locate food by

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acting as nostrils or taste buds when in low visibility conditions unlike other species

similar to the drum, but have two dorsal fins that contain ten hard spines, with the first

hard spine and a grouping of soft rays in the second. The soft dorsal (soft ray) and

caudal fins are composed of rays that are less rigid and typically branched (FFWC

2017). The soft rays are typically used to support the fins of the fish and sometimes can

play a defensive role. Near the base of the slightly concave caudal fin are one or more

dark spots that lay above the lateral line (Figures 2-6 and 2-7).

Habitat and Spawning

Red drum spend most of their life in estuaries but move to the deeper waters on

the inner coast, bay areas, and inlets during mating season (Crocker 1981). The mating

season occurs during the fall around mid-August through October depending on where

the fish dwell in Florida. Shorter daylight hours and cooler water temperatures trigger

mating habits of the red drum and peak around 9:30-10:00 p.m. Typically, spawning

occurs from its peak in September or October but cases have been found where

spawning can occur in August and December depending upon environmental

temperature (Robins 1986). The spawning process typically occurs around the bay

areas as red drum larvae have not been reported more than 12 miles from the beach or

bays. Red drum males during spawning produce a characteristic drumming sound by

rubbing specific muscles against their air bladder which produces a drumming sound

hence the name. To attract females during mating season, males begin making the

drumming sound and increases around dusk. When the males come within the

proximity of females they proceed to nudge the female’s abdomens causing a release of

the eggs which is then fertilized by the males. After the males use their drumming

sounds to attract females, the spawning process begins at night near the offshore

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waters during the summer and fall months. The larger females produce up to two million

eggs in a season which are then fertilized by the males (Figure 2-8). The fertilized eggs

take 24-36 hours to hatch after being spawned and the larvae are then carried by the

action of the tides into shallow estuaries that typically serve as low salinity nursery

areas (ASM 2017). After the larvae hatch they live off the attached yolk sac for three

days then feed on plankton while living in the water column for approximately 20 days

(USM 2017).

Mariculture is the process of producing aquaculture products by means of

creating an aquatic medium that can be completely marine or have various degrees of

marine and brackish water incorporations (Mariculture 2017). These environments can

be free floating cages, net enclosures, earth ponds, or constant water recirculating

systems (Figure 2-9) (Sivalingam 2017). In Florida mariculture ponds, red drum have

been shown to produce 20,000 to two million eggs per spawn (Roberts et al 1978).

Environmental Physical Tolerance

Though red drum can withstand substantial changes to their aquatic

environment, the mortality rate increases with drastic changes in temperature (Gunter

1941). Optimized breeding and hatching of red drum occurs at temperatures around

25°C and 30 ppt salinity, with the best growth and survival occurring in water

temperatures of 25-30°C (Holt et al 1981). Though capable of living in freshwater

conditions, red drum eggs tend to sink when the salinity is below 20 ppt, leading to a

low survival rate.

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Red Drum Production

Types of Production Phases

Two production phases are utilized for the cultivation of red drum in aquaculture,

(Figure 2-10). The broodstock phase is the production process where four to six fish are

combined in a tank, typically the male to female ratio is 1:1 Male: Female, to spawn in

tanks ranging from 10 to 17 cubic meters in width and 1.5 m in height (Holt 1990).

These tanks are connected to an external recirculating aquaculture system (RAS). RAS

Filtration systems are used to help maintain water quality by removing such

contaminants as ammonia, nitrites, dissolved organic solids, carbon dioxide, excess

nitrogen, and other suspended solids (Smith 2003). Using a broodstock production

system allows fish growers to provide a constant supply of red drum larvae that are

available for the grow-out production phase. Red drum growth is rapid during the early

part of their life spans, until 5 years when growth slows down significantly (Table 2-1

and Figure 2-11). The male and female red drum grow at the same rate and the growth

rate can vary upon location as gulf coast fish typically grow faster due to the warmer

water (USM 2017).

During the red drum spawning process, broodstock are fed a diet of primarily

squid, fish, pellet food, and shrimp (Holt 1990). After the red drum larvae become

juvenile fish, their natural diet shifts to shrimp, worms, small crabs, and other smaller

fish. Wild adult red drum are opportunistic feeders that will feed on prey such as mullet,

pinfish, spot, and other small fish. During the winter months when the water temperature

is colder the red drum diet consists primarily of fish while during the warmer summer

and fall, more shrimp and crustaceans are eaten (USM 2017).

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RAS at Mote Marine Aquaculture Park (MAP)

Aquaculture utilized at Mote Marine Aquaculture Research Park (MAP), uses a

closed aquaculture system which uses a treatment system to clean the water so it

becomes reusable instead of emitting it into the environment (Cottee 2009). Closed

aquaculture systems help to eliminate negative environmental impacts, introduction of

exotic species, and pollution of waters with chemicals and other nutrients (Naylor 2000).

A typical RAS like the one at MAP will feature oxygenation, aeration, and solids

removal through the addition of biofiltration (Piedrahita 2003). Biofilters comprise of a

collection of bacteria on the surface of a biofilm carrier media. This bacteria present in

the biofilm is the major source of nitrification which is responsible for the break down

and removal of ammonium. Biofilters have been used in conjunction with plants or algae

to successfully remove ammonium in IMTAS (van Rijn 1996).

Though effective, biofilters are costly to operate and it is required that high value

fish be grown to balance out the high cost of operation for the biofilter (Zucker 1999).

The utilization of plants and algae in conjunction with biofilters can help to provide

additional revenue opportunities as well as overall cost reduction (Graber 2009, Neori et

al., 2004).

Red drum market and trade

In 2016, the price of red drum raised in commercial aquaculture was USD $4.19-

4.63/kg, though the price fluctuates with market supply and demand (FAO 2017). These

recent sale prices reflect domestic red drum that are frozen or fresh boned fillets and

steaks ranging in weight from 170 to 340 grams.

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Red drum composition

Fish like red drum contain fat-soluble vitamins A, D and E as well as omega-3

fatty acids (Borgstrom 1961). Omega-3 and Omega-6 fatty acids are polyunsaturated

fatty acids (PUFA) and are essential in that they are not produced independently by the

human body. PUFAs play a major role in reducing the risk of heart disease and aiding in

the role of brain function (Ehrlich 2015). PUFA and monounsaturated fatty acids

(MUFA) tend to undergo oxidation due to the presence of double bonds in their

structure, while saturated fatty acids are less prone to oxidation (Delany 2000). When

compared to plant based sources of Omega-3 fatty acids, marine sources are more

prone to oxidation by the presence of long carbon chain PUFAs which are chemically

unstable (Albert 2013). Autoxidation is known to cause flavor deterioration as well as

the formation of toxic materials (Paquette 1985). Compounds like hydroperoxides, a

main product of autoxidation, breaks down to form a variety of volatile and non-volatile

secondary products when extensive lipid oxidation occurs (Paquette 1985).

Fish like red drum are also good sources of sodium, magnesium, cobalt, iodine,

fluorine, and magnesium which are helpful for bone growth, muscle building, and heart

health (Venugopal 2009). The amount of vitamins and minerals per 200 grams of red

drum can be seen in Tables 2-2 and 2-3.

Red drum flesh has a unique firmness when cooked, with an odor reminiscent of

the ocean with a fresh and fatty note (USM 2017). The color of the flesh is slightly

amber, but when cooked turns ivory. When presented to sensory evaluation panelists, it

is generally liked and has a high overall acceptance (Klanian 2015). Wang (2009),

noted a fishy, grassy, fatty and melon odor in cultured red drum linked to hexanal and 1-

penten-3-ol, likely sourced from environmental factors (Wang 2009). In the Wang

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study, the dorsal muscle of the fish tended to have the highest concentration of odor. 1-

octen-3-ol, 4-heptanal, and benzaldehyde lent strongly to the characteristic odor notes

in red drum. Cucumber odor has been associated with fresh fish and used as a quality

indicator for red drum (Josephson 1983).

Due to the high resiliency of red drum and their ease of cultivation from a

spawning, nutrition, and environmental tolerance perspective, there has been a steady

rise in production of red drum worldwide (Figure 2-12) (FAO 2017). Further research

conducted in temperature tolerance, disease resistance, and feed efficiency, may

enhance red drum as an economically and ecologically sound species to produce for

human food (FAO 2017).

Aquaponics

Despite the known health benefits of fish and vegetables, the average American

consumes less than four pounds of fish per year and an average of 92 pounds of fresh

vegetables per year which equates to one serving of vegetables per day (USDA 2010).

Systems like Aquaponics systems are able to integrate the production of fish and plants

while reducing the environmental impact of aquaculture (Adler et al., 2003; Tyson et al.,

2011). Aquaponic systems are capable of removing low concentrations of dissolved

nitrogen and phosphorous that typically accumulates in aquaculture. When plants take

up and sequester nitrogen and phosphorus, along with other nutrients, water quality is

improved (Buzby 2014). RAS are designed to grow large quantities of fish in small

volumes of water but the environmental water must be purged and treated to prevent

the buildup of waste products before the water is reused. Systems that grow secondary

vegetative crops in conjunction to the primary fish species by utilizing the by-products of

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the primary growth species are referred to as Integrated Multi-Trophic Aquaculture

Systems (IMTAS) (Rakocy 2006).

In aquaponics, aquaculture water can be treated through the production of

hydroponic plants which grow without soil or roots and are in regular contact with water

in the system (Rakocy 2012). To apply aquaponics to a marine setting, the growth of

salt tolerant vegetables must be incorporated.

The IMTAS at MAP is a marine aquaponics system and grows salt tolerable

vegetables in conjunction with red drum. Saltwort (Batis maritimia) contains high

concentrations of essential amino acids and grows in salt marshes in both North and

South America (Debez 2010). Sea purslane (Sesuvium portulacastrum) is a vegetable

with a salty taste that is popular in India and grows along coastlines in tropical and

subtropical environments (Kathiresan 1997).

The plants remove nutrients and potential sources of toxins from the culture

water, which eliminates the need for a separate biofilter (Rakocy 2006). Fish produce

nitrogen waste through excretion that is distributed in the water supply as ammonia

(NH3). Nitrifying bacteria present in the IMTA system convert the ammonia into nitrite

(NO2-) and then ultimately into nitrate (NO3-) (Madigan et al., 2003). Plants used in

IMTAS can absorb the nitrate and ammonium (NH4+) as a source of food, due to

nitrogen being the main nutrient required by most plants (Marschner 2003). Most of the

waste the fish generate is removed by a filtering system before entering the hydroponic

tanks. If waste weren’t removed, deep deposits of sludge would form and produce

methane and hydrogen sulfide, which are known to be toxic to some fish at certain

concentrations (Rakocy 2006). Research has found that nitrate toxicity occurs at 0.9 ±

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0.14 mg/L after 24 hours and 0.8 ± 0.16 mg/L after 48 hours (Wise 1989). Studies

stated that hydrogen sulphide which forms in low oxygen environments, caused

mortality in red drum at levels ≤2.0 µg/L (Lucas 2013).

Flavor Identification

Off-flavors in RAS

Earthy and muddy off-flavors and aromas have been documented since 1891,

where it was thought originally that the earthy odor came from the soil but was later

discovered to be sourced from actinomycetes (Gerber 1967). It was also noted that fish

that were subjected to water containing trace amounts of the odorous compounds would

pick up the taint and store it in the flesh. This absorption occurred through the mouth

and gills of fish and not through the skin, which required keeping them in pure running

water to eliminate the off-flavors (Gerber 1967). Research since then has confirmed that

the major source of earthy-musty taste and odor is caused by metabolites of

actinomycetes (Henley 1969, Rosen 1970). Actinomycetes in addition to fungi, bacteria,

and algae, are known to produce geosmin (GSM) and 2-methylisoborneol (MIB) which

are semi volatile compounds that have a muddy, musty odor that is identifiable in

concentrations as low as 0.015 and 0.035 µg L-1 for geosmin and MIB, respectively

(Lloyd 1998, Leo 2012). The most common taste and odor compounds in fish and

freshwater aquaculture systems are GSM and MIB as both are readily absorbed into the

lipid tissue of the aquatic organism in concentrations present in the tissue at greater

than 0.6 g/kg, which make the fish unfit for retail or commercial sale (Petersen 2011,

Persson 1980). Both compounds are saturated cyclic tertiary alcohols which makes

them resistant to oxidation by conventional water treatment methods (Figure 2-13)

(Izaguirre 1981).

25

Though geosmin and MIB are identified as the dominant source of off-flavors in

RAS, some research indicates that other compounds can be the source of off-flavor

(Petersen et al. 2014). These compounds have been linked to the presence of

Actinomycetes which have been identified not only in terrestrial aquaculture systems but

also in ocean water as part of the run off from rivers (Weyland 1969). Some researchers

have found that other bacteria, like the species found in the Myxococales order, can

produce musty and earthy off-flavors in aquaculture systems (Zaitlin & Watson 2006).

Factors like phosphate concentrations, micronutrients, and organic matter in the system

can affect water quality and promote the growth of off-flavor producing microorganisms

(Auffret 2013). In RAS, water quality can be negatively affected by the presence of

nitrogen compounds like ammonium and nitrite (Wickins, 1980; Russo & Thurston,

1991). Conditions where phosphorous concentrations were increased lead to enhanced

GSM production, while environments with increased zinc, iron and copper decreased

the production of GSM with copper being the most effective (Schrader 2001). It has

been identified that GSM and MIB grow best in aerobic, organic-rich conditions which

tend to stimulate the growth of actinomycetes, while a reduction of the compounds was

present in an anaerobic water treatment condition (Guttman 2008). Temperature,

salinity, and phosphate levels positively correlated to the growth of the microorganisms

responsible for GSM and MIB (Robertson 2006).

Gas Chromatography

Food aroma and flavors are a combination of various volatile compounds that are

sensed by the olfactory portion of the mouth and nose (Figure 2-14). Odorant receptors

are localized on the olfactory nerve in the nasal epithelium. Upon receiving an odor

compound, signals are relayed to the brain (Rinaldi 2007). Gas chromatography (GC) is

26

useful for the separation and identification of specific volatile compounds that make up

aromas. GC operates by separating the volatile compounds based upon the amount of

time it takes for them to elude from the column after being inserted. Gas

chromatography (GC) is reliant upon the different partitioning behavior that each

substance possess based upon the flowing mobile phase and stationary phase being

used to separate the component from a mixture. After being injected into the GC, the

sample is taken up by the moving gas (mobile phase) through a tube that is lined with a

film of a liquid stationary phase. As the sample travels through the column, the

components are separated in the column based upon their polarity and will elude out of

the column at different rates. As the compounds are eluted out, the detector measures

the quantity of the eluted components. If a compound has an unknown concentration, a

standard with a known concentration is introduced into the instrument and the retention

time of the unknown compounds are compared to the sample being tested to estimate

the concentration (Shimadzu 2017). The different components of a GC consist of the

column inlet, column, and detector (Figure 2-15). The column inlet is narrow metal

tubing that serves as the head of the column and is commonly considered split or

splitless which control how much sample enters the column. After the sample is injected

into the chamber using a syringe, in split mode a portion of the sample is swept by the

carrier gas while in splitless mode the entire sample is swept by the gas. For most food

samples where the concentration of the analytes are low, it is advised to use splitless

injection. Capillary columns used in GC consider length and column materials to dictate

proper usage in experiments. Capillary columns range from 10 to over 120 meters with

their inner walls being active, flexible materials which allow the column to be coiled. The

27

detector most commonly used in flavor identification are Flame Ionization Detectors

(FID) and Thermal Conductivity Detectors (TCD) (Begnaud 2016). FIDs and TCDs work

well with wide concentration ranges and are sensitive to most types of compounds. The

TCD requires the thermal conductivity of a compound to differ from the carrier gas,

while the FID works well with hydrocarbons, but is not capable of detecting water (de

Saint Laumer 2010).

Mass Spectrometry

Mass spectrometry (MS) uses the mass to charge ratio (m/z) of various ions to

quantify and identify the molecules in both simple and complex mixtures (Downard

2004). MS uses an ion source, an ion detector, and a mass analyzer to acquire the

physical property data of the sample it is testing. The MS accelerates ions though the

remainder of the system based upon the charge the molecule receives. The mass

analyzer exudes an electric or magnetic field on the ion which will cause it to deflect the

path of the individual ions based upon with charge and mass (m/z). MS analyzers can

both filter specific ions towards the detector as well as separate all the analytes for an

analysis of the whole sample (Thermofisher 2017). The ions hit the ion detector after

being deflected off the mass analyzer, and typically the ion detector are electron

multipliers which will emit a cascade of electrons as each ion comes in contact with the

detector plate (Figure 2-16) (Finehout 2004) which results in improved sensitivity as

each amplification occurs. Inside the MS is a vacuum at (10-6 to 10-8 torr) which will

remove gas molecules, neutrals, and non-sample ions that would contaminate the

results as they collide with the intended sample ions and alter their path or create non-

specific reaction products (Hoffman 2001). A computer that is connected to the MS

analyzes data the ion detector receives and produces a graph that is used to organize

28

the ions detected based upon their unique m/z and relative amount. These results are

then compared against databases collected and used to identify a molecule based upon

its specific m/z (Downard 2004).

Gas Chromatography / Mass Spectrometry (GC/MS)

Gas Chromatography Mass Spectrometry (GC/MS) is helpful in the determination

of the structure and molecular weights of unknown organic compounds in complex

solutions by comparing their spectra against a known reference spectra for the

identification and quantitation of volatile and semi-volatile organic compounds (Settle

1997). GC/MS is a combination of gas chromatography (GC) which is capable of

separating volatile compounds from an unknown solution but is not able to identify

them. Alternatively, mass spectrometry (MS) provides specific structural data with high

specificity but cannot separate compounds from solutions (Figure 2-17) (Settle 1997). In

recent years, gas chromatographs use capillary columns to separate analytes in a

mixture and their effectiveness varies on the dimensions and phase properties based

upon what compounds are being targeted. The separation of molecules occurs during

the travel down the length of the column, causing them to elute at different times the

mass spectrometer captures, ionizes, accelerates, deflects and detects the ionized

molecules individually. The mass-to-charge (m/z) ratio of the molecules is determined

as the mass spectrometer breaks down each molecule into its ionized fragments and

then detects those fragments based upon the ratio. This combination of the two

detection methods allows for a more accurate identification of a particular molecule than

with GC or MS separately. When a molecule has a particular mass spectrum and

appears at a specific retention time in a GC/MS analysis, it can be concluded that the

molecule of interest is present in the sample. (Stashenko 2014).

29

Solid Phase Microextraction

Flavor is a combination of both taste and retro nasal olfaction and crucial to the

acceptance that a consumer experiences for food (Kataoka 2000). Most foods contain

complex food matrices and consist of a wide range of organic compounds that possess

various polarities while comprising extremely low concentrations of aroma and flavor

compounds (Wilkes 2000). Solid phase microextraction (SPME) is a simple and low-

cost technique designed to analyze a wide variety of volatile and semi-volatile

compounds found in food, water and other environmental sources (Lloyd 1998). SPME

which was created by (Pawliszyn 1995), utilizes both static headspace (HS) and

dynamic headspace to allow for the accumulation of analytes through diffusion

(Pawliszyn 1995 and Jelen 2006). The primary advantages of SPME lie in its simplicity,

solvent removal, increased sensitivity, small sample volume requirement, low cost,

relative sample speed, and simple automation. The qualitative and quantitative

information is needed for accurate characterization of aroma producing compounds that

are used to determine the criteria of quality in most foods. Due to SPMEs incorporation

of multiple types of fibers and integration with GC, it can be successfully used to extract

polar and non-polar compounds that are present in gaseous, liquid, and solid samples

while easily being coupled with GC/MS, HPLC, and LC/MS. (Kataoka 2000). Since its

creation, the popularity of SPME and its utilization with MS, has been increasingly

illustrated and its experiments published about since 1990 (Figure 2-18).

SPME has been widely utilized in scientific analysis of flavors and volatiles

organic compounds using GC and GC-MS to extract them from the environment,

biological and food samples (Vas 2004). Scientists reviewed the use of SPME in the

analysis of volatile flavors in milk and dairy product utilizing on-column injection and

30

‘purge and trap’ systems (Mariaca 1997). SPME was used to analyze the volatile

compounds in fish muscle that are associated with oxidative off-flavors (Iglesias

2008).Investigation was conducted into the new trends developing around SPME and

highlighted the potential the unique microsampling technique has for the analysis of

volatile compounds in the environment (Eisert 1997). Researchers reviewed the recent

advances in SPME and discussed how though the intent was for the analysis of

environmental samples, SPME has grown to incorporate many additional applications

like flavor analysis in food products (Lord 1998). Prosen reviewed the technical facets of

SPME and detailed some applications that SPME could be applied to such as semi-

volatile constituents of vodka and organochlorine pesticides in water (Posen 1999).

Investigation was done on the analysis and binding of flavor volatiles in various dairy

products while using SPME to establish real flavor profiles as perceived by human

panelists (Stevenson 1996). Scientists reviewed the combination of SPME for aqueous

samples using capillary gas chromatography, specifically heart-cut oriented reversed-

phase liquid chromatography GC, which is useful for separating selected portions of the

main separation into a secondary column for further analysis, and analyte-isolation-

oriented analyte extraction GC in which water is directly introduced on the GC column in

where the water is eliminated and the remaining analytes are analyzed (Louter, 1999).

Experiments were conducted investigating the experimental conditions and SPME

method development that have improved fibers, matrixes, acceleration method, and

extraction time (Junting 1998). Research was conducted that reviewed further the

application of SPME when used in the analysis of organic micropollutants such as

31

toluene, pesticides, phenols, polycyclic aromatic hydrocarbons (PAH), and benzene

(Eisert 1996).

The SPME device contains a fiber assembly with a fiber holder that houses a

built-in fiber which mimics a modified syringe (Figure 2-19). The fiber consists of fused-

silica that is coated with a thin layer of several polymeric stationary phases which act as

a “sponge” as it concentrates the organic analytes on its surface during the absorption

or adsorption from the sample matrix (Kataoka 2000). An absorbent phase is coated

onto a fused silica fiber then attached to the tip of a syringe plunger. Due to the delicate

nature of the fiber, the plunger is retracted into the needle as it is being used to

penetrate the septum of a sealed vial, the fiber is then extended into the headspace

(Lloyd 1998). The fiber is usually exposed to the headspace of the sample and extracts

the volatile compounds to be used in qualitative analysis during which the sample must

undergo equilibrium. An alternative is for the tip to be immersed into a liquid sample to

then further be analyzed. During equilibrium the analyte molecules absorb onto the fiber

coating, then the fiber is retracted into the sheath. The needle is then inserted into a

heated injection port of a gas chromatograph (GC) in which the analytes are desorbed

thermally and transferred onto the head of the GC capillary column for separation and

analysis (Lloyd 1998).

The type of stationary phase selected can affect the absorption of the sample

analytes, stationary phases are defined by non-bonding, bonding, partial crosslinking or

high crosslinking (Figure 2-20). The principle of “like dissolves like” is used to assess

the affinity of a fiber for an analyte and the thickness and properties that various fibers

exhibit determine which type of fiber is most effective for different compounds. The non-

32

polar polydimethylsiloxane (PDMS) fiber is effective for extracting non-polar analytes

such as many flavor volatiles in food samples and is able to withstand high injector

temperatures around 300°C. The polarity and volatility of the analytes are the two

factors which effect sampling and chromatography. Most analytes are studied using HS-

SPME and followed by GC-GC/MS, typically a splitless injection is used since there is

no solvent and the analysis is considered very sensitive (Vas 2004).

Optimization of extraction relies on factors such as exposure time, concentration

of the analyte, and injector temperature. The extraction rate is determined by not only

the agitation rate but also the partition coefficient of the analyte between the fiber

coating and the sample matrix (Kataoka 2000). The sensitivity factor for SMPE is at a

maximum when the sample is at its equilibrium point but do to the linear relationship

between the amount of analyte adsorbed by the fiber and the initial concentration in the

sample matrix in non-equilibrium conditions, true analyte equilibrium is not necessary

for accurate and precise SPME analysis (Ai 1997). The salting out effect caused by the

addition of soluble salts like sodium chloride or potassium carbonate by supersaturating

the sample with salts to maximize the analyte extraction (Vas 2004). The time of

extraction is increased along with the fiber thickness as well as the lowering the

diffusion coefficients of the analyte molecule in the sample (Vas 2004).

Optimization of desorption is determined by factors such fiber coating, injection

depth, exposure time, and injector temperature. To ensure a high linear flow the fiber

needs to be exposed immediately after the needle is introduced to the narrow-bore GC

injector and the depth of the needle exposure should be adjusted so that the fiber is

positioned in the center of the hot injector zone (Kataoka 2000). Most analytes have an

33

optimum desorption temperature approximately equal to the boiling point of the least

volatile compound. The initial temperature of the GC column should be low to prevent

peak broadening in addition to achieving a concentration of analytes at the head of the

column. When desorbing in to the GC, a narrow-bore (0.75 mm i.d.) unpacked injection

liner helps to ensure a high gas flow that is also linear while reducing desorption time

and aiding to prevent peak broadening. Dynamic and static desorption are the two

techniques for removing analytes from the fiber during SPME interfaces. Dynamic uses

the moving mobile phase to remove analytes from the fiber while static uses

theαsoaking in the mobile phase or a strong solvent to remove analytes that are

strongly absorbed into the fiber (Vas 2004).

Direct Immersion (DI)-SPME is conducted when the fiber is immersed directly

into liquid samples, after enough time has passed for a sufficient extraction rate, the

fiber is withdrawn into the sheath of the needle, and the needle is removed from the

septum and directly injected into the injection port of the GC (Kataoka 2000). The

analytes are then desorbed from the fiber coating as the fiber is heated in the injection

port and the analytes are transferred directly to the GC column for analysis (Figure 2-

21). Though direct immersion into the sample shortens the life of the fiber in DI-SPME

by concentrating and remaining on the fiber, it is considered that DI-SPME is more

sensitive to semi or less volatile analytes present in liquid when compared to headspace

(HS)-SPME (Kataoka 2000).

Traditional sample preparation methods attempt to completely remove the

analytes of interest from sample but SPME fibers remove only an amount proportional

to the concentration of the compounds in the sample. SPME’s ability to quantitate data

34

before reaching equilibrium allows for a shorter sampling time and produces a more

economical and adaptable testing procedure. When sampling complex matrices, an

internal standard or standard additions is advised for calibration (Vas 2004). As with

many sampling techniques extraction time is a critical factor and (Figure 2-22) shows

the typical relationship between extraction time and the analytes absorption rate on the

fiber (Sigma-Aldrich 2017).

Headspace aroma capture using a gas tight syringe

The use of a gas tight syringe when conducting volatile analysis is intended to

create a cost effective, faster method of analysis while only requiring a small amount of

sample and still being able to detect low concentrations of analytes (Cho 2015). Gas

tight syringes are constructed of borosilicate glass and due to the interference fit of the

plunger and the barrel, its gas sealing properties have made the syringes a consistent

choice for GC equipment (Sigma Aldrich 2017). Static headspace analysis with MS is

rapid and useful method for the identification of off-flavor and their chemical constituents

(Marsilli 1997). To quantify odorants in boiled salmon and cod, gas tight syringes were

used to extract headspace volatiles and injected into a GC for further analysis (Milo

1996). Static headspace dilution analysis in conjunction with a gas chromatography

olfactometry utilized a gas tight syringe to quantify impactful aroma components in Thai

fish sauce (Lapsongphon 2015). It is suggested by researchers that slowly injecting the

sample and using a cold trap after extraction could help increase sensitivity of the

analysis (Schreier 1984). The cryo trapping helps to narrow the chromatographic band

and improve the detection limit.

35

Sensory Analysis

Standard sensory evaluation techniques allow for the accurate measurement of

human responses to sensory characteristics found in foods while minimizing biases

created by brands, consumer trends, and consumer perception (Lawless 2010).

Information extracted from sensory testing can then be used by food scientists to modify

or improve potential food products so they can better appeal to the consumer. (Stone

and Sidel, 2004) state that sensory evaluation is a scientific method designed to

measure, analyze, and interpret the perceived responses of products through sight,

smell, taste and hearing. Statistical methods of analysis must be conducted to

compensate for the high variability found in human observations. Variables, such as the

mood of the panelist, sensory stimulation, and past exposure to the product, must

attempt to control those variables. Prescreening panelists attempts to eliminate outside

bias but proper sensory evaluation relies on correct interpretation test results (Lawless

2010). Meiselman states that sensory evaluation is concerned with accuracy, sensitivity,

precision, and avoiding false positive data (Meiselman 1993).

The ability of a test to generate data that is considered “true” is defined as

accuracy in sensory science (Lawless 2010). Data gathered from the result of a sensory

test conducted with a group of consumers should translate to a generalization of the

opinion the population has as a whole (Lawless 2010). Type II error and minimization of

β-risk is a statistical measure to ensure the sensitivity of a test and that there are no

missing differences present between two products. A type II error is also labeled as a

false negative in which it is the error of not rejecting the null hypothesis when the

alternative hypothesis is the correct statement. It is essentially the error of failing to

notice a difference between objects when one actually exists. From a statistical

36

standpoint, beta risk is the acceptance of the null hypothesis when the alternative

hypothesis is true. This is essentially assuming there is a difference between samples

when no true difference actually exists. Missing differences can be prevented by proper

panelist training and careful experimental controls, where necessary (Lawless 2010).

There is often an error adjustment caused by the case that when human

perceptions are testing during sensory evaluations, after repeating the same test, the

results are not the same. This error variance is desired to be minimized by ensuring the

precision, accuracy, and sensitivity of the tests (Lawless 2010). Meiselman states that

some sensory tests might be adequate for some products, but not others (Meiselman

1993). Difference tests are useful to determine if consumers can tell if a product has

changed or if they prefer the newer version. Sensory perception and instrumental

measurements correlate consumer perception and quality control considerations

(Szczesniak 1987). Prevention of panelist bias is accomplished by ensuring principles of

good sensory practice. Panelist bias is the panelist’s ability to be within the accepted

range of intensity for the attributes or characteristics in the presented sample or a

control. The panelist bias can be shown in a formula (insert screen shot) where “d” is

the deviation or bias, “x” is the observed panelist value, and mu is the value of the

control or intended attribute (Meilgard 2006).

Good sensory testing principles include ensuring the testing environment is

clean, climate controlled with a 50% relative humidity (the amount of water vapor in the

air at any given time that is required to saturate the air) and temperature of around 20-

22°C, noise and distraction free, free of odors with adequate ventilation (Meilgaard and

others 2006). Panelists should be instructed how to consume and rate products

37

presented to them and all products should be properly labeled with random numbers

presented in a random order to prevent bias, a preconceived opinion of the quality of a

sample based on the brand recognition, outside source of information or influence from

other panelists (Meilgaard et. al. 2006). Samples should be presented at a standardized

temperature controlled by the panel staff that allows proper time between samples.

Additionally, water and unsalted crackers should be provided to allow panelists to

cleanse their palettes between products (Lawless 2010).

Types of Sensory Evaluation of Foods: The three types of discrimination

testing conducted in the evaluation of foods in sensory science are difference,

descriptive, and affective testing (Lawless 2010). Discrimination testing determines if

there are any perceivable differences between products using an analytical type of test

with panelists that are screened for sensory acuity. Sensory acuity is the ability of a

panelist to detect a certain limit or threshold of an attribute in a presented sample. In

some sensory tests panelists are trained to be able to generate panel terms,

comprehend panel testing concepts, and panel testing phases (Lawless 2010).

Discrimination testing is the process of utilizing sensory analysis to establish if a

detectable difference exists between two or more products. From a statistical

standpoint, the principle behind a discrimination test would be reject a null hypothesis

that says there is no detectable difference between the products presented. A triangle

test is a classic example of a difference test, typically presented to 25-40 participants

who are familiar with test procedures and have demonstrated the ability to detect

differences in products presented to them (Lawless 2010). The popularity of these tests

come from the simplicity of data analysis, given an adequate sample size to properly

38

document clear sensory differences. Replicate testing is also performed to allow for

statistical measurement. Descriptive tests determine how products differ in regard to

specific sensory characteristics utilizing panelists that are highly trained and screened

for sensory acuity and motivation, panelists are typically motivated monetarily or by

other means of compensation (Lawless 2010).

Descriptive analysis does not rely on a single sensory expert to collect sensory

data as that would present too much internal bias and would deprive the statistical data

of variance that comes from utilizing multiple panelists. Instead, descriptive analysis

uses a panel of trained individuals to extract sensory data to more accurately represent

a larger population. Descriptive sensory analysis is a complex method of sensory

discrimination and acceptance that results in a connection of similarities and differences

of a sample when compared to the complete description of the product. Results from

the descriptive analysis measure the correlation between sample ingredients or specific

production process variables with various sensory attributes (Stone and Sidel 2004).

Descriptive analysis tests are currently thought to be the most comprehensive and

informative sensory evaluation tool, as it helps to characterize changes in samples

being tested to answer questions derived from the research of food products during

their development (Lawless 2010). Sensory acceptance data generated from descriptive

analysis is often correlated to instrumental measures by means of statistical analysis, by

regression, the process of estimating the relationship between independent or

dependent variables present in samples being presented, and correlation, a measure of

the relationship between two variables as expressed by a single number to represent

the degree of the relationship (Lawless 2010). Types of notable descriptive tests

39

include: Flavor Profile method (Caul 1957), the Spectrum Method® (Meilgaard and

others 2006), and Qualitative Descriptive Analysis® (QDA®) (Stone and Sidel 2004). In

QDA® multiple product assessments are provided to panelists to assist them in making

relative evaluations to specific attributes with a high degree of precision. This is useful

since human panelists are better at assessing relative sensory characteristic

differences, and less with absolute differences (Stone and Sidel 2004).

A panel size of 10 to 12 people is suggested to gain consensus when comparing

test products used as training tools designed to illustrate specific product attributes.

Reference samples are used to help determine cohesive terminology used during the

training. Panels moderated by a panel leader, who functions as a facilitator without

interference of panelists discussion (Sensory Society 2017). Line scales are used

sometimes to help in data collection and analysis in QDA®, where a six-inch line with

anchor points on each end assist panelists during training. Panelists are able to use any

part of the scale to determine attribute intensities, resulting in relative differences

between product attributes. Relative measurements and the importance of an absolute

scale is then negated (Lawless 2010). Panelist reliability is evaluated based upon their

repeated measurements of sample attributes (Sensory Society 2017). QDA® data is

analyzed using a one-way analysis of variance (ANOVA) based on attributes and allows

for the examination of product and panelist (Sensory Society 2017). An analysis of

variance is test of the hypothesis that is used to compare the means of a variable that is

continuous and present in two or more independent comparison groups (Sullivan 2017).

ANOVA is used to test whether or not the means of several various groups are equal

and generalizes the t-test to more than two groups. The t-test is the analysis of two

40

population means through the use of statistical examination and used to test when the

variance of two normal distributions are unknown (Staff 2017). Other statistical

procedures can be applied to QDA®, such as principle component analysis, factor

analysis, multivariate analysis, and cluster analysis (Lawless 2010). Factor analysis is

useful for taking large amounts of data and condensing it into more manageable smaller

sets that are easier to extract results from while a multivariate analysis is the analysis of

data that arises from the statistical observation of more than one variable. A cluster

analysis is the grouping of a set of data points in a way that points in the same group or

cluster are more similar to each other than to those in other groups.

Visual analog scales (VAS) are commonly used for ranking the intensity of a

sensory characteristic by the panelist making a mark or slash on a measured line to

indicate intensity (Lawless 2010). An advantage of the line scale is that due to the

continuousness of the line, panelists feel less limited in their responses. The numeric

scale is limited as the panelist is only able to pick between 9 preselected values ranging

from one to nine. An issue arises if a panelist feels that a sample falls in the range

between a set of numbers as they are not able to place a mark between those points

like 4.5. Line scale allows a panelist to freely select a point anywhere along the 150cm

line without restricting the panelist to nine preselected points. When using VAS it is

suggested to employ a panel moderator to guide panelists in using the scale or

panelists will default to using sections of the scale. Anderson and Lawless conducted

experiments where they presented their panelists with examples of high and low

attributes stimuli that they perceive, which aided the panelist’s ability to orient their

responses to the scale (Anderson 1974 & Lawless 2010).

41

Affective testing aids sensory scientists in the determination of preferred products

or how well they are liked by using a hedonic test with untrained panelists, panelists are

screened for their familiarity of the products presented, an untrained panelist is a

panelist that is not able to consistently reproduce data points when given a similar

sample over different test (Lawless 2010). Affective tests are considered to be one of

the most straightforward sensory tests, as it identifies the degree of liking or disliking of

a product. A typical affective test would utilize a 9-point numeric hedonic scale given to

60-90 consumers who identified themselves as regular consumers of the product

(Lawless 2010). The Hedonic scaling provides a balanced 9-point scale for liking with a

central neutral point and increasing levels of likeability the right of the scale where (9 is

“like extremely”) and decreasing levels of likeability to the left of the scale where (1 is

“dislike extremely”). Descriptive words are equally spaced by a predetermined distance

or numeric value (Peryam and Pilgrim 1952).

Table 2-1. Typical length vs age for red drum

Age (years) Typical Length

1 12-13 2 19-21 3 24-25 4 27-29 5 30-33

USM 2017

42

Table 2-2. Vitamins in red drum fillet per ~200g

Vitamin Amount present %DV

Vitamin A 337 IU 7% Retinol 101 mcg Retinol Activity Equivalent 101 mcg Vitamin C 2.0 mg 3% Thiamin 0.1 mg 9% Riboflavin 0.3 mg 20% Niacin 4.7 mg 23% Vitamin B6 0.6 mg 30% Folate 29.7 mcg 7% Food Folate 29.7 mcg Folic Acid 0.0 mcg Dietary Folate Equivalents 29.7 mcg Vitamin B12 4.0 mcg 66% Pantothenic Acid 1.5 mg 15%

Nutrition Data 2017 Table 2-3. Minerals in red drum fillet per ~200g

Mineral Amount present %DV

Calcium 119 mg 12% Iron 1.8 mg 10% Magnesium 59.4 mg 15% Phosphorous 356 mg 36% Potassium 545 mg 16% Sodium 148 mg 6% Zinc 1.3 mg 9% Copper 0.5 mg 23% Manganese 1.4 mg 69% Selenium 24.9 mcg 36%

Nutrition Data 2017

Figure 2-1. Picture of red drum fish (Sciaenops ocellatus) (FAO 2017)

43

Figure 2-2. Coastal map of red drum habitat (GBIF 2017)

Figure 2-3. Map of Volusia county (World Maps 2017)

44

Figure 2-4. Map of Volusia county (FL Maps 2017)

Figure 2-5. Map of Indian River Lagoon (FHWA 2017)

45

Figure 2-6. Anatomy of fish (Fishing Target 2017)

Figure 2-7. External anatomy of a fish with soft dorsal fin and spiny dorsal fin identified

(Fishing Target 2017)

46

Figure 2-8. Male fish fertilizing eggs from female (Zoology 2017)

Figure 2-9. Mariculture ponds (Alloyance 2017)

47

Figure 2-10. Production cycle of red drum (FAO 2017)

Figure 2-11. Graph of age vs length of red drum (adapted from USM 2017)

48

Figure 2-12. Global aquaculture production for red drum (FAO 2017)

Figure 2-13. Chemical structure of Geosmin and 2-Methylisoborneol (Juttner 2007)

49

Figure 2-14. Diagram of human olfactory nerves (Rinaldi 2007)

50

Figure 2-13. Diagram of a gas chromatograph (Gas chromatography 2017)

Figure 2-14. Inner diagram of a mass spectrometer (Overview of Mass Spectrometry

2017)

51

Figure 2-15. Inside of a gas chromatograph/mass spectrometer (Gas chromatography–

mass spectrometry 2017)

Figure 2-16. Number of published articles in recent years related to SPME and

SPME/MS applications (Vas 2004)

52

Figure 2-17. Inner design of a SPME fiber (Kataoka 2000)

Figure 2-18. SPME fiber properties, retention vs polarity (Fanali 2017)

53

Figure 2-19. SPME process for headspace vs direct immersion SPME (Vas 2004)

Figure 2-20. Extraction time and analyte absorption rate on SPME fiber (Vas 2004)

54

CHAPTER 3 MATERIALS AND METHODS

Sample Collection

Sixty (60) pounds each of red drum fish that were grown out in a zero-discharge

IMTA system and a traditional zero-discharge RAS were collected from Mote Marine

Aquaculture Research Park (Sarasota, FL), layered in ice in insulated plastic coolers,

and transported to the University of Florida, Gainesville, for preparation and

analysis.. Insulated plastic coolers (Igloo MaxCold 98x45x41cm) were immediately

placed in a -15°F AmeriKooler (Hialeah, FL) freezer upon arrival. After complete

freezing (3-5 days), red drum samples were removed from coolers, handled with sterile

nitrile gloves, and vacuum sealed using a Kenmore Seal-n-Save (Chicago, IL) in

Kenmore Fresh Seal-n-Save Uncut 8in Wide Rolls (Chicago, IL) for continued frozen

storage at -15°F.

Sample Preparation

Vacuum-sealed red drum fish which were held for under 12 months at -15°F

before analysis, were segregated by aquaculture environment, removed from frozen

storage using sterile nitrile gloves, and the vacuum packaging was released enabling

aerobic thawing for C. botulinum control. The fish were then transferred to clear plastic

tubs, and thawed in a commercial refrigerator at 35°F for 48 hours before filleting.

Thawed red drum fish were filleted from the bone using a filet knife and the skin was

removed. After fileting, the fish to be analyzed by sensory panels-were cooked in a

Cleveland Range Steamer (Cleveland, OH) until a minimum temperature of 145°F,

measured by thermocouple (Fluke Thermometer 52II, Everett, WA). For descriptive

analysis panel training, an additional cooking method used a Kenmore (Korea) 1200

55

watt microwave oven to cook the red drum for 2 minutes and 15 seconds or until an

internal temperature of 145°F was registered using and electronic thermometer (Fisher

Scientific, Waltham, MA). Cooked red drum fillets were immediately apportioned into

one ounce (1 oz.) serving sizes and placed into 2 oz. Dart soufflé cups with lids for

sensory analysis by the panel moderator and an assistant. Estimated red drum edible

fillet yield is about 40% of total whole fish weight. Cooking loss is estimated to

contribute to another 20% weight loss.

Samples prepared for GC-GC/MS analysis, an estimated 50 grams was needed

per treatment in triplicate plus extra sample was added to accommodate for method

development and loss. SPME samples were made by taking defrosted red drum from

RAS and IMTAS and pureeing in a blender (Hamilton Beach, Glen Allen, VA) until

homogenous with 50 grams of fish sample and 50 grams of deionized water. Samples

were then labeled accordingly and the headspace was flushed with nitrogen. Samples

were then accurately weighed and transferred to 40-mL amber glass vials capped with

polypropylene caps fitted with specialized septa (Supelco, Bellefonte, PA) for DI-SPME

and gas tight syringe analysis .

Geosmin dilution samples were purchased from Supelco (Bellefonte, PA). (+/-) –

Geosmin 100µg/ml standards were serially diluted to create 1.0ppb, 0.1ppb, and

0.01ppb GSM solutions. Only the GSM has been reported but MIB solutions were also

made in the same manner and tested under the same conditions.

Additionally, approximately two pounds of intact fillets per treatment were

vacuum packaged and frozen at -20°F for further analysis.

56

Sensory Evaluation

Trained Panel

The sensory quality of red drum fish grown in RAS and IMTAS was assessed by

testing the odor sensory perception and taste intensity of fish samples. The sensory

panel used in this study was a selected and trained panel of 16 assessors with previous

experience in sensor evaluation who were familiar with the quality attributes of fresh fish

as well as being familiar with VAS when being used to assess attribute intensity.

The preliminary panel session was conducted five months after collection was

carried out to improve the ability of the panelists to recognize off-flavors and aromas

and consistently quantify sensory properties of the red drum samples grown in RAS or

IMTAS. Attributes were selected based upon their connection with quality indicators of

fresh fish by a preliminary panel of six sensory experts. The preliminary panel session

occurred six weeks before the first trained taste panel. The sensory experts were given

samples that were cooked in a microwave, unseasoned, and asked to assess the

sample for any attributes that were perceived that could be used as flavor and aroma

anchors on a VAS.

The first panel training assigned a scaled value of where the attribute anchor

would be placed on a Visual Analog Scale (VAS) which measured the intensity of the

attribute from 0-100 on a 150 mm line. A sample ballot for the trained panel can be

found in Appendix A. Reference samples were created using information received from

the preliminary taste panel conducted. Seven attributes were identified that were

present in both the RAS and IMTAS samples in varying concentrations (Table 3-1).

Reference samples were purchased 24 hours before each panel training to ensure

optimum freshness. Reference samples were prepared in an environment free of odor

57

contaminants and an estimated 70% humidity. All samples were prepared two hours

before each panel and refrigerated until the panel in a two ounce soufflé cup with a lid

immediately upon preparation. These reference samples were given to the panelists

during the panel training sessions. The red drum fish samples were profiled for aroma

intensities of the raw sample for each treatment of fish. Each treatment was labeled with

different randomly generated numbers without the panelists knowing which sample they

received but all panelists were given the same sample. After the panelists assessed

each raw sample for aroma intensity, a facilitated discussion was conducted by the

trained panelist moderator on how the sample intensities ratings occurred, which was

intended to align the sensory perception of the panelists. The samples were then

collected from the panelists and cooked in a microwave at 1200 watts (Kenmore Elite

Model 721, Chicago, IL) until an internal minimum temperature of 145° was registered

using a thermocouple (Fluke Thermometer 52II, Everett, WA); the sample cup was then

immediately capped with a lid. Samples were microwaved following the procedure

outlined for sampling aquaculture in production which uses a microwave to cook the

unseasoned fish samples until an internal temperature of 145°F is reached (Huss 1995).

The panelists then received their now cooked original samples and were asked to

assess the aroma and taste intensity of both samples, water and saltine crackers were

provided to cleanse their pallets between samples. After tasting the cooked samples, a

discussion was held with the same procedure as the raw sample. After four one-hour

training sessions, the trained panelists assessed the RAS and IMTAS samples in the

University of Florida sensory facility. Panelists were chosen for the data collection

portion of the testing after they had shown that their responses to the intensities of the

58

samples were within one standard deviation of the group’s responses. Panelists who did

not fall inside the one standard deviation of the group’s responses were invited to

participate in the testing procedure but their data was not used.

Trained Panel Testing and Data Collection

Both fish samples were analyzed by descriptive analysis test to assess the

aroma and taste intensity of red drum fish grown in RAS side-by-side with red drum fish

grown in IMTAS. All sensory evaluations were conducted in private booths with white

light. Each panelist received deionized water and unsalted crackers to cleanse their

palates between samples. A VAS was presented to trained panelists on a computer

screen using Compusense sensory analysis software with the predetermined attributes

present with 0-100 as anchors. A sample for the trained panel computer ballot can be

found in Appendix B. The trained panel test was conducted in duplicate and 16

panelists participated in the trained panel studies each.

Fish samples were randomly assigned three digit numbers using a random

number generator and sample trays used were labeled by the panelist number and

samples were arranged by corresponding numbers following the panel design layout to

ensure the orders of presentation were randomized and presented to panelists an equal

number of times. Each panelist received approximately one ounce of raw red drum fish

per environment, RAS and IMTAS. Panelists were asked to assess the aroma of the

raw samples and rate the intensity of the attributes without the use of the attribute

anchors. After completing the raw attribute assessment, the samples were steamed for

approximately three minutes until a temperature of 145°F and served to panelists within

10 minutes. Samples were then returned to panelists and they were asked to assess

the aroma and taste intensity of both samples.

59

Consumer Panel

A 60-person hedonic panel was performed at the University of Florida by

untrained, consumer panelists pre-screened for regular finfish consumption and fish

allergies by answering a questionnaire before starting the panel (Figure 3-1). Red drum

samples were steamed for approximately three minutes to an internal temperature of

145°F and served to panelists within five minutes. Fish samples were randomly

assigned three digit numbers using a random number generator and sample cups used

were labeled by the corresponding numbers. Each panelist received approximately one

ounce of cooked red drum fish per environment, RAS and IMTAS. Orders of

presentation were randomized and presented to panelists an equal number of times. In

one testing session, both fish samples were analyzed by a consumer hedonic likeability

and preference test to assess the acceptability of red drum fish grown in RAS side by

side IMTAS. All consumer evaluations were conducted in private booths with white light.

Each panelist received deionized water and unsalted crackers to cleanse their palates

between samples.

A one to nine hedonic likeability scale was presented to consumer panelists on a

computer screen using Compusense sensory analysis software, with anchors presented

for dislike extremely (1) on the far left, neither like / nor dislike (5) in the middle, and like

extremely (9) on the far left. A sample for the consumer panel computer ballot can be

found in Appendix C. After both RAS and IMTAS samples were rated, panelists were

then asked to pick which sample they preferred. Sixty panelists participated in this

study.

60

Volatile Analysis

GC-GC/MS analysis

SPME Extraction of Headspace Volatiles

Volatiles were extracted from the RAS and IMTAS samples by SPME using a 2

cm fiber (Supelco, Bellefonte, PA) comprised of coated divinylbenzene/Carboxen on

polydimethylsiloxane (DVB/CAR/PDMS) 50/30 μm via a manual SPME fiber assembly

holder for 30 minutes in a heating block at 55°C.

HS-SPME and DI-SPME extracted volatile compounds were sheathed by the

fiber assembly and immediately transferred to the injection port of a Shimadzu GC/MS

(Shimadzu, Columbia, MD) and held at 270°C in the injection port for 12 minutes

allowing volatile compound release from fiber into a 30 meter RTx-5MS capillary column

(Restek, Bellfonte, PA) with a 0.25 mm internal diameter and a 0.25 µm stationary

phase. Helium was used as the carrier gas at a flow rate of 1.1 mL/min. The oven was

initially held at 60°C for 1 minutes, then increased by 10°C/min to 300°C and held for 11

minutes. Samples were then cut by Dean’s Switch to a second DB-5 column under the

same temperature program and analyzed by MS in scanning mode. Compounds

detected by MS were compared to National Institute of Standards & Technology (NIST)

and Shimadzu Flavor & Fragrance databases for matching mass fragmentation patterns

to known compounds.

Gas Tight Syringe Extraction

Amber vials containing RAS and IMTAS samples were placed on a heating block

at 55°C for 15-20 minutes until an internal temperature read 55°C. A 1ml volume of

headspace gas was taken from the sample vial using a gas tight syringe and

immediately injected into the GC/MS. A 100 meter, CP-Sil 88 (Agilent, Santa Clara, CA)

61

capillary column with a 0.25mm internal diameter and a 0.25µm stationary phase was

used. Helium was used as the carrier gas at a flow rate of 1.31mL/min. The oven was

initially held at 150°C for zero minutes, then increased by 10°C/min to 300°C and held

for 5 minutes. Samples were then analyzed and cut based upon peaks present from FID

analysis.

Each sample was injected again under the same column parameters. The oven

was held at 100°C for zero minutes, then increased by 10°C/min to 225°C and held for

22.5 minutes. Samples were then cut by Dean’s Switch, based upon the peak times

from FID, to a second 30 meter SLB-IL11 (Supelco, Bellfonte, PA) column with 0.25mm

internal diameter and 0.2µm stationary phase under the same temperature program and

analyzed by MS in scanning mode.

Statistics

The number of trained panelists was established using a statistical table of

critical minimum values required for a significant difference ( α=0.05) from (Lawless

2010). Data from panelists during training was analyzed for statistical differences using

Microsoft Excel 2010 for Windows version 14.0.7015.1000 (Microsoft Corporation,

Redmond, WA) using one standard deviation from the collective group answers.

Trained panelist sensory scores were analyzed for statistical significance using a two-

way analysis of variance (ANOVA)(Statistical Analysis System, Cary, NC) which

allowed for panelist by sample interaction. Consumer sensory scores were analyzed

using a two-way (ANOVA) (Statistical Analysis System, Cary, NC).

62

Table 3-1. Attribute reference samples

Attribute Attribute anchor

Scale value

Verbal description Sensory definition Reference material

Cucumber 1A 90 Fresh cucumber, green The aroma associated with fresh cucumbers, similar aromas can be associated with certain species of very fresh raw fish

Diced seedless cucumbers

Seawater/ Seaweed

2A 80 Fishy, seawater, salty, musty

The aroma and flavor associated with the saltwater, specifically the sea

Chopped seaweed

Green/ Grassy

3A 70 Green, bright, grassy The aroma associated with the pronounced sharp odor of recently cut grass and green leaves

Diced green beans

Dirt 4A 30 Earthy, dirty The slightly musty aromatics associated with raw potatoes and damp humus, slightly musty notes.

Dry dirt Dirt 4B 100 Musty, muddy, dirty Moist dirt

Metallic 5A 100 Metallic odor An aromatic associated with an oxidized silver (or other oxidized metal) utensil when it is rubbed inside the mouth

Old penny

Hay 6A 30 Buttery, barn, decaying vegetation

Moldy/mildew-like aromatics associated with rotting plants.

Corn husk

Hay 6B 60 Sweet, mildew, decaying vegetation

Hay

Fresh pumpkin

7A 100 Melon rind, dirt, fruity, sweet, floral

The aroma associated with fresh melon rinds, flesh, and seeds

Diced fresh pumpkin

63

Figure 3-1. Consumer panel screener

64

CHAPTER 4 RESULTS AND DISCUSSION

Sensory Evaluation

Trained Panel

Tables 4-1 through 4-3 highlights the mean sensory scores of descriptive

analysis panelist results. There were no significant differences found between any

aroma attributes in either system. No statistically significant difference was found

between any of the aroma attributes for the raw or cooked sample of either system at

α=0.05.

The flavor attribute of “dirt” showed a significant difference in the cooked sample

with a mean value of 17.14 and 10.55 for RAS and IMTAS, respectively. The remaining

flavor attributes for the cooked RAS and IMTAS samples showed no significant

difference. The presence of a more pronounced flavor of “dirt” in the cooked RAS

sample, supports the idea that IMTAS removes the taste of “dirt” in fish flesh.

Consumer Panel

Results from the 60-person hedonic consumer sensory panel are found Table 4-

4. The likeability ratings for red drum fish cultivated in RAS and IMTAS were 6.02 and

6.05, respectively. Therefore, no significant difference in likeability was attributed

rearing environment at α=0.05.

In Table 4-4 preference differences between red drum from RAS and IMTAS

were nearly equal 29-RAS/31-IMTAS. There were no significant differences between

likeability ratings or preference.

One hypothesis was that red drum muscle tissue reared in RAS, would have a

dirtier, muddier flavor associated with it. It was also hypothesized that the dirty, muddy

65

flavor was associated with the presence of GSM and or MIB, although other aroma

compounds elicit off-flavors in fish.

Volatile Analysis

HS-SPME

Table 4-5 highlights some of the compounds extracted from the

headspace of a sample vial containing red drum raised in indoor RAS and IMTAS (and

equal amounts of salt) by HS-SPME. Table 4-5, lists 1-Penten-3-ol extracted by HS-

SPME is present in the cooked IMTAS red drum sample. 1-Penten-3-ol has an aroma

of green/grass, some descriptive analysis panelists associated this off-flavor for all of

the samples.

Aqueous samples containing GSM and MIB have used the addition of sodium

chloride (salt) and subjection to heating to aid analyte extraction from the liquid phase to

the gas phase (Grimm 2000). Separation and identification of HS-SPME extracts were

conducted by GC-GC/MS analysis.

HS-SPME didn’t prove to be very effective in identifying GSM or MIB.

Standard mixtures of GSM and MIB at varying concentrations (0.001 to 1 ppb) weren’t

identified by GC-GC/MS of SPME extraction of the red drum sample headspace. Other

researchers have reported similar results. For example, Cardin (2016) noted that

SPME encounters problems with differences in phase ratios between the SPME fiber

and the amount of sample required to low ppt concentrations, as well as the affinity of

the odor producing compounds to remain in the sample matrix. Other researchers have

concluded that SPME is not effective in the analysis of complex matrices like muscle

tissue due to the physical bonds that allow the prospective analyte to bond to the

sample matrix (Grimm 2000). Limited extraction by HS-SPME may also be associated

66

with GSM and MIB being lipophilic in nature will only partition from fish muscle into the

headspace in very low amounts making HS-SPME ineffective (Lloyd 1999). Compound

extraction limitations by SPME have been overcome by modifications of standard SPME

extraction procedures. For example, SPME recovery can be so low that isotope dilution

is needed for quantitative measurements of each targeted compound, with carryover

being as high as 10% making it difficult to establish a reliable method detection limit

(Cardin 2016). In addition, with the complex sample matrix of fish samples, it may be

unrealistic to report analyte concentrations below 0.001ppb regardless of how much

signal is present, due to the determination of practical limits of detection (Cardin 2016).

Another modification of traditional SPME techniques is microwave distillation.

Analytes of interest in complex matrices can be extracted using microwave distillation

(MD) (Grimm 2000). In MD the analytes are steam distilled from the matrix and

transferred to a flask in a chilled water bath where the steam effluent carrying the

analytes is collected. This process frees bound analytes from the food matrix (like fish

muscle tissue) and allows them to be collected in an aqueous matrix. In procedures

described by Lloyd (1999), SPME extraction is then used to concentrate the volatile

organic compounds from the aqueous solution. MD when combined with SPME allows

for a sensitive technique that analyzes the semi-volatile and thermally stable volatile

compounds in complex matrixes.

Other researchers found that steps were necessary to release volatile

compounds from fish samples. Jones (2013) conducted experiments on Barramundi

fish using static HS-SPME paired with GC/MS in which samples were homogenized

with deionized water and the volatiles were collected from the condensate after steam

67

was introduced to the sample and then cooled. Scores from the negatively associated

sensory attribute evaluation correlated with the instrumental analysis of flesh GSM

concentration when compared to a collection of key sensory attributes for the fish

samples. The Jones study suggests that instrumental analysis has the potential to be

used as a tool to estimate the impact that GSM levels will have on the overall flavor

attributes of fish samples.

However, SPME might still have some advantage over extraction systems like

purge and trap followed by solvent elution, is that it allows for subsequent analysis

without the introduction of solvents into the system (Lloyd 1999). Methods like purge

and trap have limited effectiveness due to the low recoveries and poor precision

resulting in unusable method detection limits (Cardin 2016).

DI-SPME

Table 4-6 highlights some of the compounds extracted from the aqueous portion

of a sample vial containing red drum raised in indoor RAS and IMTAS (and equal

amounts of salt) by DI-SPME. Table 4-6, lists carbamic acid extracted by DI-SPME is

present in all red drum samples raised in both environments. Carbamic acid has an

aroma of ammonia, although descriptive analysis panelists did not associate this off-

flavor for any of the samples. N-Hexadecanoic acid also found in the raw RAS and

IMTAS samples. N-Hexadecanoic has waxy like odor.

After unfavorable results were obtained from HS-SPME, a modification to the

traditional headspace method was examined. Some researchers have had success by

sticking the fiber in the aqueous portion of the sample matrix, as part of Direct

immersion (DI) – SPME. DI-SPME allows for balanced coverage of the fiber during

extraction of compounds in complex matrices (Gionfriddo 2015). However, the solid

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porous coating has the possibility of surface saturation that may occur during the

adsorption from the matrix that contains a high concentration of analytes or interfering

compounds. During saturation, nonlinear adsorption isotherms result from competitive

extraction. Compounds that have a stronger affinity for the coating displace the

compounds with less affinity for the extraction phase (Gionfriddo 2015). This reaction is

likely due to matrix compounds like suspended solids and macromolecules binding to

the analytes, especially nonpolar compounds, and reducing their availability to the

coating. Another study noted how fiber life and performance was negatively impacted by

the presence of NaCl as well as the organic materials in the compound matrix baking

onto the fiber during repeated desorption (Watson 2000).

Other reasons that GSM and MIB were difficult to extract from red drum fish

muscle tissue include fat content and the sampling of muscle tissue for analysis. Fillet

fat has been linked with increased off-flavor present in fish (Grimm 2004). Fat content of

tested fish from 0.5 to 11%, had a significant effect on the absorption of MIB, lean fish

accrued less MIB during exposure to MIB than the fatter fish (Johnsen 1992). The

bioaccumulation of GSM is dictated by the compounds hydrophobicity which is related

to the compounds structure (Schuurman and Klein 1988). Another study stated that

sampling from different areas of fin fish has correlated the flesh from the dorsal shoulder

region to be relatively low in total lipids which could be responsible for the lowered

presence of GSM in this study (Jones 2013).

Gas Tight Syringe Headspace Extraction

Table 4-7 highlights some of the compounds extracted from the headspace of a

sample vial containing red drum raised in indoor RAS and IMTAS (and equal amounts

of salt) by collecting a volume in a gas tight syringe. Table 4-7, lists 1,2-Propadiene-

69

1,3-dione extracted by gas tight syringe (with no cryo-focusing) is present in raw and

cooked RAS and IMTAS red drum samples. 1,2-Propadiene-1,3-dione has an aroma of

acrolein (burnt fat/mustard oil), although descriptive analysis panelists did not associate

this off-flavor for any of the samples. (S)-(+)-1-Cyclohexylethylamine also found in

cooked RAS sample, raw RAS and cooked IMTAS samples. (S)-(+)-1-

Cyclohexylethylamine has an amine-like (fishy) odor.

After unfavorable results were obtained from HS-SPME and DI-SPME, another

modification to the method was examined. Gas tight syringe extraction has been used

in conjunction with a purge and trap system to analyze volatile compounds in salmon in

a GC/MS (Milo 1996). The purge and trap system operate by injecting a sample with an

inert gas which forces the volatile compounds out of the sample and are then retained in

an analytical trap. The volatile compounds are desorbed by heating the trap which is

injected into the GC by backflushing the trap with carrier gas. Lapsongphon used a gas

tight syringe to analyze fish sauce volatiles and used a cooled injection port, something

similar to a cryo trap allows for cryo-focusing of the compounds in the injection port.

Even though analytical correlations of GC-GC/MS and human sensory data were

not discovered, the results of the descriptive analysis trained panel showed differences

between the RAS and IMTAS aquaculture systems. This result agrees with recent

research findings that IMTAS effectively reduces the amount of ammonia in the water

system using nitrifying bacteria to convert nitrites into nitrates (Rakocy 2006). The

Merck Index states that in acidic conditions, GSM decomposes to form odorless

compounds. Further investigation of GSM decomposition products would likely be

70

beneficial to the aquaculture industry to determine whether fish were raised in water

sources contaminated with algae responsible for GSM and MIB production.

Therefore, “dirt” flavor was associated with red drum fish raised in RAS.

However, consumers responses did not allow the measurement of “dirt” flavor, either

positively or negatively, only likeability. Thus, any correlations between the two panels

would not have statistical value. It can be said that consumer panelists weren’t

overwhelmingly positive because the mean rating was a six, where a five was “neither

like not dislike”. Even though there is no direct evidence that “dirt” or “muddy” flavor

factored into their response, it is a potential source for a diminished likeability rating.

Table 4-1. Mean aroma attribute scores for trained panels-raw samples

Table 4-2. Mean aroma attribute scores for trained panels-cooked samples

Attribute Mean score-RAS Mean score-IMTAS

Cucumber 8.52a 6.27a Seawater/ Seaweed 19.66a 17.23a Green/ Greasy 14.52a 13.96a Dirt 15.84a 14.02a Metal 6.41a 4.82a Hay 17.43a 16.55a Pumpkin 11.61a 10.32a

Table 4-3. Mean flavor attribute sensory score for trained taste panel

Attribute Mean score-RAS Mean score-IMTAS

Cucumber 8.98a 6.27a Seawater/ Seaweed 19.75a 16.43a Green/ Greasy 13.93a 11.23a Dirt 17.14a 10.55b

Attribute Mean score-RAS Mean score-IMTAS

Cucumber 19.43a 16.64a Seawater/ Seaweed 22.09a 18.64a Green/ Greasy 21.73a 19.75a Dirt 12.96a 10.39a Metal 6.18a 6.09a Hay 22.18a 21.77a Pumpkin 18.41a 17.64a

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Metal 7.36a 6.86a Hay 14.86a 14.80a Pumpkin 7.07a 6.52a

Table 4-4. Consumer sensory data

Sample Overall liking Preference

RAS 6.02a 29 IMTAS 6.05a 31

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Table 4-5. Volatile compounds identified by GC-GC/MS samples extracted with HS-SPME

Sample Compound Odor/ Aroma

Raw RAS Trifluoroethanol ethanol like

Raw RAS Benzaldehyde almond like

Raw RAS Phosphonoacetic acid** odorless

Raw IMTAS Heptane gasoline like

Raw IMTAS Propanedioic acid** odorless Raw IMTAS Nonanal* aldehyde like

Cooked RAS 1,2-benzisothiazol-3-amine N/A

Cooked RAS Nonanal* aldehyde like

Cooked RAS Phosphonoacetic acid** odorless

Cooked IMTAS Perfluorotributylamine odorless

Cooked IMTAS 1-Penten-3-ol green/grass

Cooked IMTAS Oxime strong, irritating

Cooked IMTAS Nonanal* aldehyde like

* compounds found in all samples except raw RAS ** compounds found in all samples except cooked IMTAS

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Table 4-6. Volatile compounds identified by GC-GC/MS samples extracted with DI-SPME

Sample Compound Odor/ Aroma

Raw RAS Carbamic acid* ammonia

Raw RAS 1-Alanine ethylamide N/A

Raw RAS Orcinol earthy/ moss like

Raw RAS n-Hexadecanoic acid** waxy

Raw IMTAS Carbamic acid* ammonia Raw IMTAS 2-Hydroxy-4-methoxybezaldehyde vanilla Raw IMTAS n-Hexadecanoic acid** waxy

Cooked RAS Carbamic acid* ammonia

Cooked RAS 1-(2-dimethylaminoethoxy)-2-benzamido N/A

Cooked RAS Hasmigone warm, jasmine like

Cooked RAS 1,2-hydrazinedicarbothioamide N/A

Cooked IMTAS Carbamic acid* ammonia

Cooked IMTAS 1,2,4-Benzenetricarboxylic acid N/A

Cooked IMTAS 1-Pentene gasoline like

Cooked IMTAS 11-[(3,4-dimethoxyphenyl)methyl]-2,3,7 N/A

* compounds found all samples ** compounds only found in raw RAS and IMTAS samples

74

Table 4-7. Volatile compounds identified by GC-GC/MS samples extracted with a gas tight syringe

Sample Compound Odor/ Aroma Raw RAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Raw RAS (S)-(+)-1-Cyclohexylethylamine** Amine like Raw RAS cis-3-Methylcyclohexanol none Raw RAS 2-Formylhistamine N/A Raw RAS 3-Hexyn-2-ol none Raw RAS 3-Dimethylaminoacrylonitrile None Raw RAS 2-Trifluoroacetoxydodecane N/A Raw RAS 10-pentadecenol alcohol like Raw IMTAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Raw IMTAS Diethyl Phthalate slight odor (pesticides) Raw IMTAS Cycluron odorless Raw IMTAS dl-Phenylephrine none Raw IMTAS 3-Thiophenecarboxylic acid none Raw IMTAS Benzoxazol Pyridine like (nauseating, fish-like) Raw IMTAS 1-(4-Acetamidoanilino)-3,7-dimethylbenzo N/A Raw IMTAS 7-Hydroxyimino-4,5,6,7-tetrahdrobenzofuroxide N/A Raw IMTAS Imidazole Amine like Raw IMTAS Tricyclo[4.3.1.1(3,8)]undecane-1-carboxylic acid none Cooked RAS 1,2-Propanediamine Fishy, ammoniacal Cooked RAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Cooked RAS 1-(5-Bicyclo[2.2.1]heptyl)ethylamine none Cooked RAS (S)-(+)-1-Cyclohexylethylamine** Amine like Cooked RAS Thiophene-3-ol none/ benzene like (thiophene) Cooked RAS 1-(4-Acetamidoanilino)-3,7-dimethylbenzo N/A Cooked IMTAS 1,2-Propadiene-1,3-dione* acrolein/mustard oil Cooked IMTAS 3-Butyn-1-ol none Cooked IMTAS (S)-(+)-1-Cyclohexylethylamine** Amine like

* compounds found all samples ** compounds found in all samples except raw IMTAS

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CHAPTER 5 CONCLUSIONS

Sensory analysis was conducted on red drum fish samples that were raised in

RAS or IMTAS environments. The trained taste panelists were trained to assess seven

different attributes that are associated with fresh fish, with one of the attributes (dirt)

highlighting the presence of GSM and MIB. The trained panel scores showed no

significant difference between the aroma attributes in either of the samples. However, a

significant difference was found in the flavor attribute of dirt with a higher average

intensity present in the RAS samples when compared to the IMTAS samples. The

higher score indicates that a more pronounced intensity of dirt flavor provides evidence

of an increased presence of dirty or muddy flavor, typically associated with GSM and

MIB, in RAS samples.

The consumer panel results showed no significant difference between the overall

likability or preference between the samples. This indicates that panelists could not

distinguish a difference between the two samples and that the difference between the

presence of GSM and MIB is not noticeable to the average consumer.

The SPME and gas tight syringe extracted volatiles showed the presence of

additional off-flavor compounds. Some of these additional compounds were noted to

have aromas of ammonia, green/grassy and fatty/waxy. Some of the aromas present

were not accounted for in consumer panels. For example, even though there is no

direct evidence that “dirt” or “muddy” flavor factored into their response, it is a potential

source for a diminished consumer likeability rating.

This study has revealed that there is a discernable difference in earthy and

muddy off-flavors between the two samples. Trained panelists were taught to masticate

76

by techniques accentuating retronasal perception of off-flavors, where “dirty” off-flavors

were detected in red drum fish harvested from the RAS. It is also noted that the average

consumer of fish did not distinguish a statistically relevant difference between the two

types of fish. Future research may involve the further analysis of volatile compounds in

the RAS and IMTAS samples to determine what is ultimately happening in the IMTAS

samples that lessen the impact of off-flavors commonly associated with RAS

environments.

77

APPENDIX A SAMPLE BALLOT FOR TRAINING SESSIONS FOR TRAINED TASTE PANEL

78

APPENDIX B SAMPLE BALLOT FOR TRAINED TASTE PANEL SENSORY EVALUATION

79

APPENDIX C BALLOT FOR CONSUMER PANEL SENSORY EVALUATION

TODAY'S SAMPLE: FISH

To start the test, click on the Continue button below:

Panelist Code: ________________________ Question # 1.

Please indicate your gender. Male Female Question # 2.

Please enter your age: Age __________ Question # 3.

How would you describe your racial or ethnic origin? Caucasian/White African-American/Black Hispanic/Latino Asian American Indian Other Would prefer not to say Question # 4.

How would you describe your racial background?

___________________________________________________________________________

80

Question # 5.

How often do you consume fish?

Daily 2-3 times a week Once a week 2-3 times a month Once a month 2-3 times a year Rarely I don't eat fish

Take a bite of cracker and a sip of water to rinse your mouth.

WHEN ANSWERING ANY QUESTION, MAKE

SURE THE NUMBER ON THE CUP MATCHES THE NUMBER ON THE MONITOR.

Please click on the 'Continue' button below.

Question # 6 - Sample ______

Please indicate how much you like or dislike sample 1 OVERALL. Overall Liking

Dislike extremely

Dislike very much

Dislike moderately

Dislike slightly

Neither like nor dislike

Like slightly

Like moderately

Like very much

Like extremely

1 2 3 4 5 6 7 8 9

Take a bite of cracker and a sip of water to rinse

your mouth.

WHEN ANSWERING ANY QUESTION, MAKE SURE THE NUMBER ON THE CUP MATCHES THE

NUMBER ON THE MONITOR.

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Please click on the 'Continue' button below. Question # 6 - Sample ______

Please indicate how much you like or dislike sample 2 OVERALL. Overall Liking

Dislike extremely

Dislike very much

Dislike moderately

Dislike slightly

Neither like nor dislike

Like slightly

Like moderately

Like very much

Like extremely

1 2 3 4 5 6 7 8 9

Paired Comparison

In front of you are the two samples you have tasted.

Please taste both samples again and indicate which sample you

prefer.

__________ __________

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BIOGRAPHICAL SKETCH

Daniel Clark was born in Stuart, Florida and was raised by his parents, Dale and

Diane Clark, in Palm City, Florida. He received his Associate’s degree in business

administration from Indian River State College before receiving his Associate’s degree

in culinary arts from The Culinary Institute of America in 2011. Daniel then worked in the

culinary field before deciding to return to the University of Florida and pursue a degree

in food science. After finishing his bachelor’s degree, Daniel then decided to pursue a

Master of Science in food science and human nutrition under the direction of Dr. George

Baker.