evaluationofsmartportabledeviceforfooddiagnostics:a...

Research ArticleEvaluation of Smart Portable Device for Food Diagnostics APreliminary Study on Cape Hake Fillets (M capensis andM paradoxus)

Marta Castrica1 Sara Panseri 1 Elena Siletti2 Federica Borgonovo3 Luca Chiesa 1

and Claudia M Balzaretti1

1Department of Health Animal Science and Food Safety Universita degli Studi di Milano 20133 Milan Italy2Department of Economics Management and Quantitative Methods Universita degli Studi di Milano 20133 Milan Italy3Department of Environmental Science and Policy Universita degli Studi di Milano 20133 Milan Italy

Correspondence should be addressed to Sara Panseri sarapanseriunimiit

Received 30 August 2018 Revised 7 November 2018 Accepted 26 November 2018 Published 12 February 2019

Academic Editor Hassan Arida

Copyright copy 2019Marta Castrica et alis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

e new smartphone-based food diagnostic technologies offer significant advantages over traditional methods as they can beeasily applied in various steps of the agrifood supply chain including household use and also in the food recovery field forcharitable purposes aimed at helping to reduce food waste Further advantages include the low cost the minimal equipment andnonspecialized personnel required is study evaluated the performance of two instrumental measurements of the sensors anelectronic nose (PEN3WinMuster Airsense Analytics) and a smart portable device (FOODsniffer ARS LABUS)e preliminarystudy was conducted on cape hake fillets In order to test the performance of PEN3 and FOODsniffer total volatile basic nitrogen(TVB-N) values were considered as the reference Principal component analysis (PCA) and Pearsonrsquos correlation were performedin order to compare PEN3 with TVB-N and for the FOODsniffer evaluation a one-way ANOVA was carried out A significantcorrelation was shown between PEN3 first component and TVB-N (r 092 P 001) e ANOVA results also confirmed agood agreement between FOODsniffer TVB-N (F 5199 P 001) and PEN3 (F 14317 P 001) Our simulation resultsconfirmed good performance in both methods

1 Introduction

Aquatic products (mainly fish aquatic molluscs and crus-taceans) have a critical role in the food system providingnearly 3 billion people with at least 15 of their animalprotein intake [1] In the EU the 2011 per capita con-sumption of protein from fish and seafood was 66 grams perday covering 7 of the total protein intake [2] Meat andanimal proteins (excluding fish and seafood) represented52 of the total while vegetal proteins (434 grams percapita per day) covered 41 [2]

e main commercial fish species consumed in the EUare tuna (258 kgcapita) cod (240 kgcapita) and salmon(209 kgcapita) e consumption of cod has increased by9 since 2013 [2] e FAO estimates that 30 of all the fishand seafood produced in the Europe was lost or wasted in

2009 [3] ere are several reasons for food waste in thefishing industry including (i) losses in primary fish andseafood production due to discard rates of marine catches(ii) a large proportion of purchased fish and seafood wastedby consumer households and (iii) high distribution lossesdue to deterioration during fresh fish and seafood distri-bution [3]

e development of new technologies applicable at thedifferent steps of the food supply chain can thus offer sig-nificant advantages not only for food business operators[4ndash6]

Electronic noses which have already been testedconsiderably in various fields [7ndash15] provide a rapid andpredictive response and represent a valid support com-pared to the traditional more time-consuming laboratorymethods

HindawiJournal of ChemistryVolume 2019 Article ID 2904724 7 pageshttpsdoiorg10115520192904724

e electronic nose is a directly applicable method thatrequires minimum sample pretreatment and no specificconsumables or reagents and is able to provide rapid analysis[5] Furthermore this approach is not invasive and con-sequently does not alter the product For this reason re-cently the interest and application of E-nose in foodindustry such as (i) quality control (ii) process operationsmonitoring (iii) shelf-life determination and (iv) spoilageevaluation have increased considerably [5 16] Based on theclassical definition given by Gardner and Bartlett [16] theelectronic nose is ldquoan instrument which comprises an arrayof electronic chemical sensors with partial specificity and anappropriate pattern-recognition system capable of recog-nising simple or complex odoursrdquo [17 18] e E-nosesystem consists of several components such as specifichardware with sensors electronics pumps air conditionerflow controller and dedicated software for hardwaremonitoring data preprocessing and statistical analysis Suchcharacteristics make the E-nose a device able to mimic thehuman olfactory perception and to provide a digital odourprint of the sample which can be processed by appropriatestatistical software An E-nose gas sensor array showssensitivity toward certain classes of compounds (volatileorganic compounds (VOCs)) produced by the main spoilageorganisms e alteration in the pattern of VOCs is in-dicative of the degradation processes occurring in theproducts [5]

Smartphone sensors on the contrary could be easilyused by unskilled personnel such as volunteers in charityorganizations and final consumers [19]

e emerging need to reduce food waste but also toensure food security to people in food poverty has led to thedevelopment of new technologies in the food businessaimed not only at preventing food losses and waste in theprimary production processing distribution retail andfood services but also at improving the quality of productsrecovered by charities [20ndash23]

In the third sector such as food banks where the work isdone by volunteers without specific training in the food fieldthe objective is to provide valid support in order to guaranteea safe second life to the food recovered [24]

Fishery and aquaculture products together with otheranimal and vegetable proteins are also an important sourceof protein and thus an essential component of a healthy dietFor this reason these products are also essential in the diet ofpeople in food poverty [12]

e aim of this study was to explore whether an elec-tronic nose [25] (PEN3) could be used as a fast screeningmethod for food business operators and as a support forofficial laboratory methods (TVB-N) In addition theFOODsniffer (ARS LAB US) [26] was evaluated for itspotential use as an easy-to-use sensory tool both at thedomestic level and for volunteers working for charities inorder to evaluate the acceptability of the products forconsumption by the needy

e two portable sensing devices were evaluated on capehake fillets taking the measured TVB-N values into con-sideration as the reference (gold standard) In fact theEuropean Commission defined the TVB-N as the reference

method and in the Decision of 8 March 1995 fixed the TVB-N limit values for three categories of fishery products [27] Inthis work the cape hake fillets were assessed in terms of theirTVB-N values and were measured with PEN3 whenever theFOODsniffer revealed variations

2 Materials and Methods

21 Sample Collection and Experimental Design Experimentswere performed at the Food Inspection Laboratory of theDepartment of Health Animal Science and Food Safety inMilan (Italy) One batch of cape hake (Merluccius capensisMerluccius paradoxus) was collected e batch containedfish caught in the Southeast Atlantic Ocean (FAO area 47)produced and frozen by a company based in Walvis BayNamibia (company specializing in the catching and mar-keting of frozen seafood products in the internationalmarket) and imported by an Italian company that dis-tributes frozen food to wholesalers industry and masscaterers

e batch contained fish fillets that were already skinlessand portioned into individual fillets with a medium weightof 90ndash110 g (thawed weight 85ndash100 g)

Before being analysed each fillet was washed with steriledistilled and deionized water in order to remove the glazingand the fillets were then left to thaw overnight under acontrolled temperature (0ndash4degC) In order to reproduce the useat the domestic level the defrosting procedures were carriedout as provided in the productrsquos specifications (Table 1)

On the first day of storage (day 1) 8 fillets were testedwith all three methods FOODsniffer PEN3 and TVB-NSubsequently for six consecutive days (from day 2 to day 7)eight fillets were measured with the FOODsniffer and whenthe FOODsniffer detected variations the fillets were assessedin terms of their TVB-N and were measured with PEN3 toconfirm the results In this study the variations detected bythe FOODsniffer occurred on storage days 3 and 7 Figure 1summarizes the experimental design

22 FOODsniffer FOODsniffer (ARS LAB US) created byscientists and researchers of the Kaunas University ofTechnology in cooperation with the company ARS LAB is anew and fast device used to assess the freshness of food ofanimal origin and specifically patented for the meat matrix[26 28] FOODsniffer was designed to detect whether aproduct (i) is fresh (ii) can be safely eaten after cooking or(iii) is spoiled

FOODsniffer rapidly estimates the quality and safety ofthe raw material correlating them to the levels of volatileorganic compounds present in the tested matrix through agas sensor system including at least two metal-oxidesemiconductor sensors configured to measure NH3 andCH values e technology is based on the detection of lowconcentrations of volatile compounds that are associatedwith deterioration

e device is composed of a metal-oxide sensor systemadapted to respond to the speed of changes in the con-centration of volatile compounds a processor designed toreceive and process signals incoming from the sensor system

2 Journal of Chemistry

and to turn them into a sequence of electrical signals on thebasis of variation in the concentration of volatile com-pounds and a Bluetooth device which according to thealgorithms in synchronization with the cloud provides theuser result to mobile devices (tablets or smartphones) [26]

e protocol used for the sample analysis was drawn upaccording to the FOODsniffer user manual

FOODsniffer is controlled through a dedicated smart-phone app which can be operated by nonspecialized per-sonnel It provides information on the level of freshness ofraw materials satisfactory (fresh) acceptable (to be con-sumed after cooking) and unsatisfactory (spoiled)FOODsniffer results are qualitative outputs also associatedwith colours green (fresh) orange (to be consumed aftercooking) and red (spoiled) FOODsniffer was tested in orderto evaluate its potential use as an easy-to-use sensory toolboth at the domestic level and for charity volunteers to assesswhether a food product is fit for consumption

23 Electronic Nose System e Portable Electronic NosePEN3 (WinMuster Airsense Analytics Schwerin Germany)was used in this study It has 10 metal-oxide sensors andTable 2 lists all the sensors used and their applications Eachsensor is sensitive to a specific group of compounds and itsresponse is expressed as resistivity (ohm) [25]

e instrument (PEN3) consists of three units (i) asampling and washing unit (ii) a chamber consisting of anelectrochemical gas sensor array and (iii) a pattern-recognition system During the analysis eight fillets were

kept at a constant temperature in a thermostatic water bathat 18plusmn 2degC to prevent the effects of temperature fluctuationand in order to create the correct headspace All the filletswere cut into pieces of equal weight (approximately 10 g)and each one was placed in a small sealed glass vial with acapacity of 100ml Each analysis was repeated twice esealed glass vials containing the fillets were connected to thePEN3 with a probe e headspace gas in that vials waspumped from the sampler through the sensor array at400mlmin Before and after each measurement the sensorswere cleaned by air using carbon filters Sensor response datawere recorded every second e analysis protocol wasdefined by setting up the E-nose parameters (flow rateduration of measurement etc) according to the manufac-turerrsquos instructions e analysis of each fillet lasted 640seconds e set of signals derived from the electronic noseduring the analysis takes the form of a pattern e patterndata were analysed using WinMuster (version 162 17 May2014 copyright Airsense Analytics GmbH)

24 Chemical Analysis Eight fillets were prepared for theanalysis of TVB-N levels according to Regulation (EC) no20742005 [29]

In brief 10 g (plusmn01) of the fillet was blended with 90ml ofperchloric acid 6 Subsequently 50ml of the filtrate wasintroduced into an apparatus for steam distillation and tocheck the level of alkalinisation of the extract several dropsof phenolphthalein were added Before extraction and steamdistillation a few drops of silicone antifoaming agent and65ml of sodium hydroxide solution were added e steamdistillation was regulated so that around 100ml of thedistillate could be produced in 10 minutes e distillationoutflow tube was submerged in a receiver with 100ml ofboric acid solution to which three to five drops of the in-dicator solution were added After distillation the volatilebases contained in the receiver solution were determined bytitration with a standard hydrochloric solution Eachanalysis was repeated twice as required by Regulation (EC)no 20742005 e method applied is correct if the differ-ence between the duplicates is not greater than 2mg100 gFor the blind test 50ml of perchloric acid solution was usedinstead of the extract

Finally the TVB-N concentration was calculated usingthe following equation

TVBminusNexpressed in mg100 g of sample

1113888 1113889

V1(vol of 001 HCl solution in ml for sample)minusV0(vol of 001 HCl solution in ml for sample)( 1113857 times 014 times 2 times 100

M(weight of the sample in g)

(1)

25 Statistical Analysis e data obtained from TVB-Nvalues (gold standard) PEN3 (E-nose) and FOODsnifferwere subjected to statistical analyses e aim was to

determine whether the three analysis methods could beconsidered as being equally reliable in evaluating thefreshness of the fish For each sample (cape hake fillets)

Table 1 Product specifications

Shelf lifeProduction datefreezing date 7 July 2015

Best before end 7 July 2017Conservation methodsminus18degC 18 monthsminus12degC 1 monthminus6degC 1 week0ndash4degC 3 days

Preparation method

Allow the product to thaw at roomtemperature or at refrigeration

temperature once defrosted the productmust not be frozen again and must be

consumed within 24 hours

Journal of Chemistry 3

data coming from each sensor of the electronic nose(PEN3) were analysed taking 10 seconds out of 400 secondsof the total analysis according to the stability of the sensorresponses ese values were then aggregated with theaverage to obtain a single measure A principal componentanalysis (PCA) was also performed to extract a single in-dicator of freshness to be compared with TVB-N To verifythe FOODsnier evaluation a one-way ANOVA was carriedout All statistical procedures were carried out using IBMSPSS Statistics 24 (SPSS Inc Chicago IL USA)

3 Results and Discussion

31 Chemical Results Figure 2 shows the plot of TVB-Nvalues which presents a linear behaviour over time After beingdefrosted the TVB-N values were found to increase in all lletsduring storage until they reached a maximum value after 7 daysof storage corresponding to the days in which shes are judgedunt for human consumption according to the limits providedby Regulation (CE) no 20742005 Regulation (CE) no 20742005 denes the limit values in relation to species For speciesbelonging to theMerlucciidae family the expected limit value is35mg100g esh is result is in line with the productrsquosspecications which recommends a storage not exceeding threedays at a temperature between 0 and 4degC and consumptionwithin 24 hours after thawing

32 PEN3 (E-nose) Results e eect of the number ofstorage days on the array response was evaluated As a rststep radar plots were obtained to observe whether patterndierences were developed between samples analysed indierent storage days Figure 3 shows the change of thesignal generated by the sensor array to dierent storage days(T1 T3 and T7) As can be seen the E-nose provided a verywell-dierentiated odour print useful to discriminate be-tween samples Indeed the radar plots show a clear pattern

8 fillets tested with FOODsniffer

8 fillets tested with PEN3 and TVB-N measured on 8 fillets

Storage days

1 2 3 4 5 6 7

Figure 1 Experimental design

Table 2 Sensors and their applications in PEN3

Number in the array Sensor name General description ReferenceR1 W1C Aromatic Aromatic compounds Toluene 10 ppm

R2 W5S Broad-rangeVery sensitive broad-range sensitivityreacts on nitrogen oxides and ozonevery sensitive to the negative signal

NO2 1 ppm

R3 W3C Aromatic Ammonia used as a sensor for aromatic compounds Benzene 10 ppmR4 W6S Hydrogen Mainly hydrogen selectively breath gases H2 100 ppbR5 W5C Arom-aliph Alkanes aromatic compounds less polar compounds Propane 1 ppm

R6 W1S Broad-methane Sensitive to methane (environment) ca10 ppm broad range similar to no 8 CH4 100 ppm

R7 W1W Sulphur-organic

Reacts on sulphur compounds (H2S 01 ppm)otherwise sensitive to many terpenes and sulphur

organic compounds which are importantfor smell (limonene and pyrazine)

H2S 1 ppm

R8 W2S Broad-alcohol Detects alcohols partially aromaticcompounds broad range CO 100 ppm

R9 W2W Sulphur-chlor Aromatic compounds sulphur organic compounds H2S 1 ppm

R10 W3S Methane-aliph Reacts on high concentrations gt100 ppmsometimes very selective (methane) CH4 10 ppm

0102030405060708090

100

TVB-

N (m

g10

0 g)

1 2 3 4 5 6 70Storage days

Figure 2 Measure of TVB-N on the llets


variation among T1 T3 and T7 days of storage As men-tioned by Rahman et al [30] and several other authors anE-nose is useful in many industrial processes such as foodsafety In fact in the food industry an E-nose is one of thebest methods for (i) agrifood quality monitoring (ii)freshness and shelf-life evaluation and (iii) investigating anddierentiating between dierent types of products [30] Ashighlighted by Haddi et al [31] the dierent sensor re-sponses could be due to changes in the concentration of thevolatile organic compounds emanating from each type offood products [32] e signicant dierences found amongthe samples analysed on dierent days are explained by thephysical chemical biochemical and microbiologicalchanges typical of the sh spoilage processes e PEN3sensitivity allows us to recognise the variation of VOCsemitted by the samples without giving details of speciccompounds such as biogenic amines To quantify thepresence of amine compounds the most common methodwould be the high-performance liquid chromatography(HPLC) [33] but the aim of this paper was to evaluate theperformance of the E-nose and FOODsnier

33 Analysis between PEN3 (E-nose) and TVB-N emeasures obtained by the dierent PEN3 (E-nose) sensorswere strongly correlated thus from the PCA we had a verygood result extracting a single dimension with 93 of thevariance explained which can be considered as a singleindicator of freshness for the PEN3 (E-nose) ComparingPEN3 (E-nose) the rst component and the chemicalanalysis results (TVB-N) the Pearson correlation betweenthese methods was evaluated obtaining a strong and sig-nicant correlation (r 092 P value 001) thus con-rming that the two measures are reliable alternativesFigure 4 shows the experimental results on a two-dimensional plane which identies each observation ob-tained with FOODsnier It shows that three distinctgroups of odours which correspond to satisfactory ac-ceptable and unsatisfactory levels respectively are welldistinguished in line with the results found by PEN3 (E-nose) and by TVB-N

Analysing the link between the individual sensors ofPEN3 (E-nose) and the TVB-N results a strong correlationcan be seen (min 065 in W3S and max 098 in W1W)(Table 3)

Some of the sensors (W1C W3C and W5C) showsignicantly negative correlation coecopycient values thusindicating that as the values of TVB-N increase the sensorsignals (W1C W3C and W5C) decrease

34 FOODsni er (ARS LAB US) Results FOODsnierprovides a categorical evaluation of sh freshness greenorange and red alerts which correspond to satisfactory

T1 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

005

152

253

35

1

(a)

T3 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

1816141210

86420

(b)

T7 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

160140120100

80604020

0

(c)

Figure 3 Radar plot extracted from sensor array responses at days 1 (a) 3 (b) and 7 (c) (expressed in ohm)

TVB-

N

PC1 of E-nose

FOODsniffer

Orange Green

Red

ndash200000 ndash100000 100000 20000000000

20000000

000000

40000000

60000000

80000000

Figure 4 PCA


acceptable and unsatisfactory levels of freshness re-spectively e qualitative nature of this measure does notenable the correlation between its results and those obtainedby PEN3 (E-nose) or TVB-N to be evaluated To overcomethis for the FOODsniffer we performed a one-way analysisof variance (ANOVA)

e ANOVA results confirmed a good agreement be-tween FOODsniffer TVB-N (F 5199 P 001) and PEN3(E-nose) first component factor (F 14317 P 001)

As reported in Table 4 the lower and upper limits enablea value range to be defined for TVB-N and for individualsensors of PEN3 (E-nose) in relation to the change inclassification of the FOODsniffer

4 Conclusions

In this preliminary study FOODsniffer and PEN3 wereevaluated on the basis of their predictive performance in thefood diagnostics field

ese results confirmed that a smart portable deviceassociated with good prevention practices could potentiallybe useful to reduce food waste in the agrifood supply chainand especially for household use and also in the food re-covery field for charitable purposes FOODsniffer proved tobe a valid and easy tool to use for nonspecialized personnelsuch as charity volunteers However further laboratory testsassociated with studies to test the practical feasibility of usingfood sniffers by such volunteers are necessary

As already highlighted in other studies PEN3 confirmedits excellent performance in supporting traditional labora-tory methods and proved to be a useful fast screeningmethod for food business operators

e development of new technologies is crucial in orderfor Europe to take effective action against food waste andreduce food poverty for the benefit of social economic andenvironmental sustainability

Data Availability

e data used to support the findings of this study are in-cluded within the article

Conflicts of Interest

e authors declare that they have no conflicts of interest

References

[1] A G Tacon and M Metian ldquoGlobal overview on the use offish meal and fish oil in industrially compounded aquafeedstrends and future prospectsrdquo Aquaculture vol 285 no 1ndash4pp 146ndash158 2008

[2] EUMOFA (European Market Observatory for Fisheries andAquaculture Products) 9e EU Fish Market EuropeanCommission Belgium China 2016

[3] FAO (Food and Agriculture Organization of the UnitedNations) FOOTPRINT Food Wastage Impacts on NaturalResourcesmdashSummary Report FAO Rome Italy 2013

[4] M OrsquoConnell G Valdora G Peltzer and R M Negri ldquoApractical approach for fish freshness determinations using aportable electronic noserdquo Sensors and Actuators B Chemicalvol 80 no 2 pp 149ndash154 2001

[5] O S Papadopoulou E Z Panagou F R Mohareb andG J E Nychas ldquoSensory and microbiological quality as-sessment of beef fillets using a portable electronic nose intandem with support vector machine analysisrdquo Food ResearchInternational vol 50 no 1 pp 241ndash249 2013

Table 3 Correlations between the PEN3 sensors and TVB-N values

R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)

TVB-N Pearsonrsquos correlation minus0940lowastlowast 0911lowastlowast minus0934lowastlowast 0769lowastlowast minus0903lowastlowast 0884lowastlowast 0983lowastlowast 0887lowastlowast 0964lowastlowast 0648lowastlowastSig (2-tailed) 0000 0000 0000 0000 0000 0000 0000 0000 0000 0001

lowastlowastCorrelation is significant at the 001 level (2-tailed)

Table 4 Descriptive statistics

FOODsniffer Mean Std error

95 confidenceinterval

Lowerbound

Upperbound

TVB-NGreen 1188 0782 minus0662 3037Orange 20648 0689 19018 22277Red 68813 2430 63066 74559

R1 (W1C)Green 0878 0013 0846 0910Orange 0673 0035 0590 0756Red 0242 0017 0202 0282

R2 (W5S)Green 2157 0139 1828 2487Orange 20394 0833 18425 22362Red 147926 11079 121729 174123





R7 (W1W)Green 3428 0178 3007 3849Orange 21763 0269 21128 22398Red 66042 0810 64127 67958





[6] C Di Natale G Olafsdottir S Einarsson E MartinelliR Paolesse and A DrsquoAmico ldquoComparison and integration ofdifferent electronic noses for freshness evaluation of cod-fishfilletsrdquo Sensors and Actuators B Chemical vol 77 no 1-2pp 572ndash578 2001

[7] N El Barbri E Llobet N El Bari X Correig andB Bouchikhi ldquoElectronic nose based on metal oxide semi-conductor sensors as an alternative technique for the spoilageclassification of red meatrdquo Sensors vol 8 no 1 pp 142ndash1562008

[8] A Domınguez-Aragon J A Olmedo-Martınez andE A Zaragoza-Contrera ldquoColorimetric sensor based on apoly (ortho-phenylenediamine-co-aniline) copolymer for themonitoring of tilapia (Orechromis niloticus) freshnessrdquo Sen-sors and Actuators B Chemical vol 259 pp 170ndash176 2018

[9] X Zhang H Zhou L Chang et al ldquoStudy of golden pompano(Trachinotus ovatus) freshness forecasting method by utilizingVisNIR spectroscopy combined with electronic noserdquo In-ternational Journal of Food Properties vol 21 no 1pp 1257ndash1269 2018

[10] H Zhiyi H Chenchao Z Jiajia L Jian and H GuohualdquoElectronic nose system fabrication and application in largeyellow croaker (Pseudosciaena crocea) fressness predictionrdquoJournal of Food Measurement and Characterization vol 11no 1 pp 33ndash40 2017

[11] X Li R Yang S Lin H Ye and F Chen ldquoIdentification ofkey volatiles responsible for aroma changes of egg whiteantioxidant peptides during storage by HS-SPME-GC-MSand sensory evaluationrdquo Journal of Food Measurement andCharacterization vol 11 no 3 pp 1118ndash1127 2017

[12] J Li H Feng W Liu Y Gao and G Hui ldquoDesign of aportable electronic nose system and application in K valueprediction for large yellow croaker (Pseudosciaena crocea)rdquoFood Analytical Methods vol 9 no 10 pp 2943ndash2951 2016

[13] L Zheng J Zhang Y Yu G Zhao and G Hui ldquoSpinyheadcroaker (Collichthys lucidus) quality determination usingmulti-walled carbon nanotubes gas-ionization sensor arrayrdquoJournal of Food Measurement and Characterization vol 10no 2 pp 247ndash252 2016

[14] J Jiang J Li F Zheng H Lin and G Hui ldquoRapid freshnessanalysis of mantis shrimps (Oratosquilla oratoria) by usingelectronic noserdquo Journal of Food Measurement and Charac-terization vol 10 no 1 pp 48ndash55 2016

[15] L Han J Jinghao Z Feixiang and H Guohua ldquoHairtail(Trichiurus haumela) freshness determination method basedon electronic noserdquo Journal of Food Measurement andCharacterization vol 9 no 4 pp 541ndash549 2015

[16] X Y Tian Q Cai and Y M Zhang ldquoRapid classification ofhairtail fish and pork freshness using an electronic nose basedon the PCA methodrdquo Sensors vol 12 no 2 pp 260ndash2772012

[17] J W Gardner and P N Bartlett ldquoA brief history of electronicnosesrdquo Sensors and Actuators B Chemical vol 18 no 1ndash3pp 210-211 1994

[18] A K Deisingh D C Stone and Mompson ldquoApplicationsof electronic noses and tongues in food analysisrdquo In-ternational Journal of Food Science and Technology vol 39no 6 pp 587ndash604 2004

[19] E De Boeck L Jacxsens H Goubert and M UyttendaeleldquoEnsuring food safety in food donations case study of theBelgian donationacceptation chainrdquo Food Research In-ternational vol 100 pp 137ndash149 2017

[20] M Vittuari F De Menna S Gaiani et al ldquoe second life offood an assessment of the social impact of food redistribution

activities in emilia romagna Italyrdquo Sustainability vol 9no 10 p 1817 2017

[21] A Cavaliere and V Ventura ldquoMismatch between food sus-tainability and consumer acceptance toward innovationtechnologies among millennial students the case of shelf lifeextensionrdquo Journal of Cleaner Production vol 175 pp 641ndash650 2018

[22] C M Balzaretti V Ventura S Ratti et al ldquoImproving theoverall sustainability of the school meal chain the role ofportion sizesrdquo Eating and Weight DisordersndashStudies on An-orexia Bulimia and Obesity pp 1ndash10 2018

[23] M Castrica D Tedesco S Panseri et al ldquoPet food as the mostconcrete strategy for using food waste as feedstuff within theeuropean context a feasibility studyrdquo Sustainability vol 10no 6 p 2035 2018

[24] V Milicevic G Colavita M Castrica S Ratti A Baldi andC M Balzaretti ldquoRisk assessment in the recovery of food forsocial solidarity purposes preliminary datardquo Italian Journal ofFood Safety vol 5 no 4 2016

[25] A Loutfi S Coradeschi G K Mani P Shankar andJ B B Rayappan ldquoElectronic noses for food quality a reviewrdquoJournal of Food Engineering vol 144 pp 103ndash111 2015

[26] D Gailius ldquoElectronic nose fore determination of meatfreshnessrdquo US Patent 14376 939 2014

[27] EC (European Commission) Commission decision 95149EC of 8 March 1995 fixing the total volatile basic nitrogen(TVB-N) limit values for certain categories of fishery productsand specifying the analysis methods to be used OfficialJournal of the European Union L 097 84-87 EC (EuropeanCommission) Belgium China 1995

[28] G Rateni P Dario and F Cavallo ldquoSmartphone-based fooddiagnostic technologies a reviewrdquo Sensors vol 17 no 6p 1453 2017

[29] EC (European Commission) 2005 Commission Regulation(EC) No 20742005 of European Parliament and of thecouncil of 5 December 2005 laying down implementingmeasures for certain products under Regulation (EC) No 8532004 of the European Parliament and of the Council and forthe organisation of official controls under Regulation (EC) No8542004 of the European Parliament and of the Council andRegulation (EC) No 8822004 of the European Parliament andof the Council derogating from Regulation (EC) No 8522004of the European Parliament and of the Council and amendingRegulations (EC) No 8532004 and (EC) No 8542004 Officialjournal of the European Union L 33827

[30] S Rahman T Usmani and S H Saeed ldquoReview of electronicnose and applicationsrdquo IJCCRndashInternational Journal ofCommunity Currency Research vol 3 2013

[31] Z Haddi N El Barbri K Tahri et al ldquoInstrumental as-sessment of red meat origins and their storage time usingelectronic sensing systemsrdquo Analytical Methods vol 7 no 12pp 5193ndash5203 2015

[32] L Pan and S X Yang ldquoA new intelligent electronic nosesystem for measuring and analysing livestock and poultryfarm odoursrdquo Environmental Monitoring and Assessmentvol 135 no 1ndash3 pp 399ndash408 2007

[33] S Suzuki K Kobayashi J Noda T Suzuki and K TakamaldquoSimultaneous determination of biogenic amines by reversed-phase high-performance liquid chromatographyrdquo Journal ofChromatography A vol 508 pp 225ndash228 1990


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Journal ofNanomaterials

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e electronic nose is a directly applicable method thatrequires minimum sample pretreatment and no specificconsumables or reagents and is able to provide rapid analysis[5] Furthermore this approach is not invasive and con-sequently does not alter the product For this reason re-cently the interest and application of E-nose in foodindustry such as (i) quality control (ii) process operationsmonitoring (iii) shelf-life determination and (iv) spoilageevaluation have increased considerably [5 16] Based on theclassical definition given by Gardner and Bartlett [16] theelectronic nose is ldquoan instrument which comprises an arrayof electronic chemical sensors with partial specificity and anappropriate pattern-recognition system capable of recog-nising simple or complex odoursrdquo [17 18] e E-nosesystem consists of several components such as specifichardware with sensors electronics pumps air conditionerflow controller and dedicated software for hardwaremonitoring data preprocessing and statistical analysis Suchcharacteristics make the E-nose a device able to mimic thehuman olfactory perception and to provide a digital odourprint of the sample which can be processed by appropriatestatistical software An E-nose gas sensor array showssensitivity toward certain classes of compounds (volatileorganic compounds (VOCs)) produced by the main spoilageorganisms e alteration in the pattern of VOCs is in-dicative of the degradation processes occurring in theproducts [5]

Smartphone sensors on the contrary could be easilyused by unskilled personnel such as volunteers in charityorganizations and final consumers [19]

e emerging need to reduce food waste but also toensure food security to people in food poverty has led to thedevelopment of new technologies in the food businessaimed not only at preventing food losses and waste in theprimary production processing distribution retail andfood services but also at improving the quality of productsrecovered by charities [20ndash23]

In the third sector such as food banks where the work isdone by volunteers without specific training in the food fieldthe objective is to provide valid support in order to guaranteea safe second life to the food recovered [24]

Fishery and aquaculture products together with otheranimal and vegetable proteins are also an important sourceof protein and thus an essential component of a healthy dietFor this reason these products are also essential in the diet ofpeople in food poverty [12]

e aim of this study was to explore whether an elec-tronic nose [25] (PEN3) could be used as a fast screeningmethod for food business operators and as a support forofficial laboratory methods (TVB-N) In addition theFOODsniffer (ARS LAB US) [26] was evaluated for itspotential use as an easy-to-use sensory tool both at thedomestic level and for volunteers working for charities inorder to evaluate the acceptability of the products forconsumption by the needy

e two portable sensing devices were evaluated on capehake fillets taking the measured TVB-N values into con-sideration as the reference (gold standard) In fact theEuropean Commission defined the TVB-N as the reference

method and in the Decision of 8 March 1995 fixed the TVB-N limit values for three categories of fishery products [27] Inthis work the cape hake fillets were assessed in terms of theirTVB-N values and were measured with PEN3 whenever theFOODsniffer revealed variations

2 Materials and Methods

21 Sample Collection and Experimental Design Experimentswere performed at the Food Inspection Laboratory of theDepartment of Health Animal Science and Food Safety inMilan (Italy) One batch of cape hake (Merluccius capensisMerluccius paradoxus) was collected e batch containedfish caught in the Southeast Atlantic Ocean (FAO area 47)produced and frozen by a company based in Walvis BayNamibia (company specializing in the catching and mar-keting of frozen seafood products in the internationalmarket) and imported by an Italian company that dis-tributes frozen food to wholesalers industry and masscaterers

e batch contained fish fillets that were already skinlessand portioned into individual fillets with a medium weightof 90ndash110 g (thawed weight 85ndash100 g)

Before being analysed each fillet was washed with steriledistilled and deionized water in order to remove the glazingand the fillets were then left to thaw overnight under acontrolled temperature (0ndash4degC) In order to reproduce the useat the domestic level the defrosting procedures were carriedout as provided in the productrsquos specifications (Table 1)

On the first day of storage (day 1) 8 fillets were testedwith all three methods FOODsniffer PEN3 and TVB-NSubsequently for six consecutive days (from day 2 to day 7)eight fillets were measured with the FOODsniffer and whenthe FOODsniffer detected variations the fillets were assessedin terms of their TVB-N and were measured with PEN3 toconfirm the results In this study the variations detected bythe FOODsniffer occurred on storage days 3 and 7 Figure 1summarizes the experimental design

22 FOODsniffer FOODsniffer (ARS LAB US) created byscientists and researchers of the Kaunas University ofTechnology in cooperation with the company ARS LAB is anew and fast device used to assess the freshness of food ofanimal origin and specifically patented for the meat matrix[26 28] FOODsniffer was designed to detect whether aproduct (i) is fresh (ii) can be safely eaten after cooking or(iii) is spoiled

FOODsniffer rapidly estimates the quality and safety ofthe raw material correlating them to the levels of volatileorganic compounds present in the tested matrix through agas sensor system including at least two metal-oxidesemiconductor sensors configured to measure NH3 andCH values e technology is based on the detection of lowconcentrations of volatile compounds that are associatedwith deterioration

e device is composed of a metal-oxide sensor systemadapted to respond to the speed of changes in the con-centration of volatile compounds a processor designed toreceive and process signals incoming from the sensor system












1113888 1113889



(1)






Preparation method











Storage days

1 2 3 4 5 6 7





NO2 1 ppm






H2S 1 ppm




0102030405060708090

100

TVB-

N (m

g10

0 g)









T1 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

005

152

253

35

1

(a)

T3 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

1816141210

86420

(b)

T7 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

160140120100

80604020

0

(c)


TVB-

N

PC1 of E-nose

FOODsniffer

Orange Green

Red

ndash200000 ndash100000 100000 20000000000

20000000

000000

40000000

60000000

80000000

Figure 4 PCA





4 Conclusions





Data Availability




References







R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)






Lowerbound

Upperbound
















































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls














1113888 1113889



(1)






Preparation method











Storage days

1 2 3 4 5 6 7





NO2 1 ppm






H2S 1 ppm




0102030405060708090

100

TVB-

N (m

g10

0 g)









T1 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

005

152

253

35

1

(a)

T3 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

1816141210

86420

(b)

T7 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

160140120100

80604020

0

(c)


TVB-

N

PC1 of E-nose

FOODsniffer

Orange Green

Red

ndash200000 ndash100000 100000 20000000000

20000000

000000

40000000

60000000

80000000

Figure 4 PCA





4 Conclusions





Data Availability




References







R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)






Lowerbound

Upperbound
















































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls










Storage days

1 2 3 4 5 6 7





NO2 1 ppm






H2S 1 ppm




0102030405060708090

100

TVB-

N (m

g10

0 g)









T1 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

005

152

253

35

1

(a)

T3 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

1816141210

86420

(b)

T7 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

160140120100

80604020

0

(c)


TVB-

N

PC1 of E-nose

FOODsniffer

Orange Green

Red

ndash200000 ndash100000 100000 20000000000

20000000

000000

40000000

60000000

80000000

Figure 4 PCA





4 Conclusions





Data Availability




References







R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)






Lowerbound

Upperbound
















































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls









T1 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

005

152

253

35

1

(a)

T3 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

1816141210

86420

(b)

T7 day

W2W

W3S

W1C

W5S

W3C

W6S

W5C

W1S

W1W

W2S

160140120100

80604020

0

(c)


TVB-

N

PC1 of E-nose

FOODsniffer

Orange Green

Red

ndash200000 ndash100000 100000 20000000000

20000000

000000

40000000

60000000

80000000

Figure 4 PCA





4 Conclusions





Data Availability




References







R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)






Lowerbound

Upperbound
















































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls







4 Conclusions





Data Availability




References







R1(W1C)

R2(W5S)

R3(W3C)

R4(W6S)

R5(W5C)

R6(W1S)

R7(W1W)

R8(W2S)

R9(W2W)

R10(W3S)






Lowerbound

Upperbound
















































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls







































Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls




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