rapid near infrared spectroscopic method for the detection ... › deanshipfiles ›...

S18 JOURNAL OF FOOD SCIENCE—Vol. 71, Nr. 1, 2006Published on Web 1/10/2006

© 2006 Institute of Food TechnologistsFurther reproduction without permission is prohibited

S: Sensory & Nutritive Qualities of Food

JFS S: Sensory and Nutritive Qualities of Food

Rapid Near Infrared SpectroscopicMethod for the Detection of Spoilage inRainbow Trout (Oncorhynchus mykiss) FilletMENGSHI LIN, MOJGAN MOUSAVI, MURAD AL-HOLY, ANNA G. CAVINATO, AND BARBARA A. RASCO

ABSTRACT: The possibility of using visible and short-wavelength near-infrared (SW-NIR: 600 to 1100 nm)spectroscopy to detect the onset of spoilage and to quantify microbial loads in rainbow trout (Oncorhynchusmykiss) was investigated. Spectra were acquired on the skin and flesh side of intact trout fillet portions and onminced trout muscle samples stored at 4 °C for up to 8 d or at room temperature (21 °C) for 24 h. Principalcomponent analysis (PCA) and partial least squares (PLS) chemometric models were developed to predict theonset and degree of spoilage. PCA results showed clear segregation between the control (day 1) and the samplesheld 4 d or longer at 4 °C. Clear segregation was observed for samples stored 10 h or longer at 21 °C comparedwith the control (0 h), indicating that onset of spoilage could be detected with this method. Quantitative PLSprediction models for microbial loads were established. For trout fillets, 4 °C: R = 0.97, standard error of predic-tion (SEP) = 0.38 log colony-forming units (CFU)/g (flesh side); R = 0.94, SEP = 0.53 log CFU/g (skin side); R =0.82, SEP = 0.82 log CFU/g for minced fish held at 21 °C. These results indicate that SW-NIR in combination withmultivariate statistical methods can be used to detect and monitor the spoilage process in rainbow trout andquantify microbial loads rapidly and accurately.

Keywords: SW-NIR, rainbow trout, spoilage, PCA, PLS

Introduction

There has been a growing interest in the use of near-infrared(NIR) technology as an analytical tool for agricultural and food

applications since the 1980s (Rasco and others 1991). In particular,visible and short-wavelength (SW-NIR = 600 to 1100 nm) spectros-copy has drawn considerable attention for a wide number of appli-cations in quality control in the food industry. In the SW-NIR region,various fundamental molecular vibrations, including those from C-H, O-H, N-H, C = O, and other functional groups can be detected(Williams and Norris 2001). For example, when a food is irradiatedwith NIR light, it absorbs the light with frequencies matching char-acteristic vibrations of particular functional groups, whereas thelight with other frequencies will be transmitted or reflected (Foleyand others 1998). Therefore, the biochemical components of a foodtissue determine the amount and frequency of absorbed light andthe quantity of reflected or transmitted light can be used to inferthe chemical composition of that food tissue (André and Lawler2003).

Freshness is 1 of the most important quality attributes for fishproducts, strongly affected by 2 primary factors, temperature andtime of storage. During distribution and storage of fresh fish, iceand chilled conditions are commonly applied to delay the spoilageand prolong product shelf life (Gobantes and others 1998). Guttedfish fillets may be kept in good conditions in ice for 1 wk and remainacceptable for 2 wk. The shelf life of fish stored under chilled con-

ditions is much shorter. When fish spoil, characteristic changes insensory attributes, for example, aroma, taste, texture, and appear-ance will change (Ólafsdóttir and others 1997). Fresh rainbow troutfillets are high value and perishable products. They are highly sus-ceptible to microbial spoilage and decomposition caused by endog-enous enzymes (Reddy and others 1992; Gobantes and others1998; Rasco and Bledsoe 2004). Stale trout can be observed by theirsunken eyes, changed color, generation of yellow slime and bloomon the skin, and so forth (Mills 2005).

To date, many methods have been proposed to detect and mon-itor fish freshness and spoilage including microbial enumerationmethods, volatile compound analysis, measurement of lipid oxida-tion, nucleotide and amine metabolite assays, texture measure-ment, and sensory evaluation which is the most commonly usedmethod for freshness evaluation of fish products in the seafoodindustries and retail stores (Ólafsdóttir and others 1997; Nilsen andothers 2002). However, these methods are invasive, often expen-sive and time-consuming, and, in the case of sensory analysis, con-sidered to be subjective unless a highly trained panel is used. Withthe increasing demand of high-quality seafood products, it is ofparamount importance to have a rapid detection method for fishspoilage and ensure safe and high-quality fish products on themarket.

Recently, spectroscopic methods such as NIR have been success-fully applied in the evaluation of food quality attributes. For in-stance, NIR was used to determine free fatty acid content in fish forquality assessment (Zhang and others 1997), estimate the crudelipid content in the rainbow trout muscle (Lee and others 1992),evaluate the freshness of cod and salmon (Nilsen and others 2002),study sensory quality criteria for 5 fish species (Warm and others2001), classify gender and maturity of Chinook salmon (Hamptonand others 2002), evaluate the quality of frozen minced red hake

MS 20050453 Submitted 7/27/05, Revised 9/8/05, Accepted 9/20/05. AuthorsLin, Mousavi, and Rasco are with Dept. of Food Science and Human Nutri-tion, Washington State Univ., Pullman, WA 99164-6376. Author Al-Holy iswith Dept. of Clinical Nutrition and Dietetics, Faculty of Allied Health Sci-ences, Hashemite Univ., Zarqa-Jordan. Author Cavinato is with Dept. ofChemistry and Biochemistry, Eastern Oregon Univ., La Grande, Oreg. Di-rect inquiries to author Rasco (E-mail: [email protected]).

Vol. 71, Nr. 1, 2006—JOURNAL OF FOOD SCIENCE S19

S: Se

nsor

y & N

utritiv

e Qua

lities

of Fo

od

URLs and E-mail addresses are active links at www.ift.org

Trout spoilage detection by SW-NIR . . .

(Pink and others 1999), and detect and quantify the microbialspoilage of chicken (Ellis and others 2002; Lin and others 2004).

The objectives of this study were to use visible and short-wave-length near-infrared (SW-NIR: 600 to 1100 nm) spectroscopy to de-tect and monitor the onset of spoilage and quantify the microbialloads in intact and minced rainbow trout (Oncorhynchus mykiss)fillets stored at 4 °C and 21 °C.

Materials and Methods

Sample preparationFresh boneless rainbow trout fillets were purchased from a local

retail store the same day of experiment. Fish samples were within4 d of harvest from regional culture facilities. Fillets were packagedas single layers separated by plastic packaging in a poly lined car-ton. At retail, fillets were displayed in plastic trays on ice. Twogroups of test samples were prepared with 1 group for the studyof fish spoilage under chilled conditions, and the other group forthe study of fish spoilage at room temperature. The proximatecomposition of raw rainbow trout fillets from aquaculture is 73 g/100 g moisture, 21 g/100 g protein, 5.4 g/100 g fat, and 1.4 g/100g ash, with a caloric content of 138 Kcal/g (USDA 2005) with a wa-ter activity of >0.95.

For the 1st experiment, intact trout cut fillets (15 to 20 cm, 0.2 to0.3 kg) were placed in aseptic plastic bags (Ziploc, S.C. Johnson &Son, Inc., Racine, Wis., U.S.A.) to control moisture loss and create anoxygen-limited environment similar to commercial packaging con-ditions for these products. Fish samples were kept under refriger-ated storage at 4 °C for 8 d and sampled daily. Five samples wereselected for spectral measurements at each sample interval.

For the 2nd experiment, samples (30-g each) were handled asep-tically and weighed, and comminuted for 10 s in a 12-speed blend-er (General Electric Co., Fairfield, Conn., U.S.A.). This would createa more uniform sample for spectral measurement and also acceler-ate spoilage due to increased tissue damage. The samples wereplaced inside the lid of an upturned Petri dish and spread to athickness of about 5 mm, using the inverted base of a Petri dish asa press. Then another lid was used to cover the sample, creating anoxygen-limited condition. Three replicate samples were preparedfor each time interval: 0, 2, 4, 6, 8, 10, 12, and 24 h. These sampleswere randomized, numbered, and placed on the lab bench at anambient temperature of approximately 21 °C.

Spectra collectionSW-NIR spectra were acquired in diffuse reflectance mode with

a fiber-optic probe and a DPA-20 spectrophotometer (DSquaredDevelopment, Inc., La Grande, Oreg., U.S.A.). The probe consists of32 illumination fibers arranged in a concentric circle with a singlepick-up fiber in the center. Before collection of sample spectra, areference spectrum of Spectralon (Labsphere, Inc., North Sutton,N.H., U.S.A.) was acquired. Spectralon is a thermoplastic resin withhighly reflectance behavior in the NIR region. During spectral ac-quisition of fish samples, the Spectralon was placed under the sam-ples reflecting SW-NIR light back to the pick-up fiber. The referencespectrum was automatically subtracted from the sample spectrumby the instrument in each measurement.

For the fish fillet experiment (conducted at 4 °C), 10 spectrawere acquired along the lateral line on skin side of fish fillet; where-as 10 more spectra were acquired from interior flesh side of the cutfillet. The probe was placed in direct contact with the fish skin orflesh in spectral measurement. Spectra were collected at 0.5-nmintervals from 600 to 1100 nm. Each spectrum was the average of 20scans with a 150-ms exposure time for each scan. The probe was

wiped and sanitized with 70% ethanol and blotted dry with a lenstissue after measuring each sample.

For the minced muscle experiment (conducted at room temper-ature), the probe was placed in direct contact with the sample inthe Petri dish. Ten spectra were acquired from different locationson each Petri dish with 30 spectra total collected at each time inter-val. Each spectrum was the average of 20 scans with a 50-ms expo-sure time for each scan.

Microbiological analysisBefore conducting the SW-NIR measurement, a 1-g subsample

was removed aseptically from the each sample and diluted in 9 mL0.1% peptone water. Total viable count (TVC) was determined bythe spread plating technique using Tryptic Soy Agar (TSA; DifcoLaboratories, Sparks, Md., U.S.A.). The plates were incubated at37 °C for 48 h. The TVC was recorded as a logarithmic number of thecolony-forming units (CFU) and was used as reference values inthe partial least squares (PLS) modeling. At each sampling interval,samples of about 5 g were taken and vortexed for 30 s with an equalamount of distilled water for pH measurement (Accumet AB15 pHmeter, Fisher Scientific, Pittsburgh, Pa., U.S.A.).

Statistical analysisData analysis was performed using Delight version 3.2.1

(DSquared Development). Data pre-processing algorithms such asbinning, smoothing, and 2nd derivative transformation were usedto analyze the data (Huang and others 2001, 2002). First, spectraldata were binned by 2 nm and smoothed with a Gaussian functionover 12 nm. Then a 2nd derivative transformation with a 12-nm gapwas calculated to separate overlapping absorption bands and re-move baseline offsets (Lin and others 2003).

After data pre-processing, multivariate statistical analysis suchas principal component analysis (PCA) and partial least squares(PLS) calibration models were developed. In PCA, the 1st PC con-veys the largest amount of information regarding spectral variation,followed by the 2nd PC, and so forth. The score plot allows visual-ization of segregation and clustering among spectral samples. PLSregression is a method that correlates spectral information with thereferences values. Leave-one-out cross-validation was used to cal-culate predicted values. The coefficient of correlation (R) of thepredicted values versus reference values, the standard error ofprediction (SEP), and the RPD (the ratio of the SEP to the standarddeviation of the data set) indicate the quality of the chemometricmodel (Huang and others 2001).

Results and Discussion

The log10 (TVC) values of bacteria on intact and minced rainbowtrout muscle samples stored at 4 °C and 21 °C are shown in Ta-

ble 1. TVC of <102 CFU/g for freshly caught fish and <106 CFU/g forcut fillets are common. Fish products with TVC of 107 to 108 CFU/ggenerally fail 1 or more sensory evaluation criteria (Ólafsdóttir andothers 1997). In the 4 °C study, the TVC values of trout fillets in-creased to 107 CFU/g within 1 wk and developed an unpleasantodor. Spoilage in chilled fish is primarily caused by the growth ofpsychrotrophic Gram-negative microorganisms, particularly Sh-ewanella putrefaciens, Pseudomonas spp., and Psychrobacter (Ólafs-dóttir and others 1997; Adams and Moss 2000). The initial mean pHof fresh rainbow trout fillet (day 1) was 6.50. After storage at 4 °C for8 d, the pH of the flesh dropped to 6.33. The slight drop in pH wasprobably the result of a combination of 2 factors—the growth ofspoilage microflora and enzyme activity that broke down complexproteins and fats to simpler forms, such as amino acids, free fattyacids, and so forth. Besides, very low levels (<1%) of carbohydrates


S20 JOURNAL OF FOOD SCIENCE— Vol. 71, Nr. 1 2006 URLs and E-mail addresses are active links at www.ift.org


in rainbow trout flesh also limited the degree of postmortem acid-ification during spoilage process. Because of relatively high pH,high content of unsaturated fatty acids, and the growth of psychro-trophic microorganisms, usually fish spoil faster than meat underthe same storage conditions (Adams and Moss 2000).

Typical SW-NIR spectra acquired from both skin side and fleshside of rainbow trout cut fillet are shown in Figure 1a. Because inthe SW-NIR region (600 to 1100 nm) most spectral features of dif-ferent biochemical compounds are overlapped, a 2nd derivativetransformation (12-point gap) was used to separate overlappingabsorption bands (Figure 1b). Clear differences between the spec-tra acquired from the skin side and flesh side of trout cut filletswere observed in both original and 2nd derivative transformedspectra. The prominent absorption bands for skin side spectrum aremainly in the visible region, whereas the prominent bands for fleshside spectrum are between 960 to 1060 nm (Figure 1b). These dif-ferences are primarily because the dark fish skin strongly absorbsthe visible light and contains less water content than flesh side. Theprominent band at about 960 nm arises from symmetric and anti-symmetric O-H stretch of water, as well as 2 more weaker waterbands at 840 and 750 nm (Huang and others 2002). The absorptionbands in the region of 1000 to 1060 nm are mainly from protein

functional group, such as R-CO-NH2, R-NH2, R-CO-NH-R, and R-NH-R (Williams and Norris 2001).

PCA analysis was conducted based upon mean-centered spec-tra and 5 principal components (PC) were used. For experiments onthe flesh side of rainbow fillets, day 2 samples (Figure 2a) or day 3samples (Figure 2b) could not be differentiated from the day 1 con-trol. On day 4, differentiation became apparent (Figure 2c) and byday 5, samples were completely segregated from the day 1 control(Figure 2d). These results suggest that SW-NIR can be used as afreshness indicator to predict shelf life. For example, the averagelog10(TVC) of the day 4 samples was 5.32 log cycles, which is wellbelow the rejection threshold values of 7 to 8 log CFU/g (Ólafsdóttirand others 1997). At approximately 5 log CFU/g, the fish were stillin very good conditions with no off-flavor or off-odors being ob-served. The difference of the log10 (TVC) between the day 1 controland day 4 samples was 1.62 log cycles (Table 1). These results areconsistent with the results from a previous study in chicken spoil-age, which showed that SW-NIR can be used to differentiateminced chicken samples with microbial count difference of morethan 1 log cycle (Lin and others 2004).

The PCA analysis of the spectral data collected from the skinside of rainbow trout cut fillets showed a similar trend with com-plete segregation between the samples and the day 1 control byday 5 (Figure 2h). Generally trout flesh contains about 70% to 80%water, 18% to 20% protein, 1% to 10% fat, and less than 1% carbohy-drate. The water and fat may vary with season and diet (Mills 2005).Fish skin is a less suitable surface for microbial growth than theflesh for most spoilage microflora (Adams and Moss 2000), and asa consequence, the spoilage process on the flesh side proceedsfaster than on the skin side. Because well-defined spectral changesresult from microbial growth (Ellis and others 2002), this wouldexplain why complete segregation in PCA results was observedearlier for microbial growth on the flesh compared with the skin sideof the fillets.

Figure 3 shows the PCA results for minced rainbow trout muscleheld at room temperature for 24 h. In Figure 3a the spectra from 0-h control and sample through 2-h were mixed. Figure 3b shows sim-ilar trends. However, it is noteworthy that in Figure 3c, the spectrafrom 0-h control are completely segregated from the spectra of 10-h samples. Similarly, the score plots of PCA analysis for 0-h controlwith 12-h samples and 0-h control with 24-h samples also show clearsegregations. The difference of the log10(TVC) between the 0-hcontrol and 10-h samples was 1.92 log cycles (Table 1), indicatingthat SW-NIR could differentiate between fish samples stored atroom temperature with microbial loads differing about 2 log cycles.

Unlike Fourier transform infrared (FT-IR), SW-NIR is not capableof directly detecting bacterial cells on sample surfaces due to

Figure 1—Representative short-wavelength near-infrared(SW-NIR) diffuse reflectance spectra collected from skinand flesh side of rainbow trout fillet (a) and 2nd derivativetransformation of spectra (12-point gap) (b)

Table 1—Bacteria growth on rainbow trout samplesa

4 °C 21 °C

Time (d) Log10(TVC) Time (h) Log10(TVC)

<1 3.70 0 3.00

2 3.90 2 3.043 4.15 4 3.304 5.32 6 3.505 6.65 8 4.086 6.90 10 4.927 7.48 12 5.238 7.85 24 7.46aAll measurements were taken in duplicate from each sample followingincubation on Tryptic Soy Agar (TSA) at 37 °C for 48 h. TVC = total viablecount.


S: Se

nsor

y & N

utritiv

e Qua

lities

of Fo

od



strong interfering water signals. However, SW-NIR could detect thebiochemical decomposition and changes in fish that are caused bythe activities of spoilage microflora and their catabolism of protein,lipid, and polysaccharide components that could be detected inthe SW-NIR region. By building chemometric models such as PLS,a relationship between changes in spectral features resulting frommicrobial growth and the microbial levels on fish could be estab-lished. These models were then used to predict microbial loads onsimilar samples. Figure 4 and Table 2 show the SEP values fromPLS models for both the flesh and skin side of rainbow trout held at4 °C, and for minced trout muscle samples held at 21 °C. An optimal

SEP was obtained using 6 latent variables for all 3 prediction mod-els. Results for the PLS models (6 latent variables) of the microbialloads in rainbow trout yielded R = 0.97, SEP = 0.38 log CFU/g, andRPD = 4.14 for the flesh side model; R = 0.94, SEP =.53 log CFU/g,and RPD = 2.99 for the skin side model. PLS prediction results ofminced trout samples with 6 latent variables also yielded accept-able results (R = 0.82; SEP = 0.81 log CFU/g, and RPD = 1.75). Pre-diction results for the intact rainbow trout fillets held at 4 °C werebetter than that of the minced trout samples held at room temper-ature. This may be due to a better acquisition method that wasused for intact fillet samples than that for minced samples. The

Figure 2—Principalcomponentanalysis (PCA)results forrainbow troutfillets held at4 °C for 1 (d), 2,3, 4, and 5 (m)days. a, b, c, d:flesh side; e, f,g, h: skin side.


S22 JOURNAL OF FOOD SCIENCE— Vol. 71, Nr. 1 2006 URLs and E-mail addresses are active links at www.ift.org


spectra for minced samples were acquired at different locations ofPetri dish and the background information of each location of Petridish may vary and affect spectral acquisition and the accuracy ofthe final prediction results.

Conclusions

SW-NIR diffuse reflectance spectroscopy (600 to 1100 nm) can beused to monitor the spoilage process in rainbow trout rapidly and

noninvasively. Acceptable PLS calibrations and predictions of micro-bial counts on fish samples were obtained. PCA analysis indicatethat samples can be segregated within 5 d of refrigerated storage or8 to 10 h of room temperature storage or when the difference in themicrobial levels between samples is in the range of at least 1 to 2 logcycles. This technique should be applicable for the prediction ofshelf life and microbial levels in aquatic food products before dele-terious sensory changes become apparent. The method should betransferable to other food systems composed of intact or mincedmuscle tissue. Future work should be conducted to establish therelationship to ascertain which specific changes observed in spectralfeatures correlate most closely with sensory decomposition.

AcknowledgmentsThis research was supported by the United States Dept. of Agricul-ture Natl. Research Initiative Competitive Grants Program(NRICGP) project nr 2000-01617, Aquaculture Idaho-Washington,Eastern Oregon Univ., Washington State Univ., the Univ. of Alaska(UAF 01-0048), Natl. Fisheries Inst., and the Intl. Marketing Pro-gram for Agricultural Commodities and Trade Center (IMPACT).

ReferencesAdams MR, Moss MO. 2000. Food microbiology. 2nd ed. The Royal Society of

Chemistry. Bodmin, U.K.: MPG Books.André J, Lawler IR. 2003. Near infrared spectroscopy as a rapid and inexpensive

Figure 4—Standard error of prediction (SEP) values versusnumber of latent variables from partial least squares (PLS)models for flesh side (a) and skin side (b) of rainbow troutfillets stored at 4 °C and minced trout muscle sample heldat 21 °C (c)

Figure 3—Principalcomponent analysis(PCA) results forminced rainbow troutmuscle samples held0 and 2 h (a), 0 and 8h (b), 0 and 10 h (c),and 0 and 12 h (d) at21 °C; (d), 0 h control;(m), 2 h, 8 h, 10 h, and12 h samples

Table 2—Predicting microbial loads in rainbow trout usingpartial least squares (PLS) regressiona

4 °C 21 °C

Flesh side Skin side Minced sample

Nr of spectra 400 400 292R 0.97 0.94 0.82SEP (log CFU/g) 0.38 0.53 0.82RPD 4.14 2.99 1.75Nr of latent variables 6 6 6aCFU = colony-forming units; SEP = standard error of prediction.

PLS predictionparametersand results


S: Se

nsor

y & N

utritiv

e Qua

lities

of Fo

od



means of dietary analysis for a marine herbivore, dugong Dugong dugon. MarEcol Prog Ser 257:259–66.

Ellis DI, Broadhurst D, Kell DB, Rowland JJ, Goodacre R. 2002. Rapid and quanti-tative detection of the microbial spoilage of meat by Fourier transform infraredspectroscopy and machine learning. Appl Environ Microbiol 68:2822–8.

Foley WJ, Mcllwee AP, Lawler IR, Aragones L, Woolnough, AP, Berding N. 1998.Ecological applications of near infrared spectroscopy—a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspectsof animal performance. Oecologia 116:293–305.

Gobantes I, Choubert G, Gómez R. 1998. Quality of pigmented (astaxanthin andcanthaxanthin) rainbow trout (Oncorhynchus mykiss) fillets stored under vac-uum packaging during chilled storage. J Agric Food Chem 46:4358–62.

Hampton KA, Cavinato AG, Mayes DM, Boe SJ, Hoffnagle TL. 2002. Near infraredspectroscopic classification of gender and maturity in Chinook salmon (On-corhynchus tshawytscha). Eastern Oreg Sci J 17:31–6.

Huang Y, Rogers TM, Wenz MA, Cavinato AG, Mayes DM, Bledsoe GE, Rasco BA.2001. Detection of sodium chloride in cured salmon roe by SW-NIR spectros-copy. J Agric Food Chem 49:4161–7.

Huang Y, Cavinato AG, Mayes DM, Bledsoe GE, Rasco BA. 2002. Non-destructiveprediction of moisture and sodium chloride in cold smoked Atlantic salmon(Salmo salar). J Food Sci 67:2543–7.

Lee MH, Cavinato AG, Mayes DM, Rasco BA. 1992. Noninvasive short-wave-length near-infrared spectroscopic method to estimate the crude lipid contentin the muscle of intact rainbow trout. J Agric Food Chem 40:2176–81.

Lin M, Cavinato AG, Huang Y, Rasco BA. 2003. Predicting sodium chloride con-tent in commercial king (Oncorhynchus tshawytscha) and chum (O. keta) hotsmoked salmon fillet portions by short-wavelength near-infrared (SW-NIR)spectroscopy. Food Res Int 36:761–6.

Lin M, Al-Holy M, Mousavi-Hesary M, Al-Qadiri H, Cavinato AG, Rasco BA. 2004.Rapid and quantitative detection of the microbial spoilage of chicken breasts bydiffused reflectance spectroscopy (600-1100 nm). Lett Appl Microbiol 39:148–55.

Mills A. 2005. Handling and processing rainbow trout. Rome, Italy: Food andAgriculture Org of the United Nations. Available from: http://www.fao.org/wairdocs/tan/x5945E/x5945e00.htm .

Nilsen H, Esaiassen M, Heia K, Sigernes F. 2002. Visible/near-infrared spectros-copy: a new tool for the evaluation of fish freshness. J Food Sci 67:1821–6.

Ólafsdóttir G, Martinsdóttir E, Oehlenschläger J, Dalgaard P, Jensen B, Unde-land I, Mackie IM, Henehan G, Nielsen J, Nilsen H. 1997. Methods to evaluatefish freshness in research and industry. Trends Food Sci Technol 8:258–65.

Pink J, Naczk M, Pink D. 1999. Evaluation of the quality of frozen minced red hake:use of Fourier transform near-infrared spectroscopy. J Agric Food Chem47:4280–4.

Rasco BA, Bledsoe GE. 2004. Seafood: frozen aquatic food products. In: Smith JS,Hui YH, editors. Food processing: principles and applications. Ames, Iowa:Blackwell Publishing. p 447–58.

Rasco BA, Miller CE, King TL. 1991. Utilization of NIR spectroscopy to estimatethe proximate composition of trout muscle with minimal sample pretreatment.J Agric Food Chem 39:67–72.

Reddy NR, Armstrong DJ, Rhodehamel EJ, Kautter DA. Shelf-life extension andsafety concerns about fresh fishery products packed under modified atmo-spheres: a review. J Food Safety 12:87–118.

[USDA] U.S. Dept. of Agriculture. 2005. Beltsville, MD.: Natl. Agriculture Library.Available from: www.nal.usda.gov.

Warm K, Martens H, Nielsen J. 2001. Sensory quality criteria for five fish speciespredicted from near-infrared (NIR) reflectance measurement. J Food Qual24:389–403.

Williams P, Norris K. 2001. Near-infrared technology in the agricultural andfood industries. 2nd ed. St. Paul, Minn.: American Assn. of Cereal Chemists.

Zhang HZ, Lee TC. 1997. Rapid near-infrared spectroscopic method for the de-termination of free fatty acid in fish and its application in fish quality assess-ment. J Agric Food Chem 45:3515–21.

rapid near infrared spectroscopic method for the detection ... › deanshipfiles ›...

Documents