ORIGINAL PAPER

Prediction of Aerobic Plate Count on Beef Surface UsingFluorescence Fingerprint

Masatoshi Yoshimura & Junichi Sugiyama &Mizuki Tsuta &

Kaori Fujita & Mario Shibata & Mito Kokawa &

Seiichi Oshita & Naomi Oto

Received: 21 March 2013 /Accepted: 29 July 2013# Springer Science+Business Media New York 2013

Abstract The potential of fluorescence fingerprint (FF) spec-troscopy was investigated to develop a nondestructive predic-tion method of aerobic plate count on a beef surface. Sixtysamples (e.g., 30 lean meat slices each of Australian cattle andJapanese cattle) stored aerobically at 15 °C were analyzed byfront-face fluorescence spectrophotometry. FF and aerobicplate count (APC) were measured after 0, 12, 24, 36, and48 h of storage. FFs were collected in both excitation andemission wavelength ranges of 200–900 nm. Partial least-squares regression (PLSR) performed on an FF dataset pre-dicted an APC in the bacterial contamination load range from1.7 to 7.8 logcolony-forming units (cfu)/cm2 with a predictionerror of 0.752 log cfu/cm2. The regions where the regressioncoefficient of the PLSR model was relatively high wereconsistent with those of the FF peaks of five intrinsicfluorophores: tryptophan, NAD(P)H, vitamin A, porphyrins,and flavins. This suggests that changes in the autofluorescenceof these intrinsic fluorophores due to the metabolism of bacte-rial flora on meat are reflected in the PLSR model forpredicting APC from the FF dataset. FF spectroscopy coupledwith multivariate analysis appeared to be applicable to thenondestructive determination of APC on the surface of leanbeef.

Keywords Aerobic plate count . Sanitary control . Excitationemissionmatrix . Partial least-squares regression . Beef

Introduction

The demand for and consumption ofmuscle food including bothmeat and poultry are very high in most developed countries andare increasing in developing countries. Since these foods aresusceptible tomicrobial spoilage, a number of food-borne illnessoutbreaks, caused by specific pathogenic bacteria within meatsuch as Salmonella spp., enterohemorrhagic Escherichia coli,and Campylobacter spp., have been reported worldwide(Koutsoumanis et al. 2006). However, conventional culture-based microbiological analysis methods of microbial enumera-tion or detection are very labor intensive and highly time con-suming, requiring several days to obtain results (Uyttendaele andDebevere 2006a). Several rapid methods that have been devel-oped over the last two decades to shorten analysis time are basedon microscopy (e.g., direct epifluorescent filter technique), flowcytometry, metabolic activity (e.g., conductimetry/impedance),the use of cellular components (e.g., ATP bioluminescence),immunological methods (e.g., enzyme-linked immunosorbentassays), and nucleic acid-based methods (e.g., polymerase chainreaction) (Ellis and Goodacre 2001; Uyttendaele and Debevere2006b); however, they are still inadequate because they arelaborious and slow and give retrospective information. Thiscan be a major drawback when used in monitoring proceduressuch as the HACCP system, which require fast detection.Ideally, therefore, a rapid, nondestructive, reagentless, and quan-titative method of microbiological analysis should be developedfor busy- and highly automated-processing environments.

The public concern for safety in food production calls forhigh standards of process control, which in turn requires appro-priate analytical techniques for food investigation. Among suchtechniques, spectroscopy is a good candidate as it is a rapid,nondestructive, and reagentless analytical method. Recently,fluorescence spectroscopy has become quite popular as a toolin biological science related to food technology owing to its highsensitivity and selectivity (Christensen et al. 2006; Karoui andBlecker 2011; Sadecka and Tothova 2007). Since several

M. Yoshimura is a JSPS Research Fellow.

M. Yoshimura : J. Sugiyama (*) :M. Tsuta :K. Fujita :M. ShibataFood Engineering Division, National Food Research Institute,National Agriculture and Food Research Organization, 2-1-12Kannondai, Tsukuba, Ibaraki 305-8642, Japane-mail: sugiyama@affrc.go.jp

M. Kokawa : S. Oshita :N. OtoGraduate School of Agricultural and Life Sciences, The Universityof Tokyo, 1-1-1 Yayoi, Bunkyo-ward, Tokyo 113-8657, Japan

Food Bioprocess TechnolDOI 10.1007/s11947-013-1167-8

important intrinsic fluorophores (e.g., proteins, vitamins, andcoenzymes) are inherent to food systems or microorganisms(Lacowicz 2006), the potential of this technique for applicationto food quality evaluation has attracted considerable attention.

In this study, we focused on the fluorescence fingerprint (FF)of the surface of lean beef. FF, also known as the excitation–emission matrix, is a set of fluorescence spectra acquired atconsecutive excitation wavelengths. As opposed to convention-al fluorescence spectroscopy where the emission spectra at aparticular excitation wavelength are typically studied, FF has theadvantage of obtaining more information about the fluorescentspecies present in complex food systems. Since all intrinsicfluorophores have independent and specific spectral excitationand emission profiles characterizing their unique fluorescenceproperties, FF shows a specific pattern depending on theirconstituents. Several studies in the field of food science haveused FF on various targets (Christensen et al. 2006; Karoui andBlecker 2011; Sadecka and Tothova 2007) such as in meat(Møller et al. 2003; Oto et al. 2013), olive oil (Guimet et al.2005), wine (Yin et al. 2009), beer (Sikorska et al. 2008),mycotoxin (Fujita et al. 2010), and buckwheat (Shibata et al.2011). Furthermore, FF has also been used in imaging researchto visualize the internal structure of soybean seeds (Tsuta et al.2007) and the distribution of gluten and starch in dough(Kokawa et al. 2011, 2012a, b). Recently, for the early detectionof bacterial spoilage in meat, the application of synchronousfront-face fluorescence spectroscopy on chicken breast filets(Sahar et al. 2011), conventional fluorescence spectroscopywitha portable spectrofluorimeter onminced beef (Aït-Kaddour et al.2011), and FF on lean pork loin (Oto et al. 2013) have beenreported. These studies showed the feasibility of fluorescencespectroscopy as a nondestructive monitoring method for micro-bial changes in meat during storage. However, beef meat slices,which are also heavily consumed, have not yet been studied byfluorescence spectroscopy. Since the chemical composition in-cluding endogenous fluorescent substances differs dependingon the origin of meat, the potential of this technique to evaluatebeef should also be investigated. The objective of this study wasto develop a predictionmethod for aerobic plate count (APC) ona beef surface during aerobic storage based on FF.

Material and Methods

Sample Preparation

Sixty lean beef slices of the inner thigh, consisting of 30 sliceseach of Australian cattle and Japanese cattle, were purchasedfrom a local meat retailer in Ibaraki, Japan. The meat sampleswere cut into approximately 45×45×8-mm3 slices at the storeon the day of storage and kept under 5 °C before the experi-ment. They were divided into four lots, i.e., Japanese 1 and 2and Australian 1 and 2, according to the purchase date. Each

lot (15 slices) of lean beef samples was placed in sterilizedplastic Petri dishes with lids and aerobically stored in anincubator at 15 °C. FF and microbial count were measuredafter 0, 12, 24, 36, and 48 h of storage. Three slices were usedin each measurement for each storage time.

Measurement of FF

A sample was placed between a top 0.5-mm-thick quartz plateand a bottom 1-mm-thick acrylic plate (Fig. 1a) and mountedin the sample holder of a spectrophotometer. A fluorescencespectrophotometer (F-7000, Hitachi High-Technology Corp.,Tokyo, Japan) equipped with a front-surface sample holderwas used to measure the FF of the samples. Both excitationand emission wavelength ranges were set at 200–900 nmwith10-nm intervals. FFs were collected at four points for eachslice (Fig. 1b, cross marks, nos. 1–4) on the sample surface atroom temperature. Therefore, 240 FFs (2 countries×2 lots×5storage times×3 slices×4 positions) were collected. The volt-age of the photomultiplier, which determines the amplitude offluorescence intensity, was fixed to 380 V so that the highestintensity does not exceed the dynamic range of the spectro-photometer for the FF measurement.

Microbiological Analysis

The surfaces of both the quartz plate and the beef sample werewiped with a sterile swab (Fig. 1b, 4×4 cm2 shaded area) afterthe FF measurement. Swabbing was carried out in accordancewith the method used in previous studies (Bautista et al. 1997;Oto et al. 2013), in which a sample was swabbed horizontallyand again vertically in order to sample the meat surfacematerial appropriately. The swab top was cut off and stirredwell in the phosphate-buffered saline (Sumitomo 3M Ltd.,Tokyo, Japan) until the swabbed material was dispersed.Serial dilutions of the swab sample were prepared withphosphate-buffered saline, and two plates were made for eachdilution ratio by dispensing 1 mL of dilution to PetrifilmTM

aerobic count plates (Sumitomo 3M Ltd., Tokyo, Japan).Three dilution ratios were selected by considering the growthcurve of APC on lean beef (data obtained in the preliminary

a b

Fig. 1 Sample preparation for FF and APC measurements. Cross marknos. 1–4: FF measurement position, shaded area: surfaces of both meatand quartz plate were swabbed for APC measurement

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experiment), and six Petrifilm plates per sample were used forAPC measurement. After the incubation of all Petrifilm platesfor 48 h at 35 °C, APC was determined by averaging colonycounts of two Petrifilm plates at an appropriate dilution ratio.Sixty APCs (2 countries×2 lots×5 storage times×3 samples)were determined throughout the entire experiment.

Multivariate Analysis

Data Preprocessing

The FF data were preprocessed for statistical analysis. Aschematic diagram of preprocessing performed with referenceto previous studies (Fujita et al. 2010; Shibata et al. 2011) isshown in Fig. 2 and described as follows: (a) Fluorescence isan emission with longer wavelengths than that of excitation.All data whose emission wavelength was shorter than theexcitation wavelength were therefore removed (Fig. 2a). (b)The data included scattered lights and second-, third-, andfourth-order lights, which come from an excitation lightsource. As these were much stronger than the fluorescencesignals, they were removed to perform accurate analysis(Fig. 2b). (c) The wavelength domain with excitation wave-lengths higher than 500 nm was excluded for analysis(Fig. 2c), as there was significant noise that would increaseestimation error. One thousand three hundred sixty fluores-cence intensities were obtained with the combination of exci-tation and emissionwavelengths and used as the FF dataset forregression analysis.

Regression Analysis

MATLAB 2007b (The MathWorks, Inc.) and PLS Toolbox6.7.1 (Eigenvector Research, Inc.) were used for the multivar-iate analysis of FF data. FFs of nos. 1 and 2 were used as thecalibration set, and those of nos. 3 and 4 were used as thevalidation set (Fig. 1b). The multivariate calibration of APCwas performed using partial least-squares regression (PLSR)

using SIMPLS algorithm, in which Venetian blinds cross-validation (three-way split) was applied to the calibration setto optimize the number of latent variables (LVs). The rootmean square error of cross-validation (RMSECV), that ofprediction (RMSEP), and the coefficient of determination(R2) were used as parameters to evaluate the result of analysis.Since PLSR models a linear relationship, all regressions weremade with log(FF) as explanatory variables and log(APC) asobjective variables.

Results and Discussion

Time Variation of APC

Figure 3 shows the time variation in APC determined in theJapanese (circle and cross mark plots) and Australian (triangleand square plots) beef samples. Both initial APC and growthrate varied among the lots investigated. The initial microbialload shows the amount of contamination when meat slices areprocessed. The rate of cell proliferation was considered tovary depending on growth phase of bacterial flora on the meatsurface. The difference in APC among the three slices used formeasurement at each storage time was relatively small.Combining all the data, the APC range on the beef surfacewas 1.7–7.8 log cfu/cm2.

Partial Least-Squares Regression of FF Data

Figure 4 shows the result of PLSR for predicting APC on thesurface of lean beef from FF. The result of the calibrationdataset, i.e., FFs of nos. 1 and 2 (Fig. 1b), is shown in Fig. 4a.A prediction model for APC was developed with seven latentvariables (LV=7), which gave the optimum result with thehighest correlation and the lowest RMSECV (R2=0.889;RMSECV=0.687 log cfu/cm2). The result of the validationdataset, i.e., FFs of nos. 3 and 4 (Fig. 1b), is shown in Fig. 4b.A slight overestimation was observed in the low APCrange; however, a good correlation (R2=0.819) and a small

b ca

Fig. 2 Preprocessing of fluorescence fingerprint. a Removal of nonfluorescence domain, b removal of scattering light, and c removal of noisy domain

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prediction error (RMSEP=0.752 log cfu/cm2) wereobtained. The prediction accuracy of the regression modelwas verified.

FF

Figure 5 shows the FFs of lean beef samples of Australiancattle stored at 15 °C for 48 h. Figure 5a shows those in thehigher fluorescence intensity range (0–5,000 a.u.), whileFig. 5b shows those in the lower fluorescence intensity range(0–200 a.u.). The full dynamic range of the spectrophotometerused is 0–10,000 a.u. Several fluorescence peaks (Fig. 5,peaks A–D) were observed, with each peak seemingly

corresponding to intrinsic fluorophores. Figure 6 shows thevariation in the fluorescence intensity of such fluorophoresagainst APC.

Tryptophan Fluorescence

When excited at a wavelength of 290 nm, the emission spectraof beef lean meat showed a peak intensity at 330 and 660 nm(Fig. 5a, peak A). This excitation and emission band can beassigned to the tryptophan fluorescence (Lacowicz 2006).Several previous studies showed that this fluorescence canbe used to monitor the microbial spoilage of chicken breastfilet (Sahar et al. 2011), minced beef (Aït-Kaddour et al.2011), and pork (Oto et al. 2013). Concerning the origin ofthe tryptophan fluorescence, there are three possibilities, i.e.,the tryptophan residues of proteins in bacteria (Leblanc andDufour 2002; Tourkya et al. 2009), tryptophan residues ofproteins in meat myofibrils (Skjervold et al. 2003), and freeamino acids in meat (Lawrie and Ledward 2006). The ob-served tryptophan fluorescence is assumed to be a fingerprintincluding the contributions of these three components of meatand microbial flora.

The variation in the fluorescence intensity of tryptophanagainst APC is shown in Fig. 6a. Note that the fluorescenceintensity of the dataset of Australian cattle lot 2 was lower thanthose of the other datasets, particularly when APC is morethan 106 cfu/cm2. The decrease in the fluorescence emitted bytryptophan is thought to be due to the catabolization of aminoacids in meat by the bacteria over time. This trend is consistentwith the phenomenon observed in the FF of pork (Oto et al.2013). If the tryptophan fluorescence was principally emittedfrom bacteria, the fluorescence intensity should increase along

Fig. 3 Time variation in aerobic plate count on surface of beef meat(circles and cross marks: Japanese cattle lots 1 and 2, and triangles andsquares: Australian cattle lots 1 and 2, respectively)

a b

Fig. 4 PLS regression results of aerobic plate count. a Predicted vs. measured log(APC) of calibration dataset and b predicted vs. measured log(APC) ofvalidation dataset

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with the increase in APC. It is estimated that the decrease inthe fluorescence emitted by tryptophan in meat outweighedthe increase in the fluorescence emitted by microorganisms inthis case. In the spoilage of meat under aerobic storage, almostall the bacteria in the meat microflora grow by catabolizing

low molecular weight compounds in meat such as glucose,lactic acid, amino acids, nucleotides, urea, and water-solubleproteins (Gill 1986). There is an order in which these com-pounds are catabolized by the major meat spoilage organisms,namely, the first main energy source is glucose, the second is

a b

Fig. 5 Fluorescence fingerprint of beef surface (Australian cattle, 15 °C, 48 h; a higher range 0∼5,000 a.u. and b lower range 0∼200 a.u.)

a b

c d

Fig. 6 Fluorescence peak intensity against APCs of a tryptophan, bNAD(P)H, c porphyrins, and d flavins.Circles and cross marks: Japanese cattle lots1 and 2, and big squares and small squares: Australian cattle lots 1 and 2, respectively

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lactate, and the third is amino acids (Nychas et al. 2007). Thedegradation of amino acids including tryptophan starts shortlybefore the onset of spoilage owing to glucose exhaustion (Gill1986). On the other hand, the quantity of bulk materials(proteins and lipids) does not change before spoilage devel-opment (Dainty et al. 1975). Thus, the tryptophan fluores-cence observed here is supposed to be mainly from tryptophanas free amino acid. In addition, the decrease in its intensitywhen the APC is more than 106 cfu/cm2 suggests the startingpoint of the microbial degradation of amino acids in meat.

When excited at a wavelength of 580 nm, the emissionspectra showed its peak intensity at 330 and 660 nm (Fig. 5a,peak A′). This emission band can also be assigned to thetryptophan fluorescence. However, it is supposed that trypto-phan residues were excited by stray light with the half wave-length of the excitation light, caused by low-order diffractionat the grating of the spectrophotometer. Since there was straylight that excited the meat samples, the wavelength domainwith excitation wavelengths higher than 500 nmwas excludedfrom the FF data for the analysis.

NAD(P)H Fluorescence

When excited at a wavelength of 310 nm, the emission spectrashowed a peak intensity at 440 nm (Fig. 5a, peak B). Severalstudies suggested that the fluorescence at this wavelengthdomain can be assigned to the fluorescence of NAD(P)H(i.e., that of NADH plus that of NADPH) of bacteria(Aït-Kaddour et al. 2011; Leblanc and Dufour 2002; Oto et al.2013; Sahar et al. 2011; Tourkya et al. 2009) or vitamin A inmeat fat (Skjervold et al. 2003). This fluorescence is assumed tobe a fingerprint including the contributions of these origins frombacteria andmeat. The coenzymeNAD(P)H, universally presentin living cells, is the major intermediate electron and hydrogencarrier and can be considered a useful marker of the metabolicactivity of cells. While NAD(P)H is fluorescent (excitationmaximum of 340 nm and fluorescence maximum of 460 nm),its oxidized counterpart, i.e., NAD(P)+, is not (Lacowicz 2006).NADH fluorescence can be used to classify bacterial species(Leblanc and Dufour 2002; Tourkya et al. 2009), and an emis-sion band shift can be observed among Pseudomonas,Stenotrophomonas, Xanthomonas, and Burkholderia dependingon the species (Tourkya et al. 2009). On the other hand, as thefluorescence of a meat matrix, vitamin Awas identified in 322-nm excitation and 440-nm emission bands inmeat adipose tissue(Skjervold et al. 2003).

Figure 6b shows the variation in fluorescence intensity ofpeak B against APC. Its intensity is very small against thedynamic range of the spectrophotometer, and it is difficult tofind a common trend under a single excitation emission wave-length condition. Several plots with relatively high fluores-cence intensities were assumed to be points where the emis-sion of vitamin A in adipose tissue was measured. Lean beef

was used in this study. However, it was unavoidable for asmall amount of adipose tissue to be included in the meat. Onthe other hand, in the previous study about the microbialspoilage of pork, a discriminable increase in the fluorescenceintensity of NADPH against bacterial population was ob-served when the APC is more than 107 cfu/cm2 (Oto et al.2013).

Porphyrin Fluorescence

When excited at a wavelength of 430 nm, the emission spectraof beef lean meat showed a peak intensity at 590 and 660 nm(Fig. 5b, peak C). This twin emission band can be assigned tothe porphyrin fluorescence (Christensen et al. 2006;Ramanujam 2000). The variation in the fluorescence intensityof porphyrins against APC is shown in Fig. 6c. An increase inthe fluorescence intensity, despite being very small against thedynamic range of the spectrophotometer used, was observed inthe dataset of Japanese cattle lots 1 and 2 and Australian cattlelot 1; meanwhile, there was no significant trend in the datasetof Australian cattle lot 2. The increase of fluorescence signal ofporphyrins suggests that the amount of porphyrins was in-creased. In addition, among the previous studies of autofluo-rescence in food systems (Christensen et al. 2006; Karoui andBlecker 2011), there is no precedent in which the fluorescenceof porphyrins was observed from the meat itself. Therefore, itis assumed that the origin of porphyrins was the increasingbacteria which biosynthesized it and not the postslaughtermuscle tissue which was biologically inactive.

Porphyrin biosynthesis in aerobic organisms has been ex-tensively investigated, and its pathway is well established(Jordan and Akhtar 1991). Most microorganisms and animalcells excrete very small, biologically insignificant amounts ofporphyrinogens (plus porphyrins), together with their precur-sors porphobilinogen and δ-aminolevulinic acid. This is be-cause, when the amount of porphyrinogens formed is greaterthan that which is usable for the cells, the cells preventunlimited accumulation of porphyrinogens and porphyrinsby excreting these substances (Doss and Philipp-Dormston1971). In addition, the amount of porphyrins synthesized bybacteria varies depending on the species and surroundingcondition (Doss and Philipp-Dormston 1971; Harris et al.1993). In the case of meat spoilage, genera in the familyPseudomonas, Acinetobacter, Moraxella, Aeromonas,Brochothrix thermosphacta, and Enterobacteriaceae havebeen found to be major contributors depending on theproduct type and the conditions surrounding the product(Nychas et al. 2007). Therefore, as a reason of the consid-erable difference obtained in fluorescence intensity for thesame APC (Fig. 6c), it is supposed that the amount ofporphyrins synthesized was different for each sample sincethe dominant species on the meat surface was different.

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Flavin Fluorescence

At an excitation wavelength of 460 nm, a fluorescencepeak was observed at 520 nm (Fig. 5, peak D). Thisexcitation and emission band can be assigned to theintrinsic fluorescence of flavins that are well-known in-trinsic fluorophores (Lacowicz 2006; Ramanujam 2000).Figure 6d shows the variation in the fluorescence inten-sity of flavins against APC. A slight increase in thefluorescence intensity of flavins was observed in thedataset of Japanese cattle lots 1 and 2; however, therewere no common trend in the dataset of Australian cattlelots 1 and 2. As is the case in the fluorescence ofporphyrins, a slight increase of fluorescence signal offlavins suggests the increase of flavins. Since there isno previous study in which the fluorescence of flavinswas observed from the meat itself (Christensen et al.2006; Karoui and Blecker 2011), it is assumed that theincrease of flavins was due to the biosynthesis by grow-ing bacteria on meat surface.

The flavins of bacterial cells are in the form of flavinadenine dinucleotide (FAD) and flavin mononucleotide(FMN), although there may be a trace of riboflavin as well(Benson et al. 1979; Peel 1958; Wilson and Pardee 1962).Cellular flavins, FADs, and FMNs exist mostly as cofactorsfor enzymes involved in oxidation–reduction reactions.Since the amount of flavins synthesized by growing bacte-ria also varies depending on the species and surroundingcondition (Wilson and Pardee 1962), it is estimated that theamount of flavins on the meat surface would be determinedby the dominant species of meat spoilage. This can be areason of the considerable difference observed in fluores-cence intensity of flavins for the same APC (Fig. 6d).

Regression Coefficient of PLSR

Figure 7 shows the distribution of the regression coefficient ofthe model proposed. It is considered that the wavelengthconditions with high regression coefficient contributed largelyto the PLSR model for APC prediction. The wavelengthregions related to the four types of intrinsic fluorophore men-tioned above are shown in Fig. 7 regions A to D. Figure 7region A shows the region where the tryptophan fluorescencewas observed. The highest regression coefficients were ob-served around this region; however, the peak position isdifferent from that of tryptophan. Since the decrease in tryp-tophan fluorescence intensity was assumed to have occurredowing to the onset of amino acid degradation when the APC ismore than 6.0 log cfu/cm2, its correlation with APC seems tobe weak. As an alternative explanation, other aromatic aminoacids, i.e., tyrosine and phenylalanine, fluoresce near the saidwavelength region (Lacowicz 2006). The FF data may containthe fluorescence signals of these two aromatic amino acids,although the fluorescence peaks of these two aromatic aminoacids cannot be distinguished directly. Figure 7 region Bshows the wavelength region of the NAD(P)H or vitamin Afluorescence. There were relatively high regression coeffi-cients in this region. Although no evident relationship wasobserved between the variation in fluorescence intensity underthe single excitation emission wavelength condition, the fluo-rescence signals in this region might contain information thatcorrelates with APC. Finally, regions C and D of Fig. 7 showthe wavelength regions of the fluorescence of porphyrins andflavins, respectively. The observed peaks with relatively highregression coefficients were consistent with the fluorescencepeak of each fluorophore. This result suggests that changes inintrinsic fluorophores due to the metabolism of bacterial floraon meat are reflected in the PLSR model for predicting APCfrom an FF dataset.

Conclusions

FF spectroscopy coupled with PLS regression was able tocorrelate FF with the APC in the bacterial contamination loadrange from 1.7 to 7.8 log cfu/cm2 with a prediction error ofRMSEP=0.752 log cfu/cm2. In addition to fluorescence fromtypical intrinsic fluorophores that have been observed in theprevious studies of meat, namely, tryptophan, NAD(P)H, andvitamin A, autofluorescence that might be from porphyrinsand flavins in bacteria can be monitored in the FF of beef.From the behavior of fluorescence peak intensity againstAPC, it is estimated that the fluorescence of tryptophan wasmainly from free amino acids in meat; those of NAD(P)H,porphyrins, and flavins were mainly from bacterial flora; andthat of vitamin A was mainly from adipose tissue in meat. Inthe distribution of the regression coefficient of the PLSRFig. 7 Distribution of regression coefficient of PLSR model

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model, peaks with a relatively high regression coefficient wereconsistent with the fluorescence peak of the said fluorophores.This suggests that signals of autofluorescence from intrin-sic fluorophores, which vary along with the metabolism ofmicroorganisms, are reflected in the PLSR model forpredicting APC from the FF dataset. Meanwhile, sincethe autofluorescences emitted by muscle and adipose tissuein meat are different, the presence of adipose tissue couldadversely affect the accuracy of prediction. The result inthis study supports the notion that fluorescence spectros-copy along with multivariate analysis is useful for moni-toring the behavior of various intrinsic fluorophores inmeat matrices and bacterial flora in the metabolic activityof meat. FF appeared to be applicable to the nondestruc-tive determination of APC on the surface of lean beef.

Acknowledgments This research was funded by the research anddevelopment projects for application in promoting new policy of theMinistry of Agriculture, Forestry and Fisheries (22040), Japan.

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