REVIEWRecent advances in the analysis of dairy product qualityusing methods based on the interactions of light withmatter

ERIC DUFOUR*DG Enseignement et Recherche, Ministere de l’alimentation, de l’agriculture et de la peche, 1ter avenue de Lowendal,75700, Paris 07 SP, France

*Author forcorrespondence. E-mail:eric.dufour@agriculture.gouv.fr

� 2011 Society ofDairy Technology

As demonstrated by physicists in the past centuries, light interacting with matter contains information that

may reveal the concentration or ⁄and the structure of components of the investigated matter. The use ofspectroscopy (absorption in the visible, infrared, fluorescence, Raman, etc.) in food science has increasedtremendously in the last couple of decades as it has been demonstrated that the detection and content of a

number of food constituents, as well measurement of food properties, may be achieved by measuring theradiations that is either absorbed or emitted at different wavelengths by the product. These developments

have been made possible, thanks to multivariate chemometric methods that are appropriate and usefulfor the evaluation of fluorescence or infrared spectra exhibiting slight differences such as the ones

recorded on dairy products, allowing the development of prediction models. Recently, imaging technologysuch as confocal laser scanning microscopy or hyperspectral imaging coupled to image analysis tech-

niques has successfully been used to study highly heterogeneous products such as cheese. Indeed, imageanalysis techniques such as mathematical morphology or image texture analysis make it possible to quan-tify structures in the images and to show the influence of different manufacturing processes on the protein

network microstructure of cheeses. The aim of this article is to summarise those aspects of spectroscopyand imaging methods that may have value for solving problems in dairy science and technology.

Keywords Dairy product, Quality, Structure, Spectroscopy, Imaging techniques, Chemometrics.

INTRODUCT ION

Spectroscopy is the study of the interactionbetween electromagnetic radiation and atoms, mol-ecules or other chemical species. Radiation is aform of energy that possesses both electrical andmagnetic properties and is often described as elec-tromagnetic radiation. Techniques such as ultravio-let, visible, infrared and near-infrared spectroscopyderive their names from their use of a portion ofthis electromagnetic spectrum, and can be categor-ised according to the particular wavelength beingutilised as shown in Figure 1, which also indicatesthe energy changes associated with each wave-length. And, as demonstrated by physicists in thepast centuries, light interacting with matter containsinformation about the concentration or structure ofthe investigated component (Bertrand and Dufour2006).

The most efficient techniques used for proteinstructure determination are however not adapted toinvestigate protein structure in complex food matri-ces. X-ray diffraction implies the use of crystallinesamples, which cannot be used in gel and colloidstudies. In NMR the size of the protein, as well asthe size of the adsorption surface, is a limitingfactor. Circular dichroism is a technique routinelyused to quantify the secondary structure of proteinsand peptides in solution (Dufour and Haertle1990). This technique also gives quantitative infor-mation on the secondary structures of adsorbedproteins, but it is severely limited by light scatter-ing of particles with diameters above 20 nm.For this reason, studies of secondary structurechanges in protein upon adsorption at interfacesand gelation are scarce.Since the pioneering work of Parker (1968), it is

known that front-face fluorescence spectroscopy

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doi: 10.1111/j.1471-0307.2010.00665.x

allows investigation of the fluorescence propertiesof solid, powdered, turbid and concentrated sam-ples such as dairy products. In parallel, the devel-opment of the attenuated total reflectance (ATR)device for mid-infrared spectrometers allows thesampling problems encountered, when collectingspectra from opaque and viscous samples, to beovercome. This simple and reproducible methodmade it possible to record spectra directly on dairyproducts. During the last two decades, there was ahuge development of research based on spectro-scopic methods for investigating intact food prod-ucts and exploring structure–quality relationships.The interests of fluorescence and infrared spectro-scopic methods for the nondestructive investigationof dairy products have been reviewed by severalauthors (Karoui et al. 2003, 2010; Kulmyrzaevet al. 2006; Moller-Andersen and Mortensen 2008;Karoui and Blecker 2010).This article focuses on recent developments in

the field such as synchronous fluorescence spec-troscopy (SFS), multispectral imagery and confocallaser scanning microscopy. Special attention is paidto the evaluation of the spectra and images usingrelevant chemometric methods such as parallelfactor (PARAFAC), common components andspecific weight analysis, image texture analysis ormathematical morphology.

INVEST IGAT ION OF DAIRYPRODUCTS US INGNONDESTRUCT IVEFLUORESCENCE AND INFRAREDSPECTROSCOP IC METHODS

It is generally assumed that fluorescence and Fou-rier transform mid-infrared spectroscopies do notsuffer from the above-mentioned inconveniencesand may be applied to turbid samples as far as spe-cial devices for the presentation of samples are

used. Front-face (or surface) fluorescence may pro-vide information on the structure and interactionsof food matrix components (Dufour and Riaublanc1997). The techniques used in conventional fluo-rescence spectroscopy allow to record excitationspectra or emission spectra.In general, fluorescence spectroscopy offers sev-

eral inherent advantages for the characterisation ofmolecular interactions and reactions (Lakowicz1983). First, it is 100–1000 times more sensitivethan other spectrophotometric techniques. Second,fluorescent compounds are extremely sensitive totheir environment, e.g. the changes in protein con-formation modify tryptophan quantum yield andemission wavelength. In addition, the fluorescentproperties of fluorophores are very sensitive to thechanges in solvent viscosity. Third, most fluores-cence methods are relatively rapid.The use of spectroscopy in food science has

increased tremendously in the last couple of dec-ades as it has appeared that detection and estima-tion of a number of food constituents andproperties may be achieved by measuring theamount of radiation that is either absorbed or emit-ted at different wavelengths. Absorption spectros-copy such as near infrared is now widely used infood analysis, including the estimation of proteins,carbohydrates, mineral elements, vitamins andmany additives. Emission spectroscopy hasincreased much in importance in the last decade,and several articles have shown the potential of thistechnique in the estimation of fat oxidation, colla-gen, certain vitamins, etc.Univariate analysis techniques are not always

appropriate for the study of data exhibiting slightdifferences such as the 1 or 2 nm shift observed forthe fluorescence or infrared spectra of raw- orpasteurised-milk cheeses. Principal componentanalysis (PCA), a multivariate chemometricmethod, can be used to extract information related

X ray Far UV Near UV Visible NIR MIR Far IR

Micro waves Radio frequencies (NMR)

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Figure 1 Spectral regions of interest for analytical developments.

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to protein conformation changes following milkheating from the spectral collection (Dufour andRiaublanc 1997). This method is well suited tooptimise the description of data collections with aminimum loss of information. Moreover, the eigen-vectors corresponding to the principal componentsare homologous to spectra. They provide informa-tion about the regions of the fluorescence spectrumwhich explain the differences between the samplesobserved on the similarity map. Coupling spectros-copy with multivariate chemometric methodssuch as PCA, partial least squares discriminantanalysis (PLSDA), factorial discriminant analysis,common components and specific weights analysis(CCSWA), and others considerably increasesthe yield of useful and ‘exploitable’ information(Figure 2).The potentials of spectroscopic methods coupled

with chemometrics for the nondestructive investi-gation of dairy products have been reviewed byKaroui et al. (2003), Kulmyrzaev et al. (2005,2006) and Moller-Andersen and Mortensen (2008).

SYNCHRONOUS FLUORESCENCESPECTROSCOPY COUPLED WITHPARALLEL FACTORS ANALYS ISFOR THE INVEST IGAT ION OFDA IRY PRODUCTS

Considering closely related compounds, excitationand emission profiles are very similar and arebroad and structureless for many species. It has

been demonstrated, however, that excitation–emis-sion matrix (EEM) spectroscopy and SFS can besuccessfully applied to discriminate closely relatedchemical compounds or samples (Christensenet al. 2005). EEMs are typically generated by scan-ning a monochromator to produce an emissionspectrum while another monochromator scans theexcitation wavelength incrementally (Airado-Rodriguez et al. 2009).While the techniques used in conventional fluo-

rescence spectroscopy allow to record excitationspectra or emission spectra, the excitation wave-length kex and the emission wavelength kem arescanned synchronously with a constant wavelengthinterval, Dk = kem ) kex in front-face SFS(Christensen et al. 2006). For well defined absorp-tion and quantum yield maxima, the optimumvalue of the offset Dk is set by the difference inwavelength of the emission and excitation maximawhich is known as Stoke’s shift. SFS makes it pos-sible to narrowing of the spectral band and simplifythe emission spectrum. In addition, SFS presentsan interesting advantage from our point of view: asynchronous fluorescence spectrum exhibits sharppeaks corresponding to different fluorophores,compared to a classical emission spectrum that ismainly characterised by a broad band. For exam-ple, Boubellouta and Dufour (2008) collectedsynchronous fluorescence spectra in the 250–550 nm excitation wavelength range using offsetsof 20, 40, 60, 80, 100, 120, 140, 160, 180, 200and 240 nm between excitation and emission

CheeseAnalytical

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Figure 2 Chemometric techniques applied for the extraction of qualitative or quantitative information.

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monochromators on milk samples heated between4 and 50�C. The synchronous fluorescence land-scape for milk at 4�C is shown in Figure 3. Thesynchronous fluorescence data were constructed insuch a way that x-axis represents the synchronousexcitation wavelength and the y-axis the wave-length interval (Dk), while the z-axis is plotted bylinking points of equal fluorescence intensity.New chemometric tools such as PARAFAC

and multivariate curve resolution-alternative leastsquare (Burdick 1995; Bro 1997) make it possi-ble to handle fluorescence landscapes keeping the2-dimensional structure of each measurement.PARAFAC is a chemometric decompositionmethod, and is a generalisation of PCA tohigher-order arrays. The number of PARAFACcomponents necessary to reconstruct the data isan important parameter. Several methods can beused to determine this parameter. Core consis-tency diagnostic (Bro and Kiers 2003) is oftenused to assess the model deviation of ideal multi-linearity and guide the choice of the number ofcomponents to consider. When the core consis-tency drops from a high value (above about60%) to a low value (below about 50%), it indi-cates that an appropriate number of componentshas been attained. In addition, the non-negativityhas been applied to build the components of themodels. Imposing non-negativity constraint ondecomposition model parameters of fluorescencethree-way spectral data is common practice asboth the spectral intensities and fluorophoreconcentrations are known to be positive (Boubel-louta and Dufour 2008).In this study, the objective of PARAFAC is to

resolve the synchronous fluorescence signal intothe pure spectra of each of the fluorescence com-pounds present in the set of milk spectra, i.e. toestimate the profiles of fluorophores directly

from the synchronous fluorescence landscapemeasurements. Regarding milk samples submittedto heat, Boubellouta and Dufour (2008) showedthat three components were suitable, as a core con-sistency superior to 81% and an explained varianceamounting to 94% were observed. Adding thefourth component, the core consistency decreasedto 15% and this model explained 98% of the totalvariance, suggesting that the model with four com-ponents was unstable and overfitted.The authors indicated that the loading profiles

of the first and second components performed onthe data set corresponded quite well with thecharacteristics of tryptophan and vitamin A fluo-rescence spectra respectively (Figure 4). Indeed,the shapes of spectra obtained with PARAFACwere similar to the excitation spectra of tryptophanand vitamin A spectra respectively. In addition,the optimal Dk of 50 and 100 nm for the first andthe second components agreed well with the emis-sion maxima of tryptophan residues and vitamin Arespectively. Finally, the authors assigned the thirdcomponent with optimal Dk of 60 nm to ribofla-vin fluorescence spectra. Indeed, the excitationspectrum of riboflavin in milk was characterisedby a maximum located at 448 nm, whereas theemission maximum was observed at 520 nm. Theobtained values derived from the fluorescencespectra were similar to the ones found with thePARAFAC model, strengthening the hypothesisthat the third component corresponded to the com-pound riboflavin.The loading in sample mode obtained with

PARAFAC represents the concentration mode foreach fluorophore. But, the so-called ‘concentrationmode’ could also reveal structural changes in theenvironment of the considered fluorophores asdescribed by Boubellouta and Dufour (2008). Con-sidering the mild heating of milk, the profiles of theestimated concentrations for the first and third com-ponents showed negligible variations according tothe temperature. This was explained by the mildheat treatment (4–50�C) applied to the milk whichdid not allow significant denaturation of proteins orriboflavin. Regarding, the loading of component 2corresponding to vitamin A, a significant decreasewas observed by the authors when milk sampleswere submitted to heating. In this temperature range,the triglycerides were mostly the sole milk compo-nent the structure of which was modified. Theauthors suggested that it could reflect some changesin vitamin A fluorescence properties related to themelting of triglycerides in milk fat globules. This isin agreement with previous findings reporting that

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Figure 3 Three-dimensional plot of the synchronous

fluorescence landscape for milk at 4�C.

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the shape of the vitamin A excitation spectrum iscorrelated with the physical state of the triglyceridesin the fat globule (Dufour et al. 2000).Recently, Boubellouta et al. (2009) demon-

strated that front-face SFS in combination withchemometric methods makes it possible to studythe structure of milk components and mineral equi-libria at the molecular level. Indeed, the fluores-cence properties of tryptophan residues aremodified by the changes in micelle structure andprotein interactions induced by mineral supplemen-tation. Using these spectroscopic methods coupledto chemometrics, it has been possible to demon-strate that the phenomena induced by the additionof phosphate were different from the ones observedfollowing the addition of calcium or citrate, a cal-cium-chelating agent. Fluorescence and infrareddata are spectra that allow us to derive informationon the molecular structure and interactions of milkcomponents.

COMPARISON OF STRUCTURECHANGES OF MILK COMPONENTSDURING ACID - AND RENNET-INDUCED COAGULAT IONS ASSTUDIED BY SPECTROSCOP ICAND RHEOLOGY METHODSCOUPLED WITH COMMONCOMPONENTS AND SPEC IF ICWEIGHTS ANALYS IS

Milk coagulation is the primary step in the produc-tion of most dairy products. Coagulation can beinduced by acid, rennet or both. Once a three-dimen-sional casein network is formed, it progressivelyundergoes structural modifications such as fusionand compaction of casein particles. After networkestablishment, the progressive reorganisation of thegel microstructure is also accompanied by a reduc-tion in the amount ofwater contained in casein parti-cles. The kinetics of coagulation, influenced by thecoagulant, the temperature and the structural aspectsof protein–protein and protein–fat globule interac-tions, determine the rheology properties of gels andthus their syneresis behaviour, as well as the textureof the final product.Boubellouta (2008) investigated the coagulation

kinetics (data recorded every 5 min until 300 min)of skim milk at 30�C by adding 40 lL ⁄L commer-cial rennet containing 50 mg ⁄L of chymosin or byadding glucono-d-lactone added at the concentra-tion of 1.75 g ⁄L using different techniques:dynamic oscillatory rheology, front-face SFS andATR mid-infrared spectroscopy. The rheology dataobtained included the two components of shearmodulus G*, i.e. the elastic component G¢ (storagemodulus) and the viscous component G00 (lossmodulus), the complex viscosity (g*) and thephase angle (tan d). The regions of the mid-infraredspectra located between 1700 and 1500 ⁄cm (pro-tein region), and 1500 and 900 ⁄cm (fingerprintregion) have been considered in this study. Regard-ing synchronous fluorescence, spectra were col-lected in the 250–500 nm excitation wavelengthrange using offset of 80 nm between excitationand emission monochromators.A total of four data sets containing the synchro-

nous fluorescence, the infrared spectra (AmideI&II and fingerprint regions) and the rheology dataof the acid- and rennet-induced coagulation kinet-ics were recorded. The similarities ⁄differencesbetween the four data tables can be described byCCSWA. This method consists in determining acommon space of representation for all the datasets, each table having a specific weight (or

Figure 4 Three-component non-negativity constraint

model derived from the mild-heated milk synchronous fluo-

rescence spectra. Concentration mode (a), Dk profiles (b) and

loading profiles (c).

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salience) associated with each dimension of thiscommon space. This chemometric method dealswith the co-inertia, which allows to describe theoverall information, collected and takes intoaccount the relation between the different datatables (Mazerolles et al. 2002). Similarity mapsand patterns can also be drawn. CCSWA has beenused to: (i) describe in a simple and synthetic man-ner the overall information collected; (ii) extractand use the relevant information related to thestructural changes of the studied component; (iii)provide tools for a molecular interpretation of theresults; and (iv) investigate the relations betweenstructures and rheology properties (Mazerolleset al. 2002; Kulmyrzaev and Dufour 2010).Boubellouta (2008) showed that the first two

dimensions gave different saliences for spectros-copies and rheology data sets: the first dimension(D1) expressed 89.4, 67.7 and 73.3% of the inertiaof the rheology, infrared data corresponding toAmide I&II and fingerprint spectral region bands,respectively, and a tiny part (6.3%) of the inertia ofthe fluorescence data (Table 1). On the contrary,the second dimension (D2) expressed 87.6% of theinertia of the fluorescence data and part of the iner-tia of the infrared data (21.2% and 16.5% forAmide I&II and fingerprint spectral regions respec-tively) and a tiny part (5.5%) of the inertia of therheology data (Table 1).Considering the similarity maps, the plane

defined by the components D1 and D2 allowed todiscriminate the types of coagulation (acid ⁄ rennet)and the data collected between the gelation pointsand 300 min (Figure 5a) respectively. Indeed, Her-bert et al. (1999a,b) have shown that the two typesof coagulation induced different rheology proper-ties of the gels. More interestingly, the high per-centages of variances of infrared and rheology dataexplained by component D1 suggested correlationsbetween these data sets.Considering the plane defined by the compo-

nents D2 and third dimension (D3), the data of acid

coagulation kinetic recorded between 5 and110 min had negative scores according to D2 andwere separated mainly on D3, whereas data ofacid-induced coagulation kinetic recorded between110 and 300 min were separated principally onaxis D2 (Figure 5b). The D3 expressed 2.2, 9.8and 7.9% of the inertia of fluorescence data, andinfrared data corresponding to Amide I&II andfingerprint spectral regions, respectively, and a tinypart (0.2%) of the inertia of the rheology data(Table 1). In addition, data recorded during rennet-

Table 1 Saliences for the common components 1–4 of CCSWA performed on the acid- and rennet-induced coagulation

kinetic data recorded at 30�C (Boubellouta 2008)

D1 (69.9%) D2 (27.7%) D3 (2.2%) D4 (0.1%)

Rheoloy 0.894 0.055 0.002 0.003

SFS 0.063 0.876 0.022 0.026

MIR (1700–1500 ⁄ cm) 0.677 0.212 0.098 0.002

MIR (1500–900 ⁄ cm) 0.733 0.165 0.079 0.003

D1, first dimension; D2, second dimension; D3, third dimension; D4, fourth dimension; SFS, synchronous fluorescence

spectroscopy; CCSWA, common components and specific weights analysis; MIR, mid-infrared spectra.

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Figure 5 Common components and specific weight analysis

similarity maps defined by the common components first

dimension and second dimension (D2) (a) and D2 and third

dimension (b) for the acid- and rennet-induced coagulation

kinetic data recorded at 30�C.

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induced coagulation kinetic were quite exclusivelydiscriminated according to D2 (Figure 5b).The examination of the spectral pattern in syn-

chronous fluorescence spectra associated with thecommon component D2 (Figure 6) showed simi-larity with principal components (Boubellouta2008) of the PCAs performed on synchronousfluorescence spectra recorded during acid- and ren-net-induced coagulation kinetics at 30�C. Similarconclusions were derived for the spectral patternassociated with the common component D3 (Fig-ure 6) and principal components (Boubellouta2008) of the PCAs performed on synchronous fluo-rescence spectra recorded during acid- and rennet-induced coagulation kinetics at 30�C. It appearedthat the most discriminant wavelengths andwavenumbers in the spectra recorded after thecoagulation points were similar irrespective of thecoagulation type, i.e. acid or rennet: as shown inFigure 5, these spectra were discriminated accord-ing to common component D2. But, the spectrarecorded during acid- and rennet-induced coagula-tion kinetics at 30�C showed differences as theyare discriminated according to common componentD1.Common components and specific weights anal-

ysis performed on the four data tables allowed tomanage in an efficient way the whole informationcollected. It summed up the major part of the infor-mation on three dimensions (D1, D2 and D3). Inaddition, each of the spectral data sets providedinformation on the different phases taking placeduring coagulations.It is common knowledge that the process used to

coagulate milk has broad effects on the texture of

the final product. Rheological methods are usefulfor characterising the texture of milk products andcheeses. This method made it possible to show thatthe coagulation of milk by glucono-delta-lactoneand rennet investigated in this study showed differ-ent rheology properties. It is generally assumedthat the properties of gels at the macroscopic levelare related to their molecular structure. This studyshows that infrared spectroscopy is useful for cha-racterising the changes in micelle structure beforegelation of milk and micelle aggregation duringcasein network development.Using these spectroscopic methods, it has been

possible to demonstrate that gels exhibiting differ-ent rheology properties have different structures atthe molecular level. In addition, it was possible tofollow the different steps of the gelation processes.Spectroscopic methods such as fluorescence andmid-infrared spectroscopy combined with chemo-metric tools have the potential to evaluate structureat the molecular level. Fluorescence and infrareddata are spectra that allow to derive information onthe molecular structure and interactions of the dairyproduct matrix (Herbert et al. 1999a,b; Boubellou-ta 2008). Moreover, it was also suggested that thephenomena observed at molecular (fluorescence,infrared) and macroscopic (rheology) levels arerelated to the texture of dairy products.

INVEST IGAT ION OF DAIRYPRODUCT HETEROGENE ITYUS ING IMAGING TECHNIQUES

Classical spectroscopy (fluorescence, visible andinfrared) traditionally yields information on a smallregion of a sample. Therefore, the product samplesstudied using classical spectroscopy must be rela-tively homogeneous to extract characteristicsreflecting properties of the whole product. Thepotential drawbacks of spectroscopy can be easilyeliminated by the use of microscopy and imagingtechniques, as well as by spectroscopic imagingtechniques referred to as hyperspectral imaging(Jun et al. 2007).Kulmyrzaev et al. (2008) conducted a study to

determine whether multispectral imagery combinedwith chemometrics could accurately distinguishand classify different blue cheeses. The images ofProtected Denomination of Origin (PDO) Bleud’Auvergne and Fourme d’Ambert blue cheeseswere acquired using a custom-design multispectralimager (Chevallier et al. 2006). The image acquisi-tion was conducted in the ultraviolet (360, 370 and400 nm), visible (470, 568 and 625 nm) and near-

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Figure 6 Spectral pattern associated with the common

component second dimension (—) and third dimension (- - -)

(fluorescence data sets recorded at 30�C).

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infrared (875 and 950 nm) spectral regions. Spec-tral images are multivariate and can be regarded asa three-way data structure, a ‘cube’. The authorsextracted the spectral functions (S) of image texturebased on the Fourier spectrum and image weightsfrom the raw multivariate images using an imageprocessing tool and a method of simultaneousdecomposition of covariance matrices respectively(Gonzalez et al. 2004) (Figure 7). The image anal-ysis results were organised into three matrices. Thefirst one contained the values of Sh(r) computedwith the increment of r (radius coordinate) equal to1 pixel. The first column and the first row of thismatrix contained cheese sample names and valuesof r respectively. The structure of the second matrixwas similar to that of the first one and contained thevalues of Sr(h) computed with the increment of h(angular coordinate) equal to 1 (Figure 8). The thirdmatrix was built of the computed image weightskðkÞa , in which the first column represented thecheese sample names, whereas the first row repre-sented the dimensions.Principal component analysis and PLSDA of the

spectrum functions showed a reliable discrimina-tion of the Bleu d’Auvergne and Fourme d’Ambertblue cheeses. Indeed, PLSDA performed on thefunction Sh(r) computed from the images allowed95 and 90% of good classification for Bleu d’Au-vergne and Fourme d’Ambert PDO cheeses respec-tively. In addition, examination of the imageweights using PLSDA allowed the prediction ofthe producers of the blue cheeses. This study dem-onstrated the ability of the multispectral imagerycombined with chemometrics to characterise thequality and identity of the blue cheeses in a rapidand inexpensive manner.When dealing with highly heterogeneous prod-

ucts such as cheese, characteristics obtained by

imaging a relatively large region do truly representthe nonuniform distribution of components in theheterogeneous products (Wold et al. 1999;Kulmyrzaev et al. 2008). The hyperspectral imag-ing techniques in UV, visible or mid-infraredregions combine spectroscopic and imagingsystems to collect spectral and spatial informationsimultaneously. Therefore, the amount of informa-tion obtained by hyperspectral and multispectralimaging techniques is incomparably large andmay really reflect the total variance of the productproperties.

QUANT IF ICAT ION OF SOFTCHEESE MICROSTRUCTURE US INGCONFOCAL LASER SCANNINGMICROSCOPY AND IMAGETEXTURE ANALYS IS

Soft cheeses present a wide range of structure andtexture resulting from very diversified technolo-gies. Their rheological and sensory properties arelargely governed by the structure and organisationof their components, and especially the protein net-work. Texture optimisation of soft cheeses, themajor aim for cheese manufactures, requires properknowledge of evolution of their structure from thecoagulum to the ripened stage. Light and electronmicroscopy are frequently used to characterise thestructure and organisation of the food products.However, light microscopy provides images with alimited resolution for the thick samples because ofthe large field depth of the light microscope. Com-pared with light microscopy, electron microscopyproduces images with an important resolution, butthe sample preparation for these techniques caninvolve artefacts. On the other hand, confocal laser

Figure 7 Coordinate system applied to the cheese images for

computing the spectrum function S(r, h) (r is the radial coordi-nate, h is the angular coordinate, R0 is the radius of a circle

centred at the origin).

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Figure 8 Examples of the spectrum function Sh(r) computed

for the Bleu d’Auvergne cheese (BA) and Fourme d’Ambert

cheese (FA).

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scanning microscopy, mainly used in the biomedi-cal fields, presents an important potential for themicrostructure characterisation of food products(Brooker 1995). Compared with conventional fluo-rescence microscopy, confocal microscopy allowsthe recording of thin optical sections preventingout-of-focus radiation. It provides a better signal tonoise ratio and consequently an improved resolu-tion (Laurent et al. 1994).But, microscopy techniques alone give only

descriptive information. It appears necessary: (i) toquantify the protein network structure using imageanalysis methods to objectively compare a set ofimages; and (ii) to establish a correlation betweenthe microstructure and other properties of softcheeses, such as their rheological and sensory prop-erties. However, the microscopic images of proteinnetwork of cheeses do not contain well-defined iso-lated objects, but can be defined as a repetitivearrangement of basic patterns called an imagetexture (Smolarz et al. 1989). Different image tex-ture analysis methods have been developed toextract textural features of these textured images(Serra 1982). These analysis methods describe thevisual information characterised by the grey-levelproperties of pixels and their relationships to eachother. Among these image texture analysismethods, the mathematical morphology methodhas an advantage in that it gives granulometricinformation.Few studies were carried out on the soft cheeses

to characterise the microstructure evolution duringthe ripening phase. The aim of this study was tocharacterise the protein network microstructure ofsoft cheeses using different technologies and at dif-ferent ripening stages using confocal laser scanningmicroscopy. An image analysis method issuedfrom mathematical morphology was applied onthe confocal microscopy images to quantify thestructure of different protein networks.Texture optimisation of soft cheeses, a major

aim of cheese manufacturers, requires betterknowledge of the microstructure evolution of theircomponents, and especially of the protein network.Moreover, a quantification of protein network tex-ture using image analysis methods is necessary tocompare objectively the images recorded on a setof cheese samples and to correlate the microstruc-ture of cheese protein networks and their textureproperties.Herbert (1999) investigated the following six

cheeses manufactured using different fabricationprocesses: four different mesophilic cheeses, onethermophilic cheese and an ultrafiltered cheese.

Among the mesophilic cheeses, the mesophilic 1and 2 cheeses were white mould with an acidiccurd. The mesophilic 3 and 4 cheeses correspondedto red-smear cheeses characterised by a rennetedcurd. The author selected these cheeses to considera wide structure and texture range variation. Thecheeses were studied at a young stage of ripening(25 days), except for the mesophilic 1 and 2cheeses, which were also studied at an old stage ofripening (45 days).To characterise the protein network microstruc-

ture of the different soft cheeses, cryotome sectionswere stained with Fuschin acid and images wererecorded using a Zeiss LSM 410 (Zeiss, Le Pecq,France) confocal microscope (Herbert et al. 1999b).For each sample, 10 fluorescence images of 1 lmfocal plane were recorded at different locations, andthe database contained a total of 720 images. Eachimage was composed of grey levels (256 greylevels, 512 · 512 pixels) and corresponded to asample area of 162 · 162 lm.The fluorescence images recorded by confocal

laser scanning microscopy for different soft cheesesare shown Figure 9. In these images, the protein net-work appears white, whereas the interstitial spaces,corresponding to the place of the fat globules andthe aqueous phase, appear dark. Figure 9a,b pre-sents the images for mesophilic 1 and 2 cheeses atthe young stage of ripening, respectively, whereasthe images for these same cheeses at the old stage ofripening are given in Figure 9c,d respectively. Atthe young stage of ripening, the protein networks ofthe mesophilic 1 and 2 cheeses presented a granularstructure caused by the presence of protein aggre-gates and micro-pores inside strands of network. Atthe old stage of ripening, the protein networkbecame smooth. Although there are similaritiesbetween these two cheeses, mesophilic 1 presenteda thinner protein network (network strands) and lar-ger interstitial spaces thanmesophilic 2.Mesophilic 3 and 4 cheeses, as well as thermo-

philic and ultrafiltered cheeses, were only studiedat the young stage of ripening. Both mesophilic 3and 4 cheeses presented a smooth protein network.Concerning the thermophilic cheese, the proteinnetwork also appeared smooth. However, proteinstrands are more thin than previously and the inter-stitial spaces more large. The ultrafiltered cheesehad a very thin granular structure with limitedinterstitial spaces (Herbert 1999).We then considered an image texture analysis

method to quantify the texture of the different pro-tein networks of the cheeses. The basic principle ofthis method is to compare parts of an image with a

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mask called ‘structuring element’ of various sizesand shapes and possessing a reference pixel (Serra1982). Erosion and dilation are the basic transfor-mations in the mathematical morphology method(Figure 10). Erosion consists of looking for theminimum grey-level value observed among all thepixels covered by the structuring element and toattribute this value to the reference pixel. With thesuccessive steps of erosion, both the size and the

grey-level values of the objects progressivelydecrease. The smaller are objects of the image, themore rapid is their disappearance. Dilation is a dualoperation of erosion consisting in the search for themaximum grey-level value observed in the areacovered by the structuring element and to assignthis value to the reference pixel. The successivedilation operations involve an increase for both sizeand grey-level values of the objects.

(a) (b)

(c) (d)

Figure 9 Confocal images of the protein networks of young (a) and old (c) mesophilic 1 and of young (b) and old (d) mesophilic

2 soft cheeses.

Starting image

Dilation of size 1 Dilation of size 2

Erosion of size 1 Erosion of size 2

Figure 10 Results of erosion and dilation steps performed on confocal images.

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Successive steps of erosion involve a decrease inthe sum of grey levels of the image, whereas suc-cessive dilation steps involve an increase in thesum of grey levels of the image. And, the evolutionof the sum of grey levels depends on the size ofwhite and dark objects. Therefore, to quantifyimage texture, the sum of grey levels of the imageis calculated at each erosion and dilation step.Curves showing the evolution of the grey levelssum for each erosion and dilation operation arethen built and the derivation of these latter givesthe final granulometric curves (Devaux et al.1997). These curves give information concerningthe size of both the bright fluorescent protein net-work and the dark holes, therefore characterisingthe texture of the images (Devaux et al. 1997).For this study, the author used a structuring ele-

ment corresponding to a square of 3 · 3 pixels withthe reference pixel at the centre. On the set of grey-level images acquired by confocal microscopy, 50successive steps of erosion and dilation wereapplied, corresponding to a square of size varyingbetween 1 and 32 lm. To characterise the variationsof grey-level observed in the images at each step,granulometric curves were built (Devaux et al.1997).To compare the set of fluorescence images (720)

and to emphasise the similarities and differencesbetween cheeses, the author quantified the micro-structures of the protein networks of the differentsoft cheeses. The granulometric curves derivedfrom each image were processed by PCA todescribe the main variations between the differentcheeses. The similarity map defined by the firsttwo principal components took into account 81.49and 14.39% of the total variance respectively.According to the first principal component, anopposition was observed between the ultrafiltratedcheese, mesophilic 1 and 2 cheeses at the youngripening stage and the other cheeses. The secondcomponent discriminated the thermophilic cheeseand the mesophilic 1 cheese at the old ripeningstage from the mesophilic 3 and 4 cheeses and themesophilic 2 cheese at the old ripening stage. Typi-cal granulometric patterns were then drawn tointerpret more precisely the main variationsobserved between the different cheeses in terms ofthe respective sizes of the protein network andinterstitial spaces. Considering granulometric pat-terns corresponding to the principal components 1and 2, the right-hand side characterises the size ofthe protein network, whereas the left-hand sidegives information about the size of the interstitialspaces. The first pattern indicated that the

ultrafiltered cheese and the mesophilic 1 and 2cheeses at the young ripening stage were character-ised by a thinner protein network and smaller inter-stitial spaces than for the other cheeses. Thesecond pattern showed that the thermophilic cheeseand the mesophilic 1 cheese at the old ripeningstage presented a thinner protein network and lar-ger interstitial spaces than mesophilic 3 and 4cheeses and mesophilic 2 cheese at the old ripeningstage.The discriminant ability of the data was then

investigated by applying factorial discriminant anal-ysis on the principal components. Table 2 gives theresults of classification of the images belonging tothe test samples. It appeared that the ultrafilteredcheese, the mesophilic 1 and 2 cheeses at the youngripening stage and the mesophilic 2 cheese at theold stage were very well discriminated, whereas thediscrimination of the thermophilic ⁄old mesophilic 1and mesophilic 3 and 4 cheeses was more confused.Characterisation of soft cheese microstructure

using confocal laser scanning microscopy showedsimilarities and differences of protein network mi-crostructures following soft cheese technologiesand ripening stage. Microscopic fluorescenceimages exhibited differences between the micro-structures of ultrafiltered, mesophilic 1 and 2cheeses at the young stage of ripening, mesophilic3 and 4, mesophilic 2 at the old stage of ripening,mesophilic 1 at the old stage of ripening and ther-mophilic cheeses.The quantification of protein network structure

using mathematical morphology coupled withPCA allowed quantitative assessment of the simi-larities and differences previously observed fromthe raw images. Moreover, granulometric patternsallowed the description of the microstructure fordifferent soft cheeses in terms of the respective

Table 2 Results of the Factorial Discriminant Analysis

(FDA) performed on the granulometric curves computed

from the confocal images of different cheeses (Herbert

1999)

M1j M1v M2j M2v M3 M4 TH UF

M1j 25 – 5 – – – – –

M1v – 24 – – – – 6 –

M2j 1 – 24 1 – 4 – –

M2v – – – 30 – – – –

M3 – – – – 23 4 3 –

M4 – – – 3 16 8 3 –

TH 1 9 – – – – 20 –

UF – – – – – – – 30

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sizes of the protein network and interstitial spaces(Serra 1982; Devaux et al. 1997). Finally, the fac-torial discriminant analysis performed on the gran-ulometric curves confirmed the potential of thisapproach to discriminate protein network structuresof different soft cheeses.In the study of Herbert (1999), differences in

structures were revealed at the microscopic level forthe different soft cheeses studied. Although the me-sophilic 3 and 4 cheeses (red smear) presented asmooth protein network, the mesophilic 1 and 2cheeses (white mould), at the same ripening time,exhibited a granular protein network. Granularaspect of the protein network for mesophilic 1 and 2cheeses could result from higher acidification of thecurd after gelation of milk. Acidification involvesimportant demineralisation of casein aggregates,which would become devoid of contraction strengthto merge together. Moreover, evolution of proteinnetwork during the ripening phase for the mesophil-ic 1 and 2 cheeses can be mainly attributed to pHincrease, inducing casein micelle fusion.Considering ultrafiltered and mesophilic 1

cheeses, it appeared that ultrafiltered cheesepresented very small pores, whereas mesophilic1 cheese presented large pores. The small size ofthe pores observed in ultrafiltered cheese can beexplained by the ultrafiltration of milk, whichinduces the decrease in the size of fat globules,whereas the large pores observed in mesophilic 1cheese originates from the higher percentage of fatin the processed milks (double cream).It is generally assumed that the protein network

microstructure mainly determines the textural prop-erties of soft cheeses. To establish the effects ofprotein network structure on the textural propertiesof cheeses, it appears necessary to quantify proteinnetwork structures using image analysis.

CONCLUS ION

The aim of this article was to summarise thoseaspects of spectroscopy and image methods thatmay have value for solving problems in foodscience and technology. It is important to gainmore information on the structure at the molecular,microscopic and macroscopic levels of foodproducts because it determines their properties.Spectroscopic methods are undoubtedly well suitedfor this purpose. Although fluorescence andinfrared spectroscopies are techniques whosetheory and methodology have been extensivelyexploited for studies of both chemistry and bio-chemistry, the utility of fluorescence spectroscopy

for noninvasive and nondestructive studies has notyet been fully recognised in dairy science. I hopethat this coverage will introduce a novel class oftechniques in the field of dairy science and in dairyplants. The combination of several spectroscopictechniques such as fluorescence and infrared,image instruments and the joint analyses of thesedata sets by sophisticated chemometric toolsshould be fruitful in understanding the relationbetween structure and texture of food products.New available instruments including spectrome-

ters and imaging devices (Karoui and Dufour2008; Kulmyrzaev et al. 2008) allow measuringquality attributes of foods in rapid and nondestruc-tive manner and could be used for quality controlin the dairy industry in the near future.

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