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Front face fluorescence spectroscopy and multiway analysis for process control and NFC prediction in industrially processed cookies
Chemometrics and Intelligent Laboratory Systems 93 (2008) 99–107
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Chemometrics and Intelligent Laboratory Systems j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c h e m o l a b
Front face fluorescence spectroscopy and multiway analysis for process control and NFC prediction in industrially processed cookies Jad Rizkallah a,d,⁎, Francisco J. Morales b, Lamia Ait-ameur a, Vincenzo Fogliano c, Alexia Hervieu a, Mathilde Courel d, Inès Birlouez Aragon a,d a
Institut Polytechnique LaSalle Beauvais, France Spanish National Council for Scientific Research (CSIC) Instituto del Frio, Madrid, Spain Dipartimento di Scienza degli Alimenti Università degli studi di Napoli Federico II, Italy d AgroParis Tech, Paris, France b c
A R T I C L E
I N F O
Article history: Received 12 July 2007 Received in revised form 16 April 2008 Accepted 17 April 2008 Available online 26 April 2008 Keywords: Front face fluorescence spectroscopy Neoformed contaminants Cookie Multiway arrays PARAFAC Generalized linear model
A B S T R A C T The aim of this work was to evaluate the potential of using front face fluorescence spectroscopy for rapid quantitative estimation of neoformed contaminants in industrially processed cookies. Two dimensional synchronous front face fluorescence spectra were acquired on cookies to assess the industrial process impact on the fluorescence signal and predict the neoformed contaminants content in cookies. The signal was recorded on two types of cookies; 41 samples taken from different steps of four industrial production lines and 148 cookie samples produced from experimental baking using three different temperatures with two levels of fat saturation and two types of sugar used in the formulation. After spectral pre-treatment of the acquired front face fluorescence spectroscopy data, the multiway arrays were decomposed by means of PARAFAC models. Factors extracted from the decompositions allowed identification of the main front face fluorescence spectroscopy profiles in cookies. These included native tryptophan and riboflavin, and several fluorescence profiles attributed to fat oxidation and Maillard reaction compounds. Relative intensities of the samples fluorescence profiles were then used to discriminate critical steps in the industrial baking process and to predict the content of chromatographically measured neoformed contaminants hydroxymethylfurfural, carboxymethyllysine and acrylamide in the cookies. The effects of spectral pre-treatments on decomposition and regression results were also studied. The results show that process control and neoformed contaminants estimation in industrially processed cookies can be achieved by means of front face fluorescence spectroscopy information. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Neoformed contaminants (NFC) are molecules formed during the Maillard and oxidation reactions occurring in food submitted to heat treatment [1]. Some of these molecules are suspected mutagenic and/ or carcinogenic to humans [2], specifically acrylamide [3] and Hydroxymethylfurfural (HMF) [4], whereas others such as carboxymethyllysine (CML) and the advanced glycation products [5] are supposed to increase the risk of diabetes and cardiovascular diseases. Furthermore, these NFC are usually correlated with a reduced nutritional value of food. The following work is part of the ICARE project, a European collective research aiming at evaluating NFC associated health risk and reducing the formation of these molecules, by optimization of process and development of alternative technol-
⁎ Corresponding author. Institut Polytechnique LaSalle Beauvais, France. Tel.: +33 344063876; fax: +33 344062526. E-mail address: [email protected] (J. Rizkallah). 0169-7439/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2008.04.008
ogies. Another objective of the project is to develop methods for in situ estimation of neoformed contaminants in the concerned food industries. Front face fluorescence spectroscopy (FFFS) have a good potential in achieving fast and cost efficient prediction of these molecules. The method is also pollution free given that the signal is directly acquired on the samples nondestructively without any extraction step. The FFFS signal reflects the sample's fluorescence properties distorted relatively to the optical properties, notably through quenching and scattering phenomena resulting from the physiochemical structure of the food matrix [6,7]. Therefore, any technological process inducing modifications on the fluorescence and/ or optical properties of the food sample will inevitably lead to changes of the native FFFS signal. Several studies have shown that it is possible to calibrate the acquired signal on process quality parameters of various food types using an indirect correlation approach through chemometric tools [7–9]. Examples from literature include photooxidation and thermal impact in dairy products, thermal oxidation of heated and frying oil, oxidative changes in oatmeal, and assessment of Maillard reaction during sugar processing [7].
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In particular, multivariate prediction models based on multiway PARAFAC decomposition of two dimensional fluorescence data provide robust results even in the presence of interferences in the fluorescence signal [10]. The aim of this study was to investigate the possibility to monitor the process steps and evaluate the neoformed contaminants in industrially baked cookies through FFFS signal analyzed by chemometric tools. 2. Materials and methods 2.1. Experimental samples Two types of data were analyzed. The first type included samples taken from different steps of four industrial processes (Fig. 1) used to bake four types of dough formula (Table 1). To test possible variation or drifts in the processes with oven run time, four additional sampling were carried out on the industrial process line 4. The repeated sampling was performed within a 2 h period of oven run time (after 10, 20, 40, 60 and 120 min); each cycle of process line 4 lasted around 10 min. In all, the first data set consisted of 41 samples. Sampling on an experimental baking using a laboratory scaled oven produced the second data set. Three baking temperatures were used, 150 °C (sampling at 12, 18, 25, 27 and 32 min), 200 °C (sampling at 3, 6, 9, 12, 15 and 18 min) and 230 °C (sampling at 3, 6, 9, 12 and 15 min). Independent oven runs were used to obtain duplicates of the intermediate sampling times, triplicates of the end product baked at 230 °C and quadruplicates of the end products baked at 150 and 200 °C. The experimental cookies were either made with glucose or sucrose and a low or high level of fat saturation. The second data set consisted of 148 cookie samples. 2.2. NFC chromatographic quantification 2.2.1. Carboxymethyllysine The amino-acid derivatives were analyzed by GC–MS-MS according to the method of Charissou et al. [11], after methylation of the carbonyl groups and acylation of the amine residues. Lipid extraction was performed using the Foch method and the cookie protein pellet obtained from 500 mg cookie was hydrolyzed at 110 °C for 18 h by addition of 5 ml of 6 M HCl containing the internal standard cycloleucine (10 µg/ml).
Table 1 Ingredients of the industrial and experimental cookies Wheat flour, palm oil, brown cane sugar, wheat bran, powder skimmed milk, coconut, salt. Flour (wheat, barley, rye, einkorn), brown cane sugar, palm oil, wheat syrup and extract, leavening agents, ammonium carbonate, natural vanilla aroma, salt. Industrial Wheat flour, eggs, cane sugar, wheat starch, leavening agents, acid process 3 ammonium carbonate, sweet orange essential oil, thiamine. Industrial Wheat and whole wheat flour, powder skimmed milk (1% fat), potato process 4 starch, maltitol powder and syrup, vegetable hydrogenated oil, palm oil, leavening agents, vanilla, lecithin, acesulfam-K. Experimental Wheat flour 60.67%, sugar 11.96% (factor1 = sucrose or glucose), fat 9.53% baking (factor2 = palm oil with 2 levels of hydrogenation), salt 0.19%, yeast 0.36% Industrial process 1 Industrial process 2
500 µL of dried sample was derivatized with 1 ml of thionyl chloride/ methanol solution (1.46 ml of thionyl chloride in 100 ml of methanol), heated in oven for 30 min at 110 °C and dried under nitrogen stream. Two ml dichloromethane and 400 µL of trifluoroacetic acid anhydride were then added and amine acylation was achieved at ambient temperature for 1 h. The derivatized sample was dried under nitrogen dissolved again in 1 ml dichloromethane and dried under nitrogen stream. The dried extract was dissolved in 25 µL dichloromethane and injected in the GC. Quantification was done on Finnigan PolarisQ GC/MS Benchtop Ion Trap Mass Spectrometer (Thermo Electron Corporation) in MS-MS mode. A DB5-MS capillary column (30 m × 0.25 mm, 0.25 um) and split injection mode were used. The temperature program was 2 min at 70 °C, then 5 °C/ min to 260 °C,15 °C/min to 290 °C and finally 290 °C for 5 min. The carrier gas was helium with the flow rate of 1.5 ml/min. The selected parent ion with m/z = 392 ion was isolated and fragmented using an energy of 0.90 V to obtain 4 main son-ions with respective m/z: 374 (25%), 360 (100%), 332(40%) and 206(10%). For quantification the area of the isolated parent ion was quantified using the internal standard cyclocleucin after verification that convenient fragmentation was obtained. 2.2.2. Hydroxymethylfurfural HMF determination was based on the method of Garcia-Villanova et al. [12] with slight modifications. Ground sample (500 mg) was suspended in 5 ml of deionized water in a 10 ml centrifuge tube. The tube was shaken vigorously for 1 min and clarified with 0.25 ml of
Fig. 1. Illustration of the four industrial processes, numbered circles denote the steps at which sampling was performed.
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Fig. 2. Two dimensional synchronous spectra (A) shifted to EEM (B) positions. In B the two ribbons of missing values (white bands) were used to replace the first order Rayleigh scatter and regions adjacent to the fluorescence signal acquired at the maximum synchronous delta. Regions 1 and 3 missing values created upon shifting the two dimensional EEM were replaced by zeros.
potassium ferrocyanide (15% w/v) and zinc acetate (30% w/v) solutions. The resulting mixture was centrifuged at 4000 rpm for 10 min at 4 °C. The supernatant was collected in a 10 ml volumetric flask and two further extractions were performed using 2 ml of deionized water. The extracts were filtered (0.45 μm) and 50 μL was analyzed by HPLC. The HPLC system consisted of LC-10AD class VP pumps, a SPD-M10A diode array detector and a SCL-10A class VP controller, all from Shimadzu (Japan). The mobile phase was a mixture of acetonitrile in water (5% v/v) at a flow rate of 1 ml/min under isocratic conditions and a Synergi 4µ Hydro-RP 80A, 25 × 4.6 cm, (Phenomenex) column. The UV detector was set at 280 nm and HMF was quantified using the external standard method within the range 0.025–75 mg/L. 2.2.3. Acrylamide The sample preparation procedure described by Senyuva and Gökmen [13] was used with minor modification. Finely ground sample (0.450 g) was weighed into a 10 ml centrifuge tube. The sample was spiked with 100 µL 13C3-labeled acrylamide (10,000 µg/L) to confirm acrylamide and recovery, and 5 ml water was added. The solution was allowed to stand for a few minutes and homogenised for 1 min (Ultraturrax). 500 µL Carrez I and 500 µL Carrez II solution were added, vortex and allowed to stand for 10 min. Tubes were centrifuged at 5000 rpm for 15 min at 4 °C. The clear supernatant (1 ml) was clarified onto a pre-conditioned HLB cartridge. First drops were discarded and the rest of eluate was collected in amberlite vials for LC-MS analysis. LC-ESI-MS analyses were performed as described by Morales et al. [14] by using an Agilent 1100 HPLC system (Waldbronn, Germany) consisting of a cuaternary pump, an autosampler and a temperature-controlled column oven, coupled to an Agilent 1100 MS detector equipped with electrospray ionization interface. The analytical separation was performed on an Inertsil ODS-3 column (250 × 4.6 mm, 5 µm) using the isocratic mixture of 0.2% aqueous solution of formic acid at a flow rate of 0.6 ml min− 1 at 25 °C. In order to discard early eluting peaks, the first 8.0 min of the chromatogram was diverted to waste by means of software diverted valve as it prevents contamination of the mass spectrometer with co-extractives. Data acquisition was performed in selected ion monitoring (SIM) mode using the following interface parameters: drying gas (N2, 100 psig) flow of 12 L min− 1, nebulizer pressure of 45 psig, drying gas
temperatures 350 °C, capillary voltage of 3 kV, fragmenter voltage of 70 eV. Data acquisition was performed in SIM mode. Ions monitored were m/z 72.1 for acrylamide and m/z 75.1 for 13C3-labeled acrylamide for the quantification of acrylamide in the samples. Full scan analyses were performed in the mass range 50–200 for the spectral identification of acrylamide in samples. Acrylamide calibration was built in the range of 3 to 200 µg/L. LOD and LOQ were determined to be 6 and 18 µg/kg on signal-to-noise ratio of 3 and 9, respectively. 2.3. Two dimensional front face fluorescence spectroscopy acquisition Two dimensional synchronous spectra (Fig. 2A) were acquired on a Varian Cary fluorimeter. Spectra were directly measured on grinded cookies placed in dispensable acryl cuvettes (Sarstedt®, France). Excitation spectra were recorded between 280 and 550 nm and the emission synchronous delta (Δ = λem − λexc) varied between 0 and 140 nm. Steps for excitation and emission synchronous Δ set at 8 nm and 4 nm for the industrially processed and experimentally baked samples respectively (slits= 5 nm, front face angle around 36°). To account for possible non-homogeneity, the two dimensional synchronous spectra were recorded in triplicates acquired on different sides of the cuvettes. The excitation–emission matrix (EEM) was reconstructed by placing the values of the two dimensional synchronous spectra matrix A (I excitations ⁎ J synchronous Δs) in a M (I ⁎ I + J − 1) matrix such that m(i, j) = a(i, j) for i = 1, and m(i, j + i − 1) = a(i, j) for i = 2… I. The missing values in the matrix M at λem b λexc and λem N λexc + Δmax + 16 nm (Fig. 2B) were then replaced with zeros; the remaining values of the first order Rayleigh scatter (between λem = λexc to λem = λexc + 16 nm) and the values in the region flanked by λem = λexc + Δmax and λem = λexc + Δmax + 16 nm were kept missing. 2.4. Chemometric analysis of fluorescence data and regression over NFC content 2.4.1. Multiway PARAFAC decomposition 2.4.1.1. Data arrangement. EEMs of samples belonging to the four industrial processes were arranged in a three-way array with the samples kept in the first mode and the excitation and emissions
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wavelengths assigned to mode 2 and 3; to obtain a three-way array of size 123 ⁎ 68 ⁎ 106. EEMs acquired on the experimentally baked samples were arranged in a five-way array with samples produced at different time and temperatures placed in mode 1, excitation and emission wavelengths assigned to mode 2 and 3, while the varying composition factors i.e. type of sugar used (glucose and sucrose) and level of fat saturation (high and low) were placed in two additional modes each of dimension two. The size of the resulting five-way array was 111 ⁎ 34 ⁎ 53 ⁎ 2 ⁎ 2. Experimentally baked samples were also analyzed using a three-way arrangements of the EEMs, that is, all the samples were kept in the first mode (array size 444 ⁎ 34 ⁎ 53). 2.4.1.2. Decomposition. PARAFAC models [15,16] were used to decompose the multiway arrays into sets of vectors corresponding to profiles (spectral loadings) varying only in intensity (sample mode loadings) among the samples. Iterative alternating least square algorithm [10] was used to find the PARAFAC decomposition parameters. PARAFAC models were refitted several times starting with random values to ensure the model's stability. Non-negativity constraint was imposed on all decomposition parameters. Conventional diagnosis tools such as residual analysis, deviation of multi-linearity using the core consistency diagnostic (CORCONDIA), and the interpretability of the PARAFAC loadings were used as indicators of the number of PARAFAC factors to retain and to evaluate the quality of the models. Degenerate solutions were assessed using the Tucker congruence coefficients (cosine values between all pairs of components) [15]. Scaling and multiplicative scatter correction (MSC) were applied on unfolded three-way arrays according to the excitation mode (concatenated emissions). Scaling was performed using the norm of each concatenated emission spectra on the respective emission spectral intensities. In MSC, the intercept and the slope of the regression between each of the concatenated emission spectra and the mean spectra were used to correct the intensities of the respective emission spectra [17]. For the experimentally baked samples, spectral pre-treatments were applied on the unfolded arrays arranged in three ways before arranging the data into five-way arrays. 2.4.2. Prediction of the cookies NFC content Generalized linear models (GLM) f (µy) =b0 +b1x1 +b2x2 + … +bnxn +e were used to build prediction models of chromatographically measured NFC content using the sample mode loadings of the front face PARAFAC models. In the equation f(µy) was the log function of the expected NFC content (normally distributed) and the regression coefficients bi were estimated using Fisher's scoring algorithm (iteratively reweighed least squares) for maximum likelihood estimation [18]. Through the link function, the GLM allows modelling non-linear relations between the predictors and the dependent variable. In addition, GLM can be used to model dependent variables with non-normal distribution. The multiple linear model is a special case of the generalized linear model with the link function equals the identity function and the distribution of the dependent variable expected normal [18]. The root mean square error of a leave three out cross-validation (spectral triplicates of each sample) was used to approximate the GLM models prediction error. PARAFAC sample mode loadings and NFC content of cross-validation samples were estimated using the PARAFAC loadings and NFC regression coefficients determined from calibration samples. Leverages and residual analysis were used to detect possible outliers and influential samples in the regression models [10,18]. The leverages are the diagonal elements of the hat matrix (H = X(X′X)− 1X′); they are proportional to the Mahalanobis distances between each observation and the centroid of the observations in a multidimensional space defined by correlated variables. 2.4.2.1. Software. All computations were performed using MatLab7® (The MathWorks Inc.); multiway PARAFAC decomposition was applied
using codes included in the MatLab Nway Toolbox [19]. Generalized linear models were fitted using MatLab statistics toolbox. 3. Results and discussion 3.1. Acquisition and pre-treatment Acquisition of fluorescence EEM is time consuming if sufficient resolution is needed. Usually, an EEM is acquired either by recording emission spectra at consecutive fixed excitation wavelengths or recording excitation spectra at consecutive fixed emissions wavelengths. Either way, a significant part of the acquired signal, and specifically all spectral intensities at λem b λexc do not contain any fluorescent information since emission can only occur at lower energetic levels than excitation (red shifted Stock's) and are thus physically irrelevant. An alternative approach is to use synchronous spectra acquired by simultaneously varying the excitation and emission monochromators keeping a positive difference (delta) between the two monochromators (λem = λexc + Δ). Two dimensional spectra are obtained by acquiring the excitation spectra at increasing delta values. Delta being positive, all measured points will be at λem equal or superior to λexc. Fluorescence landscapes present in the two dimensional synchronous spectra (Fig. 2A) are not low rank bilinear and consequently, cannot be appropriately described using multi-linear models. The decomposition models were therefore applied on the standard EEM reconstructed by shifting the two dimensional synchronous spectral data to a fixed emissions position. The high number of missing values created upon shifting the two dimensional synchronous spectra can be problematic for data decomposition. Regions of the EEM (Fig. 2B) known not to contain fluorescence signal (λem b λexc) and regions around second order Rayleigh were replaced by zeroes. A ribbon of missing values [20,21] was used to replace the first order Rayleigh scatter overlapping with fluorescence signal. Another ribbon was used for EEM regions adjacent to the fluorescence signal acquired at the maximum synchronous delta to avoid artefacts produced by inserting zeroes adjacent to the acquired fluorescence signal. A faster convergence of the PARAFAC model and physically relevant parameters were still obtained when part of the missing values was replaced by zeros. The same results were obtained in the study of Thygesen et al. [20].
Table 2 PARAFAC models fitted on FFFS arrays acquired on the industrial and experimental cookies with increasing number of factors Data
# Untreated data Normalized data MSC data Fac. %Var. Cor. Nbr. %Var. Cor. Nbr. %Var. Cor. It. It.
1 All industrial 2 processes (three-way array) 3 4 5 6 1 Laboratory 2 kinetics (three-way array) 3 4 5 1 Laboratory 2 kinetics (five-way array) 3 4 5
60.23 80.52 89.68 94.09 95.98 96.88 60.68 86.58 95.60 97.68 98.42 59.87 85.29 93.97 95.99 96.72
– 10 99.18 22 94.12 20 90.46 39 84.53 28 49.23 88 – 10 96.05 18 95.90 38 96.44 27 60.32 298 – 12 88.59 20 91.47 36 81.84 40 6.08 172
57.78 78.52 87.99 94.27 95.80 96.42 57.06 85.93 95.50 97.82 98.24 57.01 85.67 95.05 97.34 97.76
– 99.32 97.38 93.26 84.96 31.90 – 96.90 96.44 97.36 42.60 – 91.75 93.82 93.68 46.86
13 12 27 12 52 69 10 12 14 16 28 6 14 16 48 56
57.02 78.29 87.63 94.24 95.76 96.49 57.69 84.67 94.88 97.46 97.92 57.67 84.50 94.52 97.05 97.45
– 99.29 97.72 93.46 85.18 50.29 – 98.47 94.74 96.26 46.12 – 92.04 92.29 92.91 57.53
Nbr. It. 13 12 38 17 16 54 10 12 14 16 28 8 11 12 42 46
%Var. = Percentage of explained data variance. Cor. = CORCONDIA (Core consistency diagnostic) measuring deviation form ideal multilinearity as percent of agreement. Nbr. It. = Number of ALS iterations.
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Fig. 3. PARAFAC spectral loadings of three-way industrial processes data with increasing number of factors.
3.2. PARAFAC decomposition When samples are measured in front face mode on solid or turbid samples, the optical properties (matrix effect) will highly distort both the intensity and shape of the fluorescence landscapes [6,7] mostly through quenching and scattering phenomena. Nonetheless, several studies [7] showed that most landscapes in FFFS EEM remain bilinear of low rank and can thus be well described by parallel profiles models. Compared to other competing chemometric decomposition tools the PARAFAC model provides simpler decomposition parameters [10]. When applied on FFFS data the extracted spectral PARAFAC loadings are interpreted as fluorescence profiles distorted by the media optical and the sample mode loadings are the relative intensities of the respective front face profiles (loadings) and are dependent on fluorophores nature and concentration as well as the sample's optical property. Hence, in the FFFS context, the PARAFAC model is rather used as a soft chemometric model and the solution is only empirically valid for the data set that is being analyzed. Results of the PARAFAC decomposition models with increasing number of factors are presented in Table 2. All the reported PARAFAC figures are the results of decompositions performed on MSC treated arrays. 3.2.1. Industrial process data PARAFAC spectral loadings of the three-way array including all the industrially processed samples (Fig. 3) suggest that a five factor model is suitable to describe the data's systematic information.
Fig. 4. Industrial processes one to three (Pr.#) sample mode loadings on the 5 component PARAFAC model. Samples (acquired in spectral triplicates) belonging to the different processes are represented on different background colours. Empty circles indicate lightly treated samples deviating from the rest of the sample mode loading structure particularly on the NF profiles. Dashed rectangles and circles are used to highlight respectively process 2 cooling tunnels samples and process 3 samples left to cool at room temperature.
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Fig. 5. Industrial process line 4 PARAFAC sample loadings.
The fluorescence profiles were named in agreement with pure fluorophores known from literature to be present in cookies and having fluorescence profiles in right angle measurements similar to those obtained in the PARAFAC decomposition. The PARAFAC profiles correspond to native tryptophan (Trp − maximum intensity λexc/λem ≈ 290/350), riboflavin (Rf − max λexc/λem ≈ 350/550), and several fluor-
escence profiles generally associated to lipid oxidation and Maillard reaction compounds [7], these lasts were denoted Nf for neoformed fluorescence. Sample mode loadings of the industrial processes 1 to 3 samples acquired in spectral triplicates are plotted in Fig. 4; the industrial process line 4 sample mode loadings were excluded from this figure
Fig. 6. Spectral loadings (mode 2 and 3) of five-way PARAFAC models fitted on experimental kinetic data with increasing number of factors and plots of mode 4 and 5 loadings belong to the experimentally controlled factors.
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and will be described later on. Noticeably, the sample mode loadings of Nf profiles deviating from the rest of the structure (empty circles) are the least processed samples. This interference is probably due to major differences in optical properties and in particular quenching occurring early in the baking process upon transformation of raw samples into cookie. Another reason may be that Nf fluorescence spectral loadings overlap with native fluorescence present in lightly treated samples which also explains why this perturbation is much less evident on native tryptophan and riboflavin profile sample mode loadings. Sample mode loadings of the different profiles show that the major variability of the front face signal is highly related to the industrial process applied. Baking steps could be easily discriminated from the evolution of the profile sample mode loadings. The sample mode loadings also show that FFFS signal continue evolving when samples are left to cool at room temperature (circled samples) while cooling tunnels better stabilize the fluorescence profiles (dashed rectangles). PARAFAC sample loadings of process line 4 are presented in Fig. 5. The interference due to difference in quenching on the sample mode loading structure was more important in this data than in the other industrial processes (Fig. 4). This is mostly due to the presence of raw dough samples in this data affecting not only the relative intensity of the neoformed fluorescence but also the native riboflavin fluorescence. The interference was less evident on the Trp fluorescence sample mode loadings which decreased relatively to the baking process. The oil spray resulted in a relative decrease of Nf1 and increase of Nf2 sample mode loadings as compared to the pre-oiled samples sample mode loading. There was no clear trend and the process did not seem to significantly vary with the running time of the industrial process line 4 oven. 3.2.2. Experimentally baked data (Five-way PARAFAC) PARAFAC models were fitted on the five-way arrays comprising of EEMs acquired on the experimental samples baked at three temperatures (150, 200 and 230 °C) arranged over two additional modes comprising of the type of sugar and level of fat saturation used in the cookies formulation (Figs. 6 and 7). Additional modes in fiveway arrays had also to be constrained to non-negativity otherwise convergence problems also known as degenerate solutions [10,15] were encountered. Some models still encountered local minima and swamps especially with high number of fitted PARAFAC factors. Refitting the models several times using random starting values solved these problems and interpretable models were obtained. Fiveway array models resulted in acceptable Tucker congruence coefficients (correlations between sets of components − TC b 30%). The sample mode loading structure (Fig. 7) of all the fluorescence profiles followed the kinetic of the heat treatment being applied with the exception of Nf2 and NF3 sample mode loadings which also deviated in lightly treated samples. Cookies formulated with glucose (Fig. 6 mode 4) were characterized by a lower Trp and higher Rf profiles as compared to cookies made with sucrose, while high level of fat saturation (Fig. 6 mode 5) resulted in lower Nf1 and higher Rf profiles compared to cookies with low fat saturation. The five-way PARAFAC models also showed that oil type loadings varied much less than sugar type loadings. In all the analyzed data sets the structure of the sample mode loadings significantly improved (reduced distance between replicates and co-linearity and a better evolution with process) when scaling and especially MSC pre-treatments were applied. Scaling and MSC also resulted in faster convergence of the PARAFAC models and better CORCONDIA values when models were fitted with a relatively high Fig. 7. Sample mode loadings of the 5 way PARAFAC model fitted on kinetic data using 5 components. Samples baked at 150 °C (■), 200 °C (Δ), and 230 °C (●). Samples highlighted using dashed rectangles are the lightly treated sample of each kinetic clearly deviating from the rest of the sample mode loadings of Nf2 and Nf3.
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Fig. 8. PARAFAC spectral loadings from three-way arrangement of experimental kinetic data EEMs.
number of components. The shape of the extracted spectral parameters did not significantly change upon pre-treatment. Since the type of sugar and fat saturation level are not known to have a multiplicative effect on the FFFS profiles, it was essential to verify the suitability of the five-way arrays PARAFAC models. This was achieved through visual comparison (i) between the spectral loadings
and sample loadings obtained from the five-way arrays and the ones obtained from three-way arrangements of the respective EEMs (Table 2 and Fig. 8), and (ii) between the five-way arrangement experimentally controlled factor loadings (sugar and fat) and the mean of the three-way arrays sample loadings (figure not shown) of the respective EEMs for each level of the controlled experimental
Fig. 9. FFFS predicted and chromatographically measured content of: (A) CML in experimentally baked cookie samples, (B) HMF in experimentally baked cookie samples made with sucrose and low fat saturation baked at temperature 200 °C, (C) CML in industrial process line 1 and 4 cookies and (D) acrylamide content in industrial process 4 cookies. RMSEV is the root mean square error of a leave 3 out (triplicates of each sample) validation. Range is the minimal and maximal value of chromatographically measured NFC content. R2 is the coefficient of determination between predicted and measured values. N is the number of EEMs used in the regression. Dashed lines represent the 95% prediction levels.
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factors. Once it was verified that the same spectral loadings (Figs. 6 and 8) and sample loadings evolution were obtained in three- and five-way arrangements, the significance of the loadings corresponding to the controlled factors of the five arrays was indirectly assessed through factorial ANOVA of sample loadings obtained from three-way arrangements of the respective EEMs. ANOVA of the five components PARAFAC sample loadings obtained from three-way arrangements showed that only the sugar type used was significant for the intensity of Trp, Nf2, Nf3 and Rf (p b 0.001). The level of fat saturation and the interaction of the controlled factors did not significantly influence the intensity of the FFFS profiles. The mean intensity of the profiles corresponding for each level of the controlled factors and ANOVA significance results matched the five-way arrays controlled factor loadings shape and variability. 3.2.3. Regression of front face fluorescence spectroscopy PARAFAC sample mode loadings over chromatographically measured NFC content As mentioned earlier, the intensity of the fluorescence profiles recorded in front face mode on solid samples does by no mean reflect the true fluorophores intensity as it is highly modified by the scattering and quenching properties of the samples. Nevertheless, the PARAFAC sample mode loadings represent a measure of the modifications on the native FFFS image via the process. Consequently, the FFFS PARAFAC sample mode loadings can be used in calibration over other compounds related to process such as NFC, known to accumulate in the course of heat treatment. Chromatographically measured NFC content increased exponentially (acrylamide and HMF) or linearly (CML) with increased heating time in the experimentally baked cookies and with advancing process in the industrially baked cookies (results not shown), whereas most of the FFFS sample mode loadings followed a linear or a logarithmic evolution (Figs. 4, 5 and 7). As a result, NFC prediction through linear models such as multiple linear regressions on PARAFAC sample mode loadings or n-way PLS resulted in tailed residuals indicating that a log transformation of the dependent variable was necessary. Generalized linear models with a log link function of the normally distributed NFC content were thus used for calibration over the FFFS PARAFAC sample mode loadings. The regression models were built using the sample mode loadings of two PARAFAC models fitted with four components on three way arrangements of MSC pretreated FFFS data acquired on the industrial and experimental cookies. All the sample mode loadings used in regression resulted from PARAFAC models with CORCONDIA values higher than 90%. On average, 4 to 8 iterations were needed to find the generalized linear models coefficients of the expected NFC solution in the different validation segments. Content in CML chromatographically measured and predicted using the FFFS PARAFAC sample mode loadings in experimental samples and the industrial process lines 1 and 4 cookies are presented respectively in Fig. 9A and C. The ability of predicting simultaneously the CML content of samples with different composition based on the same FFFS decomposition and CML regression coefficients demonstrates the capacity of multiway PARAFAC decomposition and multivariate regression to handle limited amount of interference due to differences in cookie composition. Prediction of HMF in the experimental cookies baked at 200 °C with sucrose and a low level of fat saturation in the composition and acrylamide content in process line 4 samples are plotted in Fig. 9B and D. The NFC content in all the samples was well predicted by the models and only some spectral replicates were removed from the regressions based on residual analysis and leverages. The number of EEMs removed from the regressions presented in plots 9A through 9D were 18, 2, 0 and 1 respectively. The intercepts (ranging from −0.01 to
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0.35) and slopes (ranging from 0.97 to 1.001) between predicted and measured NFC content were not significantly different from the ideal zero and one values respectively. Of course, the parameters of the PARAFAC models and the coefficients of the regressions obtained in this study are only valid under the same experimental and acquisition conditions. If new interference to the FFFS spectral data is introduced by change of composition or process of the cookies, the parameters of all the chemometric models must be recalibrated. 4. Conclusion In this paper the potential of PARAFAC models applied on multiway structures to identify systematic information combining parallel FFFS profiles weighed by composition variation of cookies was demonstrated. Results of PARAFAC decomposition showed that systematic information present in FFFS data can be used in technological process control and in calibration over quality control parameters such as NFC content of cookies. The results also show that limited interferences on the signal due to composition variation, such as expected on an industrial line, can be handled through the use of spectral pretreatments, PARAFAC multiway decomposition and multivariate calibration. Consequently, good and robust NFC predictions can be achieved. Acknowledgements The authors would like to thank the European commission for financing this work through the ICARE project. We are also grateful for the SMEs of the ICARE consortium for providing the samples used for this study. References [1] P.A. Finot, Proceedings of the 8th International Maillard Symposium. August 28– October 1, 2004, Charleston, USA, Ann. N.Y. Acad. Sci., vol. 1043, 2005, pp. 1–8. [2] H. Stopper, R. Schinzel, K. Sebekova, A. Heidland, Cancer Lett. 190 (2003) 151–156. [3] E. Tareke, P. Rydberg, P. Karlsson, S. Eriksson, M. Tönqvist, J. Agric. Food Chem. 50 (2002) 4998–5006. [4] X.M. Zhang, C.C. Chan, D. Stamp, S. Minkin, M. Archer, W.R. Bruce, Carcinogenesis 14 (1993) 773–775. [5] H. Vlassara, W. Cai, J. Crandall, T. Goldberg, R. Oberstein, V. Dardaine, M. Peppa, E.J. Rayfield, Proc. Natl. Acad. Sci. U. S. A. 24 (2002) 15596–15601. [6] J.R. Lakowicz, Principles of Fluorescence Spectroscopy, Kluwer Academic/Plenum Publishers, New York, 1999, p. 237. [7] J. Christensen, Autofluorescence of Intact Food— An Exploratory Multi-way Study. PhD Thesis, The Royal Veterinary and Agricultural University, 2005, p.101. [8] M. Zandomeneghi, J. Agric. Food Chem. 47 (1999) 878–882. [9] M. Zandomeneghi, L. Carbonaro, L. Calucci, C. Pinzino, L. Galleschi, S. Ghiringhelli, J. Agric. Food Chem. 51 (2003) 2888–2895. [10] R. Bro, Multi-way analysis in the food industry. Theory, algorithms and applications, PhD Thesis, University of Amsterdam, 1998, p.290. [11] A. Charissou, L. Ait-Ameur, I. Birlouez-Aragon, J. Chromatogr. 1140 (2007) 189–194. [12] B. Garcia-Villanova, E. Guerra Hernàndez, E. Martínez-Gómez, J. Mortilla, Food Chem. 50 (1993) 4998–5006. [13] H.Z. Senyuva, V. Gökmen, Food Chem. 97 (2006) 539–545. [14] F.J. Morales, J.A. Rufian-Henares, G. Arribas-Lorenzo, Food Addit. Contam. 24 (2007) 343–350. [15] R. Bro, Chemometr. Intell. Lab. Syst. 38 (1997) 149–171. [16] R.A. Harshman, UCLA Work. Pap. Phon. 16 (1970) 1–84. [17] M.S. Dhanoa, S.J. Lister, R. Sanderson, R.J. Barnes, J. Near Infrared Spectrosc. 2 (1994) 43–47. [18] P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed., Chapman & Hall, New York, 1989, p. 511. [19] C.A. Andersson, R. Bro, Chemometr. Intell. Lab. Syst. 52 (2000) 1–4. [20] L.G. Thygesen, A. Rinnan, S. Barsberg, J.K.S. Møller, Chemometr. Intell. Lab. Syst. 71 (2004) 97–106. [21] G. Tomasi, R. Bro, Chemometr. Intell. Lab. Syst. 75 (2005) 163–180.
Contents lists available at ScienceDirect
Chemometrics and Intelligent Laboratory Systems j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / c h e m o l a b
Front face fluorescence spectroscopy and multiway analysis for process control and NFC prediction in industrially processed cookies Jad Rizkallah a,d,⁎, Francisco J. Morales b, Lamia Ait-ameur a, Vincenzo Fogliano c, Alexia Hervieu a, Mathilde Courel d, Inès Birlouez Aragon a,d a
Institut Polytechnique LaSalle Beauvais, France Spanish National Council for Scientific Research (CSIC) Instituto del Frio, Madrid, Spain Dipartimento di Scienza degli Alimenti Università degli studi di Napoli Federico II, Italy d AgroParis Tech, Paris, France b c
A R T I C L E
I N F O
Article history: Received 12 July 2007 Received in revised form 16 April 2008 Accepted 17 April 2008 Available online 26 April 2008 Keywords: Front face fluorescence spectroscopy Neoformed contaminants Cookie Multiway arrays PARAFAC Generalized linear model
A B S T R A C T The aim of this work was to evaluate the potential of using front face fluorescence spectroscopy for rapid quantitative estimation of neoformed contaminants in industrially processed cookies. Two dimensional synchronous front face fluorescence spectra were acquired on cookies to assess the industrial process impact on the fluorescence signal and predict the neoformed contaminants content in cookies. The signal was recorded on two types of cookies; 41 samples taken from different steps of four industrial production lines and 148 cookie samples produced from experimental baking using three different temperatures with two levels of fat saturation and two types of sugar used in the formulation. After spectral pre-treatment of the acquired front face fluorescence spectroscopy data, the multiway arrays were decomposed by means of PARAFAC models. Factors extracted from the decompositions allowed identification of the main front face fluorescence spectroscopy profiles in cookies. These included native tryptophan and riboflavin, and several fluorescence profiles attributed to fat oxidation and Maillard reaction compounds. Relative intensities of the samples fluorescence profiles were then used to discriminate critical steps in the industrial baking process and to predict the content of chromatographically measured neoformed contaminants hydroxymethylfurfural, carboxymethyllysine and acrylamide in the cookies. The effects of spectral pre-treatments on decomposition and regression results were also studied. The results show that process control and neoformed contaminants estimation in industrially processed cookies can be achieved by means of front face fluorescence spectroscopy information. © 2008 Elsevier B.V. All rights reserved.
1. Introduction Neoformed contaminants (NFC) are molecules formed during the Maillard and oxidation reactions occurring in food submitted to heat treatment [1]. Some of these molecules are suspected mutagenic and/ or carcinogenic to humans [2], specifically acrylamide [3] and Hydroxymethylfurfural (HMF) [4], whereas others such as carboxymethyllysine (CML) and the advanced glycation products [5] are supposed to increase the risk of diabetes and cardiovascular diseases. Furthermore, these NFC are usually correlated with a reduced nutritional value of food. The following work is part of the ICARE project, a European collective research aiming at evaluating NFC associated health risk and reducing the formation of these molecules, by optimization of process and development of alternative technol-
⁎ Corresponding author. Institut Polytechnique LaSalle Beauvais, France. Tel.: +33 344063876; fax: +33 344062526. E-mail address: [email protected] (J. Rizkallah). 0169-7439/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2008.04.008
ogies. Another objective of the project is to develop methods for in situ estimation of neoformed contaminants in the concerned food industries. Front face fluorescence spectroscopy (FFFS) have a good potential in achieving fast and cost efficient prediction of these molecules. The method is also pollution free given that the signal is directly acquired on the samples nondestructively without any extraction step. The FFFS signal reflects the sample's fluorescence properties distorted relatively to the optical properties, notably through quenching and scattering phenomena resulting from the physiochemical structure of the food matrix [6,7]. Therefore, any technological process inducing modifications on the fluorescence and/ or optical properties of the food sample will inevitably lead to changes of the native FFFS signal. Several studies have shown that it is possible to calibrate the acquired signal on process quality parameters of various food types using an indirect correlation approach through chemometric tools [7–9]. Examples from literature include photooxidation and thermal impact in dairy products, thermal oxidation of heated and frying oil, oxidative changes in oatmeal, and assessment of Maillard reaction during sugar processing [7].
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In particular, multivariate prediction models based on multiway PARAFAC decomposition of two dimensional fluorescence data provide robust results even in the presence of interferences in the fluorescence signal [10]. The aim of this study was to investigate the possibility to monitor the process steps and evaluate the neoformed contaminants in industrially baked cookies through FFFS signal analyzed by chemometric tools. 2. Materials and methods 2.1. Experimental samples Two types of data were analyzed. The first type included samples taken from different steps of four industrial processes (Fig. 1) used to bake four types of dough formula (Table 1). To test possible variation or drifts in the processes with oven run time, four additional sampling were carried out on the industrial process line 4. The repeated sampling was performed within a 2 h period of oven run time (after 10, 20, 40, 60 and 120 min); each cycle of process line 4 lasted around 10 min. In all, the first data set consisted of 41 samples. Sampling on an experimental baking using a laboratory scaled oven produced the second data set. Three baking temperatures were used, 150 °C (sampling at 12, 18, 25, 27 and 32 min), 200 °C (sampling at 3, 6, 9, 12, 15 and 18 min) and 230 °C (sampling at 3, 6, 9, 12 and 15 min). Independent oven runs were used to obtain duplicates of the intermediate sampling times, triplicates of the end product baked at 230 °C and quadruplicates of the end products baked at 150 and 200 °C. The experimental cookies were either made with glucose or sucrose and a low or high level of fat saturation. The second data set consisted of 148 cookie samples. 2.2. NFC chromatographic quantification 2.2.1. Carboxymethyllysine The amino-acid derivatives were analyzed by GC–MS-MS according to the method of Charissou et al. [11], after methylation of the carbonyl groups and acylation of the amine residues. Lipid extraction was performed using the Foch method and the cookie protein pellet obtained from 500 mg cookie was hydrolyzed at 110 °C for 18 h by addition of 5 ml of 6 M HCl containing the internal standard cycloleucine (10 µg/ml).
Table 1 Ingredients of the industrial and experimental cookies Wheat flour, palm oil, brown cane sugar, wheat bran, powder skimmed milk, coconut, salt. Flour (wheat, barley, rye, einkorn), brown cane sugar, palm oil, wheat syrup and extract, leavening agents, ammonium carbonate, natural vanilla aroma, salt. Industrial Wheat flour, eggs, cane sugar, wheat starch, leavening agents, acid process 3 ammonium carbonate, sweet orange essential oil, thiamine. Industrial Wheat and whole wheat flour, powder skimmed milk (1% fat), potato process 4 starch, maltitol powder and syrup, vegetable hydrogenated oil, palm oil, leavening agents, vanilla, lecithin, acesulfam-K. Experimental Wheat flour 60.67%, sugar 11.96% (factor1 = sucrose or glucose), fat 9.53% baking (factor2 = palm oil with 2 levels of hydrogenation), salt 0.19%, yeast 0.36% Industrial process 1 Industrial process 2
500 µL of dried sample was derivatized with 1 ml of thionyl chloride/ methanol solution (1.46 ml of thionyl chloride in 100 ml of methanol), heated in oven for 30 min at 110 °C and dried under nitrogen stream. Two ml dichloromethane and 400 µL of trifluoroacetic acid anhydride were then added and amine acylation was achieved at ambient temperature for 1 h. The derivatized sample was dried under nitrogen dissolved again in 1 ml dichloromethane and dried under nitrogen stream. The dried extract was dissolved in 25 µL dichloromethane and injected in the GC. Quantification was done on Finnigan PolarisQ GC/MS Benchtop Ion Trap Mass Spectrometer (Thermo Electron Corporation) in MS-MS mode. A DB5-MS capillary column (30 m × 0.25 mm, 0.25 um) and split injection mode were used. The temperature program was 2 min at 70 °C, then 5 °C/ min to 260 °C,15 °C/min to 290 °C and finally 290 °C for 5 min. The carrier gas was helium with the flow rate of 1.5 ml/min. The selected parent ion with m/z = 392 ion was isolated and fragmented using an energy of 0.90 V to obtain 4 main son-ions with respective m/z: 374 (25%), 360 (100%), 332(40%) and 206(10%). For quantification the area of the isolated parent ion was quantified using the internal standard cyclocleucin after verification that convenient fragmentation was obtained. 2.2.2. Hydroxymethylfurfural HMF determination was based on the method of Garcia-Villanova et al. [12] with slight modifications. Ground sample (500 mg) was suspended in 5 ml of deionized water in a 10 ml centrifuge tube. The tube was shaken vigorously for 1 min and clarified with 0.25 ml of
Fig. 1. Illustration of the four industrial processes, numbered circles denote the steps at which sampling was performed.
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Fig. 2. Two dimensional synchronous spectra (A) shifted to EEM (B) positions. In B the two ribbons of missing values (white bands) were used to replace the first order Rayleigh scatter and regions adjacent to the fluorescence signal acquired at the maximum synchronous delta. Regions 1 and 3 missing values created upon shifting the two dimensional EEM were replaced by zeros.
potassium ferrocyanide (15% w/v) and zinc acetate (30% w/v) solutions. The resulting mixture was centrifuged at 4000 rpm for 10 min at 4 °C. The supernatant was collected in a 10 ml volumetric flask and two further extractions were performed using 2 ml of deionized water. The extracts were filtered (0.45 μm) and 50 μL was analyzed by HPLC. The HPLC system consisted of LC-10AD class VP pumps, a SPD-M10A diode array detector and a SCL-10A class VP controller, all from Shimadzu (Japan). The mobile phase was a mixture of acetonitrile in water (5% v/v) at a flow rate of 1 ml/min under isocratic conditions and a Synergi 4µ Hydro-RP 80A, 25 × 4.6 cm, (Phenomenex) column. The UV detector was set at 280 nm and HMF was quantified using the external standard method within the range 0.025–75 mg/L. 2.2.3. Acrylamide The sample preparation procedure described by Senyuva and Gökmen [13] was used with minor modification. Finely ground sample (0.450 g) was weighed into a 10 ml centrifuge tube. The sample was spiked with 100 µL 13C3-labeled acrylamide (10,000 µg/L) to confirm acrylamide and recovery, and 5 ml water was added. The solution was allowed to stand for a few minutes and homogenised for 1 min (Ultraturrax). 500 µL Carrez I and 500 µL Carrez II solution were added, vortex and allowed to stand for 10 min. Tubes were centrifuged at 5000 rpm for 15 min at 4 °C. The clear supernatant (1 ml) was clarified onto a pre-conditioned HLB cartridge. First drops were discarded and the rest of eluate was collected in amberlite vials for LC-MS analysis. LC-ESI-MS analyses were performed as described by Morales et al. [14] by using an Agilent 1100 HPLC system (Waldbronn, Germany) consisting of a cuaternary pump, an autosampler and a temperature-controlled column oven, coupled to an Agilent 1100 MS detector equipped with electrospray ionization interface. The analytical separation was performed on an Inertsil ODS-3 column (250 × 4.6 mm, 5 µm) using the isocratic mixture of 0.2% aqueous solution of formic acid at a flow rate of 0.6 ml min− 1 at 25 °C. In order to discard early eluting peaks, the first 8.0 min of the chromatogram was diverted to waste by means of software diverted valve as it prevents contamination of the mass spectrometer with co-extractives. Data acquisition was performed in selected ion monitoring (SIM) mode using the following interface parameters: drying gas (N2, 100 psig) flow of 12 L min− 1, nebulizer pressure of 45 psig, drying gas
temperatures 350 °C, capillary voltage of 3 kV, fragmenter voltage of 70 eV. Data acquisition was performed in SIM mode. Ions monitored were m/z 72.1 for acrylamide and m/z 75.1 for 13C3-labeled acrylamide for the quantification of acrylamide in the samples. Full scan analyses were performed in the mass range 50–200 for the spectral identification of acrylamide in samples. Acrylamide calibration was built in the range of 3 to 200 µg/L. LOD and LOQ were determined to be 6 and 18 µg/kg on signal-to-noise ratio of 3 and 9, respectively. 2.3. Two dimensional front face fluorescence spectroscopy acquisition Two dimensional synchronous spectra (Fig. 2A) were acquired on a Varian Cary fluorimeter. Spectra were directly measured on grinded cookies placed in dispensable acryl cuvettes (Sarstedt®, France). Excitation spectra were recorded between 280 and 550 nm and the emission synchronous delta (Δ = λem − λexc) varied between 0 and 140 nm. Steps for excitation and emission synchronous Δ set at 8 nm and 4 nm for the industrially processed and experimentally baked samples respectively (slits= 5 nm, front face angle around 36°). To account for possible non-homogeneity, the two dimensional synchronous spectra were recorded in triplicates acquired on different sides of the cuvettes. The excitation–emission matrix (EEM) was reconstructed by placing the values of the two dimensional synchronous spectra matrix A (I excitations ⁎ J synchronous Δs) in a M (I ⁎ I + J − 1) matrix such that m(i, j) = a(i, j) for i = 1, and m(i, j + i − 1) = a(i, j) for i = 2… I. The missing values in the matrix M at λem b λexc and λem N λexc + Δmax + 16 nm (Fig. 2B) were then replaced with zeros; the remaining values of the first order Rayleigh scatter (between λem = λexc to λem = λexc + 16 nm) and the values in the region flanked by λem = λexc + Δmax and λem = λexc + Δmax + 16 nm were kept missing. 2.4. Chemometric analysis of fluorescence data and regression over NFC content 2.4.1. Multiway PARAFAC decomposition 2.4.1.1. Data arrangement. EEMs of samples belonging to the four industrial processes were arranged in a three-way array with the samples kept in the first mode and the excitation and emissions
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wavelengths assigned to mode 2 and 3; to obtain a three-way array of size 123 ⁎ 68 ⁎ 106. EEMs acquired on the experimentally baked samples were arranged in a five-way array with samples produced at different time and temperatures placed in mode 1, excitation and emission wavelengths assigned to mode 2 and 3, while the varying composition factors i.e. type of sugar used (glucose and sucrose) and level of fat saturation (high and low) were placed in two additional modes each of dimension two. The size of the resulting five-way array was 111 ⁎ 34 ⁎ 53 ⁎ 2 ⁎ 2. Experimentally baked samples were also analyzed using a three-way arrangements of the EEMs, that is, all the samples were kept in the first mode (array size 444 ⁎ 34 ⁎ 53). 2.4.1.2. Decomposition. PARAFAC models [15,16] were used to decompose the multiway arrays into sets of vectors corresponding to profiles (spectral loadings) varying only in intensity (sample mode loadings) among the samples. Iterative alternating least square algorithm [10] was used to find the PARAFAC decomposition parameters. PARAFAC models were refitted several times starting with random values to ensure the model's stability. Non-negativity constraint was imposed on all decomposition parameters. Conventional diagnosis tools such as residual analysis, deviation of multi-linearity using the core consistency diagnostic (CORCONDIA), and the interpretability of the PARAFAC loadings were used as indicators of the number of PARAFAC factors to retain and to evaluate the quality of the models. Degenerate solutions were assessed using the Tucker congruence coefficients (cosine values between all pairs of components) [15]. Scaling and multiplicative scatter correction (MSC) were applied on unfolded three-way arrays according to the excitation mode (concatenated emissions). Scaling was performed using the norm of each concatenated emission spectra on the respective emission spectral intensities. In MSC, the intercept and the slope of the regression between each of the concatenated emission spectra and the mean spectra were used to correct the intensities of the respective emission spectra [17]. For the experimentally baked samples, spectral pre-treatments were applied on the unfolded arrays arranged in three ways before arranging the data into five-way arrays. 2.4.2. Prediction of the cookies NFC content Generalized linear models (GLM) f (µy) =b0 +b1x1 +b2x2 + … +bnxn +e were used to build prediction models of chromatographically measured NFC content using the sample mode loadings of the front face PARAFAC models. In the equation f(µy) was the log function of the expected NFC content (normally distributed) and the regression coefficients bi were estimated using Fisher's scoring algorithm (iteratively reweighed least squares) for maximum likelihood estimation [18]. Through the link function, the GLM allows modelling non-linear relations between the predictors and the dependent variable. In addition, GLM can be used to model dependent variables with non-normal distribution. The multiple linear model is a special case of the generalized linear model with the link function equals the identity function and the distribution of the dependent variable expected normal [18]. The root mean square error of a leave three out cross-validation (spectral triplicates of each sample) was used to approximate the GLM models prediction error. PARAFAC sample mode loadings and NFC content of cross-validation samples were estimated using the PARAFAC loadings and NFC regression coefficients determined from calibration samples. Leverages and residual analysis were used to detect possible outliers and influential samples in the regression models [10,18]. The leverages are the diagonal elements of the hat matrix (H = X(X′X)− 1X′); they are proportional to the Mahalanobis distances between each observation and the centroid of the observations in a multidimensional space defined by correlated variables. 2.4.2.1. Software. All computations were performed using MatLab7® (The MathWorks Inc.); multiway PARAFAC decomposition was applied
using codes included in the MatLab Nway Toolbox [19]. Generalized linear models were fitted using MatLab statistics toolbox. 3. Results and discussion 3.1. Acquisition and pre-treatment Acquisition of fluorescence EEM is time consuming if sufficient resolution is needed. Usually, an EEM is acquired either by recording emission spectra at consecutive fixed excitation wavelengths or recording excitation spectra at consecutive fixed emissions wavelengths. Either way, a significant part of the acquired signal, and specifically all spectral intensities at λem b λexc do not contain any fluorescent information since emission can only occur at lower energetic levels than excitation (red shifted Stock's) and are thus physically irrelevant. An alternative approach is to use synchronous spectra acquired by simultaneously varying the excitation and emission monochromators keeping a positive difference (delta) between the two monochromators (λem = λexc + Δ). Two dimensional spectra are obtained by acquiring the excitation spectra at increasing delta values. Delta being positive, all measured points will be at λem equal or superior to λexc. Fluorescence landscapes present in the two dimensional synchronous spectra (Fig. 2A) are not low rank bilinear and consequently, cannot be appropriately described using multi-linear models. The decomposition models were therefore applied on the standard EEM reconstructed by shifting the two dimensional synchronous spectral data to a fixed emissions position. The high number of missing values created upon shifting the two dimensional synchronous spectra can be problematic for data decomposition. Regions of the EEM (Fig. 2B) known not to contain fluorescence signal (λem b λexc) and regions around second order Rayleigh were replaced by zeroes. A ribbon of missing values [20,21] was used to replace the first order Rayleigh scatter overlapping with fluorescence signal. Another ribbon was used for EEM regions adjacent to the fluorescence signal acquired at the maximum synchronous delta to avoid artefacts produced by inserting zeroes adjacent to the acquired fluorescence signal. A faster convergence of the PARAFAC model and physically relevant parameters were still obtained when part of the missing values was replaced by zeros. The same results were obtained in the study of Thygesen et al. [20].
Table 2 PARAFAC models fitted on FFFS arrays acquired on the industrial and experimental cookies with increasing number of factors Data
# Untreated data Normalized data MSC data Fac. %Var. Cor. Nbr. %Var. Cor. Nbr. %Var. Cor. It. It.
1 All industrial 2 processes (three-way array) 3 4 5 6 1 Laboratory 2 kinetics (three-way array) 3 4 5 1 Laboratory 2 kinetics (five-way array) 3 4 5
60.23 80.52 89.68 94.09 95.98 96.88 60.68 86.58 95.60 97.68 98.42 59.87 85.29 93.97 95.99 96.72
– 10 99.18 22 94.12 20 90.46 39 84.53 28 49.23 88 – 10 96.05 18 95.90 38 96.44 27 60.32 298 – 12 88.59 20 91.47 36 81.84 40 6.08 172
57.78 78.52 87.99 94.27 95.80 96.42 57.06 85.93 95.50 97.82 98.24 57.01 85.67 95.05 97.34 97.76
– 99.32 97.38 93.26 84.96 31.90 – 96.90 96.44 97.36 42.60 – 91.75 93.82 93.68 46.86
13 12 27 12 52 69 10 12 14 16 28 6 14 16 48 56
57.02 78.29 87.63 94.24 95.76 96.49 57.69 84.67 94.88 97.46 97.92 57.67 84.50 94.52 97.05 97.45
– 99.29 97.72 93.46 85.18 50.29 – 98.47 94.74 96.26 46.12 – 92.04 92.29 92.91 57.53
Nbr. It. 13 12 38 17 16 54 10 12 14 16 28 8 11 12 42 46
%Var. = Percentage of explained data variance. Cor. = CORCONDIA (Core consistency diagnostic) measuring deviation form ideal multilinearity as percent of agreement. Nbr. It. = Number of ALS iterations.
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Fig. 3. PARAFAC spectral loadings of three-way industrial processes data with increasing number of factors.
3.2. PARAFAC decomposition When samples are measured in front face mode on solid or turbid samples, the optical properties (matrix effect) will highly distort both the intensity and shape of the fluorescence landscapes [6,7] mostly through quenching and scattering phenomena. Nonetheless, several studies [7] showed that most landscapes in FFFS EEM remain bilinear of low rank and can thus be well described by parallel profiles models. Compared to other competing chemometric decomposition tools the PARAFAC model provides simpler decomposition parameters [10]. When applied on FFFS data the extracted spectral PARAFAC loadings are interpreted as fluorescence profiles distorted by the media optical and the sample mode loadings are the relative intensities of the respective front face profiles (loadings) and are dependent on fluorophores nature and concentration as well as the sample's optical property. Hence, in the FFFS context, the PARAFAC model is rather used as a soft chemometric model and the solution is only empirically valid for the data set that is being analyzed. Results of the PARAFAC decomposition models with increasing number of factors are presented in Table 2. All the reported PARAFAC figures are the results of decompositions performed on MSC treated arrays. 3.2.1. Industrial process data PARAFAC spectral loadings of the three-way array including all the industrially processed samples (Fig. 3) suggest that a five factor model is suitable to describe the data's systematic information.
Fig. 4. Industrial processes one to three (Pr.#) sample mode loadings on the 5 component PARAFAC model. Samples (acquired in spectral triplicates) belonging to the different processes are represented on different background colours. Empty circles indicate lightly treated samples deviating from the rest of the sample mode loading structure particularly on the NF profiles. Dashed rectangles and circles are used to highlight respectively process 2 cooling tunnels samples and process 3 samples left to cool at room temperature.
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Fig. 5. Industrial process line 4 PARAFAC sample loadings.
The fluorescence profiles were named in agreement with pure fluorophores known from literature to be present in cookies and having fluorescence profiles in right angle measurements similar to those obtained in the PARAFAC decomposition. The PARAFAC profiles correspond to native tryptophan (Trp − maximum intensity λexc/λem ≈ 290/350), riboflavin (Rf − max λexc/λem ≈ 350/550), and several fluor-
escence profiles generally associated to lipid oxidation and Maillard reaction compounds [7], these lasts were denoted Nf for neoformed fluorescence. Sample mode loadings of the industrial processes 1 to 3 samples acquired in spectral triplicates are plotted in Fig. 4; the industrial process line 4 sample mode loadings were excluded from this figure
Fig. 6. Spectral loadings (mode 2 and 3) of five-way PARAFAC models fitted on experimental kinetic data with increasing number of factors and plots of mode 4 and 5 loadings belong to the experimentally controlled factors.
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and will be described later on. Noticeably, the sample mode loadings of Nf profiles deviating from the rest of the structure (empty circles) are the least processed samples. This interference is probably due to major differences in optical properties and in particular quenching occurring early in the baking process upon transformation of raw samples into cookie. Another reason may be that Nf fluorescence spectral loadings overlap with native fluorescence present in lightly treated samples which also explains why this perturbation is much less evident on native tryptophan and riboflavin profile sample mode loadings. Sample mode loadings of the different profiles show that the major variability of the front face signal is highly related to the industrial process applied. Baking steps could be easily discriminated from the evolution of the profile sample mode loadings. The sample mode loadings also show that FFFS signal continue evolving when samples are left to cool at room temperature (circled samples) while cooling tunnels better stabilize the fluorescence profiles (dashed rectangles). PARAFAC sample loadings of process line 4 are presented in Fig. 5. The interference due to difference in quenching on the sample mode loading structure was more important in this data than in the other industrial processes (Fig. 4). This is mostly due to the presence of raw dough samples in this data affecting not only the relative intensity of the neoformed fluorescence but also the native riboflavin fluorescence. The interference was less evident on the Trp fluorescence sample mode loadings which decreased relatively to the baking process. The oil spray resulted in a relative decrease of Nf1 and increase of Nf2 sample mode loadings as compared to the pre-oiled samples sample mode loading. There was no clear trend and the process did not seem to significantly vary with the running time of the industrial process line 4 oven. 3.2.2. Experimentally baked data (Five-way PARAFAC) PARAFAC models were fitted on the five-way arrays comprising of EEMs acquired on the experimental samples baked at three temperatures (150, 200 and 230 °C) arranged over two additional modes comprising of the type of sugar and level of fat saturation used in the cookies formulation (Figs. 6 and 7). Additional modes in fiveway arrays had also to be constrained to non-negativity otherwise convergence problems also known as degenerate solutions [10,15] were encountered. Some models still encountered local minima and swamps especially with high number of fitted PARAFAC factors. Refitting the models several times using random starting values solved these problems and interpretable models were obtained. Fiveway array models resulted in acceptable Tucker congruence coefficients (correlations between sets of components − TC b 30%). The sample mode loading structure (Fig. 7) of all the fluorescence profiles followed the kinetic of the heat treatment being applied with the exception of Nf2 and NF3 sample mode loadings which also deviated in lightly treated samples. Cookies formulated with glucose (Fig. 6 mode 4) were characterized by a lower Trp and higher Rf profiles as compared to cookies made with sucrose, while high level of fat saturation (Fig. 6 mode 5) resulted in lower Nf1 and higher Rf profiles compared to cookies with low fat saturation. The five-way PARAFAC models also showed that oil type loadings varied much less than sugar type loadings. In all the analyzed data sets the structure of the sample mode loadings significantly improved (reduced distance between replicates and co-linearity and a better evolution with process) when scaling and especially MSC pre-treatments were applied. Scaling and MSC also resulted in faster convergence of the PARAFAC models and better CORCONDIA values when models were fitted with a relatively high Fig. 7. Sample mode loadings of the 5 way PARAFAC model fitted on kinetic data using 5 components. Samples baked at 150 °C (■), 200 °C (Δ), and 230 °C (●). Samples highlighted using dashed rectangles are the lightly treated sample of each kinetic clearly deviating from the rest of the sample mode loadings of Nf2 and Nf3.
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Fig. 8. PARAFAC spectral loadings from three-way arrangement of experimental kinetic data EEMs.
number of components. The shape of the extracted spectral parameters did not significantly change upon pre-treatment. Since the type of sugar and fat saturation level are not known to have a multiplicative effect on the FFFS profiles, it was essential to verify the suitability of the five-way arrays PARAFAC models. This was achieved through visual comparison (i) between the spectral loadings
and sample loadings obtained from the five-way arrays and the ones obtained from three-way arrangements of the respective EEMs (Table 2 and Fig. 8), and (ii) between the five-way arrangement experimentally controlled factor loadings (sugar and fat) and the mean of the three-way arrays sample loadings (figure not shown) of the respective EEMs for each level of the controlled experimental
Fig. 9. FFFS predicted and chromatographically measured content of: (A) CML in experimentally baked cookie samples, (B) HMF in experimentally baked cookie samples made with sucrose and low fat saturation baked at temperature 200 °C, (C) CML in industrial process line 1 and 4 cookies and (D) acrylamide content in industrial process 4 cookies. RMSEV is the root mean square error of a leave 3 out (triplicates of each sample) validation. Range is the minimal and maximal value of chromatographically measured NFC content. R2 is the coefficient of determination between predicted and measured values. N is the number of EEMs used in the regression. Dashed lines represent the 95% prediction levels.
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factors. Once it was verified that the same spectral loadings (Figs. 6 and 8) and sample loadings evolution were obtained in three- and five-way arrangements, the significance of the loadings corresponding to the controlled factors of the five arrays was indirectly assessed through factorial ANOVA of sample loadings obtained from three-way arrangements of the respective EEMs. ANOVA of the five components PARAFAC sample loadings obtained from three-way arrangements showed that only the sugar type used was significant for the intensity of Trp, Nf2, Nf3 and Rf (p b 0.001). The level of fat saturation and the interaction of the controlled factors did not significantly influence the intensity of the FFFS profiles. The mean intensity of the profiles corresponding for each level of the controlled factors and ANOVA significance results matched the five-way arrays controlled factor loadings shape and variability. 3.2.3. Regression of front face fluorescence spectroscopy PARAFAC sample mode loadings over chromatographically measured NFC content As mentioned earlier, the intensity of the fluorescence profiles recorded in front face mode on solid samples does by no mean reflect the true fluorophores intensity as it is highly modified by the scattering and quenching properties of the samples. Nevertheless, the PARAFAC sample mode loadings represent a measure of the modifications on the native FFFS image via the process. Consequently, the FFFS PARAFAC sample mode loadings can be used in calibration over other compounds related to process such as NFC, known to accumulate in the course of heat treatment. Chromatographically measured NFC content increased exponentially (acrylamide and HMF) or linearly (CML) with increased heating time in the experimentally baked cookies and with advancing process in the industrially baked cookies (results not shown), whereas most of the FFFS sample mode loadings followed a linear or a logarithmic evolution (Figs. 4, 5 and 7). As a result, NFC prediction through linear models such as multiple linear regressions on PARAFAC sample mode loadings or n-way PLS resulted in tailed residuals indicating that a log transformation of the dependent variable was necessary. Generalized linear models with a log link function of the normally distributed NFC content were thus used for calibration over the FFFS PARAFAC sample mode loadings. The regression models were built using the sample mode loadings of two PARAFAC models fitted with four components on three way arrangements of MSC pretreated FFFS data acquired on the industrial and experimental cookies. All the sample mode loadings used in regression resulted from PARAFAC models with CORCONDIA values higher than 90%. On average, 4 to 8 iterations were needed to find the generalized linear models coefficients of the expected NFC solution in the different validation segments. Content in CML chromatographically measured and predicted using the FFFS PARAFAC sample mode loadings in experimental samples and the industrial process lines 1 and 4 cookies are presented respectively in Fig. 9A and C. The ability of predicting simultaneously the CML content of samples with different composition based on the same FFFS decomposition and CML regression coefficients demonstrates the capacity of multiway PARAFAC decomposition and multivariate regression to handle limited amount of interference due to differences in cookie composition. Prediction of HMF in the experimental cookies baked at 200 °C with sucrose and a low level of fat saturation in the composition and acrylamide content in process line 4 samples are plotted in Fig. 9B and D. The NFC content in all the samples was well predicted by the models and only some spectral replicates were removed from the regressions based on residual analysis and leverages. The number of EEMs removed from the regressions presented in plots 9A through 9D were 18, 2, 0 and 1 respectively. The intercepts (ranging from −0.01 to
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0.35) and slopes (ranging from 0.97 to 1.001) between predicted and measured NFC content were not significantly different from the ideal zero and one values respectively. Of course, the parameters of the PARAFAC models and the coefficients of the regressions obtained in this study are only valid under the same experimental and acquisition conditions. If new interference to the FFFS spectral data is introduced by change of composition or process of the cookies, the parameters of all the chemometric models must be recalibrated. 4. Conclusion In this paper the potential of PARAFAC models applied on multiway structures to identify systematic information combining parallel FFFS profiles weighed by composition variation of cookies was demonstrated. Results of PARAFAC decomposition showed that systematic information present in FFFS data can be used in technological process control and in calibration over quality control parameters such as NFC content of cookies. The results also show that limited interferences on the signal due to composition variation, such as expected on an industrial line, can be handled through the use of spectral pretreatments, PARAFAC multiway decomposition and multivariate calibration. Consequently, good and robust NFC predictions can be achieved. Acknowledgements The authors would like to thank the European commission for financing this work through the ICARE project. We are also grateful for the SMEs of the ICARE consortium for providing the samples used for this study. References [1] P.A. Finot, Proceedings of the 8th International Maillard Symposium. August 28– October 1, 2004, Charleston, USA, Ann. N.Y. Acad. Sci., vol. 1043, 2005, pp. 1–8. [2] H. Stopper, R. Schinzel, K. Sebekova, A. Heidland, Cancer Lett. 190 (2003) 151–156. [3] E. Tareke, P. Rydberg, P. Karlsson, S. Eriksson, M. Tönqvist, J. Agric. Food Chem. 50 (2002) 4998–5006. [4] X.M. Zhang, C.C. Chan, D. Stamp, S. Minkin, M. Archer, W.R. Bruce, Carcinogenesis 14 (1993) 773–775. [5] H. Vlassara, W. Cai, J. Crandall, T. Goldberg, R. Oberstein, V. Dardaine, M. Peppa, E.J. Rayfield, Proc. Natl. Acad. Sci. U. S. A. 24 (2002) 15596–15601. [6] J.R. Lakowicz, Principles of Fluorescence Spectroscopy, Kluwer Academic/Plenum Publishers, New York, 1999, p. 237. [7] J. Christensen, Autofluorescence of Intact Food— An Exploratory Multi-way Study. PhD Thesis, The Royal Veterinary and Agricultural University, 2005, p.101. [8] M. Zandomeneghi, J. Agric. Food Chem. 47 (1999) 878–882. [9] M. Zandomeneghi, L. Carbonaro, L. Calucci, C. Pinzino, L. Galleschi, S. Ghiringhelli, J. Agric. Food Chem. 51 (2003) 2888–2895. [10] R. Bro, Multi-way analysis in the food industry. Theory, algorithms and applications, PhD Thesis, University of Amsterdam, 1998, p.290. [11] A. Charissou, L. Ait-Ameur, I. Birlouez-Aragon, J. Chromatogr. 1140 (2007) 189–194. [12] B. Garcia-Villanova, E. Guerra Hernàndez, E. Martínez-Gómez, J. Mortilla, Food Chem. 50 (1993) 4998–5006. [13] H.Z. Senyuva, V. Gökmen, Food Chem. 97 (2006) 539–545. [14] F.J. Morales, J.A. Rufian-Henares, G. Arribas-Lorenzo, Food Addit. Contam. 24 (2007) 343–350. [15] R. Bro, Chemometr. Intell. Lab. Syst. 38 (1997) 149–171. [16] R.A. Harshman, UCLA Work. Pap. Phon. 16 (1970) 1–84. [17] M.S. Dhanoa, S.J. Lister, R. Sanderson, R.J. Barnes, J. Near Infrared Spectrosc. 2 (1994) 43–47. [18] P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed., Chapman & Hall, New York, 1989, p. 511. [19] C.A. Andersson, R. Bro, Chemometr. Intell. Lab. Syst. 52 (2000) 1–4. [20] L.G. Thygesen, A. Rinnan, S. Barsberg, J.K.S. Møller, Chemometr. Intell. Lab. Syst. 71 (2004) 97–106. [21] G. Tomasi, R. Bro, Chemometr. Intell. Lab. Syst. 75 (2005) 163–180.