Autores: A partir do fascículo 39/9 a revista Química Nova adotou a licença CC-BY. Mais informações a respeito dessa licença podem ser obtidas aqui.

Chemometric tools and ftir-atr spectroscopy applied in milk adulterated with cheese whey |

Layane L. Vinciguerra I. Faculdade de Farmácia, Universidade Federal do Rio Grande do Sul, 90610-000 Porto Alegre - RS, Brasil Recebido em 02/08/2018 Endereço para correspondência
*e-mail: mfferrao@gmail.com RESUMO
Brazilian law forbids the addition of cheese whey in milk. However, adulteration with cheese whey is one of the most applied fraud due to its low cost. The detection of this fraud is the quantification of Caseinomacropeptide (CMP). The CMP is a constituent of the whey that can be used as adulteration marker. Thus, an analytical method capable of identifying CMP by Fourier Transform Infrared Spectra (FTIR) was developed using chemometrics methods. Firstly, we attempted to develop an exploratory analysis model by Hierarchical Grouping Analysis (HCA) and Principal Component Analysis (PCA) that indicated similarity between samples of raw milk and semi-skimmed milk. Moreover, in the PCA scores, it was possible to observe a tendency of separation between samples with different concentrations of CMP. Afterwards, multivariate regression models were used for Partial Least Squares (PLS), Partial Least Square with Interval Synergism (siPLS) and Supporting Machines with Least Squares (LS-SVM) to quantify the adulteration in different types of milk by Cheese serum through the CMP. All the models were then compared to each other and the results of the official method with Liquid Chromatography Tandem mass spectrometry (LCMS/MS) analysis used by the Ministry of Livestock and Supply (MAPA). The model LS-SVM, employing the full spectrum, obtained the best result compared to the other models (PLS and siPLS) to quantify the CMP in the milk samples. Palavras-chave: milk; adulteration; CMP; cheese whey; FTIR-ATR spectroscopy; multivariate regression.
In recent years in Brazil, it were discovered several frauds in bovine milk production and in its transportation. One of the most profitable milk frauds recently discovered was the cheese whey addition, which is prohibited by Brazilian law. This milk adulteration leads to a fat content dilution and a reduction of its nutritional value, since each 10% volume of cheese whey added reduces 8% the milk protein content. The cheese whey is a milk by-product and, consequently, it is one of the most troubling frauds to detect. Nowadays, the Brazilian official methods for detect cheese whey addition in milk are based on caseinomacropeptide (CMP) detection through liquid chromatography coupled with mass spectrometry (LC-MS) or capillary electrophoresis. Several approaches to CMP analysis in milk have been reported in the literature, such as high-performance liquid chromatography (HPLC) used to determine the CMP index and detect cheese adulteration of whey, Fourier Transformed Infrared Spectroscopy by attenuated total reflectance (FTIR-ATR) associated with chemometrics has been applied in researches as an alternative method for food matrices with low cost, rapid analysis and little or none sample pre-treatment, nondestructive and/or non-invasive avoid, high reagent consumption and has potential for portability. Moreover, the application of multivariate techniques in infrared spectroscopy enabled quantitative approach.
Samples and reagents The ultra-high temperature (UHT) milk samples (skimmed and semi-skimmed) were acquired in local markets as well as raw milk. The water used was purified through Milli-Q system (Millipore). It was also used in this work acetonitrile (Tedia), methanol (J. T. Baker), glycine (Vetec), acetic acid (J. T. Baker), porcine gastric mucous pepsin (Sigma-Aldrich), trichloroacetic acid (Merck) and formic acid (Merck). All reagents had purity grade for residue analysis. The CMP standard (Davisco Foods, EUA) and a synthetic pepsin digestion peptide (MAIPPKKNQDKTEIPTINT, Mimotopes, Australia) were used as analytical standard.
Eighteen spiked milk samples were prepared by addition of 75 mg of CMP standard in 25 mL of milk and subsequent dilution to the final concentration of 60, 120, 180, 240, 300 and 360 mg L
Figure 1. Graphical abstract of experimental steps of production and analysis of studied milk samples by FTIR-ATR
For milk analysis and separation of constituent digestion products were performed in a LCMS/MS system API 5000 AB Sciex (Foster City, CA, USA) coupled with a liquid chromatography 1100 Series (Agilent). The column was a PLRP-S (polystyrenedivinylbenzene), 150 x 4.6 mm The infrared spectra were obtained in a FTIR spectrophotometer Cary 630 (Agilent Technologies) coupled with an attenuated total reflectance accessory with ZnSe crystal and helium/neon laser. The spectral range was 4000 to 800 cm
The software Matlab 8.1 (MathWorks Inc.Natick, USA), Chemostat, The pre-processed infrared spectra as input for principal component analysis (PCA) and hierarchical component analysis (HCA) for exploratory analysis were used. The HCA was performed using the Euclidean distance and the Ward method. Partial least square regression (PLSR), synergy interval partial least square regression (siPLSR) and least square support vector machine regression (LS-SVM) algorithms were used for multivariate regression. The preprocessed spectra were used as input for PLSR. Moreover, the infrared spectra were divided in 8, 16, 32, 64 and 128 intervals with equal number of variables and the PLSR was performed in each interval. In addition, combination of these intervals two by two, three by three and four by four were made and the PLSR was performed in each combination. This approach (divide, combine) is called siPLSR. The LS-SVM was performed only in the best regression variables defined by siPLSR as well as the entire spectra. The best regression variables were defined as the variables with smaller root mean square error for cross validation (RMSECV). The meta-parameters of LS-SVM and the latent variables of PLSR and siPLSR were selected through leave-one-out cross validation method in order to minimize RMSECV. The root means square error for calibration (RMSEC), root mean square error for prediction (RMSEP) and the determination coefficient (R
Figure 2 illustrates the infrared spectrum of the adulterated milk samples in the range of 4000-800 cm
The PCA and HCA were carried out in the pre-processed spectra to evaluate the milk types differences (skimmed (LD), semi-skimmed (LS) and raw (LC)) in the adulterated samples before the quantification step. The dendrogram and the scores of PCA were depicted in the Figure 3, respectively. The three milk types are well defined in the dendrogram: the group 1 (red) is the skimmed milk, the group 2 (green) is the raw milk and the group 3 (blue) is the semi-skimmed milk. There are two exceptions: a skimmed sample (LD30), which has a large dissimilarity with the other samples, and a raw milk sample (LC5), which is more similar with the semi-skimmed than with the other raw milk samples, probably due to the change in the coordinates in which the distance is calculated after the new group is formed, since the raw and semi-skimmed samples are very close to each other. No pattern related to the CMP concentration was noticed in the dendrogram.
Figure 3. Dendrogram of the milk samples adulterated with CMP. LC - raw milk (green); LD - skimmed milk (red); and LS - semi-skimmed milk (blue)
According to the Figure 4 A), the first principal component (PC1), with 46.35% of the explained variance, separates the samples between two PCs: the raw milk samples in the negative side of PC1 and the skimmed and semi-skimmed milk samples in the positive side. The samples distribution along the second principal component (PC2), with 16.62% of explained variance, describes a pattern in relation of CMP concentration, since the concentration increase with the PC2 scores, with exception of LS5. The third principal component (PC3), with 8.26% of total variance explained, separates the raw, in the positive side, and semi-skimmed milk samples, in the negative side, Figure 4 B). Figure 5 shows the scores of the first three main components where we can observe the clear separation of the three types of milk used.
Figure 4. A) Milk samples adulterated with CMP in the PCA scores of PC1 and PC2. LC - raw milk; LD - skimmed milk; and LS - semi-skimmed milk and B) Milk samples adulterated with CMP in the PCA scores of PC1 and PC3. LC - raw milk; LD - skimmed milk; and LS - semi-skimmed milk
The PC1 loadings (Figure 6 (a)) showed a major influence of water related bands, which explained the separation between the skimmed milk to the others. On the other hand, the PC3 loadings (Figure 6 (b)) showed a major influence of the absorption bands in the 1700-1500 cm
Figure 6. Graph loadings of PC1 (A) and PC3 (B)
The pre-processed spectra were submitted to multivariate regression algorithms with the intent of develop a CMP quantification methodology with infrared spectra as input. The CMP concentrations determined by LC-MS/MS were used as reference. The CMP values determined by LC-MS/MS were 0.0053-0.4076 g L
The PLSR model (M1) with 7 latent variables presented a satisfactory RMSEC, RMSECV and RMSEP as well as a high R
Figure 7. A) CMP FTIR spectrum and B) Variance importance index for PLSR with entire spectra. The values above the red line have more influence in the model
Figures 8 and 9 show the explained variance per latent variable and the regression model for M1, respectively.
Figure 8. Explained variance per latent variable in cross validation for PLSR of entire spectra
Figure 9. LC-MS/MS versus the FTIR-ATR/PLS CMP concentrations with entire spectra. Spheres are the calibration samples; rhombus are the predicted samples
The LS-SVM models (S1-S6) were, in general, better in relation regression parameters than the PLSR models. The best LS-SVM regression was also the entire spectra model (S1) (Figure 10) that was even better than the M1 model. The S1 presented a similar RMSEC, RMSECV and R
Figure 10. LC-MS/MS versus the FTIR-ATR/LS-SVM CMP concentrations with entire spectra. Rhombus are the calibration samples; squares are the predicted samples
At last, the two models S1 and M1 presented good results for CMP quantification in milk, with good RMSEC, RMSECV, RMSEP and R
A methodology to quantify CMP in milk samples through FTIRATR associated with chemometrics was developed. The HCA and PCA separated the three milk types according to its fat and water content. In addition, the PCA showed a separation trend related to CMP content. The PLSR, siPLSR and LS-SVM models were developed and had good regression parameters. The best models used all spectra range, which indicates that the CMP information was overlapped in all spectra. The methodology developed with LS-SVM algorithm showed a better result in relation to the PLSR algorithm. Lastly, the methodology developed was suitable to detect CMP adulteration and quantify it.
The authors gratefully acknowledge the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), (Projeto nº. 133642/2014-3) for their financial support and scholarships.
1. Oliveira, G. B.; Gatti, M. D. S; Valadão, R. C., Martins, J. F. P.; Luchese, R. H.; 2. Ministério da Agricultura, Pecuária e Abastecimento (MAPA), 3. Motta, T. M. C.; Hoff, R. B.; Barreto, F.; Andrade,R. B. S; Meneghini, L. Z.; Pizzolato, T. M.; 4. Pádua Alves, É.; de Alcântara, A. L. D.; Guimarães, A. J. K.; de Santana, E. H. W.; Botaro, B. G.; Fagnani, R.; 5. Ferreira, M. P. L. V. O. I, Oliveira, M. B. P. P.; 6. Lenardon, L.; Meneghini, L. Z.; Hoff, R. B.; Motta, T. M.; Pizzolato, T. M.; Ferrão, M. F.; Bergold, A. M.; 7. Bilge, G.; Sezer, B.; Eseller, K. E.; Berberoglu, H.; Topcu, A.; Boyaci, I. H.; 8. Borin, A.; Ferrão, M. F.; Mello, C.; Maretto, D. A.; Poppi, R. J.; 9. Luna, A. S.; Pinho, J. S. A.; Machado, L. C. 10. Davis, R.; Mauer, L. J.; 11. Ministério da Agricultura, Pecuária e Abastecimento (MAPA), 12. Helfer, G. A.; Bock, F.; Marder, L.; Furtado, J. C.; Costa, A. B.; Ferrão, M. F.; 13. Noorgard, L.; Saudland, A.; Wagner, J.; Nielsen, J. P.; Munck, L.; Engelsen, S. B.; 14. Kennard, R. W.; Stone, L. A.; 15. Jensen, R. G.; 16. Walker, N. R.; Waters, R. S.; Duncan, M. A.; 17. Farrés, M.; Platikanov, S.; Tsakovski, S.; Tauler, R.; 18. Balabin, R. M.; Smirnov, S. V.; 19. Domingo, E.; Tirelli, A. A.; Nunes, C. A; Guerreiro, M. C.; Pinto, S. M.; 20. Hornik, K.; Stinchcombe; M.; White, H.; |

On-line version ISSN 1678-7064 Printed version ISSN 0100-4042

Química Nova

Publicações da Sociedade Brasileira de Química

Caixa Postal: 26037
05513-970 São Paulo - SP

Tel/Fax: +55.11.3032.2299/+55.11.3814.3602

Free access