- Email: [email protected]
NIR spectroscopy: a useful tool for rapid monitoring of processed cheeses manufacture
Journal of Food Engineering 61 (2004) 557–560 www.elsevier.com/locate/jfoodeng
NIR spectroscopy: a useful tool for rapid monitoring of processed cheeses manufacture urda L. C a
a,*
, O. Kukackov a
b
Faculty of Food and Biochemical Technology, Institute of Chemical Technology, Technick a 5, 166 28 Prague 6, Czech Republic b Nestl e Cokol adovny a.s., Mezi Vodami 47, 143 20 Praha 4, Czech Republic Received 1 August 2002
Abstract The aim of this study was to check the use of NIR technique for assessment of dry matter (DM) fat (F), crude protein (CP), pH and rheological properties of processed cheeses (penetration––P). Samples of processed cheeses were analysed by the reference methods and measured by the FT NIR spectrometer with the fibre optic probe. The calibration models were created using PLS method and verified by cross-validation. The correlation coefficient for DM is 0.998 and those for F, CP, pH and P are 0.995, 0.996, 0.945 and 0.925 respectively. The standard error of the prediction for DM, F, CP, pH and P were found 0.429, 0.997, 0.303, 0.062 and 1.330 respectively. As it follows from obtained results NIR spectroscopy can be used for the determination of the processed cheeses composition. Penetration and pH can be also estimated but with the lower precision. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Processed cheese; FT NIR spectroscopy; Calibration; Partial least square method
1. Introduction A rapid monitoring of the processed cheese manufacture is essential to obtain the high quality product with the minimal costs. As we proved in our previous papers–– NIR spectroscopy can be used for the estimation of the urda, & composition of raw milk (Kukackov a, C urda, Jindrich, 2000) and hard and semi hard cheeses (C Kukackov a, Stetina, & Divisov a, 2000). A little attention has been paid to the application of NIR spectroscopy for analysis of the processed cheeses composition (Adams, Latham, Barnett, & Poyton, 1999; Molt & Kohn, 1993). The aim of this study was therefore to check the use of NIR technique for assessment of dry matter (DM) fat (F), crude protein (CP), pH and rheological properties of processed cheeses as penetration (P).
2. Materials and methods Altogether 50 processed cheeses without flavouring were analysed for the calibration purposes. The samples
were supplied by 14 Czech producers. The reference values used for the development of the calibration models were determined by the oven drying to measure DM, by the van Gulik method to measure F, by the Kjeldahl method to measure total N content. Consistency was measured by the penetrometer PNR-10 (Petrotest Instruments GmbH & CoKG, Germany). NIR spectra in the range of 900–2500 nm were recorded on the Fourier transform spectrometer Nicolet Protege 460 (Thermo Nicolet Corp., USA) with the fibre optic probe and the Omnic software. The samples were stored in the refrigerator at 4 °C and they were left at room temperature (23 °C) not less than 12 h before NIR and penetration measurement. The NIR measurement was performed on three points of the processed cheese surface. Average spectra were taken for further evaluation. The minimal thickness of the measured cheese sample is 3 mm. An example of spectra of processed cheese with different fat content is shown on Fig. 1. The highlighted wavelengths indicate absorption of water and fat (Molt & Kohn, 1993; Williams & Fleming, 1980). 3. Results and discussion
*
Corresponding author. Tel.: +420-224-353-831; fax: +420-224-353285. urda). E-mail address: [email protected] (L. C 0260-8774/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0260-8774(03)00215-2
The Unscrambler 7 software (CAMO, Norway) was used to develop the chemometric models. The calibration
urda, O. Kukackov L. C a / Journal of Food Engineering 61 (2004) 557–560
558
Table 1 First step in the calibration procedure––the identification of outliers, the selection of wavelengths, correction of spectra and the selection number of PLS factors Measured quantity
Number of samples
Selected wavelength [nm]/ wave number [cm1 ]
Correction of spectra
Number of PLS factors
DM [%] F [%] CP [%] pH P [mm]
50 48 49 46 34
1200–2200/8500–4600 1000–2200/9600–4500 1300–2200/8000–4500 900–2400/10600–4200 900–2300/10600–4300
MSC MSC None None None
5 3 7 12 14
DM––dry matter, F––fat, CP––crude protein, P––penetration, MSC––multiplicative scatter correction, PLS––partial least squares.
was created by using the partial least square (PLS) method. Some preliminary steps were performed before the calibration. They are summarized in Table 1. Outlier samples were identified by the principal component analysis (PCA). This step together with the selection of the proper wavelength range considerably enhances the calibration results. The correction of spectra, e.g. multiplicative scatter correction (MSC) has also positive effect on the calibration results. Predicted sum of squares (PRESS) residuals graph facilitates the selection of the optimal number of PLS factors. The calibration models were verified by the crossvalidation. The capability of the calibration models to predict the concentration of the components was expressed as standard error of calibration (SEC), standard error of prediction (SEP) and correlation coefficient (R) between estimates and reference values. The accuracy and the precision of the calibration was verified by calibration coefficient of variation (CCV), prediction coefficient of variation (PCV), by the testing of the coefficients of the regression line between measured and predicted values (Mark & Workman, 1991) and by t-test for comparison of the means of two related (paired) samples for number of pairs P 30 (Massart, Vangdeginste, Deming, Michotte, & Kaufman, 1988). The regression lines for all measured quantities are shown on Figs. 2–6. The outline of the obtained calibration results
Fig. 1. VIS-NIR spectra of processed cheeses with different fat content. Highlighted wavelengths indicate absorption of water (1486 and 1954 nm) and fat.
is given in Table 2. The correlation coefficients of calibration for all measured quantities are rather high (>0.98), it means that the calibration models work well and the predicted values for the calibration samples match to the value obtained by the reference method. Values of the CCV are also acceptable with the exception of penetration. The applicability of the method can
Fig. 2. Calibration and validation results of processed cheeses for estimation of dry matter.
Fig. 3. Calibration and validation results of processed cheeses for estimation of fat.
urda, O. Kukackova / Journal of Food Engineering 61 (2004) 557–560 L. C
Fig. 4. Calibration and validation results of processed cheeses for estimation crude protein.
559
Fig. 6. Calibration and validation results of processed cheeses for estimation of penetration.
samples not included in calibration. It follows from t-test that there is no significant difference between NIR estimate and the result of reference method. The slope of the linear regression line for all constituents was close to the value 1; the intercept of the linear regression line for all constituents was close to the value 0. The significant differences were found for pH and penetration only.
4. Conclusions
Fig. 5. Calibration and validation results of processed cheeses for estimation of pH.
be assessed by cross-validation. It shows the deterioration of the correlation coefficient for pH and the penetration. The ratio SEP/SEC for pH and penetration indicates that the ruggedness for both these variables is low and there is a risk of higher difference between NIR estimate and the result of reference method for the
As it follows from obtained results NIR spectroscopy can be used for determination of the processed cheeses composition. Penetration and pH can be also estimated but with a lower precision. We can conclude from obtained results that NIR spectroscopy is very useful for at-line control of the processed cheeses manufacture. This method is rapid and non-destructive, no sample preparation is necessary. The application of NIR spectroscopy can improve the dairy production economy through the optimised laboratory efficiency, the increased product quality and the tighter production control.
Table 2 Calibration and the cross-validation results of the processed cheeses analysis by NIR spectroscopy Measured quantity DM [%] F [%] CP [%] pH P [mm]
Calibration
Cross-validation
R
SEC
CCV
R
SEP
PCV
0.999 0.996 0.998 0.984 0.992
0.361 0.888 0.216 0.033 0.434
0.85 3.66 1.56 0.58 5.32
0.998 0.995 0.996 0.945 0.925
0.429 0.997 0.303 0.062 1.330
1.01 4.12 2.15 1.07 16.29
T -test (tcrit ¼ 1:960) 0.011 0.002 0.005 0.005 0.002
DM––dry matter, F––fat, CP––crude protein, P––penetration, R––correlation coefficient, SEC––standard error of calibration, CCV––calibration coefficient of variation, SEP––standard error of prediction, PCV––prediction coefficient of variation.
560
urda, O. Kukackova / Journal of Food Engineering 61 (2004) 557–560 L. C
Acknowledgement This work was supported by Ministry of Education (CEZ: J19/98 223300005). References Adams, M. J., Latham, K., Barnett, N. W., & Poyton, A. J. (1999). Calibration models for determining moisture and fat content of processed cheese using near-infrared spectrometry. Journal of the Science of Food and Agriculture, 79, 1332–1336. urda, L., Kukackov tetina, & J., Divisova, I., (2000). Cheese C a, O., S analysis by NIR spectroscopy. In Proceedings of Syrotech, Z ilina, Slovakia.
urda, L., & Jindrich, J. (2000). Multivariate Kukackova, O., C calibration of raw cow milk using NIR spectroscopy. Czech Journal of Food Science, 18(1), 1–4. Massart, D. L., Vangdeginste, B. G. M., Deming, S. N., Michotte, Y., & Kaufman, L. (1988). Chemometrics: A textbook (p. 47). Amsterdam: Elsevier. Mark, H., & Workman, J. (1991). Statistics in spectroscopy (pp. 263– 302). San Diego: Academic Press. Molt, K., & Kohn, S. (1993). NIR-Spektroskopie–Chemometrie an Schmelzk€aseprodukten in der Qualit€atskontrolle und Prozessanalytik. Deutsche Milchwirtschaft, 44(22), 1102, pp. 1104– 1107. Williams, D. H., & Fleming, I. (1980). Spectroscopic methods in organic chemistry (3rd ed., pp. 35–73). London: McGrawHill.
NIR spectroscopy: a useful tool for rapid monitoring of processed cheeses manufacture urda L. C a
a,*
, O. Kukackov a
b
Faculty of Food and Biochemical Technology, Institute of Chemical Technology, Technick a 5, 166 28 Prague 6, Czech Republic b Nestl e Cokol adovny a.s., Mezi Vodami 47, 143 20 Praha 4, Czech Republic Received 1 August 2002
Abstract The aim of this study was to check the use of NIR technique for assessment of dry matter (DM) fat (F), crude protein (CP), pH and rheological properties of processed cheeses (penetration––P). Samples of processed cheeses were analysed by the reference methods and measured by the FT NIR spectrometer with the fibre optic probe. The calibration models were created using PLS method and verified by cross-validation. The correlation coefficient for DM is 0.998 and those for F, CP, pH and P are 0.995, 0.996, 0.945 and 0.925 respectively. The standard error of the prediction for DM, F, CP, pH and P were found 0.429, 0.997, 0.303, 0.062 and 1.330 respectively. As it follows from obtained results NIR spectroscopy can be used for the determination of the processed cheeses composition. Penetration and pH can be also estimated but with the lower precision. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Processed cheese; FT NIR spectroscopy; Calibration; Partial least square method
1. Introduction A rapid monitoring of the processed cheese manufacture is essential to obtain the high quality product with the minimal costs. As we proved in our previous papers–– NIR spectroscopy can be used for the estimation of the urda, & composition of raw milk (Kukackov a, C urda, Jindrich, 2000) and hard and semi hard cheeses (C Kukackov a, Stetina, & Divisov a, 2000). A little attention has been paid to the application of NIR spectroscopy for analysis of the processed cheeses composition (Adams, Latham, Barnett, & Poyton, 1999; Molt & Kohn, 1993). The aim of this study was therefore to check the use of NIR technique for assessment of dry matter (DM) fat (F), crude protein (CP), pH and rheological properties of processed cheeses as penetration (P).
2. Materials and methods Altogether 50 processed cheeses without flavouring were analysed for the calibration purposes. The samples
were supplied by 14 Czech producers. The reference values used for the development of the calibration models were determined by the oven drying to measure DM, by the van Gulik method to measure F, by the Kjeldahl method to measure total N content. Consistency was measured by the penetrometer PNR-10 (Petrotest Instruments GmbH & CoKG, Germany). NIR spectra in the range of 900–2500 nm were recorded on the Fourier transform spectrometer Nicolet Protege 460 (Thermo Nicolet Corp., USA) with the fibre optic probe and the Omnic software. The samples were stored in the refrigerator at 4 °C and they were left at room temperature (23 °C) not less than 12 h before NIR and penetration measurement. The NIR measurement was performed on three points of the processed cheese surface. Average spectra were taken for further evaluation. The minimal thickness of the measured cheese sample is 3 mm. An example of spectra of processed cheese with different fat content is shown on Fig. 1. The highlighted wavelengths indicate absorption of water and fat (Molt & Kohn, 1993; Williams & Fleming, 1980). 3. Results and discussion
*
Corresponding author. Tel.: +420-224-353-831; fax: +420-224-353285. urda). E-mail address: [email protected] (L. C 0260-8774/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0260-8774(03)00215-2
The Unscrambler 7 software (CAMO, Norway) was used to develop the chemometric models. The calibration
urda, O. Kukackov L. C a / Journal of Food Engineering 61 (2004) 557–560
558
Table 1 First step in the calibration procedure––the identification of outliers, the selection of wavelengths, correction of spectra and the selection number of PLS factors Measured quantity
Number of samples
Selected wavelength [nm]/ wave number [cm1 ]
Correction of spectra
Number of PLS factors
DM [%] F [%] CP [%] pH P [mm]
50 48 49 46 34
1200–2200/8500–4600 1000–2200/9600–4500 1300–2200/8000–4500 900–2400/10600–4200 900–2300/10600–4300
MSC MSC None None None
5 3 7 12 14
DM––dry matter, F––fat, CP––crude protein, P––penetration, MSC––multiplicative scatter correction, PLS––partial least squares.
was created by using the partial least square (PLS) method. Some preliminary steps were performed before the calibration. They are summarized in Table 1. Outlier samples were identified by the principal component analysis (PCA). This step together with the selection of the proper wavelength range considerably enhances the calibration results. The correction of spectra, e.g. multiplicative scatter correction (MSC) has also positive effect on the calibration results. Predicted sum of squares (PRESS) residuals graph facilitates the selection of the optimal number of PLS factors. The calibration models were verified by the crossvalidation. The capability of the calibration models to predict the concentration of the components was expressed as standard error of calibration (SEC), standard error of prediction (SEP) and correlation coefficient (R) between estimates and reference values. The accuracy and the precision of the calibration was verified by calibration coefficient of variation (CCV), prediction coefficient of variation (PCV), by the testing of the coefficients of the regression line between measured and predicted values (Mark & Workman, 1991) and by t-test for comparison of the means of two related (paired) samples for number of pairs P 30 (Massart, Vangdeginste, Deming, Michotte, & Kaufman, 1988). The regression lines for all measured quantities are shown on Figs. 2–6. The outline of the obtained calibration results
Fig. 1. VIS-NIR spectra of processed cheeses with different fat content. Highlighted wavelengths indicate absorption of water (1486 and 1954 nm) and fat.
is given in Table 2. The correlation coefficients of calibration for all measured quantities are rather high (>0.98), it means that the calibration models work well and the predicted values for the calibration samples match to the value obtained by the reference method. Values of the CCV are also acceptable with the exception of penetration. The applicability of the method can
Fig. 2. Calibration and validation results of processed cheeses for estimation of dry matter.
Fig. 3. Calibration and validation results of processed cheeses for estimation of fat.
urda, O. Kukackova / Journal of Food Engineering 61 (2004) 557–560 L. C
Fig. 4. Calibration and validation results of processed cheeses for estimation crude protein.
559
Fig. 6. Calibration and validation results of processed cheeses for estimation of penetration.
samples not included in calibration. It follows from t-test that there is no significant difference between NIR estimate and the result of reference method. The slope of the linear regression line for all constituents was close to the value 1; the intercept of the linear regression line for all constituents was close to the value 0. The significant differences were found for pH and penetration only.
4. Conclusions
Fig. 5. Calibration and validation results of processed cheeses for estimation of pH.
be assessed by cross-validation. It shows the deterioration of the correlation coefficient for pH and the penetration. The ratio SEP/SEC for pH and penetration indicates that the ruggedness for both these variables is low and there is a risk of higher difference between NIR estimate and the result of reference method for the
As it follows from obtained results NIR spectroscopy can be used for determination of the processed cheeses composition. Penetration and pH can be also estimated but with a lower precision. We can conclude from obtained results that NIR spectroscopy is very useful for at-line control of the processed cheeses manufacture. This method is rapid and non-destructive, no sample preparation is necessary. The application of NIR spectroscopy can improve the dairy production economy through the optimised laboratory efficiency, the increased product quality and the tighter production control.
Table 2 Calibration and the cross-validation results of the processed cheeses analysis by NIR spectroscopy Measured quantity DM [%] F [%] CP [%] pH P [mm]
Calibration
Cross-validation
R
SEC
CCV
R
SEP
PCV
0.999 0.996 0.998 0.984 0.992
0.361 0.888 0.216 0.033 0.434
0.85 3.66 1.56 0.58 5.32
0.998 0.995 0.996 0.945 0.925
0.429 0.997 0.303 0.062 1.330
1.01 4.12 2.15 1.07 16.29
T -test (tcrit ¼ 1:960) 0.011 0.002 0.005 0.005 0.002
DM––dry matter, F––fat, CP––crude protein, P––penetration, R––correlation coefficient, SEC––standard error of calibration, CCV––calibration coefficient of variation, SEP––standard error of prediction, PCV––prediction coefficient of variation.
560
urda, O. Kukackova / Journal of Food Engineering 61 (2004) 557–560 L. C
Acknowledgement This work was supported by Ministry of Education (CEZ: J19/98 223300005). References Adams, M. J., Latham, K., Barnett, N. W., & Poyton, A. J. (1999). Calibration models for determining moisture and fat content of processed cheese using near-infrared spectrometry. Journal of the Science of Food and Agriculture, 79, 1332–1336. urda, L., Kukackov tetina, & J., Divisova, I., (2000). Cheese C a, O., S analysis by NIR spectroscopy. In Proceedings of Syrotech, Z ilina, Slovakia.
urda, L., & Jindrich, J. (2000). Multivariate Kukackova, O., C calibration of raw cow milk using NIR spectroscopy. Czech Journal of Food Science, 18(1), 1–4. Massart, D. L., Vangdeginste, B. G. M., Deming, S. N., Michotte, Y., & Kaufman, L. (1988). Chemometrics: A textbook (p. 47). Amsterdam: Elsevier. Mark, H., & Workman, J. (1991). Statistics in spectroscopy (pp. 263– 302). San Diego: Academic Press. Molt, K., & Kohn, S. (1993). NIR-Spektroskopie–Chemometrie an Schmelzk€aseprodukten in der Qualit€atskontrolle und Prozessanalytik. Deutsche Milchwirtschaft, 44(22), 1102, pp. 1104– 1107. Williams, D. H., & Fleming, I. (1980). Spectroscopic methods in organic chemistry (3rd ed., pp. 35–73). London: McGrawHill.