Efficacy of near infrared spectroscopy to segregate raw milk from individual cows between herds for product innovation and traceability
Main Article Content
Keywords
milk segregation, near infrared spectroscopy, partial least square, soft independent modelling
Abstract
Cows with specialised characteristics and requirements can be aggregated into different herds for targeted nutri-tional management and to facilitate on-farm segregation of raw milk for the production of high-value niche dairy products, offering improved economic returns. Rapid methods for independent verification of product quality and origin are desirable to support validation and traceability of such products. This study examined the use of near infrared spectroscopy (NIRS) to segregate raw milk from individual cows of multiple breeds from different herds fed on the same or differing feeding regimes, and to correlate and evaluate the efficacy of the predictions for crude protein and the milk fatty acid (FA) phenotypes for each of the herds. Reference values and near infrared spectra were obtained from representative freeze-dried raw milk samples (n = 220) collected from 847 lactating cows of 3 breeds from the Lincoln University dairy farm in New Zealand. The feed sources (i.e. pasture or pasture with lucerne silage) significantly influenced the protein and the FA values, and these differences were reflected in NIRS analyses. The partial least square regression models for crude protein determination showed excellent results, whereas for the most dominant FA, they were not appreciable. Maximum separation was obtained between the herds on the same feeding regime (mean specificity = 95.2%) using the partial least square discriminant analysis, and its overall performance in differentiating the objects was better than that of the soft independent modelling of class analogy. The multiclass analyses conducted in this study offer improvements to current approaches for evaluating and validating raw milk for the manufacture of specific dairy products, and for enhancing product traceability.
References
Andueza, D., Agabriel, C., Constant, I., Lucas, A. and Martin, B., 2013. Using visible or near infrared spectroscopy (NIRS) on cheese to authenticate cow feeding regimes. Food Chemistry 141: 209–214. https://doi.org/10.1016/j.foodchem.2013.02.086
Ballabio, D. and Todeschini, R., 2009. Multivariate classification for qualitative analysis. Infrared spectroscopy for food quality analysis and control 83: e102. https://doi.org/10.1016/ B978-0-12-374136-3.00004-3
Barker, M. and Rayens, W., 2003. Partial least squares for discrimination. Journal of Chemometrics: A Journal of the Chemometrics Society 17: 166–173. https://doi.org/10.1002/cem.785
Chan, Y., 2003. Biostatistics 104: correlational analysis. Singapore Medical Journal 44: 614–619.
Collomb, M., Bütikofer, U., Sieber, R., Jeangros, B. and Bosset, J.-O., 2002. Composition of fatty acids in cow’s milk fat produced in the lowlands, mountains and highlands of Switzerland using high-resolution gas chromatography. International Dairy Journal 12: 649–659. https://doi.org/10.1016/S0958-6946(02)00061-4
Coppa, M., Martin, B., Agabriel, C., Chassaing, C., Sibra, C., Constant, I., Graulet, B. and Andueza, D., 2012. Authentication of cow feed-ing and geographic origin on milk using visible and near-infrared spectroscopy. Journal of Dairy Science 95: 5544–5551. https://doi. org/10.3168/jds.2011-5272
De la Roza-Delgado, B., Garrido-Varo, A., Soldado, A., Arrojo, A.G., Valdés, M.C., Maroto, F. and Pérez-Marín, D., 2017. Matching portable NIRS instruments for in situ monitoring indicators of milk composition. Food Control 76: 74–81. https://doi.org/10.1016/j. foodcont.2017.01.004
Dierking, R., Kallenbach, R. and Roberts, C., 2010. Fatty acid profiles of orchardgrass, tall fescue, perennial ryegrass, and alfalfa. Crop Science 50: 391–402. https://doi.org/10.2135/cropsci2008.12.0741
Dooley, A., Parker, W., Blair, H. and Hurley, E., 2005. Implications of on-farm segregation for valuable milk characteristics. Agricultural Systems 85: 82–97. https://doi.org/10.1016/j.agsy.2004.07.012
Ejeahalaka, K.K. and On, S.L., 2019a. Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: implications for quality assurance. Food Chemistry 295: 198–205. https:// doi.org/10.1016/j.foodchem.2019.05.120
Ejeahalaka, K.K. and On, S.L., 2019b. Effective detection and quantification of chemical adulterants in model fat-filled milk powders using NIRS and hierarchical modelling strategies. Food Chemistry: 125785. https://doi.org/10.1016/j. foodchem.2019.125785
Fleming, A., Schenkel, F., Chen, J., Malchiodi, F., Bonfatti, V., Ali, R., Mallard, B., Corredig, M. and Miglior, F., 2017. Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets. Journal of Dairy Science 100: 5073–5081. https://doi.org/10.3168/ jds.2016-12102
Frankhuizen, R., 2001. NIR analysis of dairy products. In: Ciurczak, E.W. and Burns D.A. (eds.), Handbook of near-infrared analysis. CRC Press, New York, NY, USA, pp. 499–535.
Heinrichs, J., Jones, C. and Bailey, K., 1997. Milk components: understanding the causes and importance of milk fat and protein variation in your dairy herd. Dairy Animal Science 5: 1e–8e.
Hurtaud, C., Dutreuil, M., Coppa, M., Agabriel, C. and Martin, B., 2014. Characterization of milk from feeding systems based on herbage or corn silage with or without flaxseed and authentication through fatty acid profile. Dairy Science & Technology 94: 103–123. https://doi.org/10.1007/s13594-013-0147-0
Karoui, R., Mouazen, A.M., Dufour, É., Pillonel, L., Picque, D., Bosset, J.-O. and De Baerdemaeker, J., 2006. Mid-infrared spectrometry: a tool for the determination of chemical parameters in Emmental cheeses produced during winter. Le Lait 86: 83–97. https://doi.org/10.1051/lait:2005040
Katz, G., Merin, U., Bezman, D., Lavie, S., Lemberskiy-Kuzin, L. and Leitner, G., 2016. Real-time evaluation of individual cow milk for higher cheese-milk quality with increased cheese yield. Journal of Dairy Science 99: 4178–4187. https://doi.org/10.3168/ jds.2015-10599
Lavine, B. and Workman, J.J., 2004. Chemometrics. Analytical Chemistry 76: 3365–3371. https://doi.org/10.1021/ac040053p
Marchitelli, C., Contarini, G., De Matteis, G., Crisà, A., Pariset, L., Scatà, M.C., Catillo, G., Napolitano, F. and Moioli, B., 2013. Milk fatty acid variability: effect of some candidate genes involved in lipid synthesis. Journal of Dairy Research 80: 165–173. https:// doi.org/10.1017/S002202991300006X
Martens, H. and Stark, E., 1991. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 9: 625–635. https://doi. org/10.1016/0731-7085(91)80188-F
Martin, B., Fedele, V., Ferlay, A., Grolier, P., Rock, E., Gruffat, D. and Chilliard, Y., 2004. Effects of grass-based diets on the content of micronutrients and fatty acids in bovine and caprine dairy products, Land use systems in grassland dominated regions. Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, Switzerland, 21–24 June 2004. vdf Hochschulverlag AG an der ETH Zurich, pp. 876–886.
Meilgaard, M.C., Carr, B.T. and Civille, G.V., 1999. Sensory evaluation techniques, 3rd edition. CRC Press; Taylor and Francis, New York, USA.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50: 885–900. https://doi.org/10.13031/2013.23153
Mouazen, A., Dridi, S., Rouissi, H., De Baerdemaeker, J. and Ramon, H., 2009. Prediction of selected ewe’s milk properties and differentiating between pasture and box feeding using visible and near infrared spectroscopy. Biosystems Engineering 104: 353–361. https://doi.org/10.1016/j.biosystemseng.2009.08.001
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L. and Engelsen, S.B., 2000. Interval Partial Least-Squares Regression (i PLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54: 413–419. https://doi.org/10.1366/0003702001949500
Núñez-Sánchez, N., Martínez-Marín, A., Polvillo, O., Fernández-Cabanás, V., Carrizosa, J., Urrutia, B. and Serradilla, J., 2016. Near infrared spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats. Food Chemistry 190: 244–252. https:// doi.org/10.1016/j.foodchem.2015.05.083
Oliveri, P., 2017. Class-modelling in food analytical chemistry: development, sampling, optimisation and validation issues–a tutorial. Analytica Chimica Acta 982: 9–19. https://doi.org/10.1016/j. aca.2017.05.013
Oliveri, P. and Downey, G., 2012. Multivariate class modeling for the verification of food-authenticity claims. Trends in Analytical Chemistry 35: 74–86. https://doi.org/10.1016/j.trac.2012.02.005
Powers, D.M., 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies 2: 37–63.
R Core Team, 2018. R: a language and environment for statistical computing. Available at: http://www.R-project.org.
Rugoho, I., Liu, Y. and Dewhurst, R., 2014. Analysis of major fatty acids in milk produced from high-quality grazed pasture. New Zealand Journal of Agricultural Research 57: 165–179. https:// doi.org/10.1080/00288233.2014.899505
Savitzky, A. and Golay, M.J., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36: 1627–1639. https://doi.org/10.1021/ac60214a047
Tsenkova, R., Atanassova, S., Itoh, K., Ozaki, Y. and Toyoda, K., 2000. Near infrared spectroscopy for biomonitoring: cow milk com-position measurement in a spectral region from 1,100 to 2,400 nanometers. Journal of Animal Science 78: 515–522. https://doi. org/10.2527/2000.783515x
Wishart, D.S., 2007. Current progress in computational metabolomics. Briefings in bioinformatics 8. Academic Press, Inc., New York, pp. 279–293. https://doi.org/10.1093/bib/bbm030
Wold, H., 1966. Estimation of principal components and related models by iterative least squares. Multivariate Analysis. Academic Press, Inc., New York, pp. 391–420.
Wold, S., Ruhe, A., Wold, H. and Dunn, I., WJ, 1984. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing 5: 735–743. https://doi. org/10.1137/0905052
Wold, S. and Sjostrom, M., 1977. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. Chemometrics: Theory and Application 52: 243–282. https://doi.org/10.1021/ bk-1977-0052.ch012
Woodward, S., Waghorn, G., Attwood, G. and Li, D., 2010. Ryegrass to lucerne-effects of dietary change on intake, milk yield and rumen microflora bacteria of dairy cows, Proceedings of the New Zealand Society of Animal Production. New Zealand Society of Animal Production, pp. 57–61. 23 June – 25 June 2010, Palmerston North, New Zealand.
Zimmermann, B. and Kohler, A., 2013. Optimizing Savitzky-Golay parameters for improving spectral resolution and quantification in infrared spectroscopy. Applied Spectroscopy 67: 892–902. https://doi.org/10.1366/12-06723
Ballabio, D. and Todeschini, R., 2009. Multivariate classification for qualitative analysis. Infrared spectroscopy for food quality analysis and control 83: e102. https://doi.org/10.1016/ B978-0-12-374136-3.00004-3
Barker, M. and Rayens, W., 2003. Partial least squares for discrimination. Journal of Chemometrics: A Journal of the Chemometrics Society 17: 166–173. https://doi.org/10.1002/cem.785
Chan, Y., 2003. Biostatistics 104: correlational analysis. Singapore Medical Journal 44: 614–619.
Collomb, M., Bütikofer, U., Sieber, R., Jeangros, B. and Bosset, J.-O., 2002. Composition of fatty acids in cow’s milk fat produced in the lowlands, mountains and highlands of Switzerland using high-resolution gas chromatography. International Dairy Journal 12: 649–659. https://doi.org/10.1016/S0958-6946(02)00061-4
Coppa, M., Martin, B., Agabriel, C., Chassaing, C., Sibra, C., Constant, I., Graulet, B. and Andueza, D., 2012. Authentication of cow feed-ing and geographic origin on milk using visible and near-infrared spectroscopy. Journal of Dairy Science 95: 5544–5551. https://doi. org/10.3168/jds.2011-5272
De la Roza-Delgado, B., Garrido-Varo, A., Soldado, A., Arrojo, A.G., Valdés, M.C., Maroto, F. and Pérez-Marín, D., 2017. Matching portable NIRS instruments for in situ monitoring indicators of milk composition. Food Control 76: 74–81. https://doi.org/10.1016/j. foodcont.2017.01.004
Dierking, R., Kallenbach, R. and Roberts, C., 2010. Fatty acid profiles of orchardgrass, tall fescue, perennial ryegrass, and alfalfa. Crop Science 50: 391–402. https://doi.org/10.2135/cropsci2008.12.0741
Dooley, A., Parker, W., Blair, H. and Hurley, E., 2005. Implications of on-farm segregation for valuable milk characteristics. Agricultural Systems 85: 82–97. https://doi.org/10.1016/j.agsy.2004.07.012
Ejeahalaka, K.K. and On, S.L., 2019a. Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: implications for quality assurance. Food Chemistry 295: 198–205. https:// doi.org/10.1016/j.foodchem.2019.05.120
Ejeahalaka, K.K. and On, S.L., 2019b. Effective detection and quantification of chemical adulterants in model fat-filled milk powders using NIRS and hierarchical modelling strategies. Food Chemistry: 125785. https://doi.org/10.1016/j. foodchem.2019.125785
Fleming, A., Schenkel, F., Chen, J., Malchiodi, F., Bonfatti, V., Ali, R., Mallard, B., Corredig, M. and Miglior, F., 2017. Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets. Journal of Dairy Science 100: 5073–5081. https://doi.org/10.3168/ jds.2016-12102
Frankhuizen, R., 2001. NIR analysis of dairy products. In: Ciurczak, E.W. and Burns D.A. (eds.), Handbook of near-infrared analysis. CRC Press, New York, NY, USA, pp. 499–535.
Heinrichs, J., Jones, C. and Bailey, K., 1997. Milk components: understanding the causes and importance of milk fat and protein variation in your dairy herd. Dairy Animal Science 5: 1e–8e.
Hurtaud, C., Dutreuil, M., Coppa, M., Agabriel, C. and Martin, B., 2014. Characterization of milk from feeding systems based on herbage or corn silage with or without flaxseed and authentication through fatty acid profile. Dairy Science & Technology 94: 103–123. https://doi.org/10.1007/s13594-013-0147-0
Karoui, R., Mouazen, A.M., Dufour, É., Pillonel, L., Picque, D., Bosset, J.-O. and De Baerdemaeker, J., 2006. Mid-infrared spectrometry: a tool for the determination of chemical parameters in Emmental cheeses produced during winter. Le Lait 86: 83–97. https://doi.org/10.1051/lait:2005040
Katz, G., Merin, U., Bezman, D., Lavie, S., Lemberskiy-Kuzin, L. and Leitner, G., 2016. Real-time evaluation of individual cow milk for higher cheese-milk quality with increased cheese yield. Journal of Dairy Science 99: 4178–4187. https://doi.org/10.3168/ jds.2015-10599
Lavine, B. and Workman, J.J., 2004. Chemometrics. Analytical Chemistry 76: 3365–3371. https://doi.org/10.1021/ac040053p
Marchitelli, C., Contarini, G., De Matteis, G., Crisà, A., Pariset, L., Scatà, M.C., Catillo, G., Napolitano, F. and Moioli, B., 2013. Milk fatty acid variability: effect of some candidate genes involved in lipid synthesis. Journal of Dairy Research 80: 165–173. https:// doi.org/10.1017/S002202991300006X
Martens, H. and Stark, E., 1991. Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical and Biomedical Analysis 9: 625–635. https://doi. org/10.1016/0731-7085(91)80188-F
Martin, B., Fedele, V., Ferlay, A., Grolier, P., Rock, E., Gruffat, D. and Chilliard, Y., 2004. Effects of grass-based diets on the content of micronutrients and fatty acids in bovine and caprine dairy products, Land use systems in grassland dominated regions. Proceedings of the 20th General Meeting of the European Grassland Federation, Luzern, Switzerland, 21–24 June 2004. vdf Hochschulverlag AG an der ETH Zurich, pp. 876–886.
Meilgaard, M.C., Carr, B.T. and Civille, G.V., 1999. Sensory evaluation techniques, 3rd edition. CRC Press; Taylor and Francis, New York, USA.
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50: 885–900. https://doi.org/10.13031/2013.23153
Mouazen, A., Dridi, S., Rouissi, H., De Baerdemaeker, J. and Ramon, H., 2009. Prediction of selected ewe’s milk properties and differentiating between pasture and box feeding using visible and near infrared spectroscopy. Biosystems Engineering 104: 353–361. https://doi.org/10.1016/j.biosystemseng.2009.08.001
Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L. and Engelsen, S.B., 2000. Interval Partial Least-Squares Regression (i PLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 54: 413–419. https://doi.org/10.1366/0003702001949500
Núñez-Sánchez, N., Martínez-Marín, A., Polvillo, O., Fernández-Cabanás, V., Carrizosa, J., Urrutia, B. and Serradilla, J., 2016. Near infrared spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats. Food Chemistry 190: 244–252. https:// doi.org/10.1016/j.foodchem.2015.05.083
Oliveri, P., 2017. Class-modelling in food analytical chemistry: development, sampling, optimisation and validation issues–a tutorial. Analytica Chimica Acta 982: 9–19. https://doi.org/10.1016/j. aca.2017.05.013
Oliveri, P. and Downey, G., 2012. Multivariate class modeling for the verification of food-authenticity claims. Trends in Analytical Chemistry 35: 74–86. https://doi.org/10.1016/j.trac.2012.02.005
Powers, D.M., 2011. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies 2: 37–63.
R Core Team, 2018. R: a language and environment for statistical computing. Available at: http://www.R-project.org.
Rugoho, I., Liu, Y. and Dewhurst, R., 2014. Analysis of major fatty acids in milk produced from high-quality grazed pasture. New Zealand Journal of Agricultural Research 57: 165–179. https:// doi.org/10.1080/00288233.2014.899505
Savitzky, A. and Golay, M.J., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36: 1627–1639. https://doi.org/10.1021/ac60214a047
Tsenkova, R., Atanassova, S., Itoh, K., Ozaki, Y. and Toyoda, K., 2000. Near infrared spectroscopy for biomonitoring: cow milk com-position measurement in a spectral region from 1,100 to 2,400 nanometers. Journal of Animal Science 78: 515–522. https://doi. org/10.2527/2000.783515x
Wishart, D.S., 2007. Current progress in computational metabolomics. Briefings in bioinformatics 8. Academic Press, Inc., New York, pp. 279–293. https://doi.org/10.1093/bib/bbm030
Wold, H., 1966. Estimation of principal components and related models by iterative least squares. Multivariate Analysis. Academic Press, Inc., New York, pp. 391–420.
Wold, S., Ruhe, A., Wold, H. and Dunn, I., WJ, 1984. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing 5: 735–743. https://doi. org/10.1137/0905052
Wold, S. and Sjostrom, M., 1977. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. Chemometrics: Theory and Application 52: 243–282. https://doi.org/10.1021/ bk-1977-0052.ch012
Woodward, S., Waghorn, G., Attwood, G. and Li, D., 2010. Ryegrass to lucerne-effects of dietary change on intake, milk yield and rumen microflora bacteria of dairy cows, Proceedings of the New Zealand Society of Animal Production. New Zealand Society of Animal Production, pp. 57–61. 23 June – 25 June 2010, Palmerston North, New Zealand.
Zimmermann, B. and Kohler, A., 2013. Optimizing Savitzky-Golay parameters for improving spectral resolution and quantification in infrared spectroscopy. Applied Spectroscopy 67: 892–902. https://doi.org/10.1366/12-06723
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