Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-23T22:50:49.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process

Published online by Cambridge University Press:  27 November 2012

Andreas Melfsen ,
Eberhard Hartung  and
Angelika Haeussermann
Andreas Melfsen*
Affiliation:
Christian-Albrechts-University Kiel, Institute of Agricultural Engineering, 24098, Kiel, Germany
Eberhard Hartung
Affiliation:
Christian-Albrechts-University Kiel, Institute of Agricultural Engineering, 24098, Kiel, Germany
Angelika Haeussermann
Affiliation:
Christian-Albrechts-University Kiel, Institute of Agricultural Engineering, 24098, Kiel, Germany
*
*For correspondence; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.

Keywords

On-line milk analysisraw milk quality controldiffuse reflectance NIRScalibration model robustness
Type
Research Article
Information
Journal of Dairy Research , Volume 80 , Issue 1 , February 2013 , pp. 103 - 112
Copyright
Copyright © Proprietors of Journal of Dairy Research 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aernouts, B, Polshin, E, Lammertyn, J & Saeys, W 2011 Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: reflectance or transmittance? Journal of Dairy Science 94 5315–5329Google Scholar
Brandt, M, Haeussermann, A & Hartung, E 2010 Invited review: technical solutions for analysis of milk constituents and abnormal milk. Journal of Dairy Science 93 427–436Google Scholar
Cattaneo, TMP, Cabassi, G, Profaizer, M & Giangiacomo, R 2009 Contribution of light scattering to near infrared absorption in milk. Journal of Near Infrared Spectroscopy 17 337–343Google Scholar
Chen, JY, Iyo, C, Terada, F & Kawano, S 2002 Effect of multiplicative scatter correction on wavelength selection for near infrared calibration to determine fat content in raw milk. Journal of Near Infrared Spectroscopy 10 301–307Google Scholar
Delwiche, SR & Norris, KH 1993 Classification of hard red wheat by near-infrared diffuse reflectance spectroscopy. Cereal Chemistry 70 29–35Google Scholar
Dhanoa, MS, Lister, SJ, France, J & Barnes, RJ 1999 Use of mean square prediction error analysis and reproducibility measures to study near infrared calibration equation performance. Journal of Near Infrared Spectroscopy 7 133–143Google Scholar
Friggens, NC, Ridder, C & Lovendahl, P 2007 On the use of milk composition measures to predict the energy balance of dairy cows. Journal of Dairy Science 90 5453–5467CrossRefGoogle ScholarPubMed
Guthrie, J, Wedding, B & Walsh, K 1998 Robustness of NIR calibrations for soluble solids in intact melon and pineapple. Journal of Near Infrared Spectroscopy 6 259–265CrossRefGoogle Scholar
Kawasaki, M, Kawamura, S, Nakatsuji, H & Natsuga, M 2005 Online real-time monitoring of milk quality during milking by near-infrared spectroscopy. ASAE Meeting Presentation, Paper Number: 053045Google Scholar
Kawasaki, M, Kawamura, S, Tsukahara, M, Morita, S, Komiya, M & Natsuga, M 2008 Near-infrared spectroscopic sensing system for on-line milk quality assessment in a milking robot. Computer and Electronics in Agriculture 63 22–27Google Scholar
McGlone, VA & Kawano, S 1998 Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biology and Technology 13 131–141Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012a Accuracy of milk composition analysis with near infrared spectroscopy in diffuse reflection mode. Biosystems Engineering 112 210–210CrossRefGoogle Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012b Accuracy of in-line milk composition analysis with diffuse reflectance near infrared spectroscopy. Journal of Dairy Science 95 6465–6476Google Scholar
Melfsen, A, Hartung, E & Haeussermann, A 2012c Potential of cow individual scatter correction for an improved accuracy of near infrared milk composition analysis. Journal of Near Infrared Spectroscopy 20 477–482CrossRefGoogle Scholar
Naes, T, Isaksson, T, Fearn, T & Davies, T 2004 A User-Friendly Guide To Multivariate Calibration And Classification. Chichester, UK: NIR PublicationsGoogle Scholar
Neitz, MH & Robertson, NH 1991 Composition Of Milk And Factors That Influence It. Department of Agriculture and Water Supply. Pretoria, South AfricaGoogle Scholar
Nielsen, NI, Larsen, T, Bjerring, M & Ingvartsen, KL 2005 Quarter health, milking interval, and sampling time during milking affect the concentration of milk constituents. Journal of Dairy Science 88 3186–3200Google Scholar
Peirs, A, Lammertyn, J, Ooms, K & Nicola, BM 2001 Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biology and Technology 21 189–199CrossRefGoogle Scholar
Peirs, A, Tirry, J, Verlinden, B, Darius, P & Nicola, BM 2003 Effect of biological variability on the robustness of NIR models for soluble solids content of apples. Postharvest Biology and Technology 28 269–280Google Scholar
Saranwong, S & Kawano, S 2008 System design for non-destructive near infrared analyses of chemical components and total aerobic bacteria count of raw milk. Journal of Near Infrared Spectroscopy 16 389–398Google Scholar
Schmilovitch, Z, Shmulevich, I, Notea, A & Maltz, E 2000 Near infrared spectrometry of milk in its heterogeneous state. Computers and Electronics in Agriculture 29 195–207Google Scholar
Sileoni, V, van den Berg, F, Marconi, O, Perretti, G & Fantozzi, P 2011 Internal and external validation strategies for the evaluation of long-term effects in NIR calibration models. Journal of Agricultural and Food Chemistry 59 1541–1547Google Scholar
Thomas, EV & Ge, N 2000 Development of robust multivariate calibration models. Technometrics 42 168–177CrossRefGoogle Scholar
Tsenkova, R, Atanassova, S, Toyoda, K, Ozaki, Y, Itoh, K & Fearn, T 1999 Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. Journal of Dairy Science 82 2344–2351Google Scholar
Tsenkova, R, Atanassova, S, Itoh, K, Ozaki, Y & Toyoda, K 2000 Near infrared spectroscopy for biomonitoring: Cow milk composition measurement in a spectral region from 1100 to 2 400 nanometers. Journal of Animal Science 78 515–522Google Scholar
Tsenkova, R, Atanassova, S, Ozaki, Y, Toyoda, K & Itoh, K 2001 Near-infrared spectroscopy for biomonitoring: influence of somatic cell count on cow's milk composition analysis. International Dairy Journal 11 779–783Google Scholar
Tsenkova, R, Atanassova, S, Morita, H, Ikuta, K, Toyoda, K, Iordanova, IK & Hakogi, E 2006 Near infrared spectra of cows' milk for milk quality evaluation: disease diagnosis and pathogen identification. Journal of Near Infrared Spectroscopy 14 363–370Google Scholar
Tsenkova, R, Meilina, H, Kuroki, S & Burns, DH 2009 Near infrared spectroscopy using short wavelengths and leave-one-cow-out cross-validation for quantification of somatic cells in milk. Journal of Near Infrared Spectroscopy 17 345–351Google Scholar
Wang, Y, Veltkamp, DJ & Kowalski, BR 1991 Multivariate instrument standardization. Analytical Chemistry 63 2750–2756Google Scholar
Williams, PC 2001 Implementation of near-infrared technology. In Near-Infrared Technology in the Agricultural and Food Industries, pp. 145–170. (Eds Williams, P & Norris, KH) St. Paul Minn: American Association of Cereal ChemistsGoogle Scholar
Williams, PC & Norris, K 2001 Variables affecting near-infrared spectroscopic analysis. In Near-Infrared Technology In The Agricultural And Food Industries, pp. 171–185. (Eds Williams, P & Norris), KHSt. Paul Minn: American Association of Cereal ChemistsGoogle Scholar