Literature DB >> 21962352

Mid-infrared spectrometry of milk for dairy metabolomics: a comparison of two sampling techniques and effect of homogenization.

Ben Aernouts1, Evgeny Polshin, Wouter Saeys, Jeroen Lammertyn.   

Abstract

Milk production is a dominant factor in the metabolism of dairy cows involving a very intensive interaction with the blood circulation. As a result, the extracted milk contains valuable information on the metabolic status of the cow. On-line measurement of milk components during milking two or more times a day would promote early detection of systemic and local alterations, thus providing a great input for strategic and management decisions. The objective of this study was to investigate the potential of mid-infrared (mid-IR) spectroscopy to measure the milk composition using two different measurement modes: micro attenuated total reflection (μATR) and high throughput transmission (HTT). Partial least squares (PLS) regression was used for prediction of fat, crude protein, lactose and urea after preprocessing IR data and selecting the most informative wavenumber variables. The prediction accuracies were determined separately for raw and homogenized copies of a wide range of milk samples in order to estimate the possibility for on-line analysis of the milk. In case of fat content both measurement modes resulted in an excellent prediction for homogenized samples (R(2)>0.92) but in poor results for raw samples (R(2)<0.70). Homogenization was however not mandatory to achieve good predictions for crude protein and lactose with both μATR and HTT, and urea with μATR spectroscopy. Excellent results were obtained for prediction of crude protein, lactose and urea content (R(2)>0.99, 0.98 and 0.86 respectively) in raw and homogenized milk using μATR IR spectroscopy. These results were significantly better than those obtained by HTT IR spectroscopy. However, the prediction performance of HTT was still good for crude protein and lactose content (R(2)>0.86 and 0.78 respectively) in raw and homogenized samples. However, the detection of urea in milk with HTT spectroscopy was significantly better (R(2)=0.69 versus 0.16) after homogenization of the milk samples. Based on these observations it can be concluded that μATR approach is most suitable for rapid at line or even on-line milk composition measurement, although homogenization is crucial to achieve good prediction of the fat content.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21962352     DOI: 10.1016/j.aca.2011.04.018

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  5 in total

1.  Comparative appraisal of ghee and common vegetable oils for spectral characteristics in FT-MIR reflectance spectroscopy.

Authors:  Bency Antony; Bhavbhuti M Mehta; Saurabh Sharma; K Ratnam; K D Aparnathi
Journal:  J Food Sci Technol       Date:  2018-06-18       Impact factor: 2.701

2.  Comparison of the Potential Abilities of Three Spectroscopy Methods: Near-Infrared, Mid-Infrared, and Molecular Fluorescence, to Predict Carotenoid, Vitamin and Fatty Acid Contents in Cow Milk.

Authors:  Julien Soulat; Donato Andueza; Benoît Graulet; Christiane L Girard; Cyril Labonne; Abderrahmane Aït-Kaddour; Bruno Martin; Anne Ferlay
Journal:  Foods       Date:  2020-05-06

Review 3.  Mass spectrometry-based metabolomics of volatiles as a new tool for understanding aroma and flavour chemistry in processed food products.

Authors:  Carmen Diez-Simon; Roland Mumm; Robert D Hall
Journal:  Metabolomics       Date:  2019-03-12       Impact factor: 4.290

4.  Identification of milk quality and adulteration by surface-enhanced infrared absorption spectroscopy coupled to artificial neural networks using citrate-capped silver nanoislands.

Authors:  Sherif M Eid; Sherine El-Shamy; Mohamed A Farag
Journal:  Mikrochim Acta       Date:  2022-07-29       Impact factor: 6.408

5.  Changes in the Milk Metabolome of the Giant Panda (Ailuropoda melanoleuca) with Time after Birth--Three Phases in Early Lactation and Progressive Individual Differences.

Authors:  Tong Zhang; Rong Zhang; Liang Zhang; Zhihe Zhang; Rong Hou; Hairui Wang; I Kati Loeffler; David G Watson; Malcolm W Kennedy
Journal:  PLoS One       Date:  2015-12-02       Impact factor: 3.240

  5 in total

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