Literature DB >> 22959947

Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy.

A Melfsen1, E Hartung, A Haeussermann.   

Abstract

Knowledge of daily milk composition changes can assist in monitoring dairy cow health and can help to detect nutritional imbalances. An analytical tool offering the possibility of analyzing milk during the daily milking routine would provide such information. Near-infrared (NIR) spectroscopy can analyze multiple constituents in a given substrate at the same time. In this study, a special NIR in-line milk-analyzing device was designed, and its ability to predict the contents of fat, protein, lactose, and urea and the somatic cell count in milk during the milking process was evaluated. The NIR spectra were acquired with a diode array spectrometer in diffuse reflection in the wavelength range 851 to 1649 nm. The spectra originated from a total of 785 partial milkings out of 84 composite milkings. Corresponding subsamples of the composite milkings were used for reference analysis (n=785). Excellent validation results were obtained with regard to the coefficients of determination (R(2)=0.99, 0.98, and 0.92), and standard errors of prediction (0.09, 0.05, and 0.06) for fat (%), protein (%), and lactose (%), respectively. Satisfying results were achieved for urea content (mg/L) and logarithmically transformed SCC in milk, with R(2) of 0.82 and 0.85 and standard errors of prediction of 19.3 and 0.18, respectively. The accuracy of predicting protein, lactose, and urea contents was in accordance with international recommendations for reproducibility specified for in-line analytical devices.
Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22959947     DOI: 10.3168/jds.2012-5388

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  4 in total

1.  In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.

Authors:  Diana Giannuzzi; Lucio Flavio Macedo Mota; Sara Pegolo; Luigi Gallo; Stefano Schiavon; Franco Tagliapietra; Gil Katz; David Fainboym; Andrea Minuti; Erminio Trevisi; Alessio Cecchinato
Journal:  Sci Rep       Date:  2022-05-16       Impact factor: 4.996

2.  Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy.

Authors:  Jelena Muncan; Kyoko Tei; Roumiana Tsenkova
Journal:  Sensors (Basel)       Date:  2020-12-29       Impact factor: 3.576

3.  Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds.

Authors:  Mathias Bausewein; Rolf Mansfeld; Marcus G Doherr; Jan Harms; Ulrike S Sorge
Journal:  Animals (Basel)       Date:  2022-08-19       Impact factor: 3.231

Review 4.  Optical Methods Based on Ultraviolet, Visible, and Near-Infrared Spectra to Estimate Fat and Protein in Raw Milk: A Review.

Authors:  Abraham Gastélum-Barrios; Genaro M Soto-Zarazúa; Axel Escamilla-García; Manuel Toledano-Ayala; Gonzalo Macías-Bobadilla; Daniel Jauregui-Vazquez
Journal:  Sensors (Basel)       Date:  2020-06-13       Impact factor: 3.576

  4 in total

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