Paper
12 April 2005 On-line milk spectrometry: analysis of bovine milk composition
Kyle Spitzer, Rainer Kuennemeyer, Murray Woolford, Rod Claycomb
Author Affiliations +
Proceedings Volume 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics; (2005) https://doi.org/10.1117/12.621888
Event: Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, 2004, -, Singapore
Abstract
We present partial least squares (PLS) regressions to predict the composition of raw, unhomogenised milk using visible to near infrared spectroscopy. A total of 370 milk samples from individual quarters were collected and analysed on-line by two low cost spectrometers in the wavelength ranges 380-1100 nm and 900-1700 nm. Samples were collected from 22 Friesian, 17 Jersey, 2 Ayrshire and 3 Friesian-Jersey crossbred cows over a period of 7 consecutive days. Transmission spectra were recorded in an inline flowcell through a 0.5 mm thick milk sample. PLS models, where wavelength selection was performed using iterative PLS, were developed for fat, protein, lactose, and somatic cell content. The root mean square error of prediction (and correlation coefficient) for the nir and visible spectrometers respectively were 0.70%(0.93) and 0.91%(0.91) for fat, 0.65%(0.5) and 0.47%(0.79) for protein, 0.36%(0.49) and 0.45%(0.43) for lactose, and 0.50(0.54) and 0.48(0.51) for log10 somatic cells.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyle Spitzer, Rainer Kuennemeyer, Murray Woolford, and Rod Claycomb "On-line milk spectrometry: analysis of bovine milk composition", Proc. SPIE 5852, Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, (12 April 2005); https://doi.org/10.1117/12.621888
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KEYWORDS
Proteins

Calibration

Visible radiation

Near infrared

Data modeling

Spectrometers

Absorption

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