Determination of fat and total protein content in milk using conventional digital imaging.

The applicability of conventional digital imaging to quantitative determination of fat and total protein in cow's milk, based on the phenomenon of light scatter, has been proved. A new algorithm for extracting features from digital images of milk samples has been developed. The algorithm takes into account spatial distribution of light, diffusely transmitted through a sample. The proposed method has been tested on two sample sets prepared from industrial raw milk standards, with variable fat and protein content. Partial Least-Squares (PLS) regression on the features calculated from images of monochromatically illuminated milk samples resulted in models with high prediction performance when analysed the sets separately (best models with cross-validated R(2)=0.974 for protein and R(2)=0.973 for fat content). However when analysed the sets jointly with the obtained results were significantly worse (best models with cross-validated R(2)=0.890 for fat content and R(2)=0.720 for protein content). The results have been compared with previously published Vis/SW-NIR spectroscopic study of similar samples.

[1]  T Fearn,et al.  Near-infrared spectroscopy for dairy management: measurement of unhomogenized milk composition. , 1999, Journal of dairy science.

[2]  Rudolf W. Kessler,et al.  Quantitative determination of fat and total protein in milk based on visible light scatter , 2012 .

[3]  Frans van den Berg,et al.  Review of the most common pre-processing techniques for near-infrared spectra , 2009 .

[4]  P. Williams,et al.  Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds , 1993 .

[5]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[6]  Kim H. Esbensen,et al.  Monitoring of pellet coating process with image analysis—a feasibility study , 2010 .

[7]  R. Vreeker,et al.  Phase separation in milk protein and amylopectin mixtures , 2002 .

[8]  J. Macgregor,et al.  Image texture analysis: methods and comparisons , 2004 .

[9]  Sergey Kucheryavski,et al.  Extracting useful information from images , 2011 .

[10]  J. Lammertyn,et al.  Visible and near-infrared spectroscopic analysis of raw milk for cow health monitoring: reflectance or transmittance? , 2011, Journal of dairy science.

[11]  Jeppe Revall Frisvad,et al.  Computing the scattering properties of participating media using Lorenz-Mie theory , 2007, ACM Trans. Graph..

[12]  Gerald Muschiolik,et al.  Automated image analysis as a control tool for multiple emulsions , 2005 .

[13]  M. Pinar Mengüç,et al.  Fiber optic sensor response to low levels of fat in skim milk. , 2000 .

[14]  Qi Xin,et al.  The rapid determination of fat and protein content in fresh raw milk using the laser light scattering technology , 2006 .

[15]  A. Bogomolov,et al.  Fat Globule Size Effect on Visible and Shortwave near Infrared Spectra of Milk , 2013 .

[16]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[17]  U. S. Ashworth.  Turbidimetric methods for measuring fat content of homogenized milk. , 1969 .

[18]  A. Bogomolov,et al.  Scatter-based quantitative spectroscopic analysis of milk fat and total protein in the region 400-1100nm in the presence of fat globule size variability , 2013 .