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Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds
- Bahri, A., Nawar, S., Selmi, H., Amraoui, M., Rouissi, H., Mouazen, A. M.
- Animal production science 2019 v.59 no.6 pp. 1190-1200
- algorithms, diet, ewe milk, ewes, faba beans, feeds, fiber optics, lactation, lactose, least squares, milk, milk fat, models, near-infrared spectroscopy, peas, prediction, principal component analysis, reflectance spectroscopy, spectrophotometers, standard deviation
- Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.