A portable detector on main compositions of raw and homogenized milk

Fat, protein, lactose and total solids are important indicators in evaluating the nutritional and commercial values of milk. To make the main compositions of milk be detected rapidly and conveniently, and to realize the detection in field or on-site, a portable detector on main compositions of milk was developed. The detector mainly consisted of a visible/near-infrared micro-spectrometer (650–1100 nm), a Raspberry Pi board, a micro-halogen lamp, a self-made “Y” shape optical fiber probe, a lithium battery and an organic light emitting diode display. The software of the detector was developed by using Python language to realize calibration, diffuse reflectance spectral collection, spectral pretreatment and calculation on main compositions of milk, i.e, fat, protein, lactose and total solids. One hundred and twenty raw milk and their homogenized milk were used as samples to establish the partial least squares regression (PLSR) models to predict the main compositions. The effect of several spectral preprocessing methods on prediction performance of established models were evaluated, and the results indicated that Savitzky-Golay smoothing was the best spectral pretreatment method for raw milk and Savitzky-Golay (S-G) smoothing combined with standard normal variate was the best method for homogenized milk. The precision of the detector were validated by another batch of 40 raw milk and their homogenized milk samples. The validation results showed that the root-mean-squares error of the explored detector for fat, protein, lactose and total solids contents, respectively, were 0.14%, 0.14%, 0.08% and 0.27% for raw milk, and were 0.10%, 0.12%, 0.08% and 0.21% for homogenized milk. The detector was just about 1 kg in weight and the measurement time for one sample was less than 3 s.