Determination of Protein Content of Raw Fresh Cow’s Milk Using Dielectric Spectroscopy Combined with Chemometric Methods

To investigate the feasibility of dielectric spectroscopy in determining the protein content of raw fresh milk, the dielectric spectra of dielectric constant and loss factor were obtained on 145 raw cow’s milk samples at 201 discrete frequencies from 20 to 4500 MHz using a network analyzer and an open-ended coaxial-line probe. It was found that in below 1000 MHz, there was positive linear relationship between the loss factor and the protein content with a coefficient of determination (R ²) greater than 0.66. In order to identify the most accurate method for determining the protein content, 97 milk samples were selected for calibration set, and the other 48 samples were used as prediction set by using joint x–y distance sample set partitioning method. The standard normal variate method was used to preprocess spectra. Ten, 152, and 7 variables were extracted as characteristic variables using successive projection algorithm (SPA), uninformative variable elimination (UVE) method based on partial least squares, and SPA after UVE (UVE-SPA) methods, respectively. The results showed that applying SPA after UVE was helpful to extract indispensible characteristic variables from full dielectric spectra. The models based on the least squares supporting vector machine (LSSVM) offered the best performance at the same characteristic variable extraction method when compared with those established by partial least squares regression and extreme learning machine. The best model for determining the protein content of milk was LSSVM-UVE-SPA with [Formula: see text] of 0.865, root-mean-square error of prediction set (RMSEP) of 0.094, and residual predictive deviation (RPD) of 2.604. The results indicate that the protein content of milk could be determined precisely by using dielectric spectroscopy combined with chemometric methods. The study is helpful to explore a new milk protein sensor which could be used in situ or online measurement.