A Novel Technique on Determining Water Content in Milk Using Radio-Frequency/Microwave Dielectric Spectroscopy and Chemometrics

Abstract

Water is the major component of milk. More water means less total solids concentration. Therefore, routine analysis on water content is very important in milk factory. To offer a new method for determining water content of milk in situ and in-line quality monitoring, dielectric spectroscopy, an instrumental method used to obtain spectra describing dielectric properties of materials, was used to determine the water content of milk in this study. The dielectric spectra of 161 milk samples were obtained at 201 discrete frequencies on a logarithmic scale from 20 to 4500 MHz at 25 °C. Ten, 34, and 14 optimal dielectric variables (ODVs) were extracted from the full dielectric spectra (FDS) with 402 dielectric variables by using successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and combination of CARS and SPA (CARS + SPA), respectively. Four models, including two linear models, i.e., multiple linear regression (MLR) and partial least squares regression (PLSR), and another two artificial neural network models, i.e., extreme learning machine (ELM) and least squares-support vector machine (LSSVM), were established. The artificial neural network models had better prediction performance than the linear models at the same ODV selection methods. Moreover, the ODVs extracted by CARS could give better prediction performance than SPA and CARS + SPA. Among all developed models, the FDS-LSSVM model had the lowest root mean squares error of prediction set (0.054%), followed by CARS-LSSVM with 0.094%. Few variables and high prediction precision of CARS-LSSVM have great potential in developing portable milk water detector used in situ and in-line inspection. This study indicated that dielectric spectroscopy is a promising approach for accurately determining the water content of milk.