Quantitatively determining the total bacterial count of raw goat milk using dielectric spectra

Abstract

The objective of this study was to evaluate dielectric spectra as a means of quantitatively determining total bacterial count (TBC) of raw goat milk. The dielectric spectra, including dielectric constant (ε') spectra and dielectric loss factor (ε″) spectra, and TBC of 154 raw goat milk samples were measured using network analyzer and plate count methods, respectively. Owing to the poor linear relationship between TBC in logarithm and permittivities at a single frequency, chemometrics was used to reduce noise, identify outliers, select effective variables, and divide sample sets. Several linear models, such as multiple linear regression, ridge regression, and least absolute shrinkage and selection operator, were established to determine TBC based on the effective spectra of ε', ε″, and their combination (ε'+ε″). The results indicated that the models built using the spectra of ε'+ε″ and ε' had excellent TBC prediction performance. The best model was multiple linear regression based on ε'+ε″ spectra with the residual predictive deviation of 3.26. This study shows that the dielectric spectra had great potential to quantitatively and rapidly determine TBC of raw milk.