Determining the Fat Concentration of Fresh Raw Cow Milk Using Dielectric Spectroscopy Combined with Chemometrics

Dielectric relaxation parameters combing raw milk compositions to improve the prediction performance of milk somatic cell count

BACKGROUND

Milk somatic cell count (SCC) is an international standard for identifying mastitis in dairy cows and measuring raw milk quality. Milk SCC can be predicted based on dielectric relaxation parameters (DRPs). We noted a high correlation between DRPs and the milk composition content (MCC), and so we hypothesized that combining DRPs with MCC could improve the prediction accuracy of milk SCC. The present study aimed to analyze the relationship between milk SCC, DRPs and MCC, as well as to investigate the potential of combining DRPs with MCC to improve the prediction accuracy of milk SCC.

RESULTS

The dielectric spectra (20-4500 MHz) of 276 milk samples were measured, and their DRPs (εl, εh, Δε, τ and σ) were solved by the modified Debye equation. The SCC prediction models were developed using dielectric full spectra, DRPs and DRPs combined with MCC. The results showed the correlations between DRPs (εl, εh, Δε and σ) and MCC (fat, protein, lactose and total solids) were high, and SCC exhibited a non-linear relationship with DRPs and MCC. The 5DRPs + MCC-generalized regression neural network model had the best prediction, with a standard error of prediction for prediction of 0.143 log SCC mL-1 and residual of the prediction bias of 2.870, which was superior to the models based on full spectra, DRPs and near-infrared or visible/near-infrared.

CONCLUSION

The present study has improved the prediction accuracy of milk SCC based on the DRPs combing MCC and provides a new method for dairy farming and milk quality assessment. © 2024 Society of Chemical Industry.

Recent developments on rapid detection of main constituents in milk: a review

Milk is a good source of quality fats, proteins, carbohydrates, minerals, and vitamins. Determining milk constituents is very important in dairy production and is usually conducted by means of physical or chemical processes in laboratories. These methods are time-consuming and cannot satisfy the need in practice. Developing simple, quick, cost-effective, reliable, and sensitive methods on the detection of main constituents in milk is useful for dairy farmers, manufacturers and consumers. In last decades, many rapid detection techniques such as chromatography, spectroscopy, dielectric properties, and sensors, have emerged and shown great potential in the detection of main constituents in liquid milk. In this review, the rapid detection techniques applied to determine the main constituents in milk have been reviewed. Meanwhile, the potential advantages and limitations of these techniques and recommendations for future research have also been proposed.

Evaluation of the quality of local butters: A new approach based on Raman spectroscopy and supported by the classical pycnometer method

In this study, the quality of the local Romanian butters was investigated using the classical pycnometer and optic microscopy methods, combined with Raman spectroscopy. We used a pool of 10 samples with different characteristics, and analyzed them by the three aforementioned methods. Pycnometric measurements showed a direct correlation between the fat content and the density values of the samples. Raman spectroscopy validated the results from the pycnometric measurements and the optical microscopy and indicated several other properties, such as protein content, hydration, saturation level of the polycarbonate chains, as well as the total cis isomer content and the type of arrangement preferred by the aliphatic chains (polymorphic transition). The methods employed in the present study have a strong potential to become analytical tools for the food industry and food safety agencies in order to assess the quality of butters and margarines, in a fast and cost-effective manner.

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

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.