A rapid method on identifying disqualified raw goat's milk based on total bacterial count by using dielectric spectra

Footprint analysis of CO2 in microbial community succession of raw milk and assessment of its quality

With the growing production of raw milk, interest has been increasing in its quality control. CO2, as a cold processing additive, has been studied to extend the cold storage period and improve the quality of raw milk. However, it is yet uncertain how representative microbial species and biomarkers can succeed one another at distinct critical periods during refrigeration. Therefore, the effects of CO2 treatment on the succession footprint of the microbial community and changes in quality during the period of raw milk chilling were examined by 16S rRNA analysis combined with electronic nose, and electronic tongue techniques. The results indicated that, the refrigeration time was shown to be prolonged by CO2 in a concentration-dependent way. And CO2 treatment was linked to substantial variations in beta and alpha diversity as well as the relative abundances of various microbial taxa (p < 0.01). The dominant bacterial phylum Proteobacteria was replaced with Firmicutes, while the major bacterial genera Acinetobacter and Pseudomonas were replaced with lactic acid bacteria (LAB), including Leuconostoc, Lactococcus, and Lactobacillus. From the perspective of biomarkers enriched in CO2-treated sample, almost all of them belong to LAB, no introduction of harmful toxins has been found. The assessment of the quality of raw milk revealed that CO2 improved the quality of raw milk by lowering the acidity and the rate of protein and fat breakdown, and improved the flavor by reducing the generation of volatiles, and increasing umami, richness, milk flavor and sweetness, but reducing sourness. These findings offer a new theoretical foundation for the industrial use of CO2 in raw milk.

Correlation Analysis of Microbial Contamination and Alkaline Phosphatase Activity in Raw Milk and Dairy Products

Microbial contamination in raw milk and dairy products can detrimentally affect product quality and human health. In this study, the aerobic plate count, aerobic Bacillus abundance, thermophilic aerobic Bacillus abundance, and alkaline phosphatase activity were determined in 435 raw milk, 451 pasteurized milk, and 617 sterilized milk samples collected from 13 Chinese provinces (or municipalities). Approximately 9.89% and 2.22% of raw milk and pasteurized milk samples exceeded the threshold values for the aerobic plate count, respectively. The proportions of aerobic Bacillus in raw milk, pasteurized milk, and sterilized milk were 54.02%, 14.41%, and 1.30%, respectively. The proportions of thermophilic aerobic Bacillus species were 7.36% in raw milk and 4.88% in pasteurized milk samples, and no bacteria were counted in sterilized milk. Approximately 36.18% of raw milk samples contained >500,000 mU/L of alkaline phosphatase activity, while 9.71% of pasteurized milk samples contained >350 mU/L. For raw milk, there was a positive correlation between the aerobic plate count, the aerobic Bacillus abundance, and the alkaline phosphatase activity, and there was a positive correlation between the aerobic Bacillus abundance, the thermophilic aerobic Bacillus count, and the alkaline phosphatase activity. For pasteurized milk, there was a positive correlation between the aerobic plate count, the aerobic Bacillus abundance, and the thermophilic aerobic Bacillus count; however, the alkaline phosphatase activity had a negative correlation with the aerobic plate count, the aerobic Bacillus abundance, and the thermophilic aerobic Bacillus abundance. These results facilitate the awareness of public health safety issues and the involvement of dairy product regulatory agencies in China.

Qualitative identification of mature milk adulteration in bovine colostrum using noise-reduced dielectric spectra and linear model

BACKGROUND

The rapid and accurate identification of colostrum, a strong non-homogeneous food, remains a challenge. In the present study, the dielectric spectra including the dielectric constant (ε') and loss factor (ε″) of 154 colostrum samples adulterated with 0-50% mature milk were measured from 20 to 4500 MHz.

RESULTS

The results showed that the noise-reducing spectral preprocessing, including Savitzky-Golay (S-G), second derivative (SD), and S-G + SD, was significantly better than scattering-eliminating, including standard normal variate (SNV), multiplicative scatter correction (MSC), and SNV + MSC. The combination of S-G and SD was the best. Principal component analysis results demonstrated that dielectric spectroscopy is less susceptible to the inhomogeneity of colostrum and can be used to identify doped colostrum. The identification performance of linear models was better than that of non-linear models. The established linear discriminant analysis model based on full spectra had the best accuracy rates of 99.14% and 97.37% in the calibration and validation sets, respectively. Confirmatory tests on samples from different sources confirmed the satisfactory robustness of the proposed model.

CONCLUSION

We found that the main unfavorable effect on the identification based on dielectric spectroscopy was noise interference, rather than scattering effect caused by inhomogeneity of colostrum. The satisfactory results undoubtedly cast light on rapid detection of strongly non-homogeneous foods based on dielectric spectroscopy. © 2022 Society of Chemical Industry.

SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis.

Quantitatively determining the somatic cell count of raw milk using dielectric spectra and support vector regression

To investigate the potential of dielectric spectroscopy in quantitatively determining the somatic cell count (SCC) of raw milk, the dielectric spectra of 301 raw milk samples at different SCC were collected using coaxial probe technology in the frequency range of 20 to 4,500 MHz. Standard normal variate, Mahalanobis distance, and joint x-y distances sample division were used to pretreat spectra, detect outliers, and divide samples, respectively. Principal component analysis and variable importance in projection (VIP) methods were used to reduce data dimension and select characteristic variables (CVR), respectively. The full spectra, 16 principal components obtained by principal component analysis, and 86 CVR selected by VIP were used as inputs, respectively, to establish different support vector regression models. The results showed that the nonlinear support vector regression models based on the full spectra and selected CVR using VIP had the best prediction performance, with the standard error of prediction and residual predictive deviation of 0.19 log SCC/mL and 2.37, respectively. The study provided a novel method for online or in situ detection of the SCC of raw milk in production, processing, and consumption.

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.