Determination of the Fat Content in Cow’s Milk Based on Dielectric Properties

Ultrasound Image Temperature Monitoring Based on a Temporal-Informed Neural Network

Real-time and accurate temperature monitoring during microwave hyperthermia (MH) remains a critical challenge for ensuring treatment efficacy and patient safety. This study presents a novel approach to simulate real MH and precisely determine the temperature of the target region within biological tissues using a temporal-informed neural network. We conducted MH experiments on 30 sets of phantoms and 10 sets of ex vivo pork tissues. We proposed a novel perspective: the evolving tissue responses to continuous electromagnetic radiation stimulation are a joint evolution in temporal and spatial dimensions. Our model leverages TimesNet to extract periodic features and Cloblock to capture global information relevance in two-dimensional periodic vectors from ultrasound images. By assimilating more ultrasound temporal data, our model improves temperature-estimation accuracy. In the temperature range 25-65 °C, our neural network achieved temperature-estimation root mean squared errors of approximately 0.886 °C and 0.419 °C for fresh ex vivo pork tissue and phantoms, respectively. The proposed temporal-informed neural network has a modest parameter count, rendering it suitable for deployment on ultrasound mobile devices. Furthermore, it achieves temperature accuracy close to that prescribed by clinical standards, making it effective for non-destructive temperature monitoring during MH of biological tissues.

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.

Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques

This study aimed to evaluate the applicability of electrochemical impedance spectroscopy to identify raw bovine milk adulteration with urea. Three batches of raw milk adulterated with urea were studied. Hierarchical clustering indicated that the samples could be split in three groups corresponding to low adulteration (less than 7 wt%), medium adulteration (between 8 and 16 wt%) and high adulteration (over than 16 wt%). A linear discriminant analysis was performed resulting in 90% of accuracy in classifying between groups. Besides, a partial least squares model containing three directions provided good accuracy in quantitatively predicting the urea mass fraction added to raw bovine milk. Finally, calculations using an approximated electric circuit model suggested the formation of urea aggregates that hinder charge transportation within the milk thus diminishing the solution conductivity. Results indicate that electrochemical impedance spectroscopy can be a useful, low cost and rapid tool to identify milk adulteration with urea.

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.

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.

Modeling of Dielectric and Thermal Properties of Protein-Enriched Instant Noodles as a Function of Food Chemical Composition

Dielectric property measurement instruments are expensive and not readily available. Hence, models of the dielectric and thermal properties of protein-enriched instant noodles (PEIN) were developed as a function of the product moisture, protein, ash, fat, carbohydrate, and crude fiber contents. The chicken meat, egg yolk, and seaweed in PEIN varied from 0 to 30, 0 to 15, and 0 to 6 g/100 g flour, respectively. Results revealed that protein, fat, and ash contents of PEIN increased by chicken meat, egg yolk, and seaweed supplementation. Regression equations indicated that the moisture and protein contents significantly (P ≤ 0.05) increased, while the fat content decreased the dielectric and thermal properties. The R2 values of the dielectric constant and loss factor, thermal conductivity, and specific heat equations were 0.75, 0.87, 0.78, and 0.98, respectively. Finally, no significant differences (P > 0.05) between the properties values predicted from the model equations and the experimental data (not included in the development of model equations) was observed which indicated a good model fit.

Concentration-related microwave heating processes: electromagnetic interference of Maillard reaction substrates (glucose and lysine)

Glucose and lysine are important substrates involved in the Maillard reaction.