Effect of lactose content on dielectric properties of whole milk and skim milk

Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk

To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2-20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2 = 0.998 and RP 2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.

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.

Biochemical, dielectric and surface characteristics of freeze-dried bovine colostrum whey powder

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Whey powder prepared by freeze-drying of bovine colostrum obtained from high altitude cow breed Himachali Pahari.

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Freeze-dried BCWP contained a considerable amount of amino acids, minerals and IgG.

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BCWP has a resistance of ∼57 M-ohm (Ω) which decreases with frequency and temperature.

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BCWP can find utilization as a functional food ingredient in health and infants’ formula.

Colostrum samples of recently registered cow breed “Himachali Pahari” were assorted from high altitude zone (901–2200 m). Prepared bovine colostrum whey powder (BCWP) was analysed for chemical composition, amino acids, minerals, surface morphology (SEM-EDS), FTIR and dielectric properties. Results showed that freeze-dried BCWP contained a considerable amount of nutritional parameters viz IgG (18.55 g/100 g), protein (71.72 g/100 g) and total amino acids (69.64 g/100 g). Additionally, the concentration of essential minerals was found to be adequate, and there was no presence of heavy metals. The BCWP exhibits good dielectric properties (resistance ∼57 M-Ω). SEM-EDS showed the broken up-wards layer structure with the uniform distribution of minerals on the surface. The FTIR spectra confirmed the presence of a higher proportion of β-sheets and β-turn structures in BCWP. Thus, on account of good functional and nutritive properties, BCWP could be foreseen as the future of functional food.

UHT Milk Characterization by Electrical Impedance Spectroscopy

Ultra-High Temperature (UHT) pasteurized milk is the most diffused variety of milk in Europe. In this paper, a method is presented, employing Electrical Impedance Spectroscopy to characterize the different commercial milks commonly available in grocery stores and supermarkets. The curves of the measured admittance allow for the classification of the type of milk (whole, semi-skimmed, fat-free) and to distinguish lactose-free milk. An electrical circuit model has been derived and different values of circuit parameters add interesting information on the classification of the samples. Furthermore, the characterization allows for the identification of the degradation of the milk before it is visible to the eye, thus highlighting the difference between storage in the fridge and at room temperature, and identifying expired milk.

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.

Production of Probiotic Passion Fruit (Passiflora edulis Sims f. flavicarpa Deg.) Drink Using Lactobacillus reuteri and Microencapsulation via Spray Drying

Probiotic foods offer many benefits to human health, causing increased interest in the development of new food products that exploit such benefits. However, traditional dairy foods are being replaced by other non-dairy foods to provide additional sources of benefits provided by bioactive molecules. Therefore, the objective of the present work was to study the production process of a probiotic fruit drink and then microencapsulate the probiotic pulp to stabilize the drink further. Passion fruit pulp (Passiflora edulis Sims f. flavicarpa Deg.) was fermented with Lactobacillus reuteri under different temperature conditions in combination with different pHs to find the best fermentation conditions. Different from dairy sources, the optimal conditions for the growth of Lactobacillus reuteri in the passion fruit pulp were found to be 30 °C at pH 3.18, where phenolic compounds could also be used as a secondary metabolic pathway. Spray-drying was performed using different conditions for microencapsulation. Process yields and Lactobacillus reuteri survival showed the dependency of droplet sizes, whereas phenolic compound retention was increased when higher amounts of gelatin were used. Therefore, the development of a new food product comprising a powdered fruit pulp rich in probiotic and phenolic compounds was possible.

Bovine Milk Fats and Their Replacers in Baked Goods: A Review

Milk fats and related dairy products are multi-functional ingredients in bakeries. Bakeries are critical local industries in Western countries, and milk fats represent the most important dietary lipids in countries such as New Zealand. Milk fats perform many roles in bakery products, including dough strengthening, textural softeners, filling fats, coating lipids, laminating fats, and flavor improvers. This review reports how milk fats interact with the ingredients of main bakery products. It also elaborates on recent studies on how to modulate the quality and digestibility of baked goods by designing a new type of fat mimetic, in order to make calorie- and saturated fat-reduced bakery products. It provides a quick reference for both retailers and industrial manufacturers of milk fat-based bakery products.