Rapid detection and quantification of melamine, urea, sucrose, water, and milk powder adulteration in pasteurized milk using Fourier transform infrared (FTIR) spectroscopy coupled with modern statistical machine learning algorithms.

There is an evident requirement for a rapid, efficient, and simple method to screen the authenticity of milk products in the market. Fourier transform infrared (FTIR) spectroscopy stands out as a promising solution. This work employed FTIR spectroscopy and modern statistical machine learning algorithms for the identification and quantification of pasteurized milk adulteration. Comparative results demonstrate modern statistical machine learning algorithms will improve the ability of FTIR spectroscopy to predict milk adulteration compared to partial least square (PLS). To discern the types of substances utilized in milk adulteration, a top-performing multiclassification model was established using multi-layer perceptron (MLP) algorithm, delivering an impressive prediction accuracy of 97.4 %. For quantification purposes, bayesian regularized neural networks (BRNN) provided the best results for the determination of both melamine, urea and milk powder adulteration, while extreme gradient boosting (XGB) and projection pursuit regression (PPR) gave better results in predicting sucrose and water adulteration levels, respectively. The regression models provided suitable predictive accuracy with the ratio of performance to deviation (RPD) values higher than 3. The proposed methodology proved to be a cost-effective and fast tool for screening the authenticity of pasteurized milk in the market.

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods.

Adulteration of higher priced milks with cheaper ones to obtain extra profit can adversely affect consumer health and the market. In this study, pure buffalo milk (BM), goat milk (GM), camel milk (CM), and their mixtures with 5-50% (vol/vol) cow milk or water were used. Mid-infrared spectroscopy (MIRS) combined with modern statistical machine learning was used for the discrimination and quantification of cow milk or water adulteration in BM, GM, and CM. Compared to partial least squares (PLS), modern statistical machine learning-especially support vector machines (SVM), projection pursuit regression (PPR), and Bayesian regularized neural networks (BRNN)-exhibited superior performance for the detection of adulteration. The best prediction models for the different predictive traits are as follows: The binary classification models developed by SVM resulted in differentiation of CM-cow milk, and GM/CM-water mixtures. PLS resulted in differentiation of BM/GM-cow milk and BM-water mixtures. All of the above models have 100% classification accuracy. SVM was used to develop multi-classification models for identifying the high and low proportions of cow milk in BM, GM, and CM, as well as the high and low proportions of water adulteration in BM and GM, with correct classification rates of 94%, 100%, 100%, 99%, and 100%, respectively. In addition, a PLS-based model was developed for identifying the high and low proportions of water adulteration in CM, with correct classification rates of 100%. A regression model for quantifying cow milk in BM was developed using PCA + BRNN, with RMSEV = 5.42%, and RV2 = 0.88. A regression model for quantifying water adulteration in BM was developed using PCA + PPR, with RMSEV = 1.70%, and RV2 = 0.99. Modern statistical machine learning improved the accuracy of MIRS in predicting BM, GM, and CM adulteration more effectively than PLS.

Transgenic smooth muscle expression of the human CysLT1 receptor induces enhanced responsiveness of murine airways to leukotriene D4.

Cysteinyl leukotrienes (CysLTs) exert potent proinflammatory actions and contribute to many of the symptoms of asthma. Using a model of allergic sensitization and airway challenge with Aspergillus fumigatus (Af), we have found that Th2-type inflammation and airway hyperresponsiveness (AHR) to methacholine (MCh) were associated with increased LTD(4) responsiveness in mice. To explore the importance of increased CysLT signaling in airway smooth muscle function, we generated transgenic mice that overexpress the human CysLT1 receptor (hCysLT(1)R) via the alpha-actin promoter. These receptors were expressed abundantly and induced intracellular calcium mobilization in airway smooth muscle cells from transgenic mice. Force generation in tracheal ring preparations ex vivo and airway reactivity in vivo in response to LTD(4) were greatly amplified in hCysLT(1)R-overexpressing mice, indicating that the enhanced signaling induces coordinated functional changes of the intact airway smooth muscle. The increase of AHR imposed by overexpression of the hCysLT(1)R was greater in transgenic BALB/c mice than in transgenic B6 x SJL mice. In addition, sensitization- and challenge-induced increases in airway responsiveness were significantly greater in transgenic mice than that of nontransgenic mice compared with their respective nonsensitized controls. The amplified AHR in sensitized transgenic mice was not due to an enhanced airway inflammation and was not associated with similar enhancement in MCh responsiveness. These results indicate that a selective hCysLT(1)R-induced contractile mechanism synergizes with allergic AHR. We speculate that hCysLT(1)R signaling contributes to a hypercontractile state of the airway smooth muscle.