Enhanced data preprocessing with novel window function in Raman spectroscopy: Leveraging feature selection and machine learning for raspberry origin identification.

In this study, a simple and accurate approach is proposed for enhancing the origin identification of raspberry samples using a combination of innovative Raman spectral preprocessing techniques, feature selection, and machine learning algorithms. Window function was creatively introduced and combined with baseline removal technique to preprocess the Raman spectral data, reducing the dimensionality of the raw data and ensuring the quality of the processed data. An optimization process was conducted to determine the optimal parameter for the window function, resulting in a binning window width of 5 that yielded the highest accuracy. After applying three feature selection techniques, it was found that the information gain model had the best performance in extracting discriminative spectral features. Finally, ten different machine learning algorithms were employed to construct predictive models, and the optimal models were selected. Linear Support Vector Classifier (LinearSVC), Multi-Layer Perceptron Classifier (MLPClassifier), and Linear Discriminant Analysis (LDA) achieve accuracy, precision, recall, and F1 values above 0.96, while the Random Vector Functional Link Network Classifier (RVFLClassifier) surpasses 0.93 for these performance metrics. These results demonstrate the effectiveness of the proposed approach in identifying the origin of raspberry samples with high accuracy and robustness, providing a valuable tool for agricultural product authentication and quality control.

Machine learning-assisted MALDI-TOF MS toward rapid classification of milk products.

This study established a method for rapid classification of milk products by combining matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100 ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.