Vibrational spectroscopy (Raman and infrared) and machine learning tools in food safety and composition

The integration of machine learning (ML) with vibrational spectroscopy has revolutionized the food industry, advancing the ways food quality, authenticity, and safety are analyzed. ML methods, including traditional approaches such as support vector machines (SVMs) and partial least squares regression (PLSR), and advanced deep learning techniques like neural networks (NNs), enable the efficient and precise processing of complex multivariate datasets. Vibrational spectroscopy methods-near-infrared (NIR), mid-infrared (MIR), and Raman spectroscopy- are non-destructive, versatile, and provide detailed molecular insights. The synergy between ML and these spectroscopic techniques has significantly enhanced capabilities for identifying adulterants, quantifying quality indicators, and detecting contaminants in food products. CNNs analyze spectral data to classify products, identify spoilage, and verify food origins, while RNNs excel in capturing sequential spectral patterns for monitoring storage conditions. Portable spectrometers integrated with ML algorithms offer real-time, on-site food analysis, streamlining quality assessments and safety protocols. Despite challenges such as data variability, limited labeled datasets, and the interpretability of deep learning models, advancements in ML, including data augmentation and transfer learning, are addressing these limitations. The combination of ML and vibrational spectroscopy represents a transformative leap for the food industry, providing innovative solutions to global challenges in food security, quality control, and sustainability. As these technologies continue to evolve, they promise to drive significant improvements in food analysis, ensuring safer and higher-quality food products worldwide.