2026 Volume 14 Issue 1 Pages 92-109
Raman spectroscopy, when combined with chemometric techniques, has become a powerful tool for quality assessment, process monitoring, and optimization in the vegetable processing industry. This review provides a comprehensive overview of recent advances in the use of Raman chemometric methods for processed vegetables, emphasizing their distinct advantages over other spectroscopic techniques, such as near-infrared (NIR), Fourier-transform infrared (FT-IR), and ultraviolet-visible (UV-Vis) spectroscopy. Due to its high molecular specificity, minimal water interference, and capacity to resolve detailed information on chemical structure, Raman spectroscopy is particularly well-suited for analyzing complex vegetable matrices. Integrated chemometric approaches, including principal component analysis (PCA), partial least squares regression (PLSR), support vector machines (SVM), and convolutional neural networks (CNN), have significantly improved both data interpretation and predictive performance across tasks such as quality classification, constituent quantification, and real-time process monitoring. Despite these advancements, challenges remain, including overlapping signals, limited sample diversity, and the lack of robust systems for in-line implementation. Future research should prioritize the development of transfer learning strategies, multimodal spectroscopic integration, and establish standardized automated workflows to enhance the generalizability and scalability of the models. These advances will facilitate the commercial adoption of Raman-based technologies in vegetable processing industry.
