Emerging perspectives on analytical techniques and machine learning for food metabolomics in the era of industry 4.0:...

This review systematically explores the emerging perspectives on analytical techniques and machine learning applications in food metabolomics, with a focus on their roles in the era of Industry 4.0. The study emphasizes the utilization of chromatography-mass spectrometry and proton nuclear magnetic resonance spectroscopy as primary tools for metabolic profiling, highlighting their respective strengths and limitations. LC-MS, known for its high sensitivity and specificity, faces challenges such as complex data interpretation and the need for advanced computational tools.¹H NMR offers reproducibility and quantitative accuracy but struggles with lower sensitivity compared to mass spectrometry. The review also delves into the integration of multivariate data analysis techniques like principal component analysis and partial least squares-discriminant analysis, which enhance data dimensionality reduction and pattern recognition, yet require careful validation to avoid overfitting. Furthermore, the application of machine learning algorithms, including random forests and support vector machines, is discussed in the context of improving classification and predictive tasks in food metabolomics. Practical applications of these technologies are demonstrated in areas such as quality control, nutritional studies, and food adulteration detection. The review emphasizes the need for standardization in methodologies and the development of more accessible and cost-effective analytical workflows. Future research directions include enhancing the sensitivity of ¹H NMR, integrating metabolomics with other omics technologies, and fostering data sharing to build comprehensive reference libraries. This review aims to provide a comprehensive and critical overview of the current advancements and future potentials of analytical techniques and machine learning in food metabolomics, aligning with the goals of Industry 4.0.