Emerging Trends in Amino Acid Detection: Wearable Devices and Machine Learning-Assisted Signal Processing

Abstract

As critical metabolic biomarkers, amino acids exert essential physiological functions, and their abnormal levels are closely associated with a range of diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. In recent years, amino acid analysis technologies have achieved remarkable advancements in the field of personalized healthcare. This review navigates the latest progress in amino acid analysis, with a focus on two prominent emerging trends. The first trend encompasses the development of flexible and wearable sensing devices for non-invasive, continuous amino acid monitoring in diverse biofluids, such as blood, sweat, and interstitial fluid. Particular attention is given to their design principles, operational mechanisms, practical applications, and key performance metrics. The second trend involves the application of machine learning (ML) for processing and interpreting complex response signals. Specifically, this review discusses how various ML approaches, including classical chemometric linear regression models, deep learning models, support vector machines (SVMs), ensemble learning, and tree-based models, address common challenges in complex environments, such as signal interference and nonlinear drift. Furthermore, this review outlines the current challenges and proposes future research directions, aiming to advance amino acid analysis toward more intelligent, integrated, and personalized health monitoring systems.