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Emerging Trends in Amino Acid Detection: Wearable Devices and Machine Learning-Assisted Signal Processing
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DOI：10.1039/D6AY00596A.png)
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Abstract
En 中文
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.
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