The increasing demand for a fast, field-deployable analytical technique for metabolite profiling of plants and plant-based food has promoted the development of portable spectroscopic systems. Metabolite profiling of plants is needed for understanding the biochemical composition and therapeutic potential of medicinal plants for enabling their wider use in medicine. Raman spectroscopy, which is a non-invasive and non-destructive technique, offers rapid metabolic profiling with little or no sample preparation, which can provide a practical alternative to the conventional destructive methods used for phytochemical analysis. In this study, we have designed and developed a portable Raman spectrometer capable of metabolite fingerprinting under field conditions. Our portable Raman system, coupled with advanced multivariate analysis, enables reliable metabolite profiling and classification of medicinal plants within field conditions. The designed Raman system integrates a laser source, optical fiber probe, and a spectrometer, optimised for a high signal-to-noise ratio and an embedded spectral acquisition module within a lightweight architecture suitable for in situ operation. The developed instrument demonstrated reliable acquisition of Raman fingerprints across a wide spectral range. The developed setup was employed to record spectra from the medicinal leaves, and the obtained spectral features were analyzed to identify secondary metabolites. The Raman spectrum of healthy leaves showed Raman peaks originating from cellulose, lignin, pectin, nitrate, carotenoids, anthocyanins, and other metabolites. Principal Component Analysis (PCA) was used as a chemometric approach to categorize and identify medicinal plants based on their Raman spectral signatures, enabling rapid and reliable differentiation of metabolite profiles. This work highlights the potential of integrating portable Raman spectroscopy with multivariate statistical analysis as an efficient analytical tool for metabolite profiling and rapid plant classification applications.
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Dhillon et al. (2026) studied this question.
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