Multi-Platform Metabolomics for Fraud Detection in Spices and Botanical Food Supplements: From Fingerprint Acquisition to Decision-Grade Evidence
Abstract
Fraud in herbal medicines and botanical supplements-such as biological substitution, dilution with inert material, undeclared active pharmaceutical ingredients (APIs), and intentional mislabeling-represents a significant and underestimated threat to public health. Beyond regulatory problems, fraudulent botanical products also compromise the scientific integrity of bioactive compound research. If pharmacological or clinical studies are carried out on adulterated material without knowing it, the resulting data on safety and efficacy become unreliable, and any conclusions drawn from such data are questionable. This review critically evaluates multi-platform metabolomics as an analytical decision-making framework for botanical fraud detection. Rather than cataloging available methods, we focus on the complete analytical pipeline: study design and sample strategy, multi-platform fingerprint acquisition and processing (NMR, GC-MS, LC-HRMS, HPTLC), and chemometric modeling, validation, and decision support. Particular emphasis is placed on data fusion across platforms and the requirements for producing decision-grade evidence-analytical outputs robust enough to support market monitoring and regulatory action.
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