Evaluation of handheld Raman spectrometers demonstrates accurate chemotype differentiation in cannabis, indicating rapid, non-invasive profiling capabilities.
Cannabis plants are cultivated primarily for their fiber, seeds, and phytocannabinoids (pCBs): delta‐9‐tetrahydrocannabinol (THC), cannabidiol (CBD), etc. Each plant has a certain pCBs profile usually belonging to one of the known chemotypes: THC‐dominant (I), THC/CBD‐intermediate (II), CBD‐dominant (III), etc. Important tasks for ensuring profitability and legality in the cannabis industry are the differentiation of plants by chemotype (preferably in the vegetative stage) and the determination of pCBs contents (first, psychoactive THC) in plants/products. Chromatography‐coupled methods, commonly used for this, are bulky, invasive, time‐, cost‐, and infrastructure‐consuming, and thus are low‐effective for high‐throughput measurements, especially under field conditions. Fast hand‐held Raman spectroscopy can overcome these drawbacks. This study evaluates the performance of compact hand‐held Raman spectrometers in cannabis chemotype differentiation. Two instruments with 785‐nm/46‐mW and 830‐nm/76‐mW excitations were used to measure spectra from leaves of cannabis plants (chemotypes I and III; over 60 spectra for each), as well as THC distillate and CBD standard. Classification models based on partial least squares discriminant analysis (PLS‐DA) were developed using 70% of leaves spectra for training/cross‐validation and 30% for test‐set validation. A prediction accuracy of 90% was achieved for the device with 830‐nm excitation, confirming its effectiveness for rapid, non‐invasive, in vivo differentiation of plants by chemotype. Additionally, a detailed comparative analysis of spectra from cannabis leaves, THC distillate, CBD standard, and literature data revealed numerous pCB‐associated peaks, underscoring the method's capability for in‐depth pCB analysis. Principal component analysis (PCA) significantly improves peaks identifiability, potentially enabling pCBs contents quantification with appropriate calibration.
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Komisar et al. (2025) studied this question.
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