OpenPCA and Raman mapping to decipher complex spectral datasets from multi-component samples: application to cannabis trichomes

The development of analytical techniques that decode chemical information in complex biochemical samples to discriminate different structural components may open the way for several new findings. In this study, principal component analysis (PCA) is carried out using an ad hoc Matlab coding that provides a transparent access to multivariate analysis of Raman mapping datasets. Here, we illustrated the efficacy of this method to extract meaningful results from Raman images of Cannabis sativa trichomes. A large dataset of Cannabis trichome comprising of 441 Raman spectra was examined for the first time using our OpenPCA. By mapping the chemical distribution in the trichome, we could locate the secretary vesicles in the PC score maps generated from the mapped Raman spectra. Black-box PCA solutions available in commercial software can be limited by rigid input interfaces which may prevent obtaining information by tuning the PCA analysis on selected wavenumber ranges. Hence, the OpenPCA scripts facilitate the task of obtaining key information from widely distributed range of wavenumbers that are characteristic to a specific cannabinoid, namely Δ9-THC and CBD. Overall, the PCA-coding algorithm shows advantages in decoding Raman spectra that could be extended to handle all kinds of datasets with simultaneous spatial and chemical details.