Non-destructive prediction of rosmarinic acid content in basil plants using a portable hyperspectral imaging system and ensemble learning algorithms

Abstract Background: Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining RA content in leaves are time-consuming and destructive, which poses limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. Results: The best RA prediction model was the LightGBM model with feature selection by AdaBoost algorithm and spectral pre-processing through logarithmic transformation and 2nd derivative. This model performed satisfactorily for practical screening with R2p = 0.81 and RMSEP = 3.92. The HSI images obtained using the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Conclusions: Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage.