DeepHSI: A transferable and expandable hyperspectral framework for industrial plant origin identification: A case...

Pogostemon cablin (Blanco) Benth (P.cablin), known for its unique aroma and rich chemical components, occupies an important position in the field of cosmetics, food and medicine. Identifying its origin is crucial for quality control and preventing adulteration. Traditional identification methods are time-consuming and labor-intensive, and usually require complex chemical analysis. In this study, a rapid and universal method was proposed to identify P.cablin from three major origins based on hyperspectral image (HSI) and deep learning, named DeepHSI. Furthermore, metabolomics and transcriptomics analyses were performed to validate the feasibility of HSI analysis for origins identification of P.cablin. HSI data collected under three experimental conditions (batches) were used for model training and transfer learning, which demonstrate the generality of DeepHSI. The simplified multi-origins identification model fusion mechanism ensures scalability for practical research applications and provides a paradigm for multi-classification research. These advantages provide a promising solution for rapid and nondestructive origin identification, quality control, and authenticity verification.