Phytochemical analysis : PCA · 2026
Hyperspectral Camera Imaging Tandem With HPLC-UV-ELSD Determination and Cell Bioassay for Geographical Discrimination and Quality Consistency Evaluation of Anoectochilus roxburghii.
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Xu Haixia, Zhou Qiuya, Ni Qiluo, Hu Weiyue, Xu Xiangwei, Tao Yi
Аннотация
INTRODUCTION: Anoectochilus roxburghii (AR) is a prized medicinal herb valued for its hepatoprotective effects. Its quality varies depending on geographical origin. The primary bioactive constituents include rutin, quercetin-7-O-glucoside, kaempferol-3-O-rutinoside, narcissin, quercetin, and kinsenoside. A method that enables simultaneous determination of both the content and bioactivity of the herb is therefore essential for effective quality control.
OBJECTIVE: To develop a rapid, nondestructive approach for simultaneously predicting the contents of primary bioactive constituents and hepatoprotective effects of AR using hyperspectral camera imaging (HCI) combined with deep learning models.
METHOD: Hyperspectral images of 100 AR batches were acquired using a portable Vis-NIR HCI system (389.81-1048.18 nm). The contents of six active compounds were quantified via HPLC-UV and HPLC-ELSD, while hepatoprotective activity was evaluated using an APAP-induced L02 cell injury model. Quantitative calibration models were constructed using partial least squares regression (PLSR) and the following three deep learning architectures: liquid neural network (LNN), Mamba state space model, and graph convolutional network (GCN). Their predictive performances were systematically compared. Shewhart control charts were employed to visualize batch-to-batch quality variation.
RESULT: The Mamba model demonstrated superior predictive performance across all seven quality attributes, achieving the highest coefficients of determination (Rp 2 up to 0.9972) and the lowest prediction errors. It significantly outperformed PLSR, LNN, and GCN models. Furthermore, the integration of Shewhart charts enabled effective visualization of quality consistency across batches.
CONCLUSION: This study presents a novel HCI-Mamba framework designed for the rapid, nondestructive, and multicomponent quality assessment of AR. The proposed strategy offers a high-throughput solution for AR quality control while providing a methodological framework applicable to other complex herbal medicines.
OBJECTIVE: To develop a rapid, nondestructive approach for simultaneously predicting the contents of primary bioactive constituents and hepatoprotective effects of AR using hyperspectral camera imaging (HCI) combined with deep learning models.
METHOD: Hyperspectral images of 100 AR batches were acquired using a portable Vis-NIR HCI system (389.81-1048.18 nm). The contents of six active compounds were quantified via HPLC-UV and HPLC-ELSD, while hepatoprotective activity was evaluated using an APAP-induced L02 cell injury model. Quantitative calibration models were constructed using partial least squares regression (PLSR) and the following three deep learning architectures: liquid neural network (LNN), Mamba state space model, and graph convolutional network (GCN). Their predictive performances were systematically compared. Shewhart control charts were employed to visualize batch-to-batch quality variation.
RESULT: The Mamba model demonstrated superior predictive performance across all seven quality attributes, achieving the highest coefficients of determination (Rp 2 up to 0.9972) and the lowest prediction errors. It significantly outperformed PLSR, LNN, and GCN models. Furthermore, the integration of Shewhart charts enabled effective visualization of quality consistency across batches.
CONCLUSION: This study presents a novel HCI-Mamba framework designed for the rapid, nondestructive, and multicomponent quality assessment of AR. The proposed strategy offers a high-throughput solution for AR quality control while providing a methodological framework applicable to other complex herbal medicines.