AI Article Synopsis
- The study examines how the geographical origin of medical herbs affects the quality of traditional Chinese herbal medicines.
- It uses FTIR spectroscopy and machine learning techniques (like Nearest Neighbor Method and a SVM-based classifier) to differentiate between samples of herb origins.
- The results showed a 99% accuracy in classifying samples of two types of herbs from various provinces, indicating a reliable method for determining the origin of medical herbs.
Article Abstract
Geographical origin of medical herbs is an important factor of the quality of many traditional Chinese herbal medicines. The objective of this study is to investigate whether FTIR spectroscopy coupled with pattern recognition techniques could effectively discriminate geographical origins of medical herbs. Nearest neighbor method (NNM) and a SVM-based multiclass classifier were employed to discriminate 269 angelicae dahuricae radix (ADR) samples from 4 provinces in China and 380 salviae miltiorrhizae radix (SMR) samples from 6 provinces. A leave-one-out cross-validation accuracy of 99% was achieved by the multiclass classifier. The study shows this classification scheme can be a highly accurate approach for the discrimination of medical herbs of different origins.
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