Objective: To construct scoring cards using amino acid contents as indicators to identify different main geographical origins of Cordyceps. Methods: Samples from the main geographical origins to be identified were used as positive samples, while samples from other areas served as negative samples. The amino acid content data of the training set samples were divided into bins, and the weight of evidence (WOE) and information value (IV) for each bin were calculated to optimize the binning strategy. The original data were encoded using WOE, and a logistic regression model was established. A suitable scoring system was designed according to the scoring formula, with 50 points set as the baseline when the probability of positive and negative samples was equal, determining the base score and the scores corresponding to each bin. By summing the base score with the scores corresponding to each bin for the samples to be scored, the final score of the sample could be derived. A threshold of 50 points could be used to discriminate the likelihood of positive and negative samples. Results: The scoring cards constructed for identifying Cordyceps sinensis from the Qinghai and Xizang production areas achieved identification accuracies of 0.85 and 0.90, respectively, for the test set samples. Conclusion: The two constructed scoring cards can simply and accurately identify the main production areas of Cordyceps sinensis. The integration of artificial intelligence technology with pharmaceutical analysis can significantly enhance the quality and efficiency of traditional drug quality analysis, evaluation, and control.
SHI Yan
,
CHENG Xian-long
,
WEI Feng
. Studies on the construction of scoring card models for identifying different main geographical origins of Cordyceps[J]. Chinese Journal of Pharmaceutical Analysis, 2025
, 45(7)
: 1275
-1285
.
DOI: 10.16155/j.0254-1793.2024-1035
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