AI Article Synopsis
- - A quality evaluation method was developed for various processed products of Angelica, employing data analysis via high-performance liquid chromatography and other measurements, using SPSS Clementine 11.0 software.
- - Discriminant analysis (DA) was utilized to create a classification model that differentiates between the processed Angelica products using 8 key predictor variables out of a total of 59 indexes.
- - The model achieved a high accuracy rate of 96.7% in correctly classifying the products, demonstrating that DA can effectively validate and recognize different processed Angelica products.
Article Abstract
The paper reports the development of a quality evaluation method for Angelica different processed products. The data of high-performance liquid chromatography, water, total ash and extract were analyzed with SPSS Clementine 11.0 software. Discriminant analysis (DA) established the classification model and parameter for Angelica different processed products. Fish's discriminant functions of Angelica different processed products were generated using 8 predictor variables selected from 59 indexes. The correct rate of discriminating back substitution is 96.7%. Angelica different processed products can be accurately and reliably recognized and validated with DA of SPSS Clementine 11.0 software.
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