Analysis of X-ray diffraction fingerprint of Calamina and its processed products / 中草药

Objective: To establish X-ray diffraction (XRD) fingerprint of Calamina and its processed products, compare the effects of different processing Methods on the main components of medicinal materials and determine the content of ZnO in the processed products. Methods: XRD was used to analyze 10 batches of Calamina and its processed products, and fingerprints of Calamina and its processed products were established respectively. Six different processing methods were compared, and the content of ZnO in all processed products was determined by K value method. Results: Fingerprints of Calamina and its processed products were preliminarily established. There were 23 common peaks in the fingerprints of Calamina, and there were 10 common peaks in the fingerprints of its processed products. After calcination, the ZnCO3 characteristic peak of the raw material was transformed into the characteristic peak of ZnO; The content of ZnO in the calcined product exceeded 56%. Conclusion: XRD fingerprints could be used for the identification and analysis of Calamina and its processed products. The new and reliable method was provided for quality evaluation of Calamina and its processed products.

Rapid Identification of Calamina Based on Near-infrared Diffuse Reflectance Spectroscopy and PCA-SVM Algorithm / 中国实验方剂学杂志

Objective: To establish a near-infrared diffuse reflectance spectroscopy(NIRS) identification model for crude products,counterfeit products and processed products of Calamina by principal component analysis(PCA) and support vector machine(SVM) algorithm. Method: NIRS of crude products,counterfeit products and processed products of Calamina were collected,the characteristic spectrum segments were selected,the preprocessing method and the optimum principal component number were optimized,and the PCA-SVM qualitative model was established. Result: The characteristic spectrum segment of analysis model was 7 500-4 000 cm-1.Spectra were preprocessed by the first-order derivative method(FD).The optimum principal component number was 5. And the optimum internal parameters of SVM[penalty factor(c)=0.25 and kernel function parameter(g)=8] were screened by applying the grid search algorithm.In the PCA-SVM qualitative model,the prediction accuracy rate was 100%for the 5-fold cross validation,and the prediction accuracy rates also were 100%both for training set and test set. Conclusion: PCA-SVM analysis model of NIRS for Calamina samples has a high prediction accuracy rate,and it can be used for the rapid and nondestructive identification of crude products,counterfeit products and processed products of Calamina by combining the diffuse reflection technique on solid powder.