Objective: To establish a near infrared spectral discriminant model of crude products

counterfeit products and processed products of Calamina by principal component analysis and cluster analysis. Method: Near infrared spectra of crude products

counterfeit products and processed products of Calamina were collected.Each category sample was randomly divided into training set and testing set.The spectral preprocessing methods and modeling spectral bands were screened

the principal component discriminant analysis model and the cluster analysis model were established respectively. Result: Spectrs were preprocessed by the first derivative.The characteristic spectral band of the principal component discriminant analysis model was 4 800-4 000 cm-1

and the characteristic spectral bands of the cluster analysis model were 7 300-7 000

4 800-4 000 cm-1.In the principal component discriminant analysis model

the prediction accuracy rate was 94.34%.In the cluster analysis model

the prediction accuracy rate was 96.23%. Conclusion: The principal component analysis model and cluster analysis model of near infrared spectra can be used for identification of crude products

counterfeit products and processed products of Calamina.