Objective: To establish a near infrared spectral qualitative model of Fluoritum for rapidly identifying raw

calcined

vinegar calcined products and its counterfeits based on multi-reference correlation coefficient method(MRCC) and back-propagation artificial neural networks(BP-ANN). Method: The near infrared spectra of 4 kinds of Fluoritum samples were collected

including raw products

calcined products

vinegar calcined products and counterfeit products.The second derivative and 9 points smoothing were used to calculate multiple correlation coefficients.Multiple correlation coefficients were used as input data in Matlab 2014a software

and a rapid identification model for 4 different types of Fluoritum samples was established by BP-ANN. Result: BP-ANN identification model of near infrared spectroscopy of Fluoritum was established.The prediction results of 14 batches of samples were correct

only 1 batch of samples was predicted error

the accuracy rate was 93.33%. Conclusion: The BP-ANN identification model of near infrared spectroscopy of Fluoritum can be used to distinguish among 4 different types of samples by a one-time integrated operation

and the result is accurate and reliable.In addition

based on the correlation coefficient method of near infrared spectroscopy

the multi-group correlation coefficient calculated by the multiple reference spectra is used as the input data of the network characteristics

the compression of spectral data is realized.