A novel near infrared spectroscopy(NIRS) combined with supervised pattern recognition was proposed for the rapid classification and discrimination of types of cigarettes.Standard normal variables(SNV)

multiplicative signal correction(MSC)

first derivative(FD)

second derivative(SD) and Savitzky-Golay filt(SG) and their combined spectral pre processing methods were used for the spectral data preprocessing of finished cut tobacco.A discriminant model was established by NIRS combined with three pattern recognition methods include principal component analysis(PCA)

partial least squares discriminant analysis(PLS-DA) and orthogonal partial least squares discriminant analysis(OPLS-DA)

and the prediction accuracy of classification identification was used as an evaluation index.The experimental results showed that:(1) the principal component distribution maps were intertwined

and PCA could not identify five types of finished cut tobacco.(2) The PLS-DA model for finished cut tobacco spectrum after MSC+FD pretreatment could achieve better classification and recognition results

and the prediction accuracies for the calibration set and test set were 100% and 98.3%

respectively.(3) The identification of the OPLS-DA model for the finished cut tobacco spectrum after MSC+SD pretreatment was the best

and the parameters of the model

including the fraction of the variation of X explained by the model(R2X)

the fraction of the variation of Y explained by the model(R2Y)

and the fraction of the variation of Y that can be predicted by the model according to the cross validation(Q2) were 0.485

0.907 and 0.748

respectively.The prediction accuracies for the calibration set and test set both reached to 100%.Results showed that the classification model based on NIRS combined with OPLS-DA was efficient

quick

accurate and non destructive

and provided a new and rapid identification approach for finished cut tobacco classification.