In the present study, a rapid, non-destructive and real-time detection of the adulteration in Arnebia euchroma (Royle) Johnst. (A. euchroma) samples were established using a portable near infrared spectrometer in combination with chemometrics. 37 batches of A. euchroma, 3 batches of Onosma paniculatum Bur. et Franch (O. paniculatum), 8 batches of Arnebia benthamii (Wall. ex G. Don) Johnston (A. benthamii) samples were collected. In addition, the adulterated A. euchroma samples were prepared by mixing A. euchroma with O. paniculatum or A. benthamii in different ratios, respectively. Afterwards, the near infrared spectra of the samples were acquired by a portable near infrared spectrometer. Firstly, a classification model was established by using data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) algorithms, respectively, for differentiating the A. euchroma from its adulterants. Secondly, a quantitative model for detecting the adulteration concentration in adulterated A. euchroma samples was constructed by applying PLS and support vector machine (SVM) algorithms, respectively. The classification models of DD-SIMCA and PLS-DA achieved 100% of accuracy in calibration sets, as well as an accuracy of 95.6% and 100%, respectively, in the prediction sets. Moreover, the PLS regression model registered a ratio of prediction to deviation (RPD) value and root mean square error of prediction (RMSEP) value of 2.2, 1.6 and 8.70%, 12.03%, respectively, for O. paniculatum and A. benthamii adulterants, while the SVM model yielded the RPD value and the RMSEP value of 8.2, 3.4 and 2.29%, 5.55%, respectively. In summary, portable near infrared spectrometer combined with chemometrics was verified to be capable of achieving rapid, real-time detecting of adulteration of A. euchroma.
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