Rapid detection of volatile oil in Mentha haplocalyx by near-infrared spectroscopy and chemometrics

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Abstract
Pharmacognosy Magazine,2017,13,51,439-445.
Published:July 2017
Type:Original Article
Authors:
Author(s) affiliations:

Hui Yan1, Cheng Guo1, Yang Shao2, Zhen Ouyang2
1School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, China
2School of Pharmacy, Jiangsu University, Zhenjiang, China

Abstract:

Near-infrared spectroscopy combined with partial least squares regression (PLSR) and support vector machine (SVM) was applied for the rapid determination of chemical component of volatile oil content in Mentha haplocalyx. The effects of data pre-processing methods on the accuracy of the PLSR calibration models were investigated. The performance of the final model was evaluated according to the correlation coefficient (R) and root mean square error of prediction (RMSEP). For PLSR model, the best preprocessing method combination was first-order derivative, standard normal variate transformation (SNV), and mean centering, which had Rc2 of 0.8805, Rp2 of 0.8719, RMSEC of 0.091, and RMSEP of 0.097, respectively. The wave number variables linking to volatile oil are from 5500 to 4000 cm−1 by analyzing the loading weights and variable importance in projection (VIP) scores. For SVM model, six LVs (less than seven LVs in PLSR model) were adopted in model, and the result was better than PLSR model. The Rc2 and Rp2 were 0.9232 and 0.9202, respectively, with RMSEC and RMSEP of 0.084 and 0.082, respectively, which indicated that the predicted values were accurate and reliable. This work demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in M. haplocalyx.

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