Open AccessRapid detection of volatile oil in Mentha haplocalyx by near-infrared spectroscopy and chemometrics
Author(s) -
Hui Yan,
Cheng Hai Guo,
Yilei Shao,
Zhen Ouyang
Publication year - 2017
Publication title -
pharmacognosy magazine
Language(s) - English
Resource type - Journals
eISSN - 0976-4062
pISSN - 0973-1296
DOI - 10.4103/0973-1296.211026
Subject(s) - partial least squares regression , chemometrics , variable elimination , chemistry , calibration , correlation coefficient , near infrared spectroscopy , mathematics , analytical chemistry (journal) , chromatography , artificial intelligence , statistics , computer science , optics , physics , inference
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 of 0.8805, 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 and 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 .