TY - JOUR T1 - Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics. AU - Yan,Hui, AU - Guo,Cheng, AU - Shao,Yang, AU - Ouyang,Zhen, Y1 - 2017/07/19/ PY - 2016/05/27/received PY - 2016/06/27/revised PY - 2017/8/26/entrez PY - 2017/8/26/pubmed PY - 2017/8/26/medline KW - Mentha haplocalyx KW - near-infrared spectroscopy KW - partial least squares regression KW - support vector machine KW - volatile oil SP - 439 EP - 445 JF - Pharmacognosy magazine JO - Pharmacogn Mag VL - 13 IS - 51 N2 - : 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. SUMMARY: The quality of medicine directly links to clinical efficacy, thus, it is important to control the quality of Mentha haplocalyx. 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. For SVM model, 6 LVs (less than 7 LVs in PLSR model) were adopted in model, and the result was better than PLSR model. It demonstrated that near infrared reflectance spectroscopy with chemometrics could be used to rapidly detect the main content volatile oil in Mentha haplocalyx. Abbreviations used: 1st der: First-order derivative; 2nd der: Second-order derivative; LOO: Leave-one-out; LVs: Latent variables; MC: Mean centering, NIR: Near-infrared; NIRS: Near infrared spectroscopy; PCR: Principal component regression, PLSR: Partial least squares regression; RBF: Radial basis function; RMSEC: Root mean square error of cross validation, RMSEC: Root mean square error of calibration; RMSEP: Root mean square error of prediction; SNV: Standard normal variate transformation; SVM: Support vector machine; VIP: Variable Importance in projection. SN - 0973-1296 UR - https://www.unboundmedicine.com/medline/citation/28839369/Rapid_Detection_of_Volatile_Oil_in_Mentha_haplocalyx_by_Near_Infrared_Spectroscopy_and_Chemometrics_ L2 - http://www.phcog.com/article.asp?issn=0973-1296;year=2017;volume=13;issue=51;spage=439;epage=445;aulast=Yan DB - PRIME DP - Unbound Medicine ER -