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Rapid Detection of Volatile Oil in Mentha haplocalyx by Near-Infrared Spectroscopy and Chemometrics
| Content Provider | Scilit |
|---|---|
| Author | Yan, Hui Guo, Cheng Shao, Yang Ouyang, Zhen |
| Copyright Year | 2017 |
| 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 R_{c}$^{2}$ of 0.8805, R_{p}$^{2}$ 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 R_{c}$^{2}$ and R_{p}$^{2}$ 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. Abbreviations used: $1^{st}$ der: First-order derivative; $2^{nd}$ 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 |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5551362/pdf http://www.phcog.com/article.asp?issn=0973-1296;year=2017;volume=13;issue=51;spage=439;epage=445;aulast=Yan;type=2 |
| Ending Page | 445 |
| Page Count | 7 |
| Starting Page | 439 |
| File Format | XHTML |
| ISSN | 09731296 |
| e-ISSN | 09764062 |
| DOI | 10.4103/0973-1296.211026 |
| Journal | Pharmacognosy Magazine |
| Issue Number | 51 |
| Volume Number | 13 |
| Language | English |
| Publisher | Medknow |
| Access Restriction | Open |
| Subject Keyword | Spectroscopy Mentha Haplocalyx Near-infrared Spectroscopy Partial Least Squares Regression Support Vector Machine Volatile Oil Pharmacognosy Magazine, Volume 13, Issue 51 |
| Content Type | Text |
| Resource Type | Article |
| Subject | Drug Discovery Pharmaceutical Science |
