Next Article in Journal
Previous Article in Journal
Sensors 2013, 13(10), 13820-13834; doi:10.3390/s131013820
Article

Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

1,†
, 1,†
, 2
, 1
, 1
 and 1,*
Received: 22 August 2013; in revised form: 13 September 2013 / Accepted: 24 September 2013 / Published: 14 October 2013
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [421 KB, uploaded 21 June 2014]
Abstract: Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rP values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants.
Keywords: spectral analysis; adulteration; chemometrics; least-square support vector machine (LS-SVM); partial least square regression (PLSR); competitive adaptive reweighted sampling (CARS) spectral analysis; adulteration; chemometrics; least-square support vector machine (LS-SVM); partial least square regression (PLSR); competitive adaptive reweighted sampling (CARS)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Nie, P.; Wu, D.; Sun, D.-W.; Cao, F.; Bao, Y.; He, Y. Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants. Sensors 2013, 13, 13820-13834.

AMA Style

Nie P, Wu D, Sun D-W, Cao F, Bao Y, He Y. Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants. Sensors. 2013; 13(10):13820-13834.

Chicago/Turabian Style

Nie, Pengcheng; Wu, Di; Sun, Da-Wen; Cao, Fang; Bao, Yidan; He, Yong. 2013. "Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants." Sensors 13, no. 10: 13820-13834.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert