Using combinations of principal component scores from different spectral ranges in near-infrared region to improve discrimination for samples of complex composition

被引:11
作者
Lee, Sanguk [1 ]
Chung, Hoeil [1 ]
Choi, Hangseok [2 ]
Cha, Kyungjoon [3 ]
机构
[1] Hanyang Univ, Dept Chem, Analyt Spect Lab, Seoul 133791, South Korea
[2] Samsung Biomed Res Inst, Ctr Genome Res, Seoul 135710, South Korea
[3] Hanyang Univ, Dept Math, Seoul 133791, South Korea
关键词
Sesame; Geographical origin; Principal component score; Score combination; Linear discriminant analysis; PARTIAL LEAST-SQUARES; PROCESS MONITORING METHODS; EASTMAN CHALLENGE PROBLEM; CLASSIFICATION; SPECTROSCOPY; GLUCOSE;
D O I
10.1016/j.microc.2009.10.014
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Principal component analysis (PCA) is widely used as an exploratory data analysis too[ in the field of vibrational spectroscopy, particularly near-infrared (NIR) spectroscopy. PCA represents original spectral data containing large variables into a few feature-containing variables, or scores. Although multiple spectral ranges can be simultaneously used for PCA, only one series of scores generated by merging the selected spectral ranges is generally used for qualitative analysis. Alternatively, the combined use of an independent series of scores generated from separate spectral ranges has not been exploited. The aim of this study is to evaluate the use of PCA to discriminate between two geographical origins of sesame samples, when scores independently generated from separate spectral ranges are optimally combined. An accurate and rapid analytical method to determine the origin is essentially required for the correct value estimation and proper production distribution. Sesame is chosen in this study because it is difficult to visually discriminate the geographical origins and its composition is highly complex. For this purpose, we collected diffuse reflectance near-infrared (NIR) spectroscopic data from geographically diverse sesame samples over a period of eight years. The discrimination error obtained by applying linear discriminant analysis (LDA) was improved when separate scores from two spectral ranges were optimally combined. compared to the discrimination errors obtained when scores from singly merged two spectral ranges were used. (c) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:96 / 101
页数:6
相关论文
共 13 条
[1]  
Beebe K.R., 1998, CHEMOMETRICS PRACTIC
[2]   AN INTRODUCTION TO MULTIVARIATE CALIBRATION AND ANALYSIS [J].
BEEBE, KR ;
KOWALSKI, BR .
ANALYTICAL CHEMISTRY, 1987, 59 (17) :A1007-+
[3]  
Coomans D., 1979, Anal. Chim. Acta, V112, P97
[4]  
Ivanciuc O., 2007, REV COMPUTATIONAL CH, P291, DOI [10.1002/9780470116449.ch6, DOI 10.1002/9780470116449.CH6]
[5]   Optimization of informative spectral regions for the quantification of cholesterol, glucose and urea in control serum solutions using searching combination moving window partial least squares regression method with near infrared spectroscopy [J].
Kang, Naroo ;
Kasemsumran, Sumaporn ;
Woo, Young-Ah ;
Kim, Hyo-Jin ;
Ozaki, Yukihiro .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2006, 82 (1-2) :90-96
[6]   Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem [J].
Kano, M ;
Nagao, K ;
Hasebe, S ;
Hashimoto, I ;
Ohno, H ;
Strauss, R ;
Bakshi, BR .
COMPUTERS & CHEMICAL ENGINEERING, 2002, 26 (02) :161-174
[7]   Comparison of statistical process monitoring methods: application to the Eastman challenge problem [J].
Kano, M ;
Nagao, K ;
Hasebe, S ;
Hashimoto, I ;
Ohno, H ;
Strauss, R ;
Bakshi, B .
COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (2-7) :175-181
[8]   Near-infrared spectroscopic determination of human serum albumin, γ-globulin, and glucose in a control serum solution with searching combination moving window partial least squares [J].
Kasemsumran, S ;
Du, YP ;
Murayama, K ;
Huehne, M ;
Ozaki, Y .
ANALYTICA CHIMICA ACTA, 2004, 512 (02) :223-230
[9]   Improvement of partial least squares models for in vitro and in vivo glucose quantifications by using near-infrared spectroscopy and searching combination moving window partial least squares [J].
Kasemsumran, Sumaporn ;
Du, Yi Ping ;
Maruo, Katsuhiko ;
Ozaki, Yukhiro .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2006, 82 (1-2) :97-103
[10]   Determination of total nitrogen content, pH, density, refractive index, and brix in Thai fish sauces and their classification by near-infrared spectroscopy with searching combination moving window partial least squares [J].
Ritthiruangdej, P ;
Kasemsumran, S ;
Suwonsichon, T ;
Haruthaithanasan, V ;
Thanapase, W ;
Ozaki, Y .
ANALYST, 2005, 130 (10) :1439-1445