Comparison Between Linear and Non-linear Variable Selection Methods with Applications to Spectroscopic (UV-Vis/NIR) Data

被引:0
作者
Krongchai, Chanida [1 ]
Wongsaipun, Sakunna [1 ]
Funsueb, Sujitra [1 ]
Theanjumpol, Parichat [2 ,3 ]
Jakmunee, Jaroon [1 ,4 ]
Kittiwachana, Sila [1 ,5 ]
机构
[1] Chiang Mai Univ, Fac Sci, Dept Chem, Chiang Mai 50200, Thailand
[2] Chiang Mai Univ, Fac Agr, Postharvest Technol Res Ctr, Chiang Mai 50200, Thailand
[3] Postharvest Technol Innovat Ctr, Off Higher Educ Commiss, Bangkok 10400, Thailand
[4] Chiang Mai Univ, Inst Sci & Technol Res & Dev, Chiang Mai 50200, Thailand
[5] Chiang Mai Univ, Fac Sci, Environm Sci Res Ctr, Chiang Mai 50200, Thailand
来源
CHIANG MAI JOURNAL OF SCIENCE | 2020年 / 47卷 / 01期
关键词
variable selection; multivariate calibration; partial least squares (PLS); self organizing map (SOM); spectral data analysis; PARTIAL LEAST-SQUARES; NIR SPECTROSCOPY; REGRESSION; PREDICTION; VIP;
D O I
暂无
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Variable selection aims to identify important parameters in relation to predicted responses. Selection outcomes of the important variables could be different depending on the methods used. In this research, the important variables identified using linear and non-linear variable selection methods based on partial least squares-variable important in prediction (PLS-VIP) and self organizing map-discrimination index (SOM-DI) were compared. Two datasets, near-infrared (NIR) spectra of adulterated Thai Jasmine rice and ultraviolet-visible (UV-Vis) spectra of food colorant mixtures were used for the demonstration. The advantages and disadvantages for the use of the different algorithms were compared and discussed. For the NIR data, the calibration model using supervised self organizing map (SSOM) offered better prediction results and the SOM-DI variable selection method identified the spectral changes in NIR overtone regions as significance. On the other hand, PLS calibration model resulted in higher predictive errors while the PLS-VIP variable selection captured variation from the visible region between 664 nm and 884 nm. Using the UV-Vis data, PLS appeared to put attention on only the highest absorbance region of the peak maximum absorbance. In contrast, SSOM model highlighted the variation around the isosbestic spectral regions between the mixture components. The drawback for the use of a mixture design to construct the calibration models, leading to wrong interpretation of the important variables, was also discussed.
引用
收藏
页码:160 / 174
页数:15
相关论文
共 26 条
  • [11] Prediction of liquid chromatographic retention behavior based on quantum chemical parameters using supervised self organizing maps
    Kittiwachana, Sila
    Wangkarn, Sunanta
    Grudpan, Kate
    Brereton, Richard G.
    [J]. TALANTA, 2013, 106 : 229 - 236
  • [12] THE SELF-ORGANIZING MAP
    KOHONEN, T
    [J]. PROCEEDINGS OF THE IEEE, 1990, 78 (09) : 1464 - 1480
  • [13] Application of multiple self-organizing maps for classification of soil samples in Thailand according to their geographic origins
    Krongchai, Chanida
    Funsueb, Sujitra
    Jakmunee, Jaroon
    Kittiwachana, Sila
    [J]. JOURNAL OF CHEMOMETRICS, 2017, 31 (02)
  • [14] Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer
    Liu, Fei
    Jiang, Yihong
    He, Yong
    [J]. ANALYTICA CHIMICA ACTA, 2009, 635 (01) : 45 - 52
  • [15] Self Organising Maps for distinguishing polymer groups using thermal response curves obtained by dynamic mechanical analysis
    Lloyd, Gavin R.
    Brereton, Richard G.
    Duncan, John C.
    [J]. ANALYST, 2008, 133 (08) : 1046 - 1059
  • [16] Self Organising Maps for variable selection: Application to human saliva analysed by nuclear magnetic resonance spectroscopy to investigate the effect of an oral healthcare product
    Lloyd, Gavin R.
    Wongravee, Kanet
    Silwood, Christopher J. L.
    Grootveld, Martin
    Brereton, Richard G.
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2009, 98 (02) : 149 - 161
  • [17] NIR Spectroscopy Applications for Internal and External Quality Analysis of Citrus Fruit-A Review
    Magwaza, Lembe S.
    Opara, Umezuruike Linus
    Nieuwoudt, Helene
    Cronje, Paul J. R.
    Saeys, Wouter
    Nicolai, Bart
    [J]. FOOD AND BIOPROCESS TECHNOLOGY, 2012, 5 (02) : 425 - 444
  • [18] CALIBRATION MODELING BY PARTIAL LEAST-SQUARES AND PRINCIPAL COMPONENT REGRESSION AND ITS OPTIMIZATION USING AN IMPROVED LEVERAGE CORRECTION FOR PREDICTION TESTING
    MARBACH, R
    HEISE, HM
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1990, 9 (01) : 45 - 63
  • [19] Coupled Stepwise PLS-VIP and ANN Modeling for Identifying and Ranking Aroma Components Contributing to the Palatability of Cheddar Cheese
    Morita, Airi
    Araki, Tetsuya
    Ikegami, Shoma
    Okaue, Misako
    Sumi, Masahiro
    Ueda, Reiko
    Sagara, Yasuyuki
    [J]. FOOD SCIENCE AND TECHNOLOGY RESEARCH, 2015, 21 (02) : 175 - 186
  • [20] Process Analytical Chemistry: Applications of Ultraviolet/Visible Spectrometry in Environmental Analysis: An Overview
    Ojeda, C. Bosch
    Rojas, F. Sanchez
    [J]. APPLIED SPECTROSCOPY REVIEWS, 2009, 44 (03) : 245 - 265