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Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice.
Anal Chim Acta. 2010 Feb 05; 659(1-2):229-37.AC

Abstract

Several wavelength variable selection algorithms were compared to analyze visible and near-infrared (Vis-NIR) spectra for the non-invasive quantitative determination of soluble solids content (SSC) and pH in grape juice. In order to eliminate useless variables and improve the signal/noise ratio, the pretreated full spectra were firstly calculated by different informative variable selection methods. Uninformation variable elimination (UVE) did better than interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS). Successive projections algorithm (SPA) was further operated to select variables. Finally, nine and eleven variables were obtained for respectively SSC and pH analyses. The better results of UVE-SPA-PLS models compared to those of SPA-PLS models in both SSC and pH analyses show that it is necessary to execute UVE before SPA, which can both reduce the calculation time and increase the model's performance. Furthermore, two common used calibration methods, PLS and multiple linear regression (MLR), were compared. UVE-SPA-MLR obtained better results than UVE-SPA-PLS in both SSC and pH analyses. The coefficients of determination for prediction set (r(p)(2)) and residual predictive deviation (RPD) obtained by UVE-SPA-MLR are 0.979 and 6.971 for SSC, and 0.951 and 5.432 for pH. The overall results demonstrate that it is feasible to non-invasively determine SSC and pH of grape juice using Vis-NIR spectroscopy, UVE-SPA is a powerful tool to select the efficient variables, and UVE-SPA-MLR is simple and excellent for the spectral calibration.

Authors+Show Affiliations

College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan Road, Hangzhou, Zhejiang 310029, China. ilkb8mvp@yahoo.com.cnNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20103129

Citation

Wu, Di, et al. "Hybrid Variable Selection in Visible and Near-infrared Spectral Analysis for Non-invasive Quality Determination of Grape Juice." Analytica Chimica Acta, vol. 659, no. 1-2, 2010, pp. 229-37.
Wu D, He Y, Nie P, et al. Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Anal Chim Acta. 2010;659(1-2):229-37.
Wu, D., He, Y., Nie, P., Cao, F., & Bao, Y. (2010). Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. Analytica Chimica Acta, 659(1-2), 229-37. https://doi.org/10.1016/j.aca.2009.11.045
Wu D, et al. Hybrid Variable Selection in Visible and Near-infrared Spectral Analysis for Non-invasive Quality Determination of Grape Juice. Anal Chim Acta. 2010 Feb 5;659(1-2):229-37. PubMed PMID: 20103129.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Hybrid variable selection in visible and near-infrared spectral analysis for non-invasive quality determination of grape juice. AU - Wu,Di, AU - He,Yong, AU - Nie,Pengcheng, AU - Cao,Fang, AU - Bao,Yidan, Y1 - 2009/11/26/ PY - 2009/09/08/received PY - 2009/11/17/revised PY - 2009/11/19/accepted PY - 2010/1/28/entrez PY - 2010/1/28/pubmed PY - 2010/3/31/medline SP - 229 EP - 37 JF - Analytica chimica acta JO - Anal Chim Acta VL - 659 IS - 1-2 N2 - Several wavelength variable selection algorithms were compared to analyze visible and near-infrared (Vis-NIR) spectra for the non-invasive quantitative determination of soluble solids content (SSC) and pH in grape juice. In order to eliminate useless variables and improve the signal/noise ratio, the pretreated full spectra were firstly calculated by different informative variable selection methods. Uninformation variable elimination (UVE) did better than interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS). Successive projections algorithm (SPA) was further operated to select variables. Finally, nine and eleven variables were obtained for respectively SSC and pH analyses. The better results of UVE-SPA-PLS models compared to those of SPA-PLS models in both SSC and pH analyses show that it is necessary to execute UVE before SPA, which can both reduce the calculation time and increase the model's performance. Furthermore, two common used calibration methods, PLS and multiple linear regression (MLR), were compared. UVE-SPA-MLR obtained better results than UVE-SPA-PLS in both SSC and pH analyses. The coefficients of determination for prediction set (r(p)(2)) and residual predictive deviation (RPD) obtained by UVE-SPA-MLR are 0.979 and 6.971 for SSC, and 0.951 and 5.432 for pH. The overall results demonstrate that it is feasible to non-invasively determine SSC and pH of grape juice using Vis-NIR spectroscopy, UVE-SPA is a powerful tool to select the efficient variables, and UVE-SPA-MLR is simple and excellent for the spectral calibration. SN - 1873-4324 UR - https://www.unboundmedicine.com/medline/citation/20103129/Hybrid_variable_selection_in_visible_and_near_infrared_spectral_analysis_for_non_invasive_quality_determination_of_grape_juice_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0003-2670(09)01556-6 DB - PRIME DP - Unbound Medicine ER -