Title

Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes

DOI

10.1016/j.chemolab.2004.01.003

Document Type

Journal Article

Publication Date

5-28-2004

Publication Title

Chemometrics and Intelligent Laboratory Systems

Volume

71

Issue

2

First Page

141

Last Page

150

ISSN

1697439

Keywords

Grapes, LS-SVM, MLR, NIR spectroscopy, PLSR, Robust calibration, Tartaric and Malic acidity

Abstract

Nowadays, near infrared (NIR) technology is being transferred from the laboratory to the industrial world for on-line and portable applications. As a result, new issues are arising, such as the need for increased robustness, or the ability to compensate for non-linearities in the calibration or instrument. Semi-parametric modeling has been suggested as a means for adapting to these complications. In this article, Least-Squared Support Vector Machine (LS-SVM) regression, a semi-parametric modeling technique, is used to predict the acidity of three different grape varieties using NIR spectra. The performance and robustness of LS-SVM regression are compared to Partial Least Square Regression (PLSR) and Multivariate Linear Regression (MLR). LS-SVM regression produces more accurate prediction. However, SNV pretreatment is required to improve the model robustness. © 2004 Elsevier B.V. All rights reserved.

Open Access

Green Accepted

Share

COinS