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

• Published in: Chemometrics and intelligent laboratory systems Vol. 71; no. 2; pp. 141 - 150
• Main Authors: Chauchard, F., Cogdill, R., Roussel, S., Roger, J.M., Bellon-Maurel, V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28-05-2004; Elsevier
• Subjects: Environmental Sciences; Grapes; LS-SVM; MLR; NIR spectroscopy; PLSR; Robust calibration; Tartaric and Malic acidity; MLR; LS-SVM; Grapes; Robust calibration; Tartaric and Malic acidity; NIR spectroscopy; PLSR; REGRESSION; MODELE; ACIDITE; FRUIT; INFRAROUGE; SPECTROSCOPIE; RAISIN
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2004.01.003

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.