Display options
Cite
Janik LJ, Cozzolino D, Dambergs R, et al. The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks. Anal Chim Acta. 2007;594(1):107-18doi: 10.1016/j.aca.2007.05.019.
Janik, L. J., Cozzolino, D., Dambergs, R., Cynkar, W., & Gishen, M. (2007). The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks. Analytica chimica acta, 594(1), 107-18. https://doi.org/10.1016/j.aca.2007.05.019
Janik, L J, et al. "The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks." Analytica chimica acta vol. 594,1 (2007): 107-18. doi: https://doi.org/10.1016/j.aca.2007.05.019
Janik LJ, Cozzolino D, Dambergs R, Cynkar W, Gishen M. The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks. Anal Chim Acta. 2007 Jun 26;594(1):107-18. doi: 10.1016/j.aca.2007.05.019. Epub 2007 May 21. PMID: 17560392.
Copy Download .nbib Format: NLM
Share it on

Anal Chim Acta. 2007 Jun 26;594(1):107-18. doi: 10.1016/j.aca.2007.05.019. Epub 2007 May 21.

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks.

Analytica chimica acta

L J Janik, D Cozzolino, R Dambergs, W Cynkar, M Gishen

Affiliations

  1. The Australian Wine Research Institute, P.O. Box 197, Glen Osmond, Adelaide 5064, SA, Australia. [email protected]

PMID: 17560392 DOI: 10.1016/j.aca.2007.05.019

Abstract

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.

Publication Types