The evaluation of the applicability of Fourier Transform Near-Infrared (FT-NIR) spectroscopy in the measurement of analytical parameters in must and wine

Manley, M. ; Van Zyl, A. ; Wolf, E. E. H. (2001)

CITATION: Manley, M., Van Zyl, A. & Wolf, E. E. H. 2001. The evaluation of the applicability of Fourier Transform Near-Infrared (FT-NIR) spectroscopy in the measurement of analytical parameters in must and wine. South African Journal of Enology & Viticulture, 22(2):93-100, doi:10.21548/22-2-2201.

The original publication is available at http://www.journals.ac.za/index.php/sajev

Article

Fourier transform near-infrared (FT-NIR) spectroscopy can be used as a rapid method to measure the percentage of sugar and to discriminate between different must samples in terms of their free amino nitrogen (FAN) values. It can also be used as a rapid method to discriminate between Chardonnay wine samples in terms of their malolactic fermentation (MLF) status. By monitoring the conversion of malic to lactic acid, the samples could be classified on the basis of whether MLF has started, is in progress or has been completed. Furthermore, FT-NIR spectroscopy can be used as a rapid method to discriminate between table wine samples in terms of their ethyl carbamate (EC) content. It is claimed that high concentrations of ethyl carbamate in wine can pose a health threat and has to be monitored by determining the EC content in relation to the regulatory limits set by authorities. For each of the above-mentioned parameters QUANT+™ methods were built and calibrations were derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of the percentage of sugar (r = 0.99, SEP= 0.31 °Brix). However, the correlation for the FAN predictions (r = 0.602, SEP= 272.1 g.L-1), malic acid (r = 0.64, SEP= 1.02 g.L-1), lactic acid (r = 0.61, SEP= 1.35 g.L-1) and EC predictions (r = 0.47, SEP = 3.6 μg.kg·1) were not good. The must samples could be classified in terms of their FAN values when Soft Independent Modelling by Class Analogy (SIMCA) diagnostics and validation were applied as a discriminative method, with recognition rates exceeding 80% in all cases. When SIMCA diagnostics and validation were applied to the Chardonnay and EC wine samples, recognition rates exceeding 88% and 80% respectively were obtained. These results therefore confirm that this method is successful in discriminating between samples.

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