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The effect of path length on the measurement accuracies of wine chemical parameters by UV, visible, and near-infrared spectroscopy

journal contribution
posted on 2018-03-09, 00:00 authored by N Molla, I Bakardzhiyski, Y Manolova, V Bambalov, Daniel Cozzolino, L Antonov
The use of spectral measurements using either UV, visible (VIS), or near-infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition has been extensively reported. However, little is known about the effect of path length on the UV, VIS, and NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with path length and temperature being one of the most important factors affecting the intensity of the absorptions. In this study, the effect of path length on the standard error of UV, VIS, and NIR calibration models to predict phenolic compounds was evaluated. Nineteen red and 13 white wines were analyzed in the UV, VIS, and NIR regions (200–2500 nm) in transmission mode using two effective path lengths 0.1 and 1 mm. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for phenolic compounds. These results indicated that path length has an effect on the standard error of cross validation (SECV) absolute values obtained for the PLS calibration models used to predict phenolic compounds in both red and white wines. However, no statistically significant differences were observed (p > 0.05). The practical implication of this study was that the path length of scanning for wines has an effect on the calibration accuracies; however, they are non-statistically different. Main differences were observed in the PCA score plot. Overall, well-defined protocols need to be defined for routine use of these methods in research and by the industry. © 2016 Springer Science+Business Media New York








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Springer Science and Business Media, USA

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Bulgarian Academy of Sciences; University of Food Technologies Plovdiv, Bulgaria; Agricultural University Plovdiv, Bulgaria

Author Research Institute

  • Institute for Future Farming Systems

Era Eligible

  • Yes


Food Analytical Methods