Optimisation of PLS Calibrations for Filtered and Untreated Samples towards In-Line Monitoring of Phenolic Extraction during Red-Wine Fermentations

Abstract

Infrared spectroscopy provides an efficient, robust, and multivariate means to measure phenolic levels during red-wine fermentations. However, its use is currently limited to off-line sampling. In this study, partial least squares (PLS) regression was used to investigate the possibility of using spectral data from minimally pre-treated or untreated samples for the optimisation of prediction calibrations towards an in-line monitoring set-up. The evaluation of the model performance was conducted using a variety of metrics. Limits of detection and quantification of the PLS calibrations were used to assess the ability of the models to predict lower levels of phenolics from the start of fermentation. The calibrations were shown to be useful for the quantification of phenolic compounds and phenolic parameters with minimal or no sample pre-treatment during red-wine fermentation. Upon evaluation of performance, the calibrations built for attenuated-transmission Fourier-transform mid-infrared (ATR-FT-MIR) and diffuse-reflectance Fourier-transform near-infrared (DR-FT-NIR) were shown to be the most suitable spectroscopy techniques for eventual application in an automated and in-line system with values for limits of detection and quantification being suitable for the entire duration of fermentation.