Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples

Abstract

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.