Linear and non-linear modeling to identify vinegars in blends through spectroscopic data

- Discrimination of binary blends of vinegars with different botanical origins.
- Inexpensive and quick chemical approach based on UV-vis spectroscopy.
- Real-time quality control of vinegars based on current Spanish legislation.
- No pre-treatment required for the discrimination of vinegar binary blends.
- Possible fraudulent vinegar blending identification and quality control.

The identification of vinegars produced from six different raw materials (red wine, white wine, cider, apple, molasses, and rice) in blends has been accomplished through their UV-vis spectra and different mathematical models: partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANNs). The registered spectra were mathematically treated following a linear (PLS-DA) approach and a non-linear one (ANN) based on multilayer perceptron models with different training functions. The average correct classification rate of a series of comparable internal validations was around 55% and 90%, for the PLS-DA and the ANN models respectively, which heavily favors the non-linear approach. Therefore, an accurate chemometric tool with the ability to detect specific vinegars in mixtures in an inexpensive and straightforward fashion has been designed and optimized.