Spectralprint techniques coupled with chemometric tools for vinegar classifications.

Area of Science:

• Analytical Chemistry
• Food Science
• Chemometrics

Background:

• Vinegar is a widely used product with applications in food preservation, cooking, healthcare, and cleaning.
• Accurate classification of vinegar is crucial for quality control and authenticity verification.
• Traditional classification methods may not fully capture the complex variations in vinegar composition.

Purpose of the Study:

• To classify 80 vinegar samples based on raw material, aging time, and agronomic cultivation method using spectralprint techniques and chemometrics.
• To evaluate the performance of different spectral techniques (NMR, FT-IR, NIR, UV-vis) coupled with chemometric methods (ComDim, PLS-DA) for vinegar classification.
• To determine the most effective spectral technique for distinguishing vinegar based on its origin and production parameters.

Main Methods:

• Acquisition of spectral datasets using proton nuclear magnetic resonance (¹H NMR), Fourier transform mid-infrared (FT-IR), near-infrared (NIR), and ultraviolet-visible (UV-vis) spectroscopy.
• Application of chemometric methods, including common dimension (ComDim) and partial least squares-discriminant analysis (PLS-DA), for data analysis and classification.
• Classification of vinegar samples based on raw material, aging time, and agronomic cultivation method.

Main Results:

• Proton nuclear magnetic resonance (¹H NMR) combined with partial least squares-discriminant analysis (PLS-DA) demonstrated the highest prediction performance, achieving accuracy rates between 92.3% and 100%.
• Fourier transform mid-infrared (FT-IR) and ultraviolet-visible (UV-vis) spectroscopy showed good performance, with accuracy ranging from 80.8% to 96.0%.
• Near-infrared (NIR) spectroscopy yielded classification accuracies between 69.2% and 84.0%.
• Classification based on the agronomic cultivation method was found to be more complex than classification by aging time or raw material.

Conclusions:

• Spectralprint techniques, particularly ¹H NMR with PLS-DA, are effective tools for the accurate classification of vinegar.
• The study successfully demonstrated the potential of these spectroscopic and chemometric methods for quality control and authentication of vinegar products.
• Agronomic factors present a significant challenge in vinegar classification, highlighting the need for advanced analytical approaches.