Development of a chemometrics-assisted electrochemical sensor applied to gallic acid quantification in food samples

Gallic acid (GA) is an abundant natural phenolic compound found in foods such as tea, fruits, and beverages. Quantifying GA remains a key research area in analytical chemistry. Traditional methods, such as liquid chromatography, are time-consuming, highlighting the need for faster and more efficient techniques to quantify GA concentration. This study proposes an approach based on the MCR-ALS algorithm to model second-order voltammetric data obtained by varying the scan rate. Data preprocessing and subsequent mathematical modeling enabled the quantification of GA with a detection limit of 5.9 mg L^-1, below the stipulated concentrations for the analyte in various food matrices. Quantification was achieved even in the presence of unmodeled interferences, leveraging the second-order capabilities of multivariate calibration methods. This approach allows for accurate results to be obtained in a direct, fast, and reliable manner, representing a breakthrough in food industry quality control and opening new perspectives for food quality assessment.