No abstract

This study investigated the application of infrared spectroscopy with multivariate calibration methods for at‐line monitoring of the degradation of soybean oil in industrial frying processes by determining when the acidity index and total polar materials (TPM). The infrared spectra (650–3,200 cm−1) were acquired using the attenuated total reflection accessory (ATR‐FTIR), with a resolution of 4 cm−1, and 16 scans. Partial least‐squares regression (PLS) models were evaluated for individual and simultaneous determination, and results were compared with reference methods. The individual calibration model showed standard error of prediction values of 0.09% (w/w) and 1.6% (w/w) for the acidity index and TPM, respectively. The simultaneous determination of the acidity index and TPM showed SEP values of 0.17% (w/w) and 1.6% (w/w), respectively. The results demonstrate that infrared spectroscopy combined with multivariate calibration techniques can be servant used in routine soybean oil quality control in industrial frying processes. Practical applications Foods prepared by the frying process are strongly influenced by the oil used in the process, since the product tends to absorb part of the oil during its cooking. This oil when used for a long period and exposed to high temperatures, end up suffering oxidation reactions, and can greatly reduce your shelf life. In this way the industries that use these processes for the manufacture of their products, apply a series of chemical analyzes in order to validate the quality of the oil used, these analyzes use a high amount of solvents and time. As an alternative to the use of these traditional analyzes, methods have been developed through the use of infrared spectroscopy combined with multivariate analysis tools, for the creation of partial least squares calibration models for the prediction of quality parameters of oils used in frying.