At‐line monitoring of industrial frying processes using ATR‐FTIR‐PLS method

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.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil

“…Table 1 presents the variables of interest (IV), the number of bands (N) of the preprocessing MIR spectra, RMSEC, REMSECV, RMSEP, the number of latent variables (LV), calibration R 2 (R 2 cal ), cross-validation R 2 (R 2 val ), and the R 2 of prediction (R 2 pred ). In a study by Fetter et al [63], a quadratic cross-validation error was observed, with an average value of 1.6895 with 13 latent variables in the two models proposed to predict AI in frying oils. Pre-processing in the first model employed data normalization, 1D, and MC.…”

Rapid Determination of Total Polar Compounds in Frying Oil Using ATR‐FTIR Combined with Extended Partial Least Squares Regression

“…Recently, Fetter et al. [ 37 ] also reported an ATR‐FTIR‐PLS method for determining TPCs in soybean oil used for industrial frying. They similarly optimized the model intervals with iPLS and synergy interval PLS (siPLS), finding that when spectral regions of 3200–2406, 2087–1929, 1769–1611, and 1133–975 cm −1 were used to build a TPC calibration model, the model performed best, with an RMSEP of 1.68 and an RMSEC of 0.74.…”

French Fries’ Color and Frying Process in Relation to Used Plant Oils

“…The most reliable way for the determination of stability and quality of frying oils during food preparation is the measurement of total polar matter (TPM). Those polar compounds are mainly dimers and polymers of triglycerides formed in oil at high temperatures [8]. The disposal of frying oil is recommended when the level of TPM reaches 24% (for US, Germany, and France) [9].…”