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UV–Vis spectroscopy coupled with chemometrics was used effectively to study the impact of heating on edible oils (corn oil, sunflower oil, rapeseed oil, peanut oil, soybean oil and sesame oil) and determine their acid value. Analysis of their first derivative spectra showed that the peak at 370 nm was a common indicator of the heated oils. Partial least squares regression (PLS) and principle component regression (PCR) were applied to building individual quantitative models of acid value for each kind of oil, respectively. The PLS models had a better performance than PCR models, with determination coefficients ( R 2 ) of 0.9904–0.9977 and root mean square errors (RMSE) of 0.0230–0.0794 for the prediction sets of each kind of oil, respectively. An integrate quantitative model built by support vector regression for all the six kinds of oils was also developed and gave a satisfactory prediction with a R 2 of 0.9932 and a RMSE of 0.0656. [ABSTRACT FROM AUTHOR]