Multivariate near infrared spectroscopy models for predicting the oxidative stability of biodiesel: Effect of antioxidants addition

Abstract Biodiesel, a mixture of long chain fatty acid esters, is an environmental friendly alternative to fossil fuel. This fuel is produced by a transesterification reaction between vegetable oils or animal fats and a short chain alcohol, usually methanol, in the presence of a catalyst. European governments are targeting the incorporation of 10% of biofuels in transportation fuels by 2020. Therefore, the global market for biodiesel is expected to have a significant growth in the next 10 years. According to the European legislation, from the 25 parameters that have to be analyzed to certify biodiesel quality, oxidative stability is of concern, especially when storing biodiesel for long periods. In fact, that property measures the susceptibility of biodiesel to oxidative degradation and is strongly dependent on the type of oil used in the production process and on storage conditions. Thus, EN 14214 establishes a minimum value of 6 h for the oxidative stability of biodiesel under stressed conditions of a standardized assay. This work reports the use of near infrared spectroscopy (NIRS), coupled with multivariable classification and calibration techniques, to determine the oxidative stability of biodiesel with and without antioxidants. Therefore, biodiesel samples produced from soybean, palm, rapeseed, sunflower and waste frying oils, from mixtures of these oils and also several of these samples after different storage conditions and storage periods some of them containing antioxidants (induction periods between 0.66 and 17.75 h) were used to develop the calibration models. The model for samples without antioxidants is able to estimate the oxidative stability of unknown samples with a root mean square error of prediction (RMSEP) of 0.6 h, which is similar to the reference method error (0.5 h). The introduction of samples containing antioxidants in the calibration/validation sets led to higher prediction errors (RMSEP = 1.28 h) that may be considered acceptable.