Detection and classification of diesel-biodiesel blends by LDA, QDA and SVM approaches using an electronic nose

Abstract

This study addressed the detection and classification of biodiesel from different sources using electronic nose through application of statistical training-based methods and mathematical optimization techniques. Biodiesel fuels obtained from canola oil with methanol (MK), corn oil with methanol (MZ), canola oil with ethanol (EK) and corn oil with ethanol (EZ) as well as a combined fuel (EK&MZ) were mixed with different volume percentages (2, 5, 10, 20, 80 and 100) of the petroleum diesel. Data collection was performed by application of an electronic nose equipped with 8 metal oxide semiconductor (MOS) sensors. Data analysis was conducted using different methods including linear and quadratic discriminant analysis (LDA and DDA) as well as the support vector machine (SVM). Based on the results, SVM, QDA and LDA had classification precisions of 94.8%, 94.1% and 87.1%, respectively. Moreover, the discrimination and classification precision of SVM was higher (about 95.4%) for the two groups of pure and impure fuels (various mixtures of diesel and biodiesel). For QDA and LDA methods, this precision value was 84.4% and 75.5%, respectively. Classification of B5 fuels was better in all the methods when compared with B2 and B20 fuels. Detection and classification precision of B5 biodiesels was 100%, 97.6% and 96.1% for LDA, QDA and SVM methods, respectively. Application of the overall desirability function showed that QDA method had better performance when compared to LDA and SVM as it had higher discrimination and classification ability. The performance parameters of this model were 0.941, 0.941, 0.975 and 0.850 for mean precision, sensitivity, specificity and final desirability, respectively.