A weighted principal component analysis approach for online quality determination of biodiesel using spectrophotometry data

Abstract

ABSTRACT Biodiesel fuels deteriorate when exposed to air or high temperatures. In this study, biodiesel was produced through transesterification of sunflower oil and kept at 25, 45, and 70°C for 28 days. The spectrophotometry graphs of the samples were prepared and used as inputs to three classification schemes of linear discrimination analysis, principal component analysis (PCA), and weighted PCA (WPCA) to categorize the fuels into three groups, based on their acid values. The resulting average recognition accuracies were 82%, 84%, and 96% for the three classifiers, respectively. The analysis of the classifiers outputs indicated that the wavelengths between 200 and 240 nm had the higher significant role in determining the quality of the fuels. The model assessment and error analysis of the results were performed using Hotelling T 2 statistic and Q-residual index. The analysis indicated the WPCA model, at a 5% significance level, had higher power for accurately determining the quality of biodiesel samples using their spectrophotometry data.