Identification and Quantification of Olive Oil Quality Parameters Using an Electronic Nose

Abstract

An electronic nose (EN), which is a kind of chemical sensors, was employed to check olive oil quality parameters. Fifty samples of olive oil, covering the four quality categories extra virgin, virgin, ordinary virgin and lampante, were gathered from different Palestinian cities. The samples were analysed chemically using routine tests and signals for each chemical were obtained using EN. Each signal acquisition represents the concentration of certain chemical constituents. Partial least squares (PLS) models were used to analyse both chemical and EN data. The results demonstrate that the EN was capable of modelling the acidity parameter with a good performance. The correlation coefficients of the PLS-1 model for acidity were 0.87 and 0.88 for calibration and validation sets, respectively. Furthermore, the values of the standard error of performance to standard deviation (RPD) for acidity were 2.61 and 2.68 for the calibration and the validation sets, respectively. It was found that two principal components (PCs) in the PLS-1 scores plot model explained 86% and 5% of EN and acidity variance, respectively. PLS-1 scores plot showed a high performance in classifying olive oil samples according to quality categories. The results demonstrated that EN can predict/model acidity with good precision. Additionally, EN was able to discriminate between diverse olive oil quality categories.