Determination of the oxidative stability of olive oil using an integrated system based on dielectric spectroscopy and computer vision

During storage, olive oil may suffer degradation leading to an inferior quality level when purchased and consumed. Oxidative stability is one of the most important parameters for maintaining the quality of olive oil, which affects its acceptability and market value. The current methods of predicting the oxidative stability of edible oils are costly and time-consuming. The aim of the present research is to demonstrate the use of dielectric spectroscopy integrated with computer vision for determining the oxidative stability index (OSI) of olive oil. The most effective features were selected from the extracted dielectric and visual features for each olive oil sample. Three machine learning techniques were employed to process the raw data to develop an oxidative stability prediction algorithm, including artificial neural network (ANN), support vector machine (SVM) and multiple linear regression (MLR). The predictive models showed a great agreement with the results obtained by the Rancimat instrument that was used as a reference method. The best result for modelling the oxidative stability of olive oil was obtained using SVM technique with the R-value of 0.979. It can be concluded that this new approach may be utilized as a perfect replacement for quicker and cheaper assessment of olive oil oxidation

Fusion of dielectric spectroscopy and computer vision for quality characterization of olive oil during storage

Oxidation level and quality characteristics of olive oil require monitoring during storage to ensure that their amounts are maintained in the lawful thresholds. It is especially important for licensing their commercialization as high-value virgin olive oils. The present research proposes a novel approach based on the fusion of dielectric spectroscopy and computer vision for the characterization of olive oil quality indices during storage in order to reduce the time of analysis, reagent consumption, manpower and high-cost equipment. Colour features in RGB, HSV and L∗a∗b∗ spaces were extracted as well as dielectric features in the frequency range of 40 kHz to 20 MHz for each olive oil sample. After data pre-processing, classification and prediction models were developed and compared. Several machine learning techniques were investigated for storage time classification and quality indices prediction including artificial neural network (ANN), support vector machine (SVM), Bayesian network (BN) and multiple linear regression (MLR). The best result in the classification of olive oils during the storage period was obtained by BN technique with 100% accuracy. Among predictive models, the SVM with RBF kernel had the best results (R = 0.969, 0.988 and 0.976) for prediction of peroxide value (PV), UV absorbance at 232 nm (K₂₃₂) and chlorophyll, respectively. Also, the SVM with normalized polynomial kernel had the best results (R = 0.989, 0.976, 0.969 and 0.969) for prediction of p-Anisidine value (AV), total oxidation value (TOTOX), UV absorbance at 268 nm (K₂₆₈) and carotenoid, respectively. The ANN with 40-2-1 topology gave the best result (R = 0.977) for modelling free acidity (FA). Results of this research can be utilized for developing an efficient and reliable system for olive oil quality evaluation and monitoring by industry

Design, Construction and Performance Evaluation of a Metal Oxide Semiconductor (MOS) Based Machine Olfaction (Electronic Nose) for Monitoring of Banana Ripeness

Aroma is one of the most important sensory properties of fruits and is particularly sensitive to the changes in fruit compounds. Gases involved in aroma of fruits are produced from the metabolic activities during ripening, harvest, post-harvest and storage stages. Therefore, the emitted aroma of fruits changes during the shelf-life period. The electronic nose (machine olfaction) would simulate the human sense of smell to identify and realize the complex aromas by using an array of chemical sensors. In this research, a low cost electronic nose based on six metal oxide semiconductor (MOS) sensors were designed, developed and implemented and its ability for monitoring changes in aroma fingerprint during ripening of banana was studied. The main components are used in the e-nose system include sampling system, an array of gas sensors, data acquisition system and an appropriate pattern recognition algorithm. Linear Discriminant Analysis (LDA) technique was used for classification of the extracted features of e-nose signals. Based on the results, the classification accuracy of 97/3% was obtained. Results showed the high ability of e-nose for distinguishing between the stages of ripening. It is concluded that the system can be considered as a nondestructive tool for quality control during banana shelf-life