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,whi...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.展开更多
文摘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.
