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.

Fuzzy logic classification of mature tomatoes based on physical properties fusion

Grading of fruits and vegetables is an initial step after harvesting.It is also an essential operation before packaging.In the present study,different fuzzy algorithms for classifica-tion of mature tomato were applied and evaluated based on combinations of fruit color,size and hardness.Fuzzy membership functions of hardness were established by subject-ing samples to Instron compression test as well as the rates of panelists.Each sample was also used for image processing to determine the color and size of fruit using Matlab image processing toolbox.Color and size fuzzy membership functions were established by pub-lished standard.The fuzzy If-Then rules were applied to classify the samples within five group outputs viz.“grade I”,“grade II”,“grade I-far market”,“processing”,and“storage”.Eighty-one fuzzy rules were reduced to 25 rules by combining the compatible rules.Six fuzzy algorithms with different fuzzifiers(zmf,sigmf,gbellmf)and defuzzifiers(bisector,mom,and centroid)were applied,and the outputs were compared to the panelists’classi-fications in cross tables.According to the classification results,fuzzy algorithms grouped the fruits into correct classes with 90.9%,92.3%,88.7%,87.4%,92.4%and 93.3%accuracy for 6 models,respectively.The best result was observed with zmf and sigmf,and gbellmf as fuzzifier and mom as defuzzifier with 93.3%accuracy.Overly,the results revealed that the fusion of aforementioned tomato properties based on fuzzy membership functions could accurately classify the tomatoes in correct groups for different markets.