Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration

Firouz, Mahmoud Soltani; Omid, Mahmoud; Babaei, Mehrdad; Rashvand, Mahdi

doi:10.1016/j.inpa.2021.05.001

Cited by 11 publications

(6 citation statements)

References 34 publications

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“…FS means reducing the dataset features in data preprocessing [ 25 ]. This process was undertaken in order to 1- reduce the dataset dimension for a better understanding of the data, 2- enhance the data mining algorithm's performance, 3- prevent algorithm overfitting, 4- accelerate algorithm development, and 5- simplify data visualization [ [26] , [27] , [28] , [29] ]. This study used the Chi-square test and Eta coefficient method to determine the best factors affecting mortality in COVID-19 patients.…”

Section: Methodsmentioning

confidence: 99%

Design of an artificial neural network to predict mortality among COVID-19 patients

Shanbehzadeh

Nopour²,

Kazemi-Arpanahi³

2022

Informatics in Medicine Unlocked

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Section: Methodsmentioning

confidence: 99%

Design of an artificial neural network to predict mortality among COVID-19 patients

Shanbehzadeh

Nopour²,

Kazemi-Arpanahi³

2022

Informatics in Medicine Unlocked

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“…The accuracy of the resulting ANN model is almost 98% ( Table 4 ) with only three samples misclassified ( Table 2 ). This finding can be compared with the study conducted by Firouz et al [ 52 ], who employed the classification and quantification of sesame oil adulteration and acquired 100% accuracy.…”

Section: Discussionmentioning

confidence: 70%

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Islam,

Kaczmarek,

Montowska

et al. 2023

Foods

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Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis—LDA, adaptive regression splines—MARS, support vector machine—SVM, and artificial neural networks—ANNs); (2) regression models (multiple linear regression—MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

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“…The accuracy of the resulting ANN model is almost 98% (Table 3) with only 3 samples misclassified (Table 2). This finding can be compared with the study conducted by Firouz et al [52], who employed the classification and quantification of sesame oil adulteration, and acquired 100% accuracy.…”

Section: Classification Models For Predicting Cold-pressed Flaxseed O...mentioning

confidence: 73%

“…The correlation between the observed and predicted values was 0.996 with a low RMSE value of 1.51(Table 7). The study found that ANN regression analysis demonstrated robust models for adulteration phenomena in sesame oil generated by sunflower oil, canola oil and sunflower + canola oils quantitatively [52]. Another study stated that using ANN as a pattern recognition technique for the data obtained from electronic nose could not detect the proportion of adulteration in camellia seed oil, but successfully quantified adulteration in sesame oil [53].…”

Section: Regression Models For Predicting the Concentration Of Refine...mentioning

confidence: 98%

Different Chemometric Approaches to Detect Adulteration of Cold‐Pressed Flaxseed oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Islam¹,

Kaczmarek²,

Montowska³

et al. 2023

Preprint

View full text Add to dashboard Cite

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to use the differential scanning calorimetry (DSC) technique to detect adulterations of cold-pressed flaxseed oil with refined rapeseed oil (RP). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with a RP concentration of 5, 10, 20, 30, 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for h1. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: 1) classification models (Linear Discriminant Analysis, LDA Adaptive Regression Splines, MARS, Support Vector Machine, SVM, Artificial Neural Networks, ANNs); 2) regression models (Multiple Linear Regression, MLR, MARS, SVM, ANNs, PLS) and 3) a combined model of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN&gt; SVM &gt; MARS was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R= 0.996), while other models showed goodness of fit as following MARS&gt; SVM&gt; MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) =0.986 and Q2 =0.973 were observed with the PLS model than OPLS-DA. These results demonstrate the usefulness of the DSC technique combined with chemometrics for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

show abstract

Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration

Cited by 11 publications

References 34 publications

Design of an artificial neural network to predict mortality among COVID-19 patients

Design of an artificial neural network to predict mortality among COVID-19 patients

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

Different Chemometric Approaches to Detect Adulteration of Cold‐Pressed Flaxseed oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry

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