Using an E‐nose machine for detection the adulteration of margarine in cow ghee

Ayari, Fardin; Mirzaee‐Ghaleh, Esmaeil; Rabbani, Hekmat; Heidarbeigi, Kobra

doi:10.1111/jfpe.12806

Cited by 34 publications

(13 citation statements)

References 33 publications

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“…Ayari et al. (2018b) also reported similar results regarding the adulteration detection in animal oil and edible oil (Ayari et al., 2018a, 2018b).…”

Section: Resultsmentioning

confidence: 56%

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Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

Karami

Rasekh

Mirzaee‐Ghaleh

2020

J Food Process Preserv

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Foodstuff adulteration involves addition of any low‐cost substances to the high‐price materials to reduce the content of the expensive components, and hence decrease the production cost and reach to the maximum profit. An electronic nose was used in this study to detect the adulterations in mixed edible oils. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed by Cluster analysis (CA), principle component analysis (PCA), principal component regression (PCR), linear discriminant analysis (LDA), and artificial neural network (ANN) methods with accuracy of 95, 98, 98, 88, and 97.3%, respectively. According to the results, the ANN method with structure of 8‐7‐5 showed the highest accuracy in classification of oil adulteration. Its correct classification ratio, mean square errors, and correlation (r) were 97.3%, .117211, and .0963, respectively. The results also indicated that the proposed method can be used as an alternative of the official AOCS methods to innovatively detect the edible oil adulteration with high accuracy and speed. Practical applications Lipid oxidation is one of the major causes of food spoilage especially in those containing oil. AOCS has developed various methods to evaluate the oxidation status of the oil assets. However, these chemical tests are time‐consuming, destructive, and costly and require several glassware and reagents. E‐nose could be used for real‐time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E‐nose evaluates mixture of smells released form a sample and is a reliable, nondestructive, cost‐effective, and portable method with high feasibility and speed as well as simple use. CA, PCA, and ANN methods were also applied for qualitative differentiation of different adulteration percentages in oxidized and nonoxidized oils.

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“…Ayari et al. (2018b) also reported similar results regarding the adulteration detection in animal oil and edible oil (Ayari et al., 2018a, 2018b).…”

Section: Resultsmentioning

confidence: 56%

“…In this study, the electronic nose fabricated in Razi University (Ayari, Mirzaee‐Ghaleh, Rabbani, & Heidarbeigi, 2018b) was employed to detect adulteration in edible oil. The employed system included two sections: hardware and software.…”

Section: Methodsmentioning

confidence: 99%

Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

Karami

Rasekh

Mirzaee‐Ghaleh

2020

J Food Process Preserv

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“…The values of the learning rate and momentum coefficient were 0.02 and 0.9, respectively. Neural network classification performance by the percentage of accuracy and precision of the confusion matrix was determined, using the following equations (Ayari, Mirzaee‐Ghaleh, Rabbani, & Heidarbeigi, 2018):

Accuracy = \frac{N_{TP} + N_{TN}}{N_{TP} + N_{TN} + N_{FP} + N_{FN}}

Precision = \frac{N_{TP}}{N_{TP} + N_{FP}}

where N TP , N TN , N FP , and N FN are the number of samples that are classified as true positive, true negative, false positive, and false negative, respectively.…”

Section: Methodsmentioning

confidence: 99%

Detection of ripeness grades of berries using an electronic nose

Aghilinategh

Dalvand

Anvar³

2020

Food Science & Nutrition

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The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e‐nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e‐nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10–11‐5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.

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“…Extensive survey of the literature reveals that in the past, several techniques have been employed to detect the adulterants in ghee (Rani et al, 2013;Wasnik et al, 2017;Hazra et al, 2017 andAyari et al, 2018), however it is realized that they have their own limitations in establishing the type and the level of the adulterants.…”

Section: Issn: 2319-7706 Volume 7 Number 12 (2018)mentioning

confidence: 99%