Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat)

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

doi:10.1080/10942912.2018.1505755

Cited by 41 publications

(17 citation statements)

References 24 publications

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“…These results were considerably higher than the results obtained by Ayari et al. (2018a) for detecting adulteration between the animal and edible oil. To evaluate the oxidation of Chinese‐style sausage fat with determination coefficients ( R 2 s), more than .914 and .814 were obtained during processing and storage (Gu, Sun, Tu, & Pan, 2017).…”

Section: Resultscontrasting

confidence: 69%

“…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%

“…The applied E‐nose is schematically illustrated in Figure 1. The sensor array used in this study was composed of eight metal oxide semiconductor (MOS) sensors whose features are listed in Table 1 (Ayari, Mirzaee‐Ghaleh, Rabbani, & Heidarbeigi, 2018a).…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Resultscontrasting

confidence: 69%

“…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%

See 1 more Smart Citation

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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“…Statistical analysis showed that there were significant differences among all the samples. This experiment is in agreement with the work of Jirankalgikar et al [ 12 ] and Ayari et al [ 27 ]. Using more than 10% of vegetable oil adulteration significantly reduced the RM value [ 28 ].…”

Section: Resultssupporting

confidence: 93%

Assessment of ghee adulterated with oils and fats in Bangladesh

2020

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“…As markers of the presence of some substances, VOCs can also be a useful tool for verification in industry (James, Scott, Zulfiqur, & O'Hare, 2005), environmental research (Bieganowski et al, 2018;Guz et al, 2015), medicine (Zhou, Luo, GholamHosseini, Li, & He, 2017), and even in military applications (Patel, 2014). Analyses of VOCs have been widely used to determine the quality of biomaterials (Ayari, Mirzaee-Ghaleh, Rabbani, & Heidarbeigi, 2018;Zhang, Cheng, Ma, & Li, 2017). VOCs were observed in the 1960s as markers of microbial contamination in the case of bacteria, and in the 1970s in the case of fungi (Kamiński, Libbey, Stawicki, & Wąsowicz, 1972).…”

Section: Introductionmentioning

confidence: 99%

Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal

Gancarz

Nawrocka

Rusinek

2019

Journal of Food Science

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Volatile organic compounds (VOCs) are natural markers useful in rapid assessment of adverse changes occurring in biological material. The use of an electronic nose seems to be a good, fast, and cheap method to determine particular VOCs. This paper presents a new method determination for VOCs and their concentration based on three sensorgram parameters: maximum of normalized sensor response, reaction time, and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The novelty of the method consists in the use for the first time of two parameters: reaction time and cleaning time measured from the end of the test to the half value of the maximum of normalized sensor response. The VOC sensorgrams at different VOC concentrations (26 to 3,842 ppm) were measured by an electronic nose Food Volatile Compound Analyzer (Agrinose) equipped with eight metal oxide semiconductor sensors dedicated to detect different gases. In the present studies, only six sensors that best respond to the VOCs were used. The highest responses to VOCs were obtained for two sensors—TGS2602 and AS‐MLV‐P2. The results showed that the dependence between the sensorgram parameters on VOC concentration was well described by a logarithmic curve in the whole range of concentrations. Detailed analysis revealed that the cleaning time increases with an increase in the number of carbon atoms in aliphatic molecules. The principal component analysis (PCA) was used to verify the utility of the new three parameters method in VOCs differentiation. The PCA analysis of these parameters showed that maximum of the normalized sensor response alone, which has been used for identification of particular VOCs so far, could not be regarded as a good parameter used for this purpose. Application of all the three parameters gave the best results in VOC identification. The research indicates that the use of three parameters of a volatile compound instead of only one parameter can allow precise determination of substances. Moreover, the results indicate that the analyzed parameters depend on the chemical structure of VOCs.

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Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat)

Cited by 41 publications

References 24 publications

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

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

Assessment of ghee adulterated with oils and fats in Bangladesh

Identification of Volatile Organic Compounds and Their Concentrations Using a Novel Method Analysis of MOS Sensors Signal

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