Rapid identification and quantification of sesame oils adulteration using low frequency dielectric spectroscopy combined with chemometrics

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

doi:10.1016/j.lwt.2020.110736

Cited by 21 publications

(5 citation statements)

References 37 publications

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“…Combining the data from both systems, the accuracy of classification increases in addition to the results being validated. Also, by using the two techniques, the weaknesses of one method is compensated for by the other method [110].…”

Section: E-sensing and Extra Virgin Oilmentioning

confidence: 99%

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Poeta,

Liboà,

Mistrali

et al. 2023

Sensors

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Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

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Section: E-sensing and Extra Virgin Oilmentioning

confidence: 99%

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Poeta,

Liboà,

Mistrali

et al. 2023

Sensors

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“…In 2020, Firouz and colleagues estimated the amount of adulteration in sesame oil samples by combining low-frequency dielectric spectroscopy methods (40 kHz to 20 MHz) and chemical methods. They used principal component analysis for feature reduction and artificial neural network models and support vector regression for classification ( Firouz et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

The effect of data fusion on improving the accuracy of olive oil quality measurement

Zarezadeh¹,

Aboonajmi²

2023

Food Chemistry: X

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“…Adulteration is a serious problem, particularly for expensive oils, such as sesame and olive oil. Sesame oil is extensively mixed and diluted with lower-cost oils such as canola oil [ 10 ]. Regardless of preventative regulations, some unauthorized producers add low-cost vegetable oils to sesame oil or directly add chemically synthesized sesame essential oil to low-cost oils [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…There is an important need to develop systems that can detect and quantify the adulteration of low-cost oils in sesame oil samples. Improved detection systems should be portable, low-cost, rapid, non-destructive, and utilize effective chemical sensors to quickly assess adulteration and fraud associated with commercial vegetable oils [ 10 ]. Electronic nose (e-nose) devices are particularly well suited for detecting differences in VOC emissions from plant products but are not normally designed to identify individual VOCs present in sample analytes.…”

Section: Introductionmentioning

confidence: 99%

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

Aghili,

Rasekh,

Karami

et al. 2023

Sensors

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Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25–100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography–mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82–91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

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Rapid identification and quantification of sesame oils adulteration using low frequency dielectric spectroscopy combined with chemometrics

Cited by 21 publications

References 37 publications

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nanotechnology and E-Sensing for Food Chain Quality and Safety

The effect of data fusion on improving the accuracy of olive oil quality measurement

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

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