2017
DOI: 10.1016/j.foodchem.2016.10.089
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Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis

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Cited by 52 publications
(22 citation statements)
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“…PLS calibration model was established with a good linear relationship ( R 2 = 0.980; SD = 1.433). The PLS model combined with the data obtained from FGC E‐nose were simple in contrast to previously reported use of fuzzy logic analyses to predict the frying disposal time of sunflower oils …”
Section: Discussionmentioning
confidence: 97%
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“…PLS calibration model was established with a good linear relationship ( R 2 = 0.980; SD = 1.433). The PLS model combined with the data obtained from FGC E‐nose were simple in contrast to previously reported use of fuzzy logic analyses to predict the frying disposal time of sunflower oils …”
Section: Discussionmentioning
confidence: 97%
“…E‐nose has been used for detecting edible oil oxidation . Some preliminary studies have shown the ability of E‐nose to discriminate between oils characterized by different degrees of degradation due to a prolonged frying process and to predict the frying disposal time of sunflower oils used for frying in combination with fuzzy logic analyses …”
Section: Introductionmentioning
confidence: 99%
“…The operating procedures were followed as previously reported by Upadhyay et al (2017b) to map the changes in sensor resistivity (ΔR/ R) with time. An e-nose (Fox 4000, Alpha MOS, Toulouse, France) consisting of an array of 18 MOS sensors (L, P, and T types) was used to generate the global aroma fingerprints of fried chips.…”
Section: E-nosementioning
confidence: 99%
“…The aroma comparison was made between training chips (T1-T5) and test chips (SOB 1, SO TBHQ and SO control ) collected after 0, 6, 12 and 18 h of frying to classify them. This is attributed to the evolution of volatile secondary oxidation products of oils absorbed by fried chips upon thermal incubation in e-nose and could be directly related to the degree of rancidity (Upadhyay et al, 2017b). 1a,b).…”
Section: E-nose Analysismentioning
confidence: 99%
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