Electronic nose for detection of food adulteration: a review
- PMID: 35185196
- PMCID: PMC8814237
- DOI: 10.1007/s13197-021-05057-w
Electronic nose for detection of food adulteration: a review
Abstract
The food products may attract unscrupulous vendors to dilute it with inexpensive alternative food sources to achieve more profit. The risk of high value food adulteration with cheaper substitutes has reached an alarming stage in recent years. Commonly available detection methods for food adulteration are costly, time consuming and requires high degree of technical expertise. However, a rapid and suitable detection method for possible adulterant is being evolved to tackle the aforesaid issues. In recent years, electronic nose (e-nose) system is being evolved for falsification detection of food products with reliable and rapid way. E-nose has the ability to artificially perceive aroma and distinguish them. The use of chemometric analysis together with gas sensor arrays have shown to be a significant procedure for quality monitoring in food. E-nose techniques with numerous provisions are reliable and favourable for food industry in food fraud detection. In the present review, the contributions of gas sensor based e-nose system are discussed extensively with a view to ascertain the adulteration of food products.
Keywords: Adulteration; Aroma; Chemometric analysis; E-nose; Sensor.
© Association of Food Scientists & Technologists (India) 2021.
Conflict of interest statement
Conflict of interestThe authors declare that they have no conflict of interest.
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References
-
- Al-Maskari S, Li X, Liu Q. An effective approach to handling noise and drift in electronic noses. In: Wang H, Sharaf MA, editors. Databases theory and applications. Springer, Cham: Lecture Notes in Computer Science; 2014. pp. 223–230.
-
- Ayari F, Mirzaee-Ghaleh E, Rabbani H, Heidarbeigi K. Detection of the adulteration in pure cow ghee by electronic nose method (case study: sunflower oil and cow body fat) Int J Food Prop. 2018;21(1):1670–1679.
-
- Ayari F, Mirzaee-Ghaleh E, Rabbani H, Heidarbeigi K. Using an E-nose machine for detection the adulteration of margarine in cow ghee. J Food Process Eng. 2018;41(6):e12806.
-
- Banach U, Tiebe C, Hübert T. Multigas sensors for the quality control of spice mixtures. Food Control. 2012;26(1):23–27.
-
- Bougrini M, Tahri K, Haddi Z, Saidi T, El Bari N, Bouchikhi B. Detection of adulteration in argan oil by using an electronic nose and a voltammetric electronic tongue. J Sens. 2014;2014:1–10.
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