Food Analysis Using Artificial Senses

Abstract

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Evaluation of the suitability of electronic nose based on fast GC for distinguishing between the plum spirits of different geographical origins

“…DFA is a multivariate technique for describing a mathematical function, and it allows to distinguish among predefined groups of samples [25]. The DFA 2D is shown in Fig.…”

“…To this end, wines [12][13][14][15][16][17][18][19][20][21], tequila, whiskey, vodkas [22,23] and Chinese spirit [24] were analyzed. Until now, the application of electronic nose was based on sensors, e.g., conductometric, amperometric and piezoelectric sensors [25]. However, the publications dealing with the authentication of alcoholic beverages by means of electronic nose based on fast GC are becoming more frequent lately.…”

“…However, the publications dealing with the authentication of alcoholic beverages by means of electronic nose based on fast GC are becoming more frequent lately. This specific type of electronic nose generates peaks with the highest discriminatory power, which produce a characteristic volatile profile, a so-called fingerprint, when exposed to the gas mixture [25]. The electronic nose based on fast GC was used to analyze inter alia whiskey [26,27], wines [28] and liqueurs [29,30].…”

Application of Electronic Nose Based on Fast GC for Authenticity Assessment of Polish Homemade Liqueurs Called Nalewka

“…In case only one principal component is significant for the entire class, the mean value of this component together with the associated vector becomes the model for a given class. SIMCA classifier is usually used when we deal with two or more classes that consist of tight or less tight clusters of objects (Śliwińska et al 2014;Ghasemi-Varnamkhasti et al 2012b). The classifier of statistical quality control is an effective tool for conducting the statistical monitoring of product quality.…”

The advantages of disposable screen-printed biosensors in a bioelectronic tongue for the analysis of grapes

“…There have been a range of portable electronic tongues which have been produced based on a numerous core sensing technologies. including mass, optic, electrical or electrochemical transduction (Baldwin, Bai, Plotto, & Dea, 2011;Haddi et al, 2014;Riul, Dantas, Miyazaki, & Oliveira, 2010;Rodríguez-M endez, Medina, De Saja, Apetrei, & Muñoz, 2012;Sghaier, Barhoumi, Maaref, Siadat, & JaffrezicRenault, 2009;Sliwinska, Wisniewska, Dymerski, Namiesnik, & Wardencki, 2014;Smyth & Cozzolino, 2013). Arrays of electrochemical sensors have been successfully used to discriminate between wines with different organoleptic characteristics Cet o, Apetrei, Del Valle, & Rodríguez-M endez, 2014;Cet o et al, 2012;Gay et al, 2010;Gil-S anchez et al, 2011;Guti errez, Moreno-Bar on, Pividori, Alegret, & Del Valle, 2010;Parra, Hernando, Rodríguez-M endez, & De Saja, 2004;Rodríguez-M endez et al, 2014;Zeravik, Hlavacek, Lacina, & Skl adal, 2009) or grapes of different qualities (Guti errez et al, 2011;Moreno i Codinachs et al, 2008).…”