There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Neural networks

Electronic noses for food quality : a review

Computational methods for the analysis of chemical sensor array data from volatile analytes.

The electronic nose (e-nose) is designed to crudely mimic the mammalian nose in that most contain sensors that non-selectively interact with odor molecules to produce some sort of signal that is then sent to a computer that uses multivariate statistics to determine patterns in the data. This pattern recognition is used to determine that one sample is similar or different from another based on headspace volatiles. There are different types of e-nose sensors including organic polymers, metal oxides, quartz crystal microbalance and even gas-chromatography (GC) or combined with mass spectroscopy (MS) can be used in a non-selective manner using chemical mass or patterns from a short GC column as an e-nose or "Z" nose. The electronic tongue reacts similarly to non-volatile compounds in a liquid. This review will concentrate on applications of e-nose and e-tongue technology for edible products and pharmaceutical uses.

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Electronic noses and tongues: applications for the food and pharmaceutical industries.

The paper presents the application of support vector machine (SVM) neural approach to the calibration of the electronic nose arrangement for milk recognition. The semiconductor gas sensor array mounted into the measurement test chamber has been used to measure the odour. The pre-processed sensor signals are applied to the SVM neural network performing the role of recognition and classification of the milk. The results of numerical experiments of the recognition of different types of milk have been presented and discussed.

Classification of milk by means of an electronic nose and SVM neural network

The study presents an application of the differential electronic nose to the estimation and recognition of the TNT, PETN and RDX in the complex environments under their storage. Generally there are two separate problems in the field of recognition of explosives: first – detection of the trace concentration of explosives by analytical methods (gas chromatography, ion mobility spectrometry, neutron activation analysis) and the second – the estimation of the state of explosives during their time storages in the military objects. In the second problem the concentration of a vapor of explosives is at level of sub-parts-per million where chemosensors technique is fully applicable. This paper is concerned with the second type problem. It defines the recognition criteria providing stable and robust basis for the recognition of different explosive materials. The important part of this procedure performs the 2-dimensional convolution of the differential signals formed on the basis of two sensor arrays used in the construction of the differential nose. The experimental results of the recognition of TNT, PETN and RDX have confirmed the excellent recognition ability of the developed system.

Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose

This paper studies the application of the differential electronic nose for the recognition of coffee, particularly the forgery of it, made by mixing two different quality coffee brands (the mediocre product and the high-quality coffee type), sold as the high-quality coffee brand. Since the beans are practically unrecognizable by the shape and visual inspection, the only solution to this problem is the application of the chemical analysis. The usually applied approach is the liquid chromatography. However, it is a laborious and expensive method, requiring special equipment and an experienced operator. In this paper, we propose the application of the differential electronic nose, relying its decision on the measurement of the coffee smell by the semiconductor gas sensors organized in the form of a matrix. We will show that differential electronic nose applying the special procedure of signal processing is of sufficient sensitivity for the recognition of the forgery of coffee and performs much better than the classical electronic nose (e-nose).

Recognition of Coffee Using Differential Electronic Nose

In this paper we use the electronic nose measurement system in cooperation with the support vector machine (SVM) to the classification of the gasoline with the supplement of bio-products, such as ethanol, MTBE, ETBE and benzene. The array of semiconductor sensors forming the heart of the electronic nose, responds with a signal pattern characteristic for each gasoline blend type. The SVM network working in the classification mode processes these signals and associates them with an appropriate class. It will be shown that the proposed measurement system represents an excellent tool for the recognition of different types of the gasoline blends.

Classification of gasoline with supplement of bio-products by means of an electronic nose and SVM neural network

Highlights? We developed differential electronic nose using 2 sensor arrays for recognition of cigarettes.? We applied support vector machine as the final pattern recognition unit in this artificial nose. ? We performed experiments of recognition of 11 brands of cigarettes using the artificial nose. The paper presents the application of differential electronic nose and support vector machine for recognition of the cigarette brands. Differential nose combines two identical gas sensor arrays mounted into two chambers. One array measures the gas of interest and the second the environmental air. Only differential signals of these two arrays are registered and subjected to further processing. Such organization of measurement eliminates the need for determination of the baseline value of resistance, since the differential signal is free of it. This system combined with support vector machine used as the classifier is well suited for very quick and on-line dynamic mode measurements. We have applied this system to the recognition of 11 cigarette brands on the basis of smell of their leaves. The performed experiments have proved that the developed e-nose based on the differential signals is capable to recognize the cigarette smells very quickly and with high accuracy.

K. Brudzewski

Papers

Classification of milk by means of an electronic nose and SVM neural network

Metal oxide sensor arrays for detection of explosives at sub-parts-per million concentration levels by the differential electronic nose

Recognition of Coffee Using Differential Electronic Nose

Classification of gasoline with supplement of bio-products by means of an electronic nose and SVM neural network

Differential electronic nose and support vector machine for fast recognition of tobacco