An Electronic Nose for Reliable Measurement and Correct Classification of Beverages

Abstract

This paper reports the design of an electronic nose (E-nose) prototype for reliable measurement and correct classification of beverages. The prototype was developed and fabricated in the laboratory using commercially available metal oxide gas sensors and a temperature sensor. The repeatability, reproducibility and discriminative ability of the developed E-nose prototype were tested on odors emanating from different beverages such as blackcurrant juice, mango juice and orange juice, respectively. Repeated measurements of three beverages showed very high correlation (r > 0.97) between the same beverages to verify the repeatability. The prototype also produced highly correlated patterns (r > 0.97) in the measurement of beverages using different sensor batches to verify its reproducibility. The E-nose prototype also possessed good discriminative ability whereby it was able to produce different patterns for different beverages, different milk heat treatments (ultra high temperature, pasteurization) and fresh and spoiled milks. The discriminative ability of the E-nose was evaluated using Principal Component Analysis and a Multi Layer Perception Neural Network, with both methods showing good classification results.

Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry

“…The strongest trend appears to be the expanded utilization of e-nose devices as a monitoring tool in the food industry, assuring the safety and quality of consumable plant products, continuing with the development of new methods to detect chemical contaminants [350,391], adulterations with baser elements [190,259,260], food-borne microbes and pathogens [263,351,392–395], and toxins [84,311,396] in crops and food products. Similarly, new food-analysis e-nose methods are being developed to detect changes in VOCs released from foods and beverages in storage to assess shelf-life [346,397,398] and quality [185,206,399–403], and for chemical analyses [404,405], classifications [227,232,346,406,407], and discriminations [162,218,228,408] of food types, varieties and brands. Electronic-nose applications to detect plant pests in preharvest and postharvest crops and tree species continue to expand to include new insect [54–61] and disease [111,112,339,409–413] pests, primarily microbial plant pathogens, beyond those originally reported by Wilson et al [2,106,107].…”

Detection of adulteration in freshly squeezed orange juice by electronic nose and infrared spectroscopy

“…Recently, electronic nose (E-nose) and Fourier transform infrared spectroscopy (FTIR) techniques have been gradually developed as alternatives to wet chemistry in the food industry and agriculture, mainly because they can be applied in a low-cost, rapid, and non-destructive way (Mamat et al 2011;Shen et al 2011;Králová et al 2014). E-nose comprises a series of gas sensors which have sensitivity and selectivity to volatile compounds present in the sample headspace of samples.…”

Gas classification using binary decision tree classifier

“…It diverts hydrogen, ammonia and propane in one branch, and carbon dioxide and carbon monoxide in the other branch. Sensor couple (3,4) splits carbon dioxide and carbon monoxide at internal node 1. Sensor couple (2,5) splits the hydrogen gas data in one branch and ammonia and propane data in another branch at internal node 2.…”

“…This response vector is input to the pattern recognition system for classification. The K nearest neighbor (KNN) method, multilayer perceptron (MLP), Principal component analysis (PCA), and Linear discriminant analysis (LDA) have been used for odors identification [1], [2], [3], [4], [5], [6], [7].…”