Electronic-Nose System for Classification of Fruits and Freshness Measurement using K-NN Algorithm

The main objective of this work is to design electronic nose system (E-Nose) which detects the odour and freshness of different fruits such as mango, pineapple, orange which are mainly used in food manufacturing industries. E-Nose system consists of sensor array of two with each sensor respond to different types of odours. These sensor data is analyzed with the K-nearest neighbour algorithm (K-NN Algorithm) using MATLAB for identification of different fruits. Freshness of fruit juice is determined by the measurement of pH value of juice by using pH electrode

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Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

Scientific Reports ◽

10.1038/s41598-020-79717-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chenghao Fei ◽

Chenchen Ren ◽

Yulin Wang ◽

Lin Li ◽

Weidong Li ◽

...

Keyword(s):

Electronic Nose ◽

Sensor Array ◽

Prediction Models ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Stepwise Discriminant Analysis ◽

Accuracy Rate ◽

Different Types ◽

Chemical Content ◽

Electronic Nose System

AbstractCrataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

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Electronic nose sensor development using ANN backpropagation for Lombok Agarwood classification

Research in Agricultural Engineering ◽

10.17221/26/2020-rae ◽

2020 ◽

Vol 66 (No. 3) ◽

pp. 97-103

Author(s):

Farel Ahadyatulakbar Aditama ◽

Lalu Zulfikri ◽

Laili Mardiana ◽

Tri Mulyaningsih ◽

Nurul Qomariyah ◽

...

Keyword(s):

Electronic Nose ◽

Gas Sensors ◽

Poor Quality ◽

Backpropagation Algorithm ◽

The Poor ◽

Quality Classification ◽

Electronic Nose System ◽

System Prototype

The aim of the present study is the development of an electronic nose system prototype for the classification of Gyrinops versteegii agarwood. The prototype consists of three gas sensors, i.e., TGS822, TGS2620, and TGS2610. The data acquisition and quality classification of the nose system are controlled by the Artificial Neural Network backpropagation algorithm in the Arduino Mega2650 microcontroller module. The testing result shows that an electronic nose can distinguish the quality of Gyrinops versteegii agarwood. The good-quality agarwood has an output of [1 –1], while the poor-quality agarwood has an output of [–1 1].

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Application of Electronic Nose for Evaluation of Wastewater Treatment Process Effects at Full-Scale WWTP

Processes ◽

10.3390/pr7050251 ◽

2019 ◽

Vol 7 (5) ◽

pp. 251 ◽

Cited By ~ 8

Author(s):

Grzegorz Łagód ◽

Sylwia M. Duda ◽

Dariusz Majerek ◽

Adriana Szutt ◽

Agnieszka Dołhańczuk-Śródka

Keyword(s):

Wastewater Treatment ◽

Gas Sensor ◽

Electronic Nose ◽

Sensor Array ◽

Treatment Plant ◽

Full Scale ◽

Oxide Semiconductor ◽

Pollution Level ◽

Gas Sensor Array

This paper presents the results of studies aiming at the assessment and classification of wastewater using an electronic nose. During the experiment, an attempt was made to classify the medium based on an analysis of signals from a gas sensor array, the intensity of which depended on the levels of volatile compounds in the headspace gas mixture above the wastewater table. The research involved samples collected from the mechanical and biological treatment devices of a full-scale wastewater treatment plant (WWTP), as well as wastewater analysis. The measurements were carried out with a metal-oxide-semiconductor (MOS) gas sensor array, when coupled with a computing unit (e.g., a computer with suitable software for the analysis of signals and their interpretation), it formed an e-nose—that is, an imitation of the mammalian olfactory sense. While conducting the research it was observed that the intensity of signals sent by sensors changed with drops in the level of wastewater pollution; thus, the samples could be classified in terms of their similarity and the analyzed gas-fingerprint could be related to the pollution level expressed by physical and biochemical indicators. Principal component analysis was employed for dimensionality reduction, and cluster analysis for grouping observation purposes. Supervised learning techniques confirmed that the obtained data were applicable for the classification of wastewater at different stages of the purification process.

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A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data

Sensors ◽

10.3390/s19224831 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4831 ◽

Cited By ~ 3

Author(s):

Anup Vanarse ◽

Adam Osseiran ◽

Alexander Rassau ◽

Peter van der Made

Keyword(s):

Data Processing ◽

Electronic Nose ◽

Spike Timing ◽

Sensor Data ◽

Development Environment ◽

Early Classification ◽

Recent Developments ◽

Efficient Data ◽

Olfactory Systems

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking neural network (SNN)-based classifier, implemented in a chip-emulation-based development environment, that can be seamlessly deployed on a neuromorphic system-on-a-chip (NSoC). Under three different scenarios of increasing complexity, the SNN was determined to be able to classify real-valued sensor data with greater than 90% accuracy and with a maximum latency of 3 s on the software-based platform. Highlights of this work included the design and implementation of a novel encoder for artificial olfactory systems, implementation of unsupervised spike-timing-dependent plasticity (STDP) for learning, and a foundational study on early classification capability using the SNN-based classifier.

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