Ensemble machine learning approach for electronic nose signal processing

Wijaya, Dedy Rahman; Afianti, Farah; Arifianto, Anditya; Rahmawati, Dewi; Kodogiannis, Vassilis

doi:10.1016/j.sbsr.2022.100495

Cited by 30 publications

(14 citation statements)

References 37 publications

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“…In this data article, homogeneous data sets refer to the data sets collected from different samples in almost similar environmental conditions. The homogeneous data sets are suitable for developing and testing the generalizability of machine learning models [ 2 ]. The availability of homogeneous data sets will provide a more comprehensive pattern, especially regarding the assessment of beef quality with various types of beef cuts compared to other data sets [ 3 ].…”

Section: Objectivementioning

confidence: 99%

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

et al. 2022

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Objectives In recent years, research on the use of electronic noses (e-nose) has developed rapidly, especially in the medical and food fields. Typically, e-nose is coupled with machine learning algorithms to detect or predict multiple sensory classes in a given sample. In many cases, comprehensive and complete experiments are required to ensure the generalizability of the predictive model. For this reason, homogeneous data sets are important to use. Homogeneous data sets refer to the data sets obtained from different observations in almost similar environmental condition. In this data article, e-nose homogeneous data sets are provided for beef quality classification and microbial population prediction. Data description This data set is originated from 12 type of beef cuts. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors for 2220 min. The formal standards, issued by the Meat Standards Committee, are used as a reference in labeling beef quality. Based on the number of microbial populations, meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled. The data set is formatted in "xlsx" file. Each sheet represents one beef cut. Moreover, data sets are good cases for feature selection algorithm stability test, especially to solve sensor array optimization problems.

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Section: Objectivementioning

confidence: 99%

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

et al. 2022

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“…2, the samples are dynamically selected to achieve expected variance to the selected dataset. Multiple feature selection algorithms such as chi-square, reliefF, and gini index was employed in another study 67 which seemingly improved the quality of the learning models. Further, Czarnowski et al 37 described a weighted ensemble technique of instance selection and oversampling in the case of data imbalance.…”

Section: Gas Sensor Data Analysismentioning

confidence: 99%

Review–Modern Data Analysis in Gas Sensors

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Allison

Jalajamony

et al. 2022

J. Electrochem. Soc.

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Development in the field of gas sensors has witnessed exponential growth with a multitude of applications. The diversity of the applications has led to unexpected challenges. Recent advances in data science have addressed the challenges such as selectivity, drift, aging, limit of detection, and response time. The incorporation of modern data analysis including machine learning techniques have enabled a self-sustaining gas-sensing infrastructure without human intervention. This article provides a birds-eye view on data enabled technologies in the realm of gas sensors. While elaborating the prior developments in gas-sensing related data analysis, this article is intended as an entrant for enthusiasts in the domain of data science and gas sensors.

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“…An electronic nose (e-nose) is a promising candidate designed to mimic the sense of the human nose by detecting and analyzing volatile organic compounds (VOCs) in headspace gas [9]. With a combination of sensors, such as metal oxide sensors, conducting polymers, and quartz crystal microbalance sensors, an e-nose can measure the changes in electrical resistance or impedance that result from the interaction of headspace gas in samples [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Monitoring of Microbial Communities and Chemical Characteristics in Incineration Leachate with Electronic Nose and Data Mining Techniques

et al. 2023

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Incineration leachate is a hazardous liquid waste that requires careful management due to its high levels of organic and inorganic pollutants, and it can have serious environmental and health implications if not properly treated and monitored. This study applied a novel electronic nose to monitor the microbial communities and chemical characteristics of incineration leachate. The e-nose data were aggregated using principal component analysis (PCA) and T-distributed stochastic neighbor embedding (TSNE). Random forest (RF) and gradient-boosted decision tree (GBDT) algorithms were employed to establish relationships between the e-nose signals and the chemical characteristics (such as pH, chemical oxygen demand, and ammonia nitrogen) and microbial communities (including Proteobacteria, Firmicutes, and Bacteroidetes) of the incineration leachate. The PCA-GBDT models performed well in recognizing leachate samples, achieving 100% accuracy for the training set and 98.92% accuracy for the testing data without overfitting. The GBDT models based on the original data performed exceptionally well in predicting changes in chemical parameters, with R2 values exceeding 0.99 for the training set and 0.86 for the testing set. The PCA-GBDT models also demonstrated superior performance in predicting microbial community composition, achieving R2 values above 0.99 and MSE values below 0.0003 for the training set and R2 values exceeding 0.86 and MSE values below 0.015 for the testing set. This research provides an efficient monitoring method for the effective enforcement and implementation of monitoring programs by utilizing e-noses combined with data mining to provide more valuable insights compared with traditional instrumental measurements.

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Ensemble machine learning approach for electronic nose signal processing

Cited by 30 publications

References 37 publications

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction

Review–Modern Data Analysis in Gas Sensors

Efficient Monitoring of Microbial Communities and Chemical Characteristics in Incineration Leachate with Electronic Nose and Data Mining Techniques

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