Electronic Nose Dataset for Classifying Rice Quality using Neural Network

Erlangga, Ferdy; Wijaya, Dedy Rahman; Wikusna, Wawa

doi:10.1109/icoict52021.2021.9527423

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…SVM achieved an VOLUME 11, 2023 accuracy of 0.97 by employing parameters including a C value of 10, a gamma value of 0.01, and a kernel of radial basis function. The Neural Network algorithm demonstrated high accuracy of 0.98 with optimal parameter values: hid-den_layer_sizes of (30,15,10), maximum iterations set to 200, activation function set to ReLU, and the solver parameter set to 'adam'. AdaBoost achieved an accuracy of 0.93 with parameters n_estimators set to 200 and learning_rate set to 0.3.…”

Section: B Hyperparameter Optimization For Classificationmentioning

confidence: 99%

“…An electronic nose (e-nose) is an instrument that works to imitate the principle of the sense of smell. E-nose consists of a gas sensor array as a replacement for olfactory receptors which serve to detect odors or senses to assess various samples such as beef [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], rice [15], [16], [17], [18], [19], tea [20], [21], [22], etc. It can provide a fast, accurate, and cheap solution for seafood quality detection.…”

mentioning

confidence: 99%

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Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization

Wijaya,

Syarwan,

Nugraha

et al. 2023

IEEE Access

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The fisheries sector holds great importance in the Indonesian economy, particularly in terms of its contribution to growth and development. Both the fisheries and marine industries play significant roles in driving economic activities. The rising consumption of marine fishery products has resulted in a growing market demand for high-quality and safe products. Meeting this demand necessitates the maintenance of freshness in marine fishery products. Thus, this research aims to develop a fast, cheap, accurate method utilizing an electronic nose (e-nose) and machine learning algorithms as an alternative method for assessing the freshness quality of marine fishery products (seafood). This experiment employs seven algorithms with hyperparameter optimization to obtain the best performance. Machine learning algorithms are used for classification and regression tasks. The objective is to detect the freshness quality of marine fishery products accurately (classification task) while also identifying microbial populations present in the seafood (regression task). Through extensive investigations, the classification and regression models, specifically employing the k-Nearest Neighbors algorithm, demonstrated remarkable performance, achieving a very high accuracy score. Furthermore, the regression model yielded an RMSE value of 0.03 and an R 2 value of 0.995, indicating the effectiveness of the approach in assessing and quantifying the quality attributes of marine fishery products.

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Section: B Hyperparameter Optimization For Classificationmentioning

confidence: 99%

mentioning

confidence: 99%

Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization

Wijaya,

Syarwan,

Nugraha

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared to GC-MS analysis, an e-nose provides rapid detection to obtain results in a few seconds or minutes. Previous studies in rice have shown the reliability of e-nose applications such as analyzing the volatile compounds in rice [11], distinguishing expired and non-expired rice [12], monitoring rancidity and insect infestation in brown rice [13], detecting fungal infection in jasmine brown rice [14], and identifying moldy rice [15]. In rice adulteration, Udomkun et al [16] assessed the feasibility of a commercial e-nose paired with principal component analysis (PCA) to identify the degree of adulteration in Thai jasmine rice in storage conditions.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Aznan

Viejo

Pang

et al. 2022

Sensors

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Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and non-invasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.

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Review of technology advances to assess rice quality traits and consumer perception

Aznan

Viejo

Pang

et al. 2023

Food Research International

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Electronic Nose Dataset for Classifying Rice Quality using Neural Network

Cited by 8 publications

References 9 publications

Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization

Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization

Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning

Review of technology advances to assess rice quality traits and consumer perception

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