Cited by A Novel Concentration Prediction Technique of Co Based on Bwo-Xgboost

Estimating Flavonoid and Nitrogen Status of Guava Leaves Using E-Nose and SPAD Meter DOI

Bambang Marhaenanto,

Putri Wahyulian Aningtyas,

Bayu Taruna Widjaja Putra

et al.

Agricultural Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

Detection Technologies, and Machine Learning in Food: Recent Advances and Future Trends DOI

Qiong He, Heng-Yu Huang,

Yuanzhong Wang

et al.

Food Bioscience, Journal Year: 2024, Volume and Issue: unknown, P. 105558 - 105558

Published: Nov. 1, 2024

Language: Английский

Citations

A novel concentration prediction technique of carbon monoxide (CO) based on beluga whale optimization-extreme gradient boosting (BWO-XGBoost) DOI

Fan Zhang, Zhengyang Zhu,

Jiefeng Liu

et al.

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2025, Volume and Issue: 171, P. 106045 - 106045

Published: Feb. 27, 2025

Language: Английский

Citations

Exploration of Simulated Human Olfactory System and Its Integration With Machine Learning Algorithms for Food Quality Assessment: A Review DOI

Shilpa Gite,

Moumita Karmakar,

S.D. Mokashi

et al.

Trends in Food Science & Technology, Journal Year: 2025, Volume and Issue: unknown, P. 104977 - 104977

Published: March 1, 2025

Language: Английский

Citations

Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety assessment: A review DOI

Mogos Girmatsion,

Xiaoqian Tang,

Qi Zhang

et al.

Food Research International, Journal Year: 2025, Volume and Issue: unknown, P. 116285 - 116285

Published: March 1, 2025

Language: Английский

Citations

Intelligent Grading of Green Cardamom Using Data Fusion of Electronic Nose and Computer Vision Methods DOI

Ehsan Godini,

Hemad Zareiforoush, Adel Bakhshipour

et al.

Food Science & Nutrition, Journal Year: 2025, Volume and Issue: 13(4)

Published: March 27, 2025

ABSTRACT In this research, the intelligent quality grading of green cardamom was carried out using electronic nose (e‐nose) and computer vision (CV) methods along with machine learning (ML) approaches. Cardamom samples were analyzed in three grades including Grade 1 (healthy green), 2 yellow color), 3 (immature shriveled) for capsules (Black), (Brown), (Yellow red) seeds. Three ML algorithms Decision Tree (DT), Bayesian Network (BN), Support Vector Machine (SVM) used to classify grades. Results showed that correlation‐based feature selection (CFS) algorithm decreased number input features increased classification performance. For classifying capsule based on visual features, CFS‐BN model best classifier, root mean squared error (RMSE) accuracy 0.1408 96.67%, respectively. The RMSE seeds image 0.1220 e‐nose data, CFS‐DT classifier 0.2093 93.33%, an 0.1126 96.67%. fusion CV data performance compared separate use datasets. combination 100% during both calibration evaluation stages. It can be concluded effectively develop intelligent, accurate, reliable, fast, non‐destructive system

Language: Английский

Citations

Integration of low-cost multispectral sensors and electronic nose for enhanced fermentation monitoring in tempeh production DOI

Tri Siswandi Syahputra,

Nasrul Ihsan,

Kombo Othman Kombo

et al.

Journal of Food Measurement & Characterization, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Language: Английский

Citations

Enhancing Electronic Nose Performance for Differentiating Civet and Non‐Civet Roasted Bean Coffee Using Polynomial Feature Extraction Methods DOI

Nasrul Ihsan,

Kombo Othman Kombo,

Frendy Jaya Kusuma

et al.

Flavour and Fragrance Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 25, 2024

ABSTRACT Coffee, a popular beverage worldwide, requires thorough quality assessment to ensure its authenticity and meet consumer demands. Traditional methods in the industry are often subjective, expensive, time‐consuming. This study used compact, portable electronic nose (e‐nose) with machine learning models classify distinguish between civet non‐civet roasted beans. The polynomial feature extraction method was extract important parameters from sensor response improve system performance. Classification like linear discriminant analysis (LDA), logistic regression (LR), quadratic (QDA), support vector machines (SVM) were applied samples. Among these, LDA model features yielded highest validation test accuracies, values of 0.89 ± 0.04 0.93, respectively. higher than statistical methods, which obtained accuracies 0.80 0.07 0.87, acquired e‐nose results correlated compound concentrations coffee beans measured by gas chromatography–mass spectrometry (GC–MS). These findings demonstrate system's promising potential effectively based on their aroma profiles using methods.

Language: Английский

Citations

A Novel Concentration Prediction Technique of Co Based on Bwo-Xgboost DOI

Fan Zhang, Zhengyang Zhu, Jiefeng Liu

et al.

Published: Jan. 1, 2024

Language: Английский

Citations