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

Agricultural Research, Год журнала: 2025, Номер unknown

Опубликована: Фев. 10, 2025

Язык: Английский

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

Food Bioscience, Год журнала: 2024, Номер unknown, С. 105558 - 105558

Опубликована: Ноя. 1, 2024

Язык: Английский

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

Journal of the Taiwan Institute of Chemical Engineers, Год журнала: 2025, Номер 171, С. 106045 - 106045

Опубликована: Фев. 27, 2025

Язык: Английский

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

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 104977 - 104977

Опубликована: Март 1, 2025

Язык: Английский

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

Food Research International, Год журнала: 2025, Номер unknown, С. 116285 - 116285

Опубликована: Март 1, 2025

Язык: Английский

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

Food Science & Nutrition, Год журнала: 2025, Номер 13(4)

Опубликована: Март 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

Язык: Английский

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

Journal of Food Measurement & Characterization, Год журнала: 2025, Номер unknown

Опубликована: Апрель 2, 2025

Язык: Английский

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

Flavour and Fragrance Journal, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 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.

Язык: Английский

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

Опубликована: Янв. 1, 2024

Язык: Английский

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