Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

BioResources, Год журнала: 2024, Номер 19(3), С. 4531 - 4546

Опубликована: Май 20, 2024

The quality detection of alfalfa hay is crucial for the development animal husbandry. In this study, a method based on fusion multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme Successive Projections Algorithm from spectral data. data denoised adaptive wavelet thresholding, color texture features histograms random forest algorithms, respectively. Electronic principal component analysis was used dimensionality reduction. Support Vector Machine, Extreme Learning Multi-Layer Perceptron employed to establish models image, gas information, their combination, Experimental results demonstrate that data, effectively enhances classification accuracy model. test set reaches 100%, with root mean square error determination coefficient values 0.1728 0.9239, respectively, surpassing prediction established solely individual information. This study provides new insights into detection.

Язык: Английский

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Assessment of Escherichia coli bioreporters for early detection of fungal spoilage in postharvest grape berries

Postharvest Biology and Technology, Год журнала: 2023, Номер 204, С. 112481 - 112481

Опубликована: Июль 20, 2023

Язык: Английский

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Electronic Nose-Based Monitoring of Chicken Freshness in Room and Refrigerated Storage

Опубликована: Янв. 1, 2023

Monitoring chicken freshness is vital for food safety, reducing the risk of foodborne illnesses. Electronic noses (E-nose) have emerged as valuable tools assessing quality by detecting changes in volatile compounds linked to freshness. We developed a portable E-nose assess stored at room temperature and refrigerator (4 °C) 45 days. tracked bacterial growth through Total Plate Count (TPC) identified organic (VOCs) using Gas Chromatography-Mass Spectrometry (GC-MS) over storage period. utilized Polynomial Feature Extractions (poly0, poly1, poly2) analyze sensor responses. Principal Component Analysis (PCA) reduced data dimensionality visualization, followed Linear Discriminant (LDA) classification. LDA, coupled with poly2, effectively classified based on days temperatures. To predict freshness, we employed Support Vector Regression (SVR) our device. Results showed high predictive accuracy: values 0.95 0.93 0.74 3.89 under refrigerated conditions, respectively. Our study demonstrated that VOCs correlate samples varying durations conditions. Notably, poly2 feature extraction model excelled predicting surpassing other polynomial orders. This highlights significance an appropriate discerning different conditions durations. showcases potential technology monitoring offering insights into dynamics VOC various settings.

Язык: Английский

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Machine Learning to Detect Fungal Infections in Stored Pome Fruits via Mass Spectrometry Data: Industry, Economic, and Social Implications

Journal of Advances in Information Technology, Год журнала: 2024, Номер 15(10), С. 1174 - 1183

Опубликована: Янв. 1, 2024

Язык: Английский

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