Food Reviews International, Год журнала: 2025, Номер unknown, С. 1 - 18
Опубликована: Апрель 18, 2025
Язык: Английский
Food Control, Год журнала: 2025, Номер unknown, С. 111234 - 111234
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1
Journal of Food Composition and Analysis, Год журнала: 2025, Номер unknown, С. 107532 - 107532
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
LWT, Год журнала: 2025, Номер unknown, С. 117412 - 117412
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Clean Technologies, Год журнала: 2025, Номер 7(1), С. 26 - 26
Опубликована: Март 14, 2025
Waste management is one of the key areas where circular models should be promoted, as it plays a crucial role in minimizing environmental impact and conserving resources. Effective material identification classification are essential for optimizing recycling processes selecting appropriate production equipment. Proper sorting materials enhances both efficiency sustainability systems. The proposed study explores potential using cost-effective strategy based on hyperspectral imaging (HSI) to classify space waste products, an emerging challenge management. Specifically, investigates use HSI sensors operating near-infrared range detect identify classification. Analyses focused textile plastic materials. results show promising further research, suggesting that approach capable effectively identifying classifying various categories predicted images achieve exceptional sensitivity specificity, ranging from 0.989 1.000 0.995 1.000, respectively. Using cost-effective, non-invasive technology could offer significant improvement over traditional methods classification, particularly challenging context operations. implications this work how enables development geared toward sustainable hence proper distinction they allow better recovery end-of-life management, ultimately contributing more efficient recycling, valorization, practices.
Язык: Английский
Процитировано
0
Molecules, Год журнала: 2025, Номер 30(6), С. 1357 - 1357
Опубликована: Март 18, 2025
(1) Background: Soybean storage quality is crucial for subsequent processing and consumption, making it essential to explore an objective, rapid, non-destructive technology assessing its quality. (2) Methods: crude fatty acid value important indicator evaluating the of soybeans. In this study, three types soybeans were subjected accelerated aging analyze trends in values. The study focused on acquiring raw spectral information using hyperspectral imaging technology, preprocessing by derivative method (1ST, 2ND), multiplicative scatter correction (MSC), standard normal variate (SNV). feature variables extracted a variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA). Partial least squares regression (PLSR), support vector machine (SVM), extreme learning (ELM) models developed predict values optimal model was used visualize dynamic distribution these (3) Results: exhibited positive correlation with time, functioning as direct soybean 1ST-VISSA-SVM predictive values, achieving coefficient determination (R2) 0.9888 root mean square error (RMSE) 0.1857 enabling visualization related chemical information. (4) Conclusions: has been confirmed that possesses capability rapid detection
Язык: Английский
Процитировано
0
Plant Phenomics, Год журнала: 2025, Номер unknown, С. 100042 - 100042
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Food Reviews International, Год журнала: 2025, Номер unknown, С. 1 - 18
Опубликована: Апрель 18, 2025
Язык: Английский
Процитировано
0