Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms.
Food Chem
; 448: 139103, 2024 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-38547708
ABSTRACT
The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearson-competitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC. As a result, three CIs, six ORs, and twelve WFs were selected for PC and WGC datasets. For single-modal data, the back-propagation neural network exhibited superior accuracy, with estimation accuracies (WF > OR > CI). For multi-modal data, the random forest regression paired with OR + WF + CI showed the highest validation accuracy. Utilizing the Gini impurity, WF outweighed OR and CI in the PC and WGC models. The amalgamation of MLs with multimodal data harnessed the synergies among various remote sensing sources, substantially augmenting model precision and stability.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Proteínas de Plantas
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Triticum
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Algoritmos
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Aprendizaje Automático
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Glútenes
Idioma:
En
Revista:
Food Chem
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Food chem
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Food chemistry
Año:
2024
Tipo del documento:
Article
País de afiliación:
China