Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis

Sicheng Yang; Yang Cao; Chuanjie Li; Juan Manuel Castagnini; Francisco Jose Barba; Changyao Shan; Jianjun Zhou

doi:10.1016/j.crfs.2024.100695

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Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis

Journal article

Open access

Peer reviewed

Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis

Sicheng Yang, Yang Cao, Chuanjie Li, Juan Manuel Castagnini, Francisco Jose Barba, Changyao Shan and Jianjun Zhou

Current research in food science, Vol.8, 100695

2024

DOI: https://doi.org/10.1016/j.crfs.2024.100695

PMID: 38362161

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Abstract

Grain drying

Hyperspectral imaging

Partial least squares model

Visualization

This study proposes a recognition model for different drying methods of grain using hyperspectral imaging technology (HSI) and multivariate analysis. Fresh harvested grain samples were dried using three different methods: rotating ventilation drying, mechanical drying, and natural drying. Hyperspectral images of the samples were collected within the 388–1065 nm band range. The spectral features of the samples were extracted using principal component analysis (PCA), while the texture features were extracted using second-order probability statistical filtering. Partial least squares regression (PLSR) drying models with different characteristics were established. At the same time, a BPNN (Back-propagation neural network, BPNN) based on spectral texture fusion features was established to compare the recognition effects of different models. Texture analysis indicated that the mean-image had the clearest contour, and the texture characteristics of mechanical drying were smaller than those of rotating ventilation drying and natural drying. The BPNN model established using spectral-texture feature variables showed the best performance in distinguishing grain in different drying modes, with a prediction model obtained based on the correlation coefficients of special variables. The spectral and texture feature values were fused for pseudo-color visualization expression, and the three drying methods of grain showed different colors. This study provides a reference for non-destructive and rapid detection of grain with different drying methods. [Display omitted]

Details

Title: Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis
Authors/Creators: Sicheng Yang - Huanggang Central Hospital
Yang Cao - Academy of State Administration of Grain, Beijing, 100037, China
Chuanjie Li - College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing, 163319, Heilongjiang, China
Juan Manuel Castagnini - Universitat de València
Francisco Jose Barba - Universitat de València
Changyao Shan - Murdoch University
Jianjun Zhou - Instituto de Agroquímica y Tecnología de Alimentos
Publication Details: Current research in food science, Vol.8, 100695
Publisher: Elsevier B.V
Identifiers: 991005637962007891
Murdoch Affiliation: Murdoch University
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.244 Chemometrics; 2.244.499 NIR Spectroscopy
Web Of Science research areas: Food Science & Technology
ESI research areas: Agricultural Sciences