Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis
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. Hyperspect...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
|
| Series: | Current Research in Food Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665927124000212 |
| _version_ | 1797316000071286784 |
|---|---|
| author | Sicheng Yang Yang Cao Chuanjie Li Juan Manuel Castagnini Francisco Jose Barba Changyao Shan Jianjun Zhou |
| author_facet | Sicheng Yang Yang Cao Chuanjie Li Juan Manuel Castagnini Francisco Jose Barba Changyao Shan Jianjun Zhou |
| author_sort | Sicheng Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:12:56Z |
| format | Article |
| id | doaj.art-a43b3d4083d44e1291576fe4755b4036 |
| institution | Directory Open Access Journal |
| issn | 2665-9271 |
| language | English |
| last_indexed | 2024-03-08T03:12:56Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Food Science |
| spelling | doaj.art-a43b3d4083d44e1291576fe4755b40362024-02-13T04:07:17ZengElsevierCurrent Research in Food Science2665-92712024-01-018100695Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysisSicheng Yang0Yang Cao1Chuanjie Li2Juan Manuel Castagnini3Francisco Jose Barba4Changyao Shan5Jianjun Zhou6Huanggang Public Testing Center, No.128 Huangzhou Avenue, Huanggang City, Hubei Province, ChinaAcademy of State Administration of Grain, Beijing, 100037, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural Reclamation University, Daqing, 163319, Heilongjiang, ChinaNutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, SpainNutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, SpainCollege of Science, Health, Engineering and Education, Murdoch University, Perth, 6150, AustraliaNutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, Spain; Department of Biotechnology, Institute of Agrochemistry and Food Technology-National Re-search Council (IATA-CSIC), Agustin Escardino 7, 46980, Paterna, Spain; Corresponding author. Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, Universitat de València, Avda. Vi-cent Andrés Estellés, s/n, 46100, Burjassot, SpainThis 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.http://www.sciencedirect.com/science/article/pii/S2665927124000212Grain dryingHyperspectral imagingPartial least squares modelVisualization |
| spellingShingle | Sicheng Yang Yang Cao Chuanjie Li Juan Manuel Castagnini Francisco Jose Barba Changyao Shan Jianjun Zhou Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis Current Research in Food Science Grain drying Hyperspectral imaging Partial least squares model Visualization |
| title | Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis |
| title_full | Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis |
| title_fullStr | Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis |
| title_full_unstemmed | Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis |
| title_short | Enhancing grain drying methods with hyperspectral imaging technology: A visualanalysis |
| title_sort | enhancing grain drying methods with hyperspectral imaging technology a visualanalysis |
| topic | Grain drying Hyperspectral imaging Partial least squares model Visualization |
| url | http://www.sciencedirect.com/science/article/pii/S2665927124000212 |
| work_keys_str_mv | AT sichengyang enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT yangcao enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT chuanjieli enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT juanmanuelcastagnini enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT franciscojosebarba enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT changyaoshan enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis AT jianjunzhou enhancinggraindryingmethodswithhyperspectralimagingtechnologyavisualanalysis |