Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods
(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6724 |
| _version_ | 1827705427878477824 |
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| author | Youngwook Seo Ahyeong Lee Balgeum Kim Jongguk Lim |
| author_facet | Youngwook Seo Ahyeong Lee Balgeum Kim Jongguk Lim |
| author_sort | Youngwook Seo |
| collection | DOAJ |
| description | (1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated. |
| first_indexed | 2024-03-10T16:02:58Z |
| format | Article |
| id | doaj.art-a68198ef9f8e4bf98f580d095fe60532 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:02:58Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a68198ef9f8e4bf98f580d095fe605322023-11-20T15:09:53ZengMDPI AGApplied Sciences2076-34172020-09-011019672410.3390/app10196724Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric MethodsYoungwook Seo0Ahyeong Lee1Balgeum Kim2Jongguk Lim3Department of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, KoreaDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, KoreaDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, KoreaDepartment of Agricultural Engineering, National Institute of Agricultural Sciences, 310 Nongsaengmyeong-ro, Deokjin-gu, Jeonju 54875, Korea(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.https://www.mdpi.com/2076-3417/10/19/6724Hyperspectral imageVNIRFluorescenceSWIRMultivariate analysis algorithm |
| spellingShingle | Youngwook Seo Ahyeong Lee Balgeum Kim Jongguk Lim Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods Applied Sciences Hyperspectral image VNIR Fluorescence SWIR Multivariate analysis algorithm |
| title | Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods |
| title_full | Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods |
| title_fullStr | Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods |
| title_full_unstemmed | Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods |
| title_short | Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods |
| title_sort | classification of rice and starch flours by using multiple hyperspectral imaging systems and chemometric methods |
| topic | Hyperspectral image VNIR Fluorescence SWIR Multivariate analysis algorithm |
| url | https://www.mdpi.com/2076-3417/10/19/6724 |
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