Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm
Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determini...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/5/1512 |
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| author | Min-Jee Kim Woo-Hyeong Yu Doo-Jin Song Seung-Woo Chun Moon S. Kim Ahyeong Lee Giyoung Kim Beom-Soo Shin Changyeun Mo |
| author_facet | Min-Jee Kim Woo-Hyeong Yu Doo-Jin Song Seung-Woo Chun Moon S. Kim Ahyeong Lee Giyoung Kim Beom-Soo Shin Changyeun Mo |
| author_sort | Min-Jee Kim |
| collection | DOAJ |
| description | Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R<sup>2</sup> and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits. |
| first_indexed | 2024-04-25T00:20:27Z |
| format | Article |
| id | doaj.art-cc862e5ef2914264bbf5cc05ab8567f9 |
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| language | English |
| last_indexed | 2024-04-25T00:20:27Z |
| publishDate | 2024-02-01 |
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| series | Sensors |
| spelling | doaj.art-cc862e5ef2914264bbf5cc05ab8567f92024-03-12T16:55:02ZengMDPI AGSensors1424-82202024-02-01245151210.3390/s24051512Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection AlgorithmMin-Jee Kim0Woo-Hyeong Yu1Doo-Jin Song2Seung-Woo Chun3Moon S. Kim4Ahyeong Lee5Giyoung Kim6Beom-Soo Shin7Changyeun Mo8Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of KoreaInterdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of KoreaInterdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon 24341, Republic of KoreaEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD 20705, USADepartment of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of KoreaProtected Horticulture Research Institute, National Institute of Horticultural and Herbal Science, Haman 52054, Republic of KoreaDepartment of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of KoreaDepartment of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of KoreaCitrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R<sup>2</sup> and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.https://www.mdpi.com/1424-8220/24/5/1512hyperspectral imagingsoluble solid contentcitrus fruitpartial least-squares regressioneffective-wavelength selection |
| spellingShingle | Min-Jee Kim Woo-Hyeong Yu Doo-Jin Song Seung-Woo Chun Moon S. Kim Ahyeong Lee Giyoung Kim Beom-Soo Shin Changyeun Mo Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm Sensors hyperspectral imaging soluble solid content citrus fruit partial least-squares regression effective-wavelength selection |
| title | Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm |
| title_full | Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm |
| title_fullStr | Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm |
| title_full_unstemmed | Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm |
| title_short | Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm |
| title_sort | prediction of soluble solid content in citrus fruit using visible near infrared hyperspectral imaging based on effective wavelength selection algorithm |
| topic | hyperspectral imaging soluble solid content citrus fruit partial least-squares regression effective-wavelength selection |
| url | https://www.mdpi.com/1424-8220/24/5/1512 |
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