{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T18:02:42Z","timestamp":1781200962615,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Department of Science and Technology Development Plan Project","award":["20220203112SF"],"award-info":[{"award-number":["20220203112SF"]}]},{"name":"Jilin Provincial Department of Science and Technology Development Plan Project","award":["JJKH20210039KJ"],"award-info":[{"award-number":["JJKH20210039KJ"]}]},{"name":"Jilin Provincial Department of Science and Technology Development Plan Project","award":["GH21228"],"award-info":[{"award-number":["GH21228"]}]},{"name":"Jilin Provincial Department of Education Science and Technology Research Project","award":["20220203112SF"],"award-info":[{"award-number":["20220203112SF"]}]},{"name":"Jilin Provincial Department of Education Science and Technology Research Project","award":["JJKH20210039KJ"],"award-info":[{"award-number":["JJKH20210039KJ"]}]},{"name":"Jilin Provincial Department of Education Science and Technology Research Project","award":["GH21228"],"award-info":[{"award-number":["GH21228"]}]},{"name":"\u201814th Five-Year\u2019 Plan project of Education and Science in Jilin Province","award":["20220203112SF"],"award-info":[{"award-number":["20220203112SF"]}]},{"name":"\u201814th Five-Year\u2019 Plan project of Education and Science in Jilin Province","award":["JJKH20210039KJ"],"award-info":[{"award-number":["JJKH20210039KJ"]}]},{"name":"\u201814th Five-Year\u2019 Plan project of Education and Science in Jilin Province","award":["GH21228"],"award-info":[{"award-number":["GH21228"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model\u2019s prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields.<\/jats:p>","DOI":"10.3390\/s24186111","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Estimation Model for Maize Multi-Components Based on Hyperspectral Data"],"prefix":"10.3390","volume":"24","author":[{"given":"Hang","family":"Xue","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Beihua University, Jilin 132021, China"},{"name":"College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiping","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Beihua University, Jilin 132021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"ref_1","first-page":"177","article-title":"Classification and Identification of Soybean Varieties by Density Functional Theory Combined with Raman Spectroscopy","volume":"10","author":"Wang","year":"2022","journal-title":"J. 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