Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging
The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficul...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2025.1460523/full |
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| author | Guoliang Wang Min Liu Hongtao Xue Erhu Guo Aiying Zhang |
| author_facet | Guoliang Wang Min Liu Hongtao Xue Erhu Guo Aiying Zhang |
| author_sort | Guoliang Wang |
| collection | DOAJ |
| description | The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficult to apply on a large scale for nutrient deficiency diagnosis and do not meet the original intention of precise agricultural development. This study set up five different gradient treatments for organic fertilizer (sheep manure) application. Hyperspectral imaging combined with chemometrics was employed to achieve rapid and non-destructive detection of the content of amylose and amylopectin in foxtail millet flour. The aim of this study was to determine the optimal dosage of organic fertilizers for application. Spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), and iterated retaining informative variables (IRIVs) was employed for key band extraction. Partial least squares regression (PLSR) was then used to establish the prediction model and regression equation, which was used to visualize the two components. Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC–RF–IRIV–PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23 g/(100 g), respectively. Similarly, the prediction model for amylopectin using MSC–CARS–IRIV–PLSR also demonstrated good performance, with the R and RMSE values predicted to be 0.59 and 7.34 g/(100 g), respectively. The results of visualization and physicochemical determination showed that the amount of amylopectin accumulation was highest, and the amount of amylose was lowest, under the application of 22.5 t/ha of organic fertilizer. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research. |
| format | Article |
| id | doaj-art-0421ee82f7f14278af4dc0a753fa78ea |
| institution | DOAJ |
| issn | 2673-6187 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Remote Sensing |
| spelling | doaj-art-0421ee82f7f14278af4dc0a753fa78ea2025-08-20T03:11:33ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872025-02-01610.3389/frsen.2025.14605231460523Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imagingGuoliang Wang0Min Liu1Hongtao Xue2Erhu Guo3Aiying Zhang4Millet Research Institute, Shanxi Agricultural University, Changzhi, ChinaMillet Research Institute, Shanxi Agricultural University, Changzhi, ChinaCollege of Agriculture, Shanxi Agricultural University, Jinzhong, ChinaMillet Research Institute, Shanxi Agricultural University, Changzhi, ChinaMillet Research Institute, Shanxi Agricultural University, Changzhi, ChinaThe levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficult to apply on a large scale for nutrient deficiency diagnosis and do not meet the original intention of precise agricultural development. This study set up five different gradient treatments for organic fertilizer (sheep manure) application. Hyperspectral imaging combined with chemometrics was employed to achieve rapid and non-destructive detection of the content of amylose and amylopectin in foxtail millet flour. The aim of this study was to determine the optimal dosage of organic fertilizers for application. Spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), and iterated retaining informative variables (IRIVs) was employed for key band extraction. Partial least squares regression (PLSR) was then used to establish the prediction model and regression equation, which was used to visualize the two components. Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC–RF–IRIV–PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23 g/(100 g), respectively. Similarly, the prediction model for amylopectin using MSC–CARS–IRIV–PLSR also demonstrated good performance, with the R and RMSE values predicted to be 0.59 and 7.34 g/(100 g), respectively. The results of visualization and physicochemical determination showed that the amount of amylopectin accumulation was highest, and the amount of amylose was lowest, under the application of 22.5 t/ha of organic fertilizer. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.https://www.frontiersin.org/articles/10.3389/frsen.2025.1460523/fullhyperspectral imagingfoxtail milletamylose and amylopectinchemometricsvisualization |
| spellingShingle | Guoliang Wang Min Liu Hongtao Xue Erhu Guo Aiying Zhang Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging Frontiers in Remote Sensing hyperspectral imaging foxtail millet amylose and amylopectin chemometrics visualization |
| title | Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| title_full | Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| title_fullStr | Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| title_full_unstemmed | Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| title_short | Simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| title_sort | simultaneous determination of the amylose and amylopectin content of foxtail millet flour by hyperspectral imaging |
| topic | hyperspectral imaging foxtail millet amylose and amylopectin chemometrics visualization |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2025.1460523/full |
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