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ORIGINAL RESEARCH article

Front. Remote Sens.
Sec. Multi- and Hyper-Spectral Imaging
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1460523

Simultaneous Determination of Amylose and Amylopectin Content of Foxtail Millet Flour with Hyperspectral Imaging

Provisionally accepted
Guoliang Wang Guoliang Wang 1Min Liu Min Liu 1Hongtao Xue Hongtao Xue 2Erhu Guo Erhu Guo 1Aiying Zhang Aiying Zhang 1*
  • 1 Shanxi Agricultural University Millet Research Institute, Changzhi, China
  • 2 College of Agriculture, Shanxi Agricultural University, jinzhong , China, jinzhong, China

The final, formatted version of the article will be published soon.

    The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizer can affect the ratio of amylose and amylopectin components. The determination of these components is typically done 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 agriculture development. This study set up five different gradient treatments of 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 application dosage of organic fertilizer. The 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 (IRIV) were employed for key band extraction. The partial least squares regression (PLSR) was then used to establish the prediction model and the regression equation, that was used to visualize the two components. Results demonstrated 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 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 condition of applying 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.

    Keywords: hyperspectral imaging, foxtail millet, Amylose and Amylopectin, chemometrics, visualization

    Received: 06 Jul 2024; Accepted: 15 Jan 2025.

    Copyright: © 2025 Wang, Liu, Xue, Guo and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Aiying Zhang, Shanxi Agricultural University Millet Research Institute, Changzhi, China

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