Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia

Ge Chao-hong; Li Wei-ming; Zhao Hai-long

doi:doi:10.11648/j.aff.20251406.11

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Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia

Ge Chao-hong

, Li Wei-ming^*

, Zhao Hai-long

Published in Agriculture, Forestry and Fisheries (Volume 14, Issue 6)

Received: 30 September 2025 Accepted: 16 October 2025 Published: 22 November 2025

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Abstract

In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.

Published in	Agriculture, Forestry and Fisheries (Volume 14, Issue 6)
DOI	10.11648/j.aff.20251406.11
Page(s)	226-231
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Xanthoceras Sorbifolium, Fat Content, NIRS, Prediction Model

References

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APA Style

Chao-hong, G., Wei-ming, L., Hai-long, Z. (2025). Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agriculture, Forestry and Fisheries, 14(6), 226-231. https://doi.org/10.11648/j.aff.20251406.11

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ACS Style

Chao-hong, G.; Wei-ming, L.; Hai-long, Z. Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agric. For. Fish. 2025, 14(6), 226-231. doi: 10.11648/j.aff.20251406.11

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AMA Style

Chao-hong G, Wei-ming L, Hai-long Z. Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agric For Fish. 2025;14(6):226-231. doi: 10.11648/j.aff.20251406.11

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@article{10.11648/j.aff.20251406.11,
  author = {Ge Chao-hong and Li Wei-ming and Zhao Hai-long},
  title = {Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia
},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {14},
  number = {6},
  pages = {226-231},
  doi = {10.11648/j.aff.20251406.11},
  url = {https://doi.org/10.11648/j.aff.20251406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20251406.11},
  abstract = {In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.
},
 year = {2025}
}

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TY  - JOUR
T1  - Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia

AU  - Ge Chao-hong
AU  - Li Wei-ming
AU  - Zhao Hai-long
Y1  - 2025/11/22
PY  - 2025
N1  - https://doi.org/10.11648/j.aff.20251406.11
DO  - 10.11648/j.aff.20251406.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 226
EP  - 231
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20251406.11
AB  - In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.

VL  - 14
IS  - 6
ER  -

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Author Information

Ge Chao-hong

Cotton Research Institute, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China

Contact Email

http://orcid.org/0009-0004-5485-2396
Li Wei-ming

Cotton Research Institute, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China

Contact Email

http://orcid.org/0009-0004-0684-5669
Zhao Hai-long

Shijiazhuang Seed Management Station, Shijiazhuang, China

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Plain Text BibTeX RIS

APA Style

Chao-hong, G., Wei-ming, L., Hai-long, Z. (2025). Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agriculture, Forestry and Fisheries, 14(6), 226-231. https://doi.org/10.11648/j.aff.20251406.11

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ACS Style

Chao-hong, G.; Wei-ming, L.; Hai-long, Z. Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agric. For. Fish. 2025, 14(6), 226-231. doi: 10.11648/j.aff.20251406.11

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AMA Style

Chao-hong G, Wei-ming L, Hai-long Z. Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia. Agric For Fish. 2025;14(6):226-231. doi: 10.11648/j.aff.20251406.11

Copy | Download

@article{10.11648/j.aff.20251406.11,
  author = {Ge Chao-hong and Li Wei-ming and Zhao Hai-long},
  title = {Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia
},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {14},
  number = {6},
  pages = {226-231},
  doi = {10.11648/j.aff.20251406.11},
  url = {https://doi.org/10.11648/j.aff.20251406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20251406.11},
  abstract = {In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Establishment and Verification of Near-infrared Spectral Prediction Model for Fat Content of Xanthoceras Sorbifolia

AU  - Ge Chao-hong
AU  - Li Wei-ming
AU  - Zhao Hai-long
Y1  - 2025/11/22
PY  - 2025
N1  - https://doi.org/10.11648/j.aff.20251406.11
DO  - 10.11648/j.aff.20251406.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 226
EP  - 231
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20251406.11
AB  - In order to realize the nondestructive and rapid detection of fat content of Xanthoceras sorbifolia and meet the screening of breeding materials and industrial processing requirements of X.sorbifolia, 46 X.sorbifolia were selected as the standard sample set, the results showed that the fat content of 46 apricot kernel kernels was 49.38%~68.98% an average content of 61.62%. The fat content of the seed kernel was determined by the Soxhlet extraction method, and the spectral data of the sample was collected by the near-infrared spectroscopy (NIRS) technology, and the Unscrambler software was used to construct the NIRS prediction model of X.sorbifolia fat content by the partial least squares (PLS) method. The results showed that the regression curve R-Square (determination coefficient) of the model was 0.9856, and the RMSE (standard error) was 0.4149, which could be used for effective prediction. At the same time, 32 X.sorbifolia samples not participating in the modeling were selected as validation materials to further carry out external test on the prediction effect of the model. The results showed that the external test regression curve R-Square was 0.9014, RMSE was 0.8259, and the predicted value of fat content was in good agreement with the chemical value.

VL  - 14
IS  - 6
ER  -

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