Xinjiang Agricultural Sciences ›› 2023, Vol. 60 ›› Issue (3): 582-589.DOI: 10.6048/j.issn.1001-4330.2023.03.008
• Horticultural Special Local Products·Physiology and Biochemistry • Previous Articles Next Articles
MA Wenqiang1(), LIU Jia1, SHEN Xiaohe1, CHEN Zhongyuan2, YANG Liling1(), ZHANG Man3()
Received:
2022-07-30
Online:
2023-03-20
Published:
2023-04-18
Correspondence author:
YANG Liling (1980-), female, Sichuan Province, researcher, doctor, research direction of the forestry and fruit production and processing equipment technology, (E-mail)Supported by:
马文强1(), 刘佳1, 沈晓贺1, 陈中原2, 杨莉玲1(), 张漫3()
通讯作者:
杨莉玲(1980-),四川江津人,研究员,博士,研究方向为林果生产加工装备,(E-mail)411450712@qq.com;作者简介:
马文强(1986-),新疆库车人,副研究员,博士,研究方向为林果智能化检测,(E-mail)mwq4530@163.com
基金资助:
CLC Number:
MA Wenqiang, LIU Jia, SHEN Xiaohe, CHEN Zhongyuan, YANG Liling, ZHANG Man. Prediction Model of Nitrogen Content in Walnut Leaves Based on Spectrum[J]. Xinjiang Agricultural Sciences, 2023, 60(3): 582-589.
马文强, 刘佳, 沈晓贺, 陈中原, 杨莉玲, 张漫. 核桃叶片氮元素含量的光谱预测模型[J]. 新疆农业科学, 2023, 60(3): 582-589.
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预处理方法 Pretreatment method | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
未处理 None | 5 | 0.644 8 | 0.842 4 | 2.796 4 | 0.596 0 |
多元散射校正 MSC | 5 | 0.616 8 | 0.870 3 | 1.411 5 | 0.721 4 |
Tab.1 Comparison of predictive modeling of nitrogen content between original Spectra and MSC pretreatment spectra
预处理方法 Pretreatment method | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
未处理 None | 5 | 0.644 8 | 0.842 4 | 2.796 4 | 0.596 0 |
多元散射校正 MSC | 5 | 0.616 8 | 0.870 3 | 1.411 5 | 0.721 4 |
预处理方法 Pretreatment method | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
标准正态化 SNV | 5 | 0.617 7 | 0.870 8 | 1.443 | 0.723 3 |
一阶微分 FD | 5 | 0.221 4 | 0.946 6 | 3.633 5 | 0.404 5 |
二阶微分 SD | 5 | 0.332 | 0.930 5 | 3.982 1 | 0.386 1 |
卷积平滑滤波 Savitzky- Golay S-G | 5 | 0.609 7 | 0.880 1 | 1.246 | 0.756 8 |
Tab.2 Comparison of prediction modeling of nitrogen content in different spectral pretreatment methods
预处理方法 Pretreatment method | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
标准正态化 SNV | 5 | 0.617 7 | 0.870 8 | 1.443 | 0.723 3 |
一阶微分 FD | 5 | 0.221 4 | 0.946 6 | 3.633 5 | 0.404 5 |
二阶微分 SD | 5 | 0.332 | 0.930 5 | 3.982 1 | 0.386 1 |
卷积平滑滤波 Savitzky- Golay S-G | 5 | 0.609 7 | 0.880 1 | 1.246 | 0.756 8 |
分解层数 Decompo sition layers | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
1 | 5 | 0.595 | 0.893 | 1.127 | 0.771 4 |
2 | 5 | 0.632 1 | 0.862 4 | 1.471 | 0.728 |
3 | 5 | 0.880 3 | 0.791 2 | 2.492 7 | 0.632 3 |
4 | 5 | 0.896 4 | 0.796 5 | 2.653 | 0.646 6 |
Tab.3 Comparison of predictive modeling of nitrogen content based on wavelet denoising pretreatment spectra with different decomposition layers
分解层数 Decompo sition layers | PLSR 主成分数 Number of principal components of PLSR | 校正集 Calibration set | 验证集 Validation set | ||
---|---|---|---|---|---|
均方 根误差 RMES (mg/g) | 决定 系数 R2 | 均方 根误差 RMSE (mg/g) | 决定 系数 R2 | ||
1 | 5 | 0.595 | 0.893 | 1.127 | 0.771 4 |
2 | 5 | 0.632 1 | 0.862 4 | 1.471 | 0.728 |
3 | 5 | 0.880 3 | 0.791 2 | 2.492 7 | 0.632 3 |
4 | 5 | 0.896 4 | 0.796 5 | 2.653 | 0.646 6 |
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