基于降噪处理的蒙古栎木材气干密度NIRS定标模型
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摘要
分别采用卷积平滑法、小波变换法对蒙古栎木材近红外光谱(NIRS)做去噪处理,并讨论两者混合去噪时,处理顺序变化对光谱去噪效果的影响,最后应用偏最小二乘法(partial least squares regression,PLS)和主成分回归法建立蒙古栎木材气干密度近红外定标模型。结果表明,当平滑点数为3,db5小波分解层数为2时,以平滑 小波方式去噪效果最好,其信噪比(SNR)为18.546,均方根误差为0.04。平滑 小波去噪后,基于PLS的蒙古栎木材密度近红外校正模型决定系数由0.767提高到0.902,校正均方根误差降低了35.32%,预测集决定系数为0.860,内部交叉验证和预测均方根误差分别达到最低,剩余预测偏差为2.67。因此,近红外光谱技术可实现蒙古栎木材气干密度快速预测,合理选择处理参数和建模方法可以有效提高模型精度。
Savitzky-Golay smoothing and wavelet transform were applied to the near infrared spectrum( NIRS) of oak( Quercus mongolica Fisch. ex Ledeb) wood for the noise elimination. Denoising effects were discussed by switching the pretreatment order of the two methods. Partial least squares regression( PLS) and principal component regression( PCR) were used for model development of oak wood air-dry density prediction. The results showed that the best denoising effects were associated with spectrum pretreatment of SG smoothing followed by wavelet transform with three smoothing points and two db5 wavelet decomposition layers. The signal to noise ratio( SNR) was 18. 546 and the root mean square error of calibration was 0. 04. For the calibration model of NIR-based oak wood air-dry density prediction developed with PLS,the determination coefficient( R~2) was 0. 902 for the calibration model with correct root mean square error reduced by 35. 32% compared to the model developed with raw spectrum. R~2 for prediction was 0. 860 with residual prediction deviation of 2. 67. This study indicated that NIR could be used for rapid determination of oak wood air-dry density,and determination of reasonable processing parameters as well as modeling methods could effectively improve the model accuracy.
Savitzky-Golay smoothing and wavelet transform were applied to the near infrared spectrum( NIRS) of oak( Quercus mongolica Fisch. ex Ledeb) wood for the noise elimination. Denoising effects were discussed by switching the pretreatment order of the two methods. Partial least squares regression( PLS) and principal component regression( PCR) were used for model development of oak wood air-dry density prediction. The results showed that the best denoising effects were associated with spectrum pretreatment of SG smoothing followed by wavelet transform with three smoothing points and two db5 wavelet decomposition layers. The signal to noise ratio( SNR) was 18. 546 and the root mean square error of calibration was 0. 04. For the calibration model of NIR-based oak wood air-dry density prediction developed with PLS,the determination coefficient( R~2) was 0. 902 for the calibration model with correct root mean square error reduced by 35. 32% compared to the model developed with raw spectrum. R~2 for prediction was 0. 860 with residual prediction deviation of 2. 67. This study indicated that NIR could be used for rapid determination of oak wood air-dry density,and determination of reasonable processing parameters as well as modeling methods could effectively improve the model accuracy.
引文
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