Ding Yong-Jun, Li Min-Zan, Zheng Li-Hua, Zhao Rui-Jiao, Li Xiu-Hua, An Deng-Kui
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Nov;31(11):2936-9.
In quantitative analysis of spectral data, noises and background interference always degrade the accuracy of spectral feature extraction. The wavelet transform is multi-scale decomposition used to reduce the noise and improve the analysis precision. On the other hand, the wavelet transform denoising is often followed by destroying the efficiency information. The present research introduced two indexes to control the scale of decomposition, the smoothness index (SI) and the time shift index (TSI). When the parameters satisfied TSI < 0.01 and SI > 0.100 4, the noise of spectral characteristic was reduced. In the meanwhile, the reflection peaks of biochemical components were reserved. Through analyzing the correlation between denoised spectrum and chlorophyll content, some spectral characteristics parameters reflecting the changing tendency of chlorophyll content were chosen. Finally, the partial least squares regression (PLSR) was used to develop the prediction model of the chlorophyll content of tomato leaf. The result showed that the predictiong model, which used the values of absorbance at 366, 405, 436, 554, 675 and 693 nm as input variables, had higher predictive ability (calibration coefficient was 0. 892 6, and validation coefficient was 0.829 7) and better potential to diagnose tomato growth in greenhouse.
在光谱数据的定量分析中，噪声和背景干扰总是会降低光谱特征提取的准确性。小波变换是一种用于减少噪声并提高分析精度的多尺度分解方法。另一方面，小波变换去噪之后往往会破坏有效信息。本研究引入了两个指标来控制分解尺度，即平滑度指标（SI）和时移指标（TSI）。当参数满足TSI < 0.01且SI > 0.100 4时，光谱特征的噪声得到降低。与此同时，生化成分的反射峰得以保留。通过分析去噪光谱与叶绿素含量之间的相关性，选择了一些反映叶绿素含量变化趋势的光谱特征参数。最后，采用偏最小二乘回归（PLSR）建立番茄叶片叶绿素含量的预测模型。结果表明，以366、405、436、554、675和693 nm处的吸光度值作为输入变量的预测模型具有较高的预测能力（校正系数为0.892 6，验证系数为0.829 7），并且在诊断温室番茄生长方面具有较好的潜力。
