为了提高基于近红外光谱的温室番茄叶绿素含量预测精度， 采用小波变换消除光谱中的随机噪声。 但是在去噪的同时， 也会降低有效信息量。 因此， 引入平滑指数(SI)和时移指数(TSI)对去噪效果进行量化， 以控制变换尺度， 获得最佳变换效果。 实验表明TSI<0.01且SI>0.1004时， 在去噪的同时， 也能保留反映生化参量的特征峰， 从而实现自适应小波去噪。 通过小波变换反射率与叶绿素含量的相关分析， 提取了反映叶绿素含量变化的特征波段， 使用偏最小二乘法建立了叶绿素含量预测模型， 结果表明使用384， 405， 436， 554， 675和693 nm处的吸光度建立的模型， 预测系数Rc达到0.892 6， 验证系数Rv达到0.829 7， 可以作为温室番茄营养状态快速诊断的技术基础。

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.