基于小波变换的番茄总糖近红外无损检测

分别采用小波消噪、常数偏移消除等11种光谱预处理方法，对番茄总糖含量（质量分数）的近红外光谱进行预处理，通过偏最小二乘法定量校正模型预测值比较得出，小波消噪是适合番茄近红外光谱的最佳预处理方法，小波消噪的总糖质量分数近红外光谱优选区域为11 998.9～6 097.8 cm-1和4 601.3～4 246.5 cm-1，在此光谱区内建立的番茄总糖质量分数偏最小二乘法模型预测值与实测值的相关系数为0.930，内部交叉验证均方差为0.466%，校正标准差为0.469%，预测标准差为0.260%。试验结果表明：小波消噪后建立的近红外光谱模型能准确地对番茄总糖含量进行快速无损检测。

Nondestructive examination of total sugar in tomatoes with near infrared spectroscopy based on wavelet transform

The noise of the near infrared spectrum of tomato was eliminated respectively in 11 kinds of spectral preprocessing methods of the wavelet denoising (WD), the constant offset (COE) and so on, WD was examined to be the optimal spectrum preprocessing method by comparing the prediction of the partial least squares (PLS) calibration model, And by optimizing several wavelength ranges of WD NIR spectra, the best wavelength ranges of 11 998.9-6 097.8 cm-1 and 4 601.3-4 246.5 cm-1 was obtained, in this spectral region, PLS model with WD spectrum was established, which gave a correlation coefficient (R) between prediction and actual of 0.930, a root mean square error of cross validation (RMSECV) of 0.466%, and a standard error of calibration (RMSEC) of 0.469%, as well as a standard error of prediction (RMSEP) of 0.260%. The results showed that the FT-NIR model treated by WD is feasible to detect total sugar content of tomatoes rapidly and nondestructively.