番茄维生素C含量近红外预测光谱的小波去噪  

Wavelet Denoising in Prediction Model of Tomato Vitamin C Content Using NIR Spectroscopy

作　　者：李天华[1] 施国英[1] 魏珉[1] 汪健民[2] 侯加林[1] 

机构地区：[1]山东农业大学机械与电子工程学院,泰安271018 [2]山东农业大学化学与材料科学学院,泰安271018

出　　处：《农业机械学报》2013年第S1期200-204,共5页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(31071830);山东省现代农业产业技术体系建设专项资助项目(SDARS-2010-2-3-1)

摘　　要：为剔除预测番茄维生素C含量的近红外光谱数据中的噪声信息,利用Matlab 7.0小波工具箱对光谱数据进行去噪处理。为得到最佳去噪效果,在dbN小波基中分别考察db2至db9小波去噪时模型的评价参数,得到db6小波为最佳小波;考察db6小波在分解层数从3到7变化时模型的评价参数,得到最佳分解层数5。以信噪比和均方根误差对不同阈值方式下的去噪效果进行评价,得到硬阈值的启发式去噪方法去噪效果最佳。将重构后的光谱用偏最小二乘法建立预测模型,得到预测相关系数为0.907,校正集的标准偏差和预测集样本的标准偏差分别为0.819、0.905,模型预测准确率为88.3%。去噪后的模型参数均好于原始信号所建模型参数,表明小波技术用于番茄维生素C预测的光谱去噪是可行的。For removing the noise in the spectral data,Matlab 7.0 wavelet toolbox was used to denoise the data.In order to get the best denoising effect,the model evaluation parameters were investigated from the db2 to db9 wavelet respectively in dbN wavelet basis,and the db6 was thought to be the best wavelet according to the investigation result.Using the db6 wavelet,the optimal decomposition layer was five when changed from three to seven.For getting the best denoising method,the signal-to-noise ratio( SNR) and root mean square error( RMSE) were used to evaluate denoising effect in different thresholds,and the hard threshold value of the heuristic denoising method was observed to be the best one.The prediction model was built using the reconstruction spectrum by partial least squares( PLS)method.The correlation coefficient of the proposed model was 0.907.The root mean square error values of calibration and prediction were 0.819 and 0.905.The performance index was 88.3%.The model parameters using wavelet denoising were better than the original signal.It was showed that wavelet denoising was feasible in prediction of tomato vitamin C with NIR spectroscopy.

关 键 词：番茄 维生素C 含量检测 近红外光谱 小波去噪 偏最小二乘法 

分 类 号：TS207.3[轻工技术与工程—食品科学]