欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对可见／近红外光与杨梅汁酸度存在非线性相关的特点，提出了应用偏最小二乘（PLS）法预测线性部分和人工神经网络（ANN）预测非线性部分，结合两种方法综合预测杨梅汁酸度值，通过比较r，RMSEP，Bias的值来检验该方法．其中PLS模型用于寻找与杨梅汁酸度值有关的敏感波段，预测杨梅汁酸度的线性部分，将这些敏感波段对应的光谱吸光度值作为人工神经网络的输入，并将杨梅汁酸度的实际测量值减去PLS模型校正值，获得的差额部分作为神经网络的输出，建立一个差额神经网络预测杨梅汁酸度的非线性部分．46个样本用于建模，30个样本用于预测．结果表明该方法对样本的预测相关系数r=0．939，RMSEP=0．218，Bias=-0．121，好于只使用PLS模型的相关系数r=0．921，RMSEP=0．228，Bias=-0．132．
英文摘要：
Aiming at the nonlinear correlation characteristic of visible/near infrared spectra and the corresponding acidity of bayberry juice, one mixed algorithm was presented to predict the acidity of bayberry juice with partial least squares （PLS） and artificial neural network （ANN）. The values of correlation coefficient （r）, the root mean squared error of prediction （RMSEP） , and bias were used to estimate the mixed model. PLS was used to find some sensitive spectra related to acidity in juice, and the values of spectral absorptance corresponding to them were regarded as the input neurons of ANN. Remnant values by subtracting standard values and validation values were regarded as the output neurons of ANN. The calibration equation developed from them was used to predict the constituent values for the independent spectra of 30 samples. The results indicate that the observed results by using PLS-ANN （r = 0. 939, RMSEP = 0.218, Bias = -0. 121 ） are better than those obtained by PLS （r =0.921, RMSEP =0.228, Bias = -0. 132）.
同期刊论文项目
同项目期刊论文
Study on the relationship between spectral properties of oilseed rape leaves and their chlorophyll c
Spectral characterization and N content prediction of soil with different particle size and moisture
Variation rules of the nitrogen content of the oilseed rape at growth stage using SPAD and visible-N
Determination of Protein Content of Auricularia auricula Using Near Infrared Spectroscopy Combined w
Application of Successive Projections Algorithm to Nondestructive Determination of Total Amino Acids
Application of image texture for the sorting of tea categories using multi-spectral imaging techniqu
Chlorophyll assessment and sensitive wavelength exploration for tea (Camellia sinensis) based on ref
Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy
Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial
Detecting the quality of glycerol monolaurate: A method for using Fourier transform infrared spectro
Study on Application of Multi-Spectral Image Texture to Discriminating Rice Categories Based on Wave
期刊信息
位置：