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Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Chen, Liping 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China; Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China; Minist Agr, Key Lab Agriinformat, Beijing 100097, Peoples R China; Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Model analysis;Hyperspectral imaging;Peach;Soluble solids content;Nondestructive detection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2017 年 142 卷

页码：

收录情况： SCI

摘要： Soluble solids content (SSC) is one of the most important factors determining the quality and price of fresh fruits. This study was carried out to compare the different models for more robust and accurate evaluation of SSC in 'Pinggu' peaches using hyperspectral imaging in the visible and near infrared spectral range (400-1000 nm). The local region models and multi-region combination model based on full wavelengths were established and compared by partial least squares, respectively. The results of model analysis showed that multi-region combination model was superior to local region models due to insensitivity to the variation of the local regions of interest. Then, four typical wavelength selection algorithms including Monte Carlo cross-validation, successive projections algorithm, competitive adaptive reweighted sampling and random frog were utilized to select the effective wavelengths (EWs) for rapid quantitative determination of SSC, respectively. It was found that random frog was the most effective algorithm for local region models and multi-region combination model in SSC analyses. Considering the practical industrial application, finally, different type of models developed based on EWs selected by random frog were applied to predict the SSC of whole 'Pinggu' peaches from new test samples. EW-multi-region combination model achieved the optimal prediction results with r(p), of 0.86 and RMSEP of 0.67. The overall results of this study revealed that the hyperspectral imaging coupled with effective wavelength selection can be used to non-invasively and fast measure the SSC of 'Pinggu' peaches and a more robust and accurate model could be established based on multi-region information rather than any local region. These results can provide a useful reference for global evaluation of the internal quality attributes of fresh fruits.

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