Non-destructive determination of soluble solids content using a multi-region combination model in hybrid citrus

文献类型： 外文期刊

第一作者： Li, Yongjie

作者： Li, Yongjie;Jin, Guoqiang;Jiang, Xin;Yi, Shilai;Tian, Xi

作者机构：

关键词： Vis/near infrared; Spectrum measurement position; Soluble solids content; The multi-region model

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2020 年 104 卷

页码：

收录情况： SCI

摘要： Fruit is complicated natural production and the chemical components are heterogeneous at different position, the comprehensive quality evaluation for intact fruit is the fairest way for avoiding the nonuniform distribution. In order to build a higher accuracy model for soluble solids content (SSC) evaluation, the visible/near infrared spectra of citrus were collected from stem, equator and navel positions to analyze the influence of spectrum measurement position on the prediction accuracy of SSC. The SSC value gradually reduced from navel to equator and stem positions by sequence, but the trend of peel thickness of these three regions were opposite of SSC. The coefficient correlation of SSC between intact fruit and three local regions reached remarkable level, which proved it was feasible to build an accurate model for SSC prediction of intact fruit using the spectral information of local positions. Then separate local models based on specific position spectrum (stem, equator and navel) were built, the result showed the equator position was more suitable to evaluate SSC of intact fruit than navel and stem positions due to the better prediction accuracy, however the unpredictability and variation of the spectral collection position is a challenge to the prediction ability of intact fruit quality. Next multi-region combination models that fusing spectral information of multiple positions were developed, the combination models of 'Equator + Navel' and 'Stem + Equator + Navel' achieved optimal performance than other combination models and all separate local models, with the correlation coefficient of prediction set (R-pre) and root mean square error of prediction (RMSEP) of 0.8507, 0.8424 and 0.6015 degrees Brix, 0.5901 degrees Brix, respectively. It indicated that the peel thickness interfered the acquisition of spectral information of flesh layer, but the accuracy and robust of the prediction model could be improved through fusing the spectral information of multiple regions. Therefore, the fusion of multi-information sets should be deserved more attention to build a practicable model that is not sensitive to the variation of spectrum measurement position for SSC evaluation of intact citrus fruit.

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