An optimal zone combination model for on-line nondestructive prediction of soluble solids content of apple based on full-transmittance spectroscopy

文献类型： 外文期刊

第一作者： Tian, Xi

作者： Tian, Xi;Fan, Shuxiang;Li, Jiangbo;Huang, Wenqian;Chen, Liping;Tian, Xi;Fan, Shuxiang;Li, Jiangbo;Huang, Wenqian;Chen, Liping

作者机构： Beijing Res Ctr Intelligent Equipment Agr, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China;Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Fruit orientation; 'Fuji' apples; Full-transmittance spectra; Malus domestica; VIS/NIR spectroscopy; Zone combination model

期刊名称：BIOSYSTEMS ENGINEERING （ 2020影响因子：4.123； 五年影响因子：4.508 ）

ISSN： 1537-5110

年卷期： 2020 年 197 卷

页码：

收录情况： SCI

摘要： High accuracy on-line estimation for fruit internal quality is still a challenge due to varying geometric structures and orientations. In this study, multiple full-transmittance spectra were collected using short-integration-time mode for each apple. The signal-to-noise ratio of each collected spectrum changed with the measurement position of the fruit due to the heterogeneity of internal composition. To explore the distribution character of transmittance spectra across the apple structure to guide the development of an on-line fruit quality determination system, a methodology which we called 'zone combination modelling' was proposed for selecting the most effective spectra for SSC prediction. The orientation of stem-calyx axis vertical was selected as the preferred orientation for quality prediction of 'Fuji' apple based on the analysis of the variation and quality of full-transmittance spectra. The most ineffective and most effective zone combinations for SSC prediction were determined by investigating the effect of transmittance spectra within different zone combinations on SSC prediction ability. Ten effective wavelengths selected from the most efficient zone combination were used to develop an optimal prediction model. Results showed that the contribution of different spectral measurement zones to SSC prediction capability varied and that in particular, those collected from the apple core zone should be removed when building SSC prediction models. The coefficient of determination and root mean square errors of prediction and validation sets of SSC, respectively, were 0.733 and 0.61%, 0.721 and 0.71% for the optimal model, indicating that zone combinations model was promising for SSC prediction of apple. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

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