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Non-destructive evaluation of soluble solids content of apples using a developed portable Vis/NIR device

文献类型： 外文期刊

作者： Fan, Shuxiang 1 ; Wang, Qingyan 1 ; Tian, Xi 1 ; Yang, Guiyan 1 ; Xia, Yu 1 ; Li, Jiangbo 1 ; Huang, Wenqian 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Agriinformat, Beijing 100097, Peoples R China

关键词： Soluble solids content; NIR; apple; Portable device; On-tree

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

ISSN： 1537-5110

年卷期： 2020 年 193 卷

页码：

收录情况： SCI

摘要： A portable visible and near-infrared (Vis/NIR) device could evaluate and monitor internal qualities of fruit on-tree, as well as during storage conditions after harvest. A portable Vis/NIR device which consisted of a commercial spectrometer in the spectral range of 400-1000 nm, an interactance fibre optic probe, a novel switch system, and a microcontroller, was developed and its ability for apple soluble solids content (SSC) prediction was evaluated. A switch system was designed for spectra collection, resulting in the acquisition of three spectra for each measurement of apple fruit, namely the white reference, dark reference, and sample spectrum, which can be used to correct the spectrum of apple fruit dynamically. The results showed that the dynamic correction was more promising than the static correction in which the reference spectra were obtained only once. A model for SSC was built using partial least square (PLS), with the coefficient of determination of prediction (R-p(2)), the root mean square error of prediction (RMSEP), and the ratio of the standard deviation of the reference destructive SSC to the RMSEP (RPD) of 0.777, 0.561%, and 2.114, respectively. The model was then embedded in the custom software to make it possible for the portable device to predict SSC of apple directly, followed by validation using independent sets. The validation results gave R-p(2), RMSEP, and RPD of 0.764, 0.672%, 2.029, respectively for SSC prediction under laboratory conditions, and 0.684, 2.777%, and 0.381, respectively for apples on-tree. The prediction results in the field were improved dramatically using the model built by the field data, with R-p(2), RMSEP and RPD of 0.690, 0.604%, and 1.794, respectively. The overall results showed that the developed device had considerable potential to detect the SSC of apple in practical situations. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

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