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Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method

文献类型： 外文期刊

作者： Fan, Shuxiang 1 ; Li, Jiangbo 1 ; Xia, Yu 1 ; Tian, Xi 1 ; Guo, Zhiming 4 ; Huang, Wenqian 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

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

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

4.Jiangsu Uni

关键词： Soluble solids content; NIR; Apple; Biological variability; Partial least squares; Calibration transfer

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：5.537； 五年影响因子：5.821 ）

ISSN： 0925-5214

年卷期： 2019 年 151 卷

页码：

收录情况： SCI

摘要： The long-term performance of a near-infrared (NIR) calibration model for soluble solids content (SSC) prediction has been investigated using apples with biological variability collected from 2012 to 2018. The NIR spectrum in the range of 4000-10,000 cm(-1) was acquired around equator position for each sample. Partial least squares (PLS) was used to develop calibration model based on the samples harvested in 2012 and 2013. The model was then applied to predict the SSC of samples in five separate data sets collected from 2014 to 2018, resulting in a lower performance with higher RMSEP values in the range of 0.704-1.716%. After applying the slope and bias (S/B) correction method, ten samples were selected from each prediction set and used to adjust the model; the prediction results for five independent prediction sets were improved, with RMSEP values ranging from 0.501% to 0.654%. Subsequently, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) methods were used to select the most effective wavelengths for the determination of SSC. The calibration model built with 15 wavelengths, combined with the S/B correction method, could replace the full spectral range to detect the SSC of apples over a long period of time, with R-p and RMSEP for five prediction sets being 0.919, 0.937, 0.908, 0.896, 0.924 and 0.592, 0.637, 0.513, 0.523, 0.500%, respectively. Overall, the proposed method in this study could make the model valid and robust over a long time and make the biological variability a negligible interference for SSC prediction, thereby providing potential for SSC prediction in practical application.

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