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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

Author:
Fan, Shuxiang, Li, Jiangbo, Xia, Yu, Tian, Xi, Guo, Zhiming, Huang, Wenqian
Source:
Postharvest biology and technology 2019 v.151 pp. 79-87
ISSN:
0925-5214
Subject:
algorithms, apples, data collection, least squares, models, near-infrared spectroscopy, prediction, total soluble solids, wavelengths
Abstract:
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 Rp 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.
Agid:
6304005