Development of NIRS calibration curves for sugars in baked sweetpotato

Abstract

BackgroundVariability in sugar content between raw and cooked sweetpotato storage roots impact nutritional and dietary importance with implications for consumer preference. High‐throughput phenotyping is required to breed varieties that satisfy consumer preferences.ResultsNear‐infrared reflectance spectroscopy (NIRS) calibration curves were developed for analysing sugars in baked storage roots using 147 genotypes from a population segregating for sugar content and other traits. The NIRS prediction curves had high coefficients of determination in calibration (R2c) of 0.96 (glucose), 0.93 (fructose), 0.96 (sucrose), and 0.96 (maltose). The corresponding coefficients of determination for cross‐validation (R2cv) were 0.92 (glucose), 0.89 (fructose), 0.96 (sucrose) and 0.93 (maltose) and were similar to the R2c for all sugars measured. The ratios of the standard deviation of the reference set to the standard error of cross‐validation were greater than three for all sugars. These results confirm the applicability of the NIRS curves in efficiently determining sugar content in baked sweetpotato storage roots. External validation was performed on an additional 70 genotypes. Coefficients of determination (r2) were 0.88 (glucose), 0.88 (fructose), 0.86 (sucrose) and 0.49 (maltose). The results were comparable to those found for the calibration and cross‐validation in fructose, glucose, and sucrose, but were moderate for maltose due to the low variability of maltose content in the population.ConclusionsNIRS can be used for screening sugar content in baked sweetpotato storage roots in breeding programs and can be used to assist with the development of improved sweetpotato varieties that better meet consumer preferences. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from ‘Farm’ to ‘Table’: Progresses, challenges, trends, and prospects

“…All these quality indicators during the period of sweetpotato roots storage were investigated by NIR analysis and a rapid and reagent-independent approach was expected to be developed as a alternative tool, and the specific results are summarized and shown in Appendix. Through comparative analysis, it was observed that the vast majority of indicators of sweetpotato roots were well evaluated by linear calibration algorithms (PLS & MLR) with R 2 P larger than 0.80 ( Magwaza, Naidoo, Laurie, Laing, & Shimelis, 2016 ; Su & Sun, 2017a ; Bu, Li, & Yan, 2018 ; Bu et al, 2018 ; Tian, Huang, Bai, Lv, & Sun, 2019 ; Tian et al, 2021 ; He et al, 2022 ; Xiao et al, 2022 ; He et al, 2023 ; Tang et al, 2023 ; He et al, 2023 ; He et al, 2023 ), while the maltose, cellulose, and minerals were poorly predicted ( Amankwaah et al, 2023 ; Lebot, Malapa, & Jung, 2013 ; Lebot, Ndiaye, & Malapa, 2011 ), which may due to the very small amounts of the three substances, as NIR sensor typically performs better in predicting substances with higher contents ( Porep, Kammerer, & Carle, 2015 ). In a few studies, only calibration datasets were applied for modeling, while no prediction datasets were provided, which meant that the reliability and robustness of the results had not been further verified ( Kamruzzaman & Villordon, 2022 ; Tang et al, 2013 ; Tang, Li, & Ma, 2008 ).…”

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

“…Previously, NIRS-based prediction models have been extensively developed for characterizing many crops such as maize, potato, cassava, rice, and pulses (14)(15)(16)(17)(18)(19). NIRS models are a reliable technique for various biochemical estimations such as moisture, dietary fiber, ash, fatty acids, oils, protein, and sugar content with a minimum sample requirement (15,(20)(21)(22)(23). Since these biochemical attributes determine the functionality of adzuki and rice bean germplasm, NIRS-based prediction modeling can be used for proximate analysis, and other constituents can contribute to the selection of the best crop varieties with a higher content of desired biochemical and nutritional constituents, such as protein, oil, fiber, minerals, and vitamins, accelerating the process of developing high-yielding varieties through breeding.…”