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An independent validation reveals the potential to predict Hagberg–Perten falling number using spectrometers

Plant phenome journal, 2023-12, Vol.6 (1), p.n/a [Peer Reviewed Journal]

2023 The Authors. published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop Science Society of America. ;ISSN: 2578-2703 ;EISSN: 2578-2703 ;DOI: 10.1002/ppj2.20070

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  • Title:
    An independent validation reveals the potential to predict Hagberg–Perten falling number using spectrometers
  • Author: Chen, Chun‐Peng James ; Hu, Yang ; Li, Xianran ; Morris, Craig F. ; Delwiche, Stephen ; Carter, Arron H. ; Steber, Camille ; Zhang, Zhiwu
  • Is Part Of: Plant phenome journal, 2023-12, Vol.6 (1), p.n/a
  • Description: The Hagberg–Perten falling number (HFN) method is the international standard used to evaluate the damage to wheat (Triticum aestivum) grain quality due to preharvest sprouting (PHS) and late maturity alpha‐amylase (LMA). However, the HFN test requires specialized laboratory facilities and is time consuming. Spectrometers were known as a potential tool for quick HFN assessment, but none of the studies have validated the assessment results across different datasets. In this study, an independent validation was conducted using independent samples and spectral instruments. The calibration set had 462 grain samples of 92 varieties grown at 24 locations in 2019 and examined using a near‐infrared spectrometer. In the validation set, 19 varieties collected from 10 locations in 2 years that experienced either PHS or LMA were scanned with a hyperspectral camera. The association between spectra and HFN was modeled by partial least square regression. As a result, the independent validation correlation accuracy was r = 0.72 and a mean absolute error of 56 s. Furthermore, this study showed a cost‐effective alternative using only 10 spectral bands to predict HFN, and it achieved better performance than the full spectrum of the hyperspectral system. In conclusion, this is the first study that showed the potential that wheat HFN could be predicted on an independent dataset measured by a different instrument. The result suggested that spectrometers can potentially serve as a faster alternative for plant breeders to develop varieties resistant to PHS and LMA, and for growers to screen damaged grains in transportation processes. Core Ideas This study is the first to validate a calibrated spectroscopy model of Hagberg–Perten falling number on an independent dataset. Spectral selection is a cost‐effective strategy for replacing hyper spectrometers in predicting falling numbers. The unsupervised selection strategy presented in this study performed better than the supervised approaches. The presented model has a validation accuracy of r = 0.72, but a high bias of 56 seconds. Application of the spectroscopy model to a hyperspectral image of wheat kernels revealed biological insights.
  • Publisher: Wiley
  • Language: English
  • Identifier: ISSN: 2578-2703
    EISSN: 2578-2703
    DOI: 10.1002/ppj2.20070
  • Source: DOAJ Directory of Open Access Journals
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