Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for <italic>Malus micromalus Makino</italic> Based on Near-Infrared Spectroscopy

Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for <italic>Malus micromalus Makino</italic> Based on Near-Infrared Spectroscopy

To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of Malus micromalus Makino, rapid and non-destructive prediction models of SSC were studied using least-square support vector regression (LS-SVR), partial least squares regressi...

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Bibliographic Details
Main Authors: Qiang Gao, Meili Wang, Yangyang Guo, Xiaoqiang Zhao, Dongjian He
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Least-square support vector regression
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Malus micromalus Makino</italic>
near-infrared spectroscopy
soluble solids content
successive projection algorithm
Online Access:https://ieeexplore.ieee.org/document/8825778/

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https://ieeexplore.ieee.org/document/8825778/

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