Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i>

Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in <i>Brassica juncea</i>

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in <i>Brassica juncea</i> leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin content...

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Bibliographic Details
Main Authors: Jae-Hyeong Choi, Soo Hyun Park, Dae-Hyun Jung, Yun Ji Park, Jung-Seok Yang, Jai-Eok Park, Hyein Lee, Sang Min Kim
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Agriculture
Subjects:
hyperspectral image
partial least squares regression
prediction models
root mean square error of prediction
standard normal variate
total anthycyanins
Online Access:https://www.mdpi.com/2077-0472/12/10/1515

Internet

https://www.mdpi.com/2077-0472/12/10/1515

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