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Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea

Authors :
Jae-Hyeong Choi
Soo Hyun Park
Dae-Hyun Jung
Yun Ji Park
Jung-Seok Yang
Jai-Eok Park
Hyein Lee
Sang Min Kim
Source :
Agriculture; Volume 12; Issue 10; Pages: 1515
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.

Details

ISSN :
20770472
Volume :
12
Database :
OpenAIRE
Journal :
Agriculture
Accession number :
edsair.doi.dedup.....d11e84e7ad383e609434ed0e136af949
Full Text :
https://doi.org/10.3390/agriculture12101515