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

Jae-Hyeong Choi, Soo Hyun Park, Dae-Hyun Jung, Yun Ji Park, Jung-Seok Yang, Jai-Eok Park, Hyein Lee and Sang Min Kim ()
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Jae-Hyeong Choi: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Soo Hyun Park: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Dae-Hyun Jung: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Yun Ji Park: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Jung-Seok Yang: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Jai-Eok Park: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Hyein Lee: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea
Sang Min Kim: Smart Farm Research Center, KIST Gengneung Institute of Natural Products, Gangneung 25451, Korea

Agriculture, 2022, vol. 12, issue 10, 1-12

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 (R V 2 = 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.

Keywords: hyperspectral image; partial least squares regression; prediction models; root mean square error of prediction; standard normal variate; total anthycyanins; total carotenoids; total chlorophylls; total glucosinolates; total phenolics (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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