Choi, Jae Hyeong Park, Soo Hyun Jung Dae-Hyun Park, Yun Ji YANG, JUNG-SEOK Park Jai-Eok Lee, HyeIn KIM, SANG MIN 2024-01-12T02:36:56Z 2024-01-12T02:36:56Z 2022-09-26 2022-10 2077-0472 https://pubs.kist.re.kr/handle/201004/76003 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. English MDPI AG Hyperspectral Imaging-Based Multiple Predicting Models for Functional Component Contents in Brassica juncea Article 10.3390/agriculture12101515 1 Agriculture , v.12, no.10 Agriculture 12 10 Y scie scopus 000872098200001 Agronomy Agriculture Article ALLYL ISOTHIOCYANATE ANTHOCYANIN CONTENT MUSTARD POLYPHENOL total chlorophylls total glucosinolates total phenolics hyperspectral image partial least squares regression prediction models root mean square error of prediction standard normal variate total anthycyanins total carotenoids