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

高光谱成像 偏最小二乘回归 规范化(社会学) 数学 牡荆素 均方误差 模式识别(心理学) 生物系统 人工智能 计算机科学 生物 统计 生物化学 类黄酮 社会学 人类学 抗氧化剂
作者
Jae-Hyeong Choi,Soo Hyun Park,Soo Hyun Park,Yun Ji Park,Jung‐Seok Yang,Jai-Eok Park,Hyein Lee,Sang Min Kim
出处
期刊:Agriculture [Multidisciplinary Digital Publishing Institute]
卷期号:12 (10): 1515-1515 被引量:4
标识
DOI:10.3390/agriculture12101515
摘要

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.
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