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

被引:10
作者
Choi, Jae-Hyeong [1 ,2 ]
Park, Soo Hyun [1 ]
Jung, Dae-Hyun [1 ,3 ]
Park, Yun Ji [1 ]
Yang, Jung-Seok [1 ]
Park, Jai-Eok [1 ]
Lee, Hyein [1 ]
Kim, Sang Min [1 ,2 ]
机构
[1] KIST Gengneung Inst Nat Prod, Smart Farm Res Ctr, Kangnung 25451, South Korea
[2] Univ Sci & Technol, KIST Sch, Dept Biomed Sci & Technol, Seoul 02792, South Korea
[3] Kyung Hee Univ, Dept Smart Farm Sci, Yongin 17104, South Korea
来源
AGRICULTURE-BASEL | 2022年 / 12卷 / 10期
关键词
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; ALLYL ISOTHIOCYANATE; ANTHOCYANIN CONTENT; MUSTARD; POLYPHENOL;
D O I
10.3390/agriculture12101515
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
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 (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.
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页数:12
相关论文
共 37 条
[11]  
Hye Kim mi, 2012, [Journal of The Korean Society of Food Culture, 한국식생활문화학회지], V27, P719
[12]  
Ihsan M, 2019, INT C ADV COMP SCI I, P157, DOI [10.1109/ICACSIS47736.2019.8979775, 10.1109/icacsis47736.2019.8979775]
[13]  
Jeong JinCheol Jeong JinCheol, 2015, Korean Journal of Crop Science / Hanguk Jakmul Hakhoe Chi, V60, P231, DOI 10.7740/kjcs.2015.60.2.231
[14]   Anthocyanin Management in Fruits by Fertilization [J].
Jezek, Mareike ;
Zoerb, Christian ;
Merkt, Nikolaus ;
Geilfus, Christoph-Martin .
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2018, 66 (04) :753-764
[15]   Measurement of Environmentally Influenced Variations in Anthocyanin Accumulations in Brassica rapa subsp. Chinensis (Bok Choy) Using Hyperspectral Imaging [J].
Kim, Hyo-suk ;
Yoo, Ji Hye ;
Park, Soo Hyun ;
Kim, Jun-Sik ;
Chung, Youngchul ;
Kim, Jae Hun ;
Kim, Hyoung Seok .
FRONTIERS IN PLANT SCIENCE, 2021, 12
[16]  
Kumar V., 2011, Humanit. Med, V1, DOI [10.5667/tang.2011.0005,2.1-2.16, DOI 10.5667/TANG.2011.0005]
[17]  
Lee Jeong-Eun,, 2020, [Asian Journal of Beauty and Cosmetology, 아시안뷰티화장품학술지], V18, P283, DOI 10.20402/ajbc.2020.0038
[18]   Potential of Hyperspectral Imaging for Rapid Prediction of Anthocyanin Content of Purple-Fleshed Sweet Potato Slices During Drying Process [J].
Liu, Yunhong ;
Sun, Yue ;
Xie, Anguo ;
Yu, Huichun ;
Yin, Yong ;
Li, Xin ;
Duan, Xu .
FOOD ANALYTICAL METHODS, 2017, 10 (12) :3836-3846
[19]   Enzymatic inhibition by allyl isothiocyanate and factors affecting its antimicrobial action against Escherichia coli O157:H7 [J].
Luciano, Fernando B. ;
Holley, Richard A. .
INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY, 2009, 131 (2-3) :240-245
[20]  
Malabed R.S., 2014, P SALLE U RES C 2014, P1