小麦盐与物理损伤胁迫下果胶高光谱反演模型

doi:10.3964/j.issn.1000-0593(2022)09-2935-06

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Abstract Wheat is the primary grain crop in our country and plays a pivotal role in developing the national economy. However, abiotic stress factors such as salt and physical damage have gradually become essential factors restricting wheat yield and quality. Studies have shown that the cell wall is an important line of defence for plant cells to resist adversity and stress directly. Under salt stress, cell osmotic pressure increases, and the permeability of the plasma membrane will be affected to a certain extent. In order to maintain cell morphology and structure, pectin and other polysaccharides in plant cell walls will be transformed and changed to varying degrees. Physical damage will deepen the degree of lipid peroxidation of plant cell membranes, increase membrane permeability, and lead to the loss and degradation of nutrients. The damaged site and its surrounding cells will also be embolized to block the invasion of bacteria. Pectin, which is the main component of cell wall and can reflect the integrity and permeability of the plant cell wall and membrane system, can be used as an important factor in studying the response mechanism of plant internal substances under stress. At present, commonly used pectin detection methods such as Gravimetric Colorimetric, Liquid chromatography, etc. are cumbersome to operate, not real-time, and large sample consume. There is an urgent need for a simple, fast and non-destructive detection method. This paper used hydroponics to research the wheat (Yannong 0428). The sodium chloride (NaCl) solution was applied to culture medium, and acupuncture was carried out on both sides of the main vein of the first leaf of wheat to simulate the salt damage and the physical damage caused by insect bites, respectively. The pectin and its hyperspectral information of wheat leaf were also collected and processed. The correlation analysis method was used to screen the sensitive spectral band. The three modeling methods of principal component regression (PCR), partial least squares (PLS), and stepwise multiple linear regression (SMLR) were combined with multiple scattering correction processing (MSC), standard normal transformation processing (SNV), first derivative processing (FD), convolution smoothing (SG), and Norris derivative filter processing (NDF) to establish a pectin content inversion model. Finally, the model established by PLS+SNV+FD+NDF method was selected as the optimal model, and its performance was also tested. The results showed that the predicted value of pectin content was consistent with the measured value, and the coefficient of determination (R²) and root mean square error (RMSE) were determined to be 0.997 6 and 0.35, respectively. The repeatability of the predicted value was good, and the relative standard deviation (RSD) was 1.2%. This study uses a new method to realize the high-precision, fast and non-destructive detection of wheat pectin, which is helpful to the in-depth study of the mechanism of the wheat response to stress and provides a reference for the prediction of the stress degree of field crops and the accurate control of planting environment.

Key words：Wheat; Hyperspectral technique; Pectin; Salt stress; Physical damage; Model prediction

Received: 2021-07-21 Accepted: 2021-10-21

ZTFLH:

S512

Corresponding Authors: DANG Jing-min E-mail: jmdang@jlu.edu.cn

Cite this article:

PIAO Zhao-jia,YU Hai-ye,ZHANG Jun-he, et al. Hyperspectral Inversion Model of Pectin Content in Wheat Under Salt and Physical Damage Stresses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2935-2940.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)09-2935-06 OR https://www.gpxygpfx.com/EN/Y2022/V42/I09/2935

[1] YE Zhi-biao, LI Wen-juan(叶志标，李文娟). Chinese Journal of Agricultural Resources and Regional Planning(中国农业资源与区划)，2019，40(3)：158.
[2] YANG Jin-song(杨劲松). Acta Pedological Sinica(土壤学报)，2008，45(5)：837.
[3] GAO Jia-min, GAO Su-hong, GAO Bao-jia(高佳敏, 高素红, 高宝嘉). Acta Agriculturae Boreali-Occidentalis Sinica(西北农业学报), 2019, 28(9)：1543.
[4] ZHU Yu-peng, MENG Xiang-hao, GAI Wei-ling, et al(朱玉鹏, 孟祥浩, 盖伟玲, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2017, 33(19)：1.
[5] Bychkov A L, Podgorbunskikh E M, Ryabchikova E I, et al. Cellulose, 2017, 25(1): 1.
[6] Rajendran S K, Lin I W, Chen M J, et al. BMC Plant Biology, 2014, 14(1): 112.
[7] WANG Xue-hua, DAI Li(王学华, 戴力). Scientia Agricultura Sinica(中国农业科学), 2016, 49(22)：4323.
[8] ZHANG Fang-yan, PU Biao, CHEN An-jun(张方艳, 蒲彪, 陈安均). Food Science and Technology(食品科技), 2013, 38(12)：280.
[9] TAO A-li, ZHANG Guo-sheng(陶阿丽, 张国升). Journal of Chifeng University·Natural Science Edition(赤峰学院学报·自然科学版), 2012, 28(11)：108.
[10] WU Yu-ping, YANG Guang-yu, WANG Dong-dan(吴玉萍，杨光宇，王东丹). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2004, 21(1)：183.
[11] TONG Qing-xi, ZHANG Bing, ZHANG Li-fu(童庆禧, 张兵, 张立福). Journal of Remote Sensing(遥感学报), 2016, 20(5)：689.
[12] PENG Yao-qi, XIAO Ying-xin, LI Xin-xing, et al(彭要奇, 肖颖欣, 李鑫星, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(5)：1356.
[13] XIAO Jing-lin, QIN Mei, LI Xiao-feng, et al(肖京林, 覃美, 黎晓峰, 等). Guangdong Agricultural Sciences(广东农业科学), 2020, 47(9)：73.
[14] Endler A, Kesten C, Schneider R, et al. Cell, 2015, 162(6): 1353.
[15] Li W, Qu H. Journal of Pharmaceutical & Biomedical Analysis, 2010, 52(4): 425.