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Spectral Analysis of Changes in Photosynthetic Pigment Composition in Leaves of Sweet Cherry Tree Under Rain-Shelter Cultivation Based on Raman and FTIR |
ZHANG Hui-min1, 2, HOU Qian-dong2, WU Ya-wei3, TU Kai2, LI Quan4, WEN Xiao-peng1, 2* |
1. College of Forest/Institute for Forest Resources & Environment of Guizhou, Guizhou University, Guiyang 550025, China
2. Institute of Agro-bioengineering/The Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education), Guizhou University, Guiyang 550025, China
3. Institute of Fruit Tree Research, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China
4. Kaili University, Kaili 556011, China |
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Abstract To improve the of quality and yield of sweet cherry fruits, sweet cherry trees were cultivated under rain-shelter to avoid the problems of low fruit setting rate, falling fruit and fruit malformation in southern China. Sweet cherry trees under rain-shelter cultivation had an obvious negative effect on photosynthesis. In both plants and algae, photosynthetic pigments such as chlorophylls and carotenoids play irreplaceable roles in light harvesting and mediating stress responses to a variety of endogenous stimuli. This research aimed to detect changes of photosynthetic pigments in leaves that affect photosynthesis of fruit trees quickly and conveniently. The experiment took sweet cherry leaves in two different cultivation pattern, open-field and rain-shelter cultivation, as the research objects, and determined its Raman spectrum in the range of 200~3 500 cm-1. The analysis is performed, and the characteristic peaks are calibrated and designated from three wave number bands of 400~800, 800~1 250 and 1 250~1 650 cm-1. According to the Raman spectrum characteristic value, it is concluded that the sweet cherry leaves have a relatively small Raman scattering. Sensitivity is mainly concentrated in the 500~1 700cm-1 band. The analysis of Raman spectrum in the range of 960~1 800 cm-1 found that characterizes the carotenoids (lycopene, β-carotene and lutein) mainly contains 4 main peaks, which are 1 526, 1 157, 1 005 and 960 cm-1, the Raman intensity of leaves of sweet cherry tree under open-field cultivation is significantly lower than that of rain-shelter cultivation. 1 157 and 1 526 cm-1 are also the Raman spectrum characteristic peaks of chlorophyll. Overall analysis shows that photosynthetic pigment content in leaves of sweet cherry under open-field cultivation is lower than that of under rain-shelter cultivation. The characteristic spectral lines of 1 157, 1 520 and 1 526 cm-1 correspond to symmetrical stretching vibrations of C—C single bond and C═C double bond, and their relative strengths can be used as the basis for judging the content of cellulose, carotenoids and chlorophyll in sweet cherry leaves. Fourier transform infrared spectroscopy (FTIR) characterizes that the vibration peak position and vibration intensity of chlorophyll are weak, the vibration coupling is complex and difficult to identify. The second derivative treatment of the infrared spectrum of the chemical composition in sweet cherry leaves was used to derive the peak position and enhance the resolution of the spectrum. The characteristic peaks of β-carotene at 1 437 and 1 551 cm-1 are obvious. Compared to rain-shelter cultivation, sweet cherry leaves showed lower absorbance at these two characteristic peaks, indicating that the content of β-carotene in leaves sweet cherry tree under open-field cultivation is less than that of rain-shelter cultivation. These findings provide the theoretical basis for the spectroscopy research of photosynthetic pigments in plant leaves under different cultivation pattern.
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Received: 2020-03-10
Accepted: 2020-07-07
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Corresponding Authors:
WEN Xiao-peng
E-mail: xpwengzu@163.com
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[1] Cinta Pinzaru S, Müller Cs, Tomšic S, et al. Journal of Raman Spectroscopy, 2015, 46(7): 597.
[2] CHEN Chang-shui, SHI Xing-zhe, LI Qian, et al(陈长水, 师星哲, 李 谦, 等). Chinese Journal of Quantum Electronics(量子电子学报), 2017,(5): 513.
[3] You W, Wang Z, Lu F, et al. Spectroscopy Letters, 2017,50(7): 387.
[4] Radu A I, Ryabchykov O, Bocklitz T W, et al. Analyst, 2016, 141: 4447.
[5] LIU Yan-de, CHENG Meng-jie, HAO Yong, et al(刘燕德, 程梦杰, 郝 勇, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(6): 1768.
[6] Bhosale P, Ermakov I V, Ermakova M R, et al. Journal of Agricultural and Food Chemistry, 2004, 52(11): 3281.
[7] Bicanic D, Dimitrovski D, Luterotti S, et al. Food Chemistry, 2010, 121(3): 832.
[8] Juliano Antônio Sebben, Juliana da Silveira Espindola, Lucas Ranzan. Food Chemistry, 2017, 245: 1224.
[9] ZHOU Li-jie, ZHOU Huan, LI Jia-jia, et al(周丽洁, 周 欢, 李佳佳, 等). Journal of Forestry Engineering(林业工程学报), 2019, 4(1): 74.
[10] JIN Ke-xia, WANG Kun, CUI He-shuai, et al(金克霞, 王 坤, 崔贺帅, 等). Scientia Silvae Science(林业科学), 2018, 54(3): 147.
[11] WANG De-kai, PEI Ke-mei, LIU He-qin(汪得凯, 裴克梅, 刘合芹). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2008, (1): 243.
[12] SUN Xiao-ling, XU Yue-fei, MA Lu-yi, et al(孙小玲, 许岳飞, 马鲁沂, 等). Chinese Journal of Plant Ecology(植物生态学报), 2010, 34(8): 989.
[13] Tamás Lóránd, József Deli, Péter Molnár. Helvetica Chimica Acta, 2002, 85(6): 1691. |
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