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Detection of Protease Deterioration Factor in Tomato by Fluorescence Sensor Array |
LI Meng-yao1, 2, WANG Shu-ya1, XIE Yun-feng1, LIU Yun-guo3*, ZHAI Chen1* |
1. Nutrition & Health Research Institute, COFCO Corporation, Beijing Key Laboratory of Nutrition & Health and Food Safety, Beijing 102209, China
2. College of Life Science and Technology, Xinjiang University, Urumqi 830002, China
3. College of Life Sciences, Linyi University, Linyi 276000, China |
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Abstract Based on fluorescence spectroscopy, combined with immunofluorescence and fluorescence probe, a fluorescence sensor array detection technology of tomato protease deterioration factor was established. In this study, a quantum dot fluorescent probe capable of being recognized by pectinase, catalase and superoxide dismutase antibodies was synthesized by carbodiimide method. Immunofluorescence analysis of pectinase, catalase and superoxide dismutase was established based on the principle of antigen-antibody specific recognition, and the influence of its reaction parameters was investigated. Under the optimized reaction conditions (60-min reaction time at 37 ℃), the immunofluorescence intensity presented excellent linearity with the activities of pectinase, catalase and superoxide dismutase by studying the corresponding fluorescence spectrum changes. The detection range of activity was 0.05~500,0.02~800 and 0.5~900 U·mL-1, respectively. The correlation coefficients were 0.989 4, 0.993 8, 0.981 9, and the detection limits were 5.0×10-3, 2.0×10-3, 5.0×10-2 U·mL-1, respectively. Compared with the existing analysis method, the method is simple in operation, low detection line and linear range. Based on the near-infrared fluorescent probe method, a novel hydrosoluble near-Infrared fluorescence off-on probe has been developed for detecting carboxylesterase and polyphenol oxidase activities. The probe was designed by introducing (4-acetoxybenzyl)oxy and 3-hydroxybenzyloxy respectively as quenching and recognizing moiety to the decomposed product of IR-783, which exhibits excellent near-infrared fluorescence feature and good water solubility. The responding mechanism of the novel probe to carboxylesterase and polyphenol oxidase was investigated. By studying the corresponding fluorescence spectrum changes at 37 ℃ pH 7.4, it was found that the presence of carboxylesterase and polyphenol oxidase will cut off the bonds that connect the fluorophore with a recognition moiety in the probe, resulting in the release of the fluorophore, which achieves the purpose of detecting carboxylesterase and polyphenol oxidase. Moreover, the release amount of the fluorophore is linearly related to the activity of carboxylesterase (0.01~0.3 U·mL-1) and polyphenol oxidase (10~70 U·mL-1). This behaviour leads to the development of a simple and sensitive fluorescent method for assaying carboxylesterase and polyphenol oxidase activity, with detection limits of 3.4×10-3 and 1.1×10-2 U·mL-1, respectively. The correlation coefficients were 0.997 2 and 0.991 0, respectively. Compared with the existing near-infrared fluorescent probe method, the near-infrared fluorescent probe synthesized in this study has better water solubility and higher specificity. The research realized the one-time and visual array detection of various enzyme activities in the sample, which is based on the multi-function fluorescent microplate reader to set the corresponding excitation wavelength and emission wavelength for different fluorescent substances. Through the sample spike recovery experiment, it is found that the recovery rate of the method is within the range of 90.0%~102.3%, the coefficient of variation is <15%, and it has good specificity, which indicated that the method established in this study has good accuracy and is expected to be applied.
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Received: 2019-10-29
Accepted: 2020-02-14
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Corresponding Authors:
LIU Yun-guo, ZHAI Chen
E-mail: zhaichen@cofco.com
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[1] DENG Hong-jun, MAO Lin-chun(邓红军, 茅林春). Journal of Food Safety & Quality(食品安全质量检测学报), 2018, 9(11): 2744.
[2] Gisela P O, Norma A M H, Ileana T, et al. Journal of Food Biochemistry, 2019, 43(3): 12770.
[3] Park S J, Kim Y J, Kang J S, et al. Analytical Chemistry, 2018, 90: 9465.
[4] Kodani S D, Barthelemy M, Kamita S G, et al. Analytical Biochemistry, 2017, 539: 81.
[5] Hadwan M H. BMC Biochemistry, 2018, 19(1): 7.
[6] Chiaki M, Satoru M, Sayaka K, et al. Analytical Biochemistry, 2017, 526: 43.
[7] Levine S R, Beatty K E. Chem. Commun., 2016, 52: 1835.
[8] Zhao M J, Zhang T P, Yu F J, et al. Biochem, Pharmacology, 2018, 152: 293.
[9] Zhou H, Tang J, Zhang J, et al. J. Mater. Chem. B, 2019, 7: 2989.
[10] Jin Q, Feng L, Wang D D, et al. Biosens. and Bioelectronics, 2016, 83: 193.
[11] LI Yan-dong, HAN Xue, WU Yu-yang, et al(李研东, 韩 雪, 吴雨洋, 等). Quality and Safety of Agro-Products(农产品质量与安全), 2017, (5): 83.
[12] Liu Z, Liu S. Analytical & Bioanalytical Chemistry, 2018, 410(17): 4145.
[13] Ouyang S Y, Zhang Z W, He T, et al. Toxins, 2017, 9(4): 137.
[14] Zhao L N, Hu S M, Meng Q W, et al. Journal of Molecular Recognition,2018, 31(8): 2712.
[15] Zhang J H, Li Z, Tian X W, et al. Chem. Commun., 2019, 55: 9463. |
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