Qualitative and Quantitative Analysis of Two Plant Pathogens by Three Dimensional Fluorescence Spectroscopy Combined with Second Order Correction Algorithm

CAI Luning; LIU Xueru; CHEN Lei; LI Xin; GU Shaobin

doi:10.13386/j.issn1002-0306.2021090083

Volume 43 Issue 9

Apr. 2022

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Science and Technology of Food Industry > 2022 > 43(9): 1-12. > DOI: 10.13386/j.issn1002-0306.2021090083

CAI Luning, LIU Xueru, CHEN Lei, et al. Qualitative and Quantitative Analysis of Two Plant Pathogens by Three Dimensional Fluorescence Spectroscopy Combined with Second Order Correction Algorithm[J]. Science and Technology of Food Industry, 2022, 43(9): 1−12. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021090083.

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Qualitative and Quantitative Analysis of Two Plant Pathogens by Three Dimensional Fluorescence Spectroscopy Combined with Second Order Correction Algorithm

CAI Luning^1,,
LIU Xueru¹,
CHEN Lei¹,
LI Xin^{1, 2, 3, ,},
GU Shaobin^1, ,

1.
College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471023, China
2.
Key Laboratory of Microbial Resources Exploitation and Utilization, Henan University of Science and Technology, Luoyang 471023, China
3.
Henan Engineering Research Center of Food Microbiology, Luoyang 471023, China

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Received Date: September 06, 2021
Available Online: March 01, 2022

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Abstract

Pseudomonas syringae pv. Lachrymans-8 and Fusarium graminearum-ACCC37687 were used to quickly identify the pathogenic microorganisms of plant fungal diseases and bacterial diseases. To explore the feasibility of rapid identification of plant fungal and bacterial diseases by three-dimensional fluorescence spectroscopy. By collecting the three-dimensional fluorescence spectrum data of gradient mixed bacterial solution samples, the data were analyzed by second-order correction algorithm alternating trilinear decomposition (ATLD), parallel factor analysis (PARAFAC), self weighted trilinear decomposition (SWATLD), alternating penalty trilinear decomposition (APTLD) and first-order algorithm partial least squares regression coefficient method (PLS), and the characteristic excitation and emission wavelengths were extracted. The concentration prediction model was established by multiple linear regression between the fluorescence intensity data of characteristic wavelength and the absorbance (OD₆₀₀) of bacterial solution at 600 nm wavelength. The prediction performance of the model was measured by the left one out cross validation (LOOCV), so as to realize the qualitative and quantitative analysis of single component in complex bacterial solution mixed system. The fluorescence characteristic peaks of Pseudomonas syringae were excitation/emission=285 nm/340 nm, 290 nm/340 nm, 285 nm/332.4 nm, 280 nm/361.6 nm, 295 nm/361.6 nm. The fluorescence characteristic peaks of Fusarium graminearum were excitation/emission=380 nm/468 nm, 390 nm/512 nm, 340 nm/511.2 nm, 415 nm/511.2 nm. The results showed that the concentration prediction model of Pseudomonas syringae-8 (R²_cv=0.92441191, RMSEP=0.005163633, R=0.961463421) was better than that of Fusarium graminearum-ACCC37687 (R²_cv=0.583953931, RMSEP=0.027653679, R=0.764168784). The results of this study provide an available method for rapid identification of fungal and bacterial hazards.

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Science and Technology of Food Industry

Qualitative and Quantitative Analysis of Two Plant Pathogens by Three Dimensional Fluorescence Spectroscopy Combined with Second Order Correction Algorithm

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