研究目的
To study differences in the secondary metabolites originating from different lighting conditions and lettuce varieties using FIMS combined with ANOVA–PCA.
研究成果
The combination of FIMS fingerprinting and ANOVA–PCA provides a useful tool for the characterization of the sources of variance in plant materials regarding to genetic, environmental, and management factors. Different light sources and lettuce varieties showed significant differences in secondary metabolites.
研究不足
The study focused on two varieties of lettuce and three light conditions. The findings may not be generalizable to other varieties or conditions. The sensitivity of the method to other environmental factors was not explored.
1：Experimental Design and Method Selection:
The study used FIMS combined with ANOVA–PCA to analyze the secondary metabolites in lettuce under different light conditions and varieties. Ultra-high-performance liquid chromatography–high-resolution accurate mass spectrometry was used for putative marker compound identification.
2：Sample Selection and Data Sources:
Two varieties of lettuce, Romaine and Lollo Rossa, were grown under three different light conditions (sunlight, white light, and fluorescent light). Samples were collected at weeks 2, 3, 4, 5, 6, 7, 8, and 9 after germination.
3：List of Experimental Equipment and Materials:
The FIMS system consisted of a Thermo LTQ Orbitrap XL mass spectrometer with an Agilent 1290 UHPLC system. Chemicals used included Optima grade water and acetonitrile from Fisher Scientific, and MS grade formic acid from Sigma/Aldrich.
4：Experimental Procedures and Operational Workflow:
The flow injection was passed through a guard column to minimize potential contamination for the MS system. Mobile phases consisted of
5：1% formic acid in H2O and 1% formic acid in acetonitrile with isocratic elution at
40 (v/v) at a flow rate of 0.4 mL/min for 10 min. Electrospray ionization (ESI) was performed in negative ion mode from m/z 100–
6：4 mL/min for 10 min. Electrospray ionization (ESI) was performed in negative ion mode from m/z 100–Data Analysis Methods:
1000.
5. Data Analysis Methods: The data were analyzed using ANOVA–PCA method. The normalized, mean-centered matrix was used to construct ten sub-matrices, the means and residuals matrix for each experimental variable.
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