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Fig. 1. a in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS

Fig. 1. a) Total ion chromatogram (TIC) for the L. gibba sample, b) Extracted ion chromatogram of GSH and PCs from the TIC of L. gibba, c) Extracted ion chromatogram of GSH and PCs from the standard solution at 10 μg mL 1.

opennotspecifiedMar 2021View details →

Fig. 2 in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS

Fig. 2. Ishikawa diagram representing the main sources of uncertainties for measuring GSH and PC concentration in aquatic plants.

Fig. 11. Experiment 3 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 11. Experiment 3, mean GC/MS peak area (±SE) of branches, buds, and needles in blue and Norway spruce. Means with different letters are significantly different within each species (ANOVA, Tukey's HSD).

Fig. 3 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 3. Sample GC traces of blue spruce (B1), Norway spruce (N1), and cedar of Lebanon (C1). Compounds were identified by retention indices, mass spectra (NIST library), and reference comparisons. Identified compounds are: (1) tricyclene, (2) α-pinene, (3) camphene, (4) sabinene, (5) β-pinene, (6) β-myrcene, (7) 3-carene, (8) limonene, (9) β-phellandrene (coeluting with limonene), (10) 1,8-cineole, (11) γ-terpinene, (12) terpinolene, (13) camphor, (14) α-terpineol, and (15) β-caryophyllene.

Fig. 2. Chemical structures for terpenes detected from branch tip samples. Numbering follows Table 1 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 2. Chemical structures for terpenes detected from branch tip samples. Numbering follows Table 1 (elution order).

Fig. 12. Experiment 4 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 12. Experiment 4, PCA of B1, B7, B8, N1, N7, N11, and C1 run on a chiral column (standardized TIC responses). Largest circles show 95% confidence limits for each tree species, small circles show 95% confidence limits for each tree.

Fig. 1 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 1. Mean abundance of Norway volatiles vs time (left) and the logarithm of average abundance of Norway vs AHS oven temperature (right).

Fig. 9. Experiment 2 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 9. Experiment 2, comparison of N1, N7, and N11 GC traces (standardized TIC responses). Different colored sample at right (N1 – blue, N7 – green, and N11 – red) correspond to colored peaks at left (as indicated by arrows). N1 samples had higher percentages of β-pinene, N7 samples had higher percentages of α-pinene and limonene (and β-phellandrene), and N11 had higher percentages of camphene, β-myrcene and 1,8-cineole. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 10. Experiment 3 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 10. Experiment 3, PCA of B1–B11 and N1–N11 run over two days (standardized TIC responses). Circles show 95% confidence limits.

Fig. 5. Experiment 1 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 5. Experiment 1, PCA of B1, N1, and C1 over time (standardized TIC responses). Samples were collected and analyzed roughly every week over an 8-month time period (September 2018 through April 2019). Circles show 95% confidence limits.

Fig. 8. Experiment 2 in Headspace GC-MS analysis of differences in intra- and interspecific Terpene profiles of Picea pungens Engelm. and P. abies (L.) Karst

Fig. 8. Experiment 2, PCA of B1–B11, N1–N11, and C1 run over two days (standardized TIC responses). Circles show 95% confidence limits.

Fig. 4 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 4. Evaluation of searching blocks against those of BMDMS-NP. Blocks in (A) and (C) are from NIST17 and those in (B) and (D) are from MoNA. Lines marked with 'x' in (C) and (D) show the performance after application of precursor m/z filter had been adopted prior to calculation of the similarity score between blocks.

Fig. 3 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 3. Overview of BMDMS-NP. (A) The MS/MS library was established by two types of spectrometers with several conditions. (B) BMDMS-NP consists of information about compound spectra. The program provided by GitHub helped to search and build an in-house library. (C) Spectrum search is performed using the concept of data block. The spectra of the library are grouped as shown by the bold lines in the table. The user can group input spectral data into a block and search against BMDMS-NP and get ranking and scores for the searches.

Fig. 1 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 1. Principal component analysis of molecular fingerprints between the 'natural product/for sale' subset of ZINC and BMDMS-NP. The graph describes that the structural diversity of BMDMS-NP is overlapped with that of ZINC. In order to compare intuitively, the ZINC points were randomly selected to be the same quantity as the BMDMS-NP points and the 95% confidence interval ellipses are added.

Fig. 2 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

Fig. 2. Venn diagram representing the number of overlapped metabolites between libraries. The number of each set was determined using InChIKey as the compound identifier.

Fig. 2 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)

Fig. 2. (A) Molecular network of the molecular family of flavonoid glycosides and phenylethanoid extracted from the MN of the fruit extract of Crescentia cujete. (B) Proposed fragmentation pathway observed in the MS/MS spectrum naringin (33).

Fig. 1 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)

Fig. 1. UPLC-MS/MS based molecular networking in negative ionization mode of the fruit extract of Crescentia cujete. AG: alkyl glycosides, BC: benzoyl and cinnamoyl derivatives, FG1-3: flavonoid glucosides, PE: phenylethanoid derivatives, IG1-2: iridoids glycosides. Node text indicates the parent ion, node color shows the chemical group (green: n-alkyl sugars, sky blue: benzoyl derivatives, dark blue: cinnamoyl derivatives, red: flavonoids glycosides, purple: phenylpropanoids derivatives, and gold: iridoid glycosides). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)

Fig. 3. Chemical structures of iridoid glycosides (11-14, 21 and 24) isolated from the fruit of Crescentia cujete.

opennotspecifiedMay 2020View details →

Fig. 5. A in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow

Fig. 5. A boxplot showing the ion intensities of MS/MS feature 10 (gallic acid) in extracts which were active (IC50 <30 μg/mL) and inactive against α-glucosidase.