In this study, 41 tomato samples were investigated by means of stable isotope ratios (d 13 C, d 18 O and d 2 H), elemental content, phenolic compounds and pesticides in order to classify them, according to growing conditions and geographical origin. Using investigated parameters, stepwise linear discriminant analysis was applied and the differences that occurred between tomato samples grown in greenhouses compared to those grown on field, and also between Romanian and abroad purchased samples were pointed out. It was shown that Ti, Ga, Te, d 2 H and d 13 C content were able to differentiate Romanian tomato samples from foreign samples, whereas Al, Sc, Se, Dy, Pb, d 18 O, 4,4 0 -DDT could be used as markers for growing regime (open field vs. greenhouse). For the discrimination of different tomato varieties (six cherry samples and fourteen common sorts) grown in greenhouse, phenolic compounds of 20 samples were determined. In this regard, dihydroquercetin, caffeic acid, chlorogenic acid, rutin, rosmarinic acid, quercetin and naringin were the major phenolic compounds detected in our samples. The phenolic profile showed significant differences between cherry tomato and common tomato. The contents of the chlorogenic acid and rutin were significantly higher in the cherry samples (90.27-243.00 lg/g DW and 160.60-433.99 lg/g DW respectively) as compared to common tomatoes (21.30-88.72 lg/g DW and 24.84-110.99 lg/g DW respectively). The identification of dihydroquercetin is of particular interest, as it had not been reported previously in tomato fruit.
The present work reports the photoluminescence (PL) and photocatalytic properties of multi-walled carbon nanotubes (MWCNTs) decorated with Fe-doped ZnO nanoparticles. MWCNT:ZnO-Fe nanocomposite samples with weight ratios of 1:3, 1:5 and 1:10 were prepared using a facile synthesis method. The obtained crystalline phases were evidenced by X-ray diffraction (XRD). X-ray Photoelectron spectroscopy (XPS) revealed the presence of both 2+ and 3+ valence states of Fe ions in a ratio of approximately 0.5. The electron paramagnetic resonance EPR spectroscopy sustained the presence of Fe3+ ions in the ZnO lattice and evidenced oxygen vacancies. Transmission electron microscopy (TEM) images showed the attachment and distribution of Fe-doped ZnO nanoparticles along the nanotubes with a star-like shape. All of the samples exhibited absorption in the UV region, and the absorption edge was shifted toward a higher wavelength after the addition of MWCNT component. The photoluminescence emission spectra showed peaks in the UV and visible region. Visible emissions are a result of the presence of defects or impurity states in the material. All of the samples showed photocatalytic activity against the Rhodamine B (RhB) synthetic solution under UV irradiation. The best performance was obtained using the MWCNT:ZnO-Fe(1:5) nanocomposite samples, which exhibited a 96% degradation efficiency. The mechanism of photocatalytic activity was explained based on the reactive oxygen species generated by the nanocomposites under UV irradiation in correlation with the structural and optical information obtained in this study.
Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs.
Edible mushrooms have been recognized as highly nutritional food for a long time, due to their specific flavor, texture and also for therapeutic effects. This study proposes a new simple approach, based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushrooms species from Romanian spontaneous flora, namely Armillaria mellea, Boletus edulis and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) manage to 100% classify the three species and the cross validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for holdout set the percent raised at 94.74%. The lowered values obtained for the training set was due to one C. cibarius sample, two B. edulis and five A. mellea, which were placed to other species. Anyway, for holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified investigated mushroom samples according to their species, meaning that in every partition the predominant specie had the biggest DOMs, while samples belonging to other specie had lower DOMs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.