NMR Metabolomics and Chemometrics of Lettuce, Lactuca sativa L., under Different Foliar Organic Fertilization Treatments

Lanzotti, Virginia; Anzano, Attilio; Grauso, Laura; Zotti, Maurizio; Sacco, Adriana; Senatore, Mauro; Moreno, Mauro; Diano, Marcello; Parente, Maddalena; Esposito, Serena; Termolino, Pasquale; Palomba, Emanuela; Zoina, A.; Mazzoleni, Stefano

doi:10.3390/plants11162164

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…In total, 12 spectra were acquired. Quantification was performed by signal integration relative to the internal standard, DSS, as described in Lanzotti et al [34]. The region of the solvent peaks was excluded from the analysis.…”

Section: H-nmr Analysismentioning

confidence: 99%

Chemical Analysis and Antimicrobial Activity of Moringa oleifera Lam. Leaves and Seeds

et al. 2022

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Moringa oleifera is a traditional food crop widespread in Asiatic, African, and South American continents. The plant, able to grow in harsh conditions, shows a high nutritional value and medicinal potential evidencing cardioprotective, anti-inflammatory, antioxidant, and antimicrobial properties. The purpose of this study was the phytochemical analysis of M. oleifera and the identification of the antimicrobial compounds by combining a chemical approach with in vitro tests. The metabolite profile of M. oleifera polar and apolar extracts of leaves and seeds were investigated by using Nuclear Magnetic Resonance spectroscopy and Gas Chromatography-Mass Spectrometry. The antimicrobial activity of all of the obtained extract was evaluated against four bacterial pathogens (Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa and Salmonella enterica). The chemical analysis provided a wide set of metabolites that were identified and quantified. Moreover, apolar extracts from seeds showed a significant concentration-dependent antimicrobial activity against S. aureus and S. epidermidis, (4 mg/mL reduced the viability up to 50%) that was associated to the content of specific fatty acids. Our results remarked the advantages of an integrated approach for the identification of plant metabolites and its use in association with biological tests to recognize the compounds responsible for bioactivity without compounds purification.

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Section: H-nmr Analysismentioning

confidence: 99%

Chemical Analysis and Antimicrobial Activity of Moringa oleifera Lam. Leaves and Seeds

et al. 2022

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“…All kinds of plant damage caused by them are called pests. After insects injured or exposed to pathogens, the organic content of the plant will change [ 9 ] causing its mutation, leading to wilting and even death [ 10 ]. In recent years, crops are always hard to escape the attack of pests.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Cheng

Cui

et al. 2023

Plants

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Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.

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Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

Singh,

Al-Nemi,

Alahmari

et al. 2023

Metabolomics

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Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the eld of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various complex mixtures of plants, honey, milk, and biological specimens, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra. To tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals. We have implemented this comprehensive approach to various NMR techniques, including 1D 1 H presaturation (presat), 2D J-resolved (RES), 2D 1 H-1 H Total Correlation Spectroscopy (TOCSY), and 2D 1 H-13 C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-esh, honey, a standard stock solution of glucose, and nine amino acids, and fetal bovine serum.The outcomes of this approach have been signi cant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. This, in turn, enables the identi cation of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quanti cation of metabolites in statistical studies in the eld of metabolomics.

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NMR Metabolomics and Chemometrics of Lettuce, Lactuca sativa L., under Different Foliar Organic Fertilization Treatments

Cited by 8 publications

References 29 publications

Chemical Analysis and Antimicrobial Activity of Moringa oleifera Lam. Leaves and Seeds

Chemical Analysis and Antimicrobial Activity of Moringa oleifera Lam. Leaves and Seeds

High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

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