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Singh U, Al-Nemi R, Alahmari F, Emwas AH, Jaremko M. Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies. Metabolomics 2023; 20:7. [PMID: 38114836 DOI: 10.1007/s11306-023-02069-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/16/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, 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. OBJECTIVES The aim of this study is 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. METHODS We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS). RESULTS The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites. CONCLUSION This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.
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Affiliation(s)
- Upendra Singh
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah, 23955-6900, Saudi Arabia
| | - Ruba Al-Nemi
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah, 23955-6900, Saudi Arabia
| | - Fatimah Alahmari
- Department of Nanomedicine Research, Institute for Research & Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Abdul-Hamid Emwas
- Core Lab of NMR, King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah, 23955-6900, Saudi Arabia.
| | - Mariusz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah, 23955-6900, Saudi Arabia.
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Classification of Polish Natural Bee Honeys Based on Their Chemical Composition. Molecules 2022; 27:molecules27154844. [PMID: 35956789 PMCID: PMC9369904 DOI: 10.3390/molecules27154844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/20/2022] [Accepted: 07/27/2022] [Indexed: 12/04/2022] Open
Abstract
The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.
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Melissopalynology analysis, determination of physicochemical parameters, sugars and phenolics in Maltese honey collected in different seasons. JOURNAL OF THE SERBIAN CHEMICAL SOCIETY 2022. [DOI: 10.2298/jsc211214033b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Malta, a country renowned for its honey, has not been extensively mentioned
in investigations based on honey. In addition to many parameters, the
collection period affects honey quality, precisely due to the different
floral composition that exists during a certain season. Therefore, the
significance of this study refers to the provision of data on honey from
Malta collected during the autumn, spring, and summer seasons.
Melissopalynological analysis, determination of physicochemical parameters,
and the use of analytical chromatographic methods enabled detailed analysis
of these honey. Principal component analysis (PCA) provided the
differentiation of Maltese honey depending on the harvest season. Lotus
pollen, followed by Eucalyptus, predominated in all honey samples.
Characteristic compounds for summer honey were pinocembrin, galangin,
kaempferol, chrysin, p-hydroxybenzoic acid, vanillic acid, and maltotriose,
while quercetin 3-O-galactoside, ferulic acid, ellagic acid, protocatechuic
acid, luteolin 7-O-glucoside, and melibiose were specific for autumn honey.
A higher amount of p-coumaric acid, genistein, catechin, as well as the
content of many sugars were found in spring samples. To our best knowledge,
this is the first scientific work dealing with a detailed chemical analysis
of Maltese honey.
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Rachineni K, Rao Kakita VM, Awasthi NP, Shirke VS, Hosur RV, Chandra Shukla S. Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification. Curr Res Food Sci 2022; 5:272-277. [PMID: 35141528 PMCID: PMC8816647 DOI: 10.1016/j.crfs.2022.01.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/25/2021] [Accepted: 01/09/2022] [Indexed: 02/01/2023] Open
Abstract
Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes. NMR helps to identify the fingerprints of honey chemical constituents. Combined NMR and ML tools can determine the type of adulteration in honey. Supervised classification schemes, Logistic regression, DNN, and LGBM are utilized. Corn, brown rice, and jaggery adulterations are discriminated in honey.
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Affiliation(s)
- Kavitha Rachineni
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
- Corresponding author.
| | - Veera Mohana Rao Kakita
- UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Kalina Campus, Santacruz, Mumbai, 400 098, India
| | - Neeraj Praphulla Awasthi
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
| | - Vrushali Siddesh Shirke
- Export Inspection Agency – Mumbai, E-3, Industrial Area (MIDC), Andheri East, Mumbai, 400 093, India
| | - Ramakrishna V. Hosur
- UM-DAE Centre for Excellence in Basic Sciences, University of Mumbai, Kalina Campus, Santacruz, Mumbai, 400 098, India
| | - Satish Chandra Shukla
- Export Inspection Agency- Chennai (Head Office), 6th Floor CMDA Tower-II, No: 1 Gandhi Irwin Road, Egmore, Chennai, 600008, India
- Corresponding author.
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Lorenc Z, Paśko S, Pakuła A, Teper D, Sałbut L. An attempt to classify the botanical origin of honey using visible spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:5272-5277. [PMID: 33647165 DOI: 10.1002/jsfa.11176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/04/2021] [Accepted: 03/01/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND The production of honey, and especially the unifloral varieties, is limited by factors such as weather conditions or the availability of nectar flow and honeydew. This results in a deficit in supply leading to the adulteration of honey. If they are not properly labeled, customers cannot distinguish artificial / synthetic products from real honey. Currently, the basic, commonly used method for determining the varieties of honey (botanical origin) is palynological analysis. However, this procedure is quite difficult owing to the dearth of experienced staff in the field of melissopalynology. RESULTS A method for identifying and classifying natural honey accurately based on its botanical origin has therefore been proposed. This analysis would rely on the visible light spectra transmitted through a relatively thin layer of the substance of interest, regardless of deviations in thickness. We present algorithms for analyzing the transmittance spectra-parametrization based on polynomial approximation (PMA) and applying a method for spectra selection and reduction (SSR) and a classical classification model (decision tree). A comparison is presented of the classification of four varieties of honey, confirmed by pollen analysis, obtained from the analysis of optically measured transmittance spectra of the samples. The algorithms that are compared contain a decision tree that uses raw data, data reduced by principal component analysis (PCA), and data after calculations based on the proposed algorithms alone (PMA and SSR) and together with the PCA method. CONCLUSION This novel method produced outstanding results in comparison with the standard PCA method and is helpful in identifying the botanical origin of honey effectively. © 2021 Society of Chemical Industry.
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Affiliation(s)
- Zofia Lorenc
- Faculty of Mechatronics, Institute of Micromechanics and Photonics, Warsaw University of Technology, Warsaw, Poland
| | - Sławomir Paśko
- Faculty of Mechatronics, Institute of Micromechanics and Photonics, Warsaw University of Technology, Warsaw, Poland
| | - Anna Pakuła
- Faculty of Mechatronics, Institute of Micromechanics and Photonics, Warsaw University of Technology, Warsaw, Poland
| | - Dariusz Teper
- Apiculture Division, Research Institute of Horticulture, Puławy, Poland
| | - Leszek Sałbut
- Faculty of Mechatronics, Institute of Micromechanics and Photonics, Warsaw University of Technology, Warsaw, Poland
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Dimitrakopoulou ME, Matzarapi K, Chasapi S, Vantarakis A, Spyroulias GA. Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants. J Food Sci 2021; 86:4417-4429. [PMID: 34459510 DOI: 10.1111/1750-3841.15873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/28/2021] [Accepted: 07/02/2021] [Indexed: 11/28/2022]
Abstract
In this study, non-targeted 1 H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.
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Affiliation(s)
| | - Konstantina Matzarapi
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Styliani Chasapi
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Apostolos Vantarakis
- Department of Public Health, Medical School, University of Patras, Patras, Greece
| | - Georgios A Spyroulias
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
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