NMR-Based Analysis of Pomegranate Juice Using Untargeted Metabolomics Coupled with Nested and Quantitative Approaches

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts

We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.

Rapid quantification of the adulteration of pomegranate juices by Raman spectroscopy and chemometrics

The juice drink industry has repeatedly been exposed to adulteration. Unscrupulous producers, for example, use cheap juice for substitution in the pursuit of more significant economic benefits, which presents a tremendous challenge for the control of the quality of drinks. The objective of this study was to apply Raman spectroscopy combined with chemometrics to rapidly quantify the adulteration concentration of apple juice or grape juice in pomegranate juice. Two supervised learning algorithms: partial least squares regression (PLSR) and support vector machine regression (SVR) were used to analyze the Raman spectra of 114 samples. The coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD) of the prediction set when using PLSR and SVR to predict the adulterated concentration of apple juice in pomegranate juice were 0.9357 and 0.9465, 6.446% and 5.974%, 3.945 and 4.322, respectively. The R2, RMSE, and RPD of the prediction set when using PLSR and SVR to predict the adulteration concentration of grape juice in pomegranate juice were 0.9501 and 0.9502, 6.334% and 5.571%, and 4.475 and 4.481, respectively. It was concluded that Raman spectroscopy combined with chemometrics has excellent potential for application as a rapid quantitative method to detect adulterated concentrations of pomegranate juice.

A comparative evaluation of low-field and high-field NMR untargeted analysis: Authentication of virgin coconut oil adulterated with refined coconut oil as a case study

Despite the advances in low-field nuclear magnetic resonance (NMR), there are limited spectroscopic applications for untargeted analysis and metabolomics. To evaluate its potential, we combined high-field and low-field NMR with chemometrics for the differentiation between virgin and refined coconut oil and for the detection of adulteration in blended samples. Although low-field NMR has less spectral resolution and sensitivity compared to high-field NMR, it was still able to achieve a differentiation between virgin and refined coconut oils, as well as between virgin coconut oil and blends, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and random forest techniques. These techniques were not able to distinguish between blends with different levels of adulteration; however, partial least squares regression (PLSR) enabled the quantification of adulteration levels for both NMR approaches. Given the significant benefits of low-field NMR, including economic and user-friendly analysis and fitting in an industrial environment, this study establishes the proof of concept for its utilization in the challenging scenario of coconut oil authentication. Also, this method has the potential to be used for other similar applications that involve untargeted analysis.

Characterization of Bioactivity of Selective Molecules in Fruit Wines by FTIR and NMR Spectroscopies, Fluorescence and Docking Calculations

Fourier transform infrared (FTIR) and proton nuclear magnetic resonance (1H NMR) spectroscopies were applied to characterize and compare the chemical shifts in the polyphenols' regions of some fruit wines. The obtained results showed that FTIR spectra (1800-900 cm-1) and 1H NMR (δ 6.5-9.3 ppm) of different fruit wines can be used as main indices of the year of vintage and quality of fruit wines. In addition to the classical determination of antioxidant profiles and bioactive substances in wines, fluorometric measurements were used to determine the interactions of wine substances with the main human serum proteins. The results showed relatively high binding properties of wines with the highest one for pomegranate, followed by kiwifruit and persimmon wines. The interactions of vitamin C, catechin and gallic acid with human serum albumin (HSA) were also examined by docking studies. The docking calculations showed that gallic acid has a stronger binding affinity compared to catechin and vitamin C. The stronger binding affinity of gallic acid may be due to three hydrogen bonds and pi-pi interactions. The fluorescence and docking studies proved that only the bioactive compounds of wines and not the amount of alcohol have high binding properties to human serum proteins. The emphasis in this report was made on the utility of FTIR, NMR and fluorescence of wines as a mean of wine authentication and its fingerprint. The findings, based on polyphenols from fruits and fruit wines, their bioactivity and health properties, offer valuable insights for future endeavours focused on designing healthy food products.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) ¹H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T₂ filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D ¹H NMR methods such as 1D ¹H presat and 1D ¹H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D ¹H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Metabolomic NMR analysis and organoleptic perceptions of pomegranate wines: Influence of cultivar and yeast on the product characteristics

Pomegranate (Punica granatum L.) fruits are a historical agricultural product of the Mediterranean basin that became increasingly popular in the latest years for being rich in antioxidants and other micronutrients, and are extensively commercialized as fruits, juice, jams and, in some Eastern countries, as a fermented alcoholic beverage. In this work, four different pomegranate wines specifically designed using combinations of two cultivars (Jolly Red and Smith) and two yeast starters with markedly different characteristics (Saccharomyces cerevisiae Clos and Saccharomyces cerevisiae ex-bayanus EC1118) were analyzed. The chemical characterization of the wines together with the originating unfermented juices was performed by ¹H NMR spectroscopy metabolomic analysis. The full spectra were used for unsupervised and supervised statistical multivariate analysis (MVA), namely Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and sparse PCA (SPCA). The MVA of the wines showed a clear discrimination between the cultivars, and a smaller, yet significant, discrimination between the yeasts used. In particular, a higher content of citrate and gallate was observed for the Smith cv. and, on the contrary, a statistically significant higher content of fructose, malate, glycerol, 2,3 butanediol, trigonelline, aromatic amino acids and 4-hydrophenylacetate was observed in Jolly Red pomegranate wines samples. Significant interaction among the pomegranate cultivar and the fermenting yeast was also observed. Sensorial analysis was performed by a panel of testing experts. MVA of tasting data showed that the cultivar significantly affected the organoleptic parameters considered, while the yeast had a minor impact. Correlation analysis between NMR-detected metabolites and organoleptic descriptors identified several potential sensorially-active molecules as those significantly impacting the characteristics of the pomegranate wines.

Application of Band-Selective HSQC NMR in Species Discrimination and Adulteration Identification of Panax Linn

The quality of Panax Linn products available in the market is threatened by adulteration with different Panax species, such as Panax quinquefolium (PQ), Panax ginseng (PG), and Panax notoginseng (PN). In this paper, we established a 2D band-selective heteronuclear single quantum coherence (bs-HSQC) NMR method to discriminate species and detect adulteration of Panax Linn. The method involves selective excitation of the anomeric carbon resonance region of saponins and non-uniform sampling (NUS) to obtain high-resolution spectra in less than 10 min. The combined strategy overcomes the signal overlap limitation in ¹H NMR and the long acquisition time in traditional HSQC. The present results showed that twelve well-separated resonance peaks can be assigned in the bs-HSQC spectra, which are of high resolution, good repeatability, and precision. Notably, the identification accuracy of species was found to be 100% for all tests conducted in the present study. Furthermore, in combination with multivariate statistical methods, the proposed method can effectively determine the composition proportion of adulterants (from 10% to 90%). Based on the PLS-DA models, the identification accuracy was greater than 80% when composition proportion of adulterants was 10%. Thus, the proposed method may provide a fast, practical, and effective analysis technique for food quality control or authenticity identification.

An Optimized Two-Dimensional Quantitative Nuclear Magnetic Resonance Strategy for the Rapid Quantitation of Diester-Type C19-Diterpenoid Alkaloids from Aconitum carmichaelii

With the development of nuclear magnetic resonance (NMR) spectrometers and probes, two-dimensional quantitative nuclear magnetic resonance (2D qNMR) technology with a high signal resolution and great application potential has become increasingly accessible for the quantitation of complex mixtures. However, the requirement that the relaxation recovery time be equal to at least five times T₁ (longitudinal relaxation time) makes it difficult for 2D qNMR to simultaneously achieve high quantitative accuracy and high data acquisition efficiency. By comprehensively using relaxation optimization and nonuniform sampling, we successfully established an optimized 2D qNMR strategy for HSQC experiments at the half-hour level and then accurately quantified the diester-type C₁₉-diterpenoid alkaloids in Aconitum carmichaelii. The optimized strategy had the advantages of high efficiency, high accuracy, good reproducibility, and low cost and thus could serve as a reference to optimize 2D qNMR experiments for quantitative analysis of natural products, metabolites, and other complex mixtures.

Extraction and Quantitation of Phytosterols from Edible Brown Seaweeds: Optimization, Validation, and Application

Brown seaweeds are known as important marine food sources, from which phytosterols have been recognized as functional food components with multiple health-beneficial effects. However, studies on phytosterol extraction and quantitation from edible brown seaweeds are limited. In the present work, extraction methods for seaweed phytosterols were compared and optimized by one-factor-at-one-time method and response surface methodology. Moreover, the quantitation method of total sterols and major sterol components, including fucosterol, saringosterol, and ostreasterol, was established and validated using 1H NMR. Furthermore, the developed extraction and determination methods were applied to investigate three common edible seaweeds from Japan (Hijiki, Wakame, and Kombu). As a result, the finally optimized conditions were ultrasound-assisted extraction with CHCl3-MeOH 2:3 for 15 min followed by saponification with 1.65 mL of 1.85 M KOH for 14.5 h. Based on the developed methods, phytosterols in three seaweeds were compared, and Hijiki showed an abundant total sterol amount (2.601 ± 0.171 mg/g DW), significantly higher than Wakame (1.845 ± 0.137 mg/g DW) and Kombu (1.171 ± 0.243 mg/g DW). Notably, the composition of the sterol components varied in different seaweeds. These findings might help the nutritional investigation and functional food development concerning phytosterols from seaweeds.

NOAH-(15N/13C)-CEST NMR supersequence for dynamics studies of biomolecules

An NMR supersequence is introduced for the rapid acquisition of ¹⁵N-CEST and methyl-¹³C-CEST experiments in the same pulse sequence for applications to proteins. The high sensitivity and accuracy allows the simultaneous quantitative characterization of backbone and side-chain dynamics on the millisecond timescale ideal for routine screening for alternative protein states.

Rapid Detection and Quantification of Adulterants in Fruit Juices Using Machine Learning Tools and Spectroscopy Data

Fruit juice production is one of the most important sectors in the beverage industry, and its adulteration by adding cheaper juices is very common. This study presents a methodology based on the combination of machine learning models and near-infrared spectroscopy for the detection and quantification of juice-to-juice adulteration. We evaluated 100% squeezed apple, pineapple, and orange juices, which were adulterated with grape juice at different percentages (5%, 10%, 15%, 20%, 30%, 40%, and 50%). The spectroscopic data have been combined with different machine learning tools to develop predictive models for the control of the juice quality. The use of non-supervised techniques, specifically model-based clustering, revealed a grouping trend of the samples depending on the type of juice. The use of supervised techniques such as random forest and linear discriminant analysis models has allowed for the detection of the adulterated samples with an accuracy of 98% in the test set. In addition, a Boruta algorithm was applied which selected 89 variables as significant for adulterant quantification, and support vector regression achieved a regression coefficient of 0.989 and a root mean squared error of 1.683 in the test set. These results show the suitability of the machine learning tools combined with spectroscopic data as a screening method for the quality control of fruit juices. In addition, a prototype application has been developed to share the models with other users and facilitate the detection and quantification of adulteration in juices.

Ultrahigh-Resolution NMR with Water Signal Suppression for a Deeper Understanding of the Action of Antimetabolic Drugs on Diffuse Large B-Cell Lymphoma

Ultrahigh-resolution NMR has recently attracted considerable attention in the field of complex samples analysis. Indeed, the implementation of broadband homonuclear decoupling techniques has allowed us to greatly simplify crowded ¹H spectra, yielding singlets for almost every proton site from the analyzed molecules. Pure shift methods have notably shown to be particularly suitable for deciphering mixtures of metabolites in biological samples. Here, we have successfully implemented a new pure shift pulse sequence based on the PSYCHE method, which incorporates a block for solvent suppression that is suitable for metabolomics analysis. The resulting experiment allows us to record ultrahigh-resolution 1D NOESY ¹H spectra of biofluids with suppression of the water signal, which is a crucial step for highlighting metabolite mixtures in an aqueous phase. We have successfully recorded pure shift spectra on extracellular media of diffuse large B-cell lymphoma (DLBCL) cells. Despite a lower sensitivity, the resolution of pure shift data was found to be better than that of the standard approach, which provides a more detailed vision of the exo-metabolome. The statistical analyses carried out on the resulting metabolic profiles allow us to successfully highlight several metabolic pathways affected by these drugs. Notably, we show that Kidrolase plays a major role in the metabolic pathways of this DLBCL cell line.

Expanding current applications and permitting the analysis of larger intact samples by means of a 7 mm CMP-NMR probe

Comprehensive multiphase NMR combines the ability to study and differentiate all phases (solids, gels, and liquids) using a single NMR probe. The general goal of CMP-NMR is to study intact environmental and biological samples to better understand conformation, organization, association, and transfer between and across phases/interfaces that may be lost with conventional sample preparation such as drying or solubilization. To date, all CMP-NMR studies have used 4 mm probes and rotors. Here, a larger 7 mm probehead is introduced which provides ∼3 times the volume and ∼2.4 times the signal over a 4 mm version. This offers two main advantages: (1) the additional biomass reduces experiment time, making 13C detection at natural abundance more feasible; (2) it allows the analysis of larger samples that cannot fit within a 4 mm rotor. Chicken heart tissue and Hyalella azteca (freshwater shrimp) are used to demonstrate that phase-based spectral editing works with 7 mm rotors and that the additional biomass from the larger volumes allows detection with 13C at natural abundance. Additionally, a whole pomegranate seed berry (aril) and an intact softgel capsule of hydroxyzine hydrochloride are used to demonstrate the analysis of samples too large to fit inside a conventional 4 mm CMP probe. The 7 mm version introduced here extends the range of applications and sample types that can be studied and is recommended when 4 mm CMP probes cannot provide adequate signal-to-noise (S/N), or intact samples are simply too big for 4 mm rotors.

Evaluation of Fumaric Acid and Maleic Acid as Internal Standards for NMR Analysis of Protein Precipitated Plasma, Serum, and Whole Blood

Significant advances have been made in unknown metabolite identification and expansion of the number of quantifiable metabolites in human plasma, serum, and whole blood using NMR spectroscopy. However, reliable quantitation of metabolites is still a challenge. A major bottleneck is the lack of a suitable internal standard that does not interact with the complex blood sample matrix and also does not overlap with metabolite peaks apart from exhibiting other favorable characteristics. With the goal of addressing this challenge, a comprehensive investigation of fumaric and maleic acids as potential internal standards was made along with a comparison with the conventional standards, TSP (trimethylsilylpropionic acid) and DSS (trimethylsilylpropanesulfonic acid). Both fumaric acid and maleic acid exhibited a surprisingly high performance with a quantitation error <1%, while the TSP and DSS caused an average error of up to 35% in plasma, serum, and whole blood. Further, the results indicate that while fumaric acid is a robust standard for all three biospecimens, maleic acid is suitable for only plasma and serum. Maleic acid is not suited for the analysis of whole blood due to its overlap with coenzyme peaks. These findings provide new opportunities for improved and accurate quantitation of metabolites in human plasma, serum, and whole blood using NMR spectroscopy. Moreover, the use of protein precipitation prior to NMR analysis mirrors the sample preparation commonly used for mass spectrometry based metabolomics, such that these findings further strengthen efforts to combine and compare NMR and MS based metabolite data of human plasma, serum, and whole blood for metabolomics based research.

Analysis and Authentication of Avocado Oil Using High Resolution NMR Spectroscopy

Avocado oil is a food product of high commercial and nutritional value. As a result, it can be a subject of adulteration similar to other high-value edible oils, such as olive oil. For olive oil and many other foods products, NMR spectroscopy has been successfully used for authentication and quality assessment. In this study, we apply NMR analysis to avocado oil to differentiate it from other oils including olive, canola, high-oleic (HO) safflower, HO sunflower and soybean oil using commercial and lab-made samples of avocado oils. NMR allowed the rapid analysis of the fatty acid profile and detection of minor compounds, such as sterols, oxidation products, and hydrolysis products, which can be used to assess oil quality and authenticity. The NMR assignment was conducted using traditional 2D NMR and the novel NOAH super-sequences. Combining chemometrics with NMR enabled us to differentiate between avocado oil and other oils. Avocado oil has compositional similarities with other vegetable oils, such as HO sunflower and HO safflower oil, which can be used as potential adulterants. Despite these similarities, NMR-based metabolomics captured differences in the levels of certain compounds including fatty acids, terpenes, sterols, and oxidation products to detect adulteration and for quality control purposes.