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.

A New UPLC-qTOF Approach for Elucidating Furan and 2-Methylfuran Metabolites in Human Urine Samples after Coffee Consumption.

We have investigated urine samples after coffee consumption using targeted and untargeted approaches to identify furan and 2-methylfuran metabolites in urine samples by UPLC-qToF. The aim was to establish a fast, robust, and time-saving method involving ultra-performance liquid chromatography-quantitative time-of-flight tandem mass spectrometry (UPLC-qToF-MS/MS). The developed method detected previously reported metabolites, such as Lys-BDA, and others that had not been previously identified, or only detected in animal or in vitro studies. The developed UPLC-qToF method detected previously reported metabolites, such as lysine-cis-2-butene-1,4-dial (Lys-BDA) adducts, and others that had not been previously identified, or only detected in animal and in vitro studies. In sum, the UPLC-qToF approach provides additional information that may be valuable in future human or animal intervention studies.

Untargeted Characterization of Chestnut (Castanea sativa Mill.) Shell Polyphenol Extract: A Valued Bioresource for Prostate Cancer Cell Growth Inhibition.

Chestnut seeds are used for fresh consumption and for the industrial preparation of derivatives, such as chestnut flour. During industrial processing, large amounts of by-products are generally produced, such as leaves, flowers, shells and burs. In the present study, chestnut shells were extracted by boiling water in order to obtain polyphenol-rich extracts. Moreover, for the removal or non-phenolic compounds, a separation by preparative reverse phase chromatography in ten fractions was carried out. The richest fractions in terms of phenolic content were characterized by means of untargeted high-resolution mass spectrometric analysis together with a dedicated and customized data processing workflow. A total of 243 flavonoids, phenolic acids, proanthocyanidins and ellagitannins were tentatively identified in the five richest fractions. Due its high phenolic content (450.03 µg GAE per mg of fraction), one tumor cell line (DU 145) and one normal prostate epithelial cell line (PNT2) were exposed to increasing concentration of fraction 3 dry extract for 24, 48 and 72 h. Moreover, for DU 145 cell lines, increase of apoptotic cells and perturbation of cell cycle was demonstrated for the same extract. Those outcomes suggest that chestnut industrial by-products could be potentially employed as a source of bioresources.

Metabolite Profiles of Raw Peanut Seeds Reveal Differences between Market-Types.

Peanuts (Arachis hypogaea L.) are prized for their flavor and popular worldwide as food or as food ingredients. The raw peanut seed contains the precursor compounds to roasted peanut flavor and has the potential to be manipulated through traditional breeding methods. However, little is known about the metabolome of the raw seeds. Comprehensive metabolite profiles of both raw runner and Virginia-type peanuts were determined. Using a system incorporating several methodologies including (RP)/UPLC-MS/MS and HILIC/UPLC-MS/MS, along with quantitation of fatty acids, free amino acids, and tocopherols, 365 metabolites were identified and of these, 52 were significantly different between market types (P < 0.05). Higher levels of gamma-glutamylalanine, oxylipins, purine metabolites, and alpha-ketoglutarate derived members of the glutamate family of amino acids defined the Virginia-type, while runner-type peanuts were differentiated by their ethylmalonate and eicosenoate content. This study presents a comprehensive analysis of the raw peanut seed, providing knowledge of the range of small molecules present in peanuts. The new information presented here will enable future research for peanut quality improvement. PRACTICAL APPLICATION: Peanuts are widely used as snack foods and as food ingredients. Knowledge of the secondary metabolite compounds in raw peanuts is needed to determine their importance in peanut flavor and nutritional quality. This report used a nontargeted analytical approach for the identification of these types of compounds in peanuts for the first time. These data were supplemented with quantitative analysis of free amino acids and tocopherols and discussed as potential flavor precursors and health promoting compounds.

Fusion of near-infrared and fluorescence spectroscopy for untargeted fraud detection of Chinese tea seed oil using chemometric methods.

BACKGROUND: This paper investigated the feasibility of data fusion of near-infrared (NIR) and fluorescence spectroscopy for rapid analysis of cheap vegetable oils in Chinese Camellia oleifera Abel. (COA) oil. Because practical frauds usually involve adulterations of multiple known and unknown cheap oils, traditional analytical methods aimed at detecting one or more known adulterants are insufficient to identify adulterated COA oil. Therefore, untargeted analysis was performed by developing class models of pure COA oil using robust one-class partial least squares (OCPLS). RESULTS: The most accurate OCPLS model was obtained with fusion of standard normal variate (SNV)-NIR and SNV-fluorescence spectra with sensitivity of 0.954 and specificity of 0.91. Robust OCPLS could detect adulterations with 2% (w/w) or more cheap oils, including rapeseed oil, sunflower seed oil, corn oil and peanut oil. CONCLUSION: Fusion of NIR and fluorescence data and chemometrics provided enhanced capacity for rapid and untargeted analysis of multiple adulterations in Chinese COA oils. © 2018 Society of Chemical Industry.

Screening of drugs and homeopathic products from Atropa belladonna seed extracts: Tropane alkaloids determination and untargeted analysis.

Homeopathic products are still a controversial issue in modern medicine, understood as complementary or alternative medicine (CAM). In this particular case, homeopathic products prepared from Atropa belladonna extracts may present specific problems due to the effects derived from its components. This article applies a simple, rapid, reliable method to the analysis of different homeopathic products obtained from Atropa belladonna; drugs containing high concentration of plant extracts; and Atropa belladonna seeds. The method was based on a simple solid-phase preconcentration method followed by ultra-high pressure liquid chromatography (UHPLC) coupled to high resolution mass spectrometry using Exactive-Orbitrap as an analyser. An in-house database was set and atropine and scopolamine were the compounds detected at highest concentrations in homeopathic products from Atropa belladonna extracts (4.57 and 2.56 µg/kg, respectively), in Belladonna ointment (4007 and 1139 µg/kg, respectively) and Belladonna seeds (338 and 32.1 mg/kg, respectively). Other tropane alkaloids such as tropine, apoatropine, aposcopolamine, tropinone, homatropine, and anisodamine were detected at lower concentrations (0.04-1.36 µg/kg). When untargeted analysis was performed, other tropane alkaloids were identified in the tested samples, such as ecgonine (0.003 µg/kg), benzoylecgonine (0.56 µg/kg), calystegines A (19.6 µg/kg), B (33.1 µg/kg), and C (1.01 µg/kg). Finally other compounds present in the homeopathic products, such as sugars (fructose, glucose, and lactose) or amino acids (valine, ornithine, leucine, and phenylalanine), were identified.

Integrated analytical assets aid botanical authenticity and adulteration management.

This article reviews and develops a perspective for the meaning of authenticity in the context of quality assessment of botanical materials and the challenges associated with discerning adulterations vs. contaminations vs. impurities. Authentic botanicals are by definition non-adulterated, a mutually exclusive relationship that is confirmed through the application of a multilayered set of analytical methods designed to validate the (chemo)taxonomic identity of a botanical and certify that it is devoid of any adulteration. In practice, the ever-increasing sophistication in the process of intentional adulteration, as well as the growing number of botanicals entering the market, altogether necessitate a constant adaptation and reinforcement of authentication methods with new approaches, especially new technologies. This article summarizes the set of analytical methods - classical and contemporary - that can be employed in the authentication of botanicals. Particular emphasis is placed on the application of untargeted metabolomics and chemometrics. An NMR-based untargeted metabolomic model is proposed as a rapid, systematic, and complementary screening for the discrimination of authentic vs. potentially adulterated botanicals. Such analytical model can help advance the evaluation of botanical integrity in natural product research.

Untargeted metabolomic analysis of tomato pollen development and heat stress response.

KEY MESSAGE: Pollen development metabolomics. Developing pollen is among the plant structures most sensitive to high temperatures, and a decrease in pollen viability is often associated with an alteration of metabolite content. Most of the metabolic studies of pollen have focused on a specific group of compounds, which limits the identification of physiologically important metabolites. To get a better insight into pollen development and the pollen heat stress response, we used a liquid chromatography-mass spectrometry platform to detect secondary metabolites in pollen of tomato (Solanum lycopersicum L.) at three developmental stages under control conditions and after a short heat stress at 38 °C. Under control conditions, the young microspores accumulated a large amount of alkaloids and polyamines, whereas the mature pollen strongly accumulated flavonoids. The heat stress treatment led to accumulation of flavonoids in the microspore. The biological role of the detected metabolites is discussed. This study provides the first untargeted metabolomic analysis of developing pollen under a changing environment that can serve as reference for further studies.

A comprehensive quality evaluation method by FT-NIR spectroscopy and chemometric: Fine classification and untargeted authentication against multiple frauds for Chinese Ganoderma lucidum.

The origins and authenticity against frauds are two essential aspects of food quality. In this work, a comprehensive quality evaluation method by FT-NIR spectroscopy and chemometrics were suggested to address the geographical origins and authentication of Chinese Ganoderma lucidum (GL). Classification for 25 groups of GL samples (7 common species from 15 producing areas) was performed using near-infrared spectroscopy and interval-combination One-Versus-One least squares support vector machine (IC-OVO-LS-SVM). Untargeted analysis of 4 adulterants of cheaper mushrooms was performed by one-class partial least squares (OCPLS) modeling for each of the 7 GL species. After outlier diagnosis and comparing the influences of different preprocessing methods and spectral intervals on classification, IC-OVO-LS-SVM with standard normal variate (SNV) spectra obtained a total classification accuracy of 0.9317, an average sensitivity and specificity of 0.9306 and 0.9971, respectively. With SNV or second-order derivative (D2) spectra, OCPLS could detect at least 2% or more doping levels of adulterants for 5 of the 7 GL species and 5% or more doping levels for the other 2 GL species. This study demonstrates the feasibility of using new chemometrics and NIR spectroscopy for fine classification of GL geographical origins and species as well as for untargeted analysis of multiple adulterants.