NMR-based and chemometric approaches applicable to adulteration studies for assessment of the botanical origin of edible oils.
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ABSTRACT:
SUBMITTER: Ravaglia LM
PROVIDER: S-EPMC6342785 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Food adulteration is a challenge faced by consumers and researchers. Due to DNA fragmentation during oil processing, it is necessary to discover metabolic markers alternative to DNA for adulteration detection of edible oils. However, the contents of metabolic markers vary in response to various factors, such as plant species, varieties, geographical origin, climate, and cultivation measures. Thus, it is difficult to identify a universal marker for all adulterants that may be present in some authentic samples. Currently, the specificity and selectivity of metabolic biomarkers are difficult to validate. Therefore, this study developed a screening strategy based on plant metabolic networks by developing a targeted analytical method for 56 metabolites in a metabolic network, using liquid/liquid extraction-liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified a chain of 11 metabolites that were related to isoflavonoid biosynthesis, which were detected in soybean oils but not rapeseed oils. Through multiple-marker mutual validation, these metabolites can be used as species-specific universal markers to differentiate soybean oil from rapeseed oil. Moreover, this method provides a model for screening characteristic markers of other edible vegetable oils and foods.
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Project description:Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.
| S-EPMC8701812 | biostudies-literature
Project description:Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (-LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%-60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and -LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and -LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.
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Project description:In this work, we introduce an NMR-based screening method for the fatty acid composition analysis of edible oils. We describe the evaluation and optimization needed for the automated analysis of vegetable oils by low-field NMR to obtain the fatty acid composition (FAC). To achieve this, two scripts, which automatically analyze and interpret the spectral data, were developed. The objective of this work was to drive forward the automated analysis of the FAC by NMR. Due to the fact that this protocol can be carried out at low field and that the complete process from sample preparation to printing the report only takes about 3 min, this approach is promising to become a fundamental technique for high-throughput screening. To demonstrate the applicability of this method, the fatty acid composition of extra virgin olive oils from various Spanish olive varieties (arbequina, cornicabra, hojiblanca, manzanilla, and picual) was determined by ¹H-NMR spectroscopy according to this protocol.
| S-EPMC4772056 | biostudies-literature
Project description:Extra-virgin olive oil (383 samples; EVOOs) of three consecutive harvesting years from nine Italian regions were collected and submitted to an ¹H NMR-chemometric protocol to characterize the samples according to their origin (geographical area and variety). A more complete assignment of the olive oil ¹H spectrum in CDCl? and DMSOd? was reported identifying 24-methylencycolartanol. A single classification model provided the discrimination of EVOOs among the three geographical macro-areas (North, Islands, Center-South), whereas a hierarchical approach based on breaking the overall classification problem into a series of smaller linear discriminant analysis (LDA) sub-models was tested to differentiate olive oils according to their geographical regions. Specific compounds responsible for olive oil characterization were identified.
| S-EPMC6523492 | biostudies-literature
Project description:The recent introduction of compact or low-field (LF) NMR spectrometers that use permanent magnets, giving rise to proton (1H) NMR frequencies between 40 and 80 MHz, have opened up new areas of application. The two main limitations of the technique are its insensitivity and poor spectral resolution. However, this study demonstrates that the chemometric treatment of LF 1H NMR spectral data is suitable for unveiling medicines as adulterants of slimming dietary supplements (DS). To this aim, 66 DS were analyzed with LF 1H NMR after quick and easy sample preparation. A first PLS-DA model built with the LF 1H NMR spectra from forty DS belonging to two classes of weight-loss DS (non-adulterated, and sibutramine or phenolphthalein-adulterated) led to the classification of 13 newly purchased test samples as natural, adulterated or borderline. This classification was further refined when the model was made from the same 40 DS now considered as representing three classes of DS (non-adulterated, sibutramine-adulterated, and phenolphthalein-adulterated). The adulterant (sibutramine or phenolphthalein) was correctly predicted as confirmed by the examination of the 1H NMR spectra. A limitation of the chemometric approach is discussed with the example of two atypical weight-loss DS containing fluoxetine or raspberry ketone.
| S-EPMC7179456 | biostudies-literature
Project description:Adulteration remains an issue in the dietary supplement industry, including botanical supplements. While it is common to employ a targeted analysis to detect known adulterants, this is difficult when little is known about the sample set. With this study, untargeted metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS) was employed to detect adulteration in botanical dietary supplements. A training set was prepared by combining Hydrastis canadensis L. with a known adulterant, Coptis chinensis Franch., in ratios ranging from 5 to 95% adulteration. The metabolomics datasets were analyzed using both unsupervised (principal component analysis and composite score) and supervised (SIMCA) techniques. Palmatine, a known H. canadensis metabolite, was quantified as a targeted analysis comparison. While the targeted analysis was the most sensitive method tested in detecting adulteration, statistical analyses of the untargeted metabolomics datasets detected adulteration of the goldenseal samples, with SIMCA providing the greatest discriminating potential. Graphical abstract.
| S-EPMC7321857 | biostudies-literature
Project description:The aim of the study was to examine and compare oxidative stability of refined (peanut, corn, rice bran, grapeseed, and rapeseed) oils. The oils were subject a Schaal Oven Test (temperature 63 ± 1 °C) and a Rancimat test (temperature 120 °C) and their stability was compared at the 1st and 12th month of storage. Changes in the peroxide (PV) and anisidine (AnV) values in the thermostat test were the fastest in rapeseed oil and grapeseed oil. The best quality was preserved by peanut and corn oils both in the first and the twelfth month of storage. The induction times for the rice bran, corn, peanut, and rapeseed oils were similar from 4.77 h to 5.02 h in the first month and from 3.22 h to 3.77 h in the twelfth month. The shortest induction times were determined for grapeseed oil: 2.4 h and 1.6 h, respectively. A decrease of oxidative stability of about 30% was found in all the oils after 12 months of storage. The PV of 10, determined in the thermostat and Rancimat tests, were achieved at the latest in corn oil and the fastest in rice bran oil.
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Project description:BackgroundEdible oils have proven health benefits in the prevention and treatment of various disorders since the establishment of human era. This study was aimed to appraise neuropharmacological studies on the commonly used edible oils including Cinnamomum verum (CV), Zingiber officinale (ZO) and Cuminum cyminum (CC).MethodsThe oils were analyzed via GC-MS for identifications of bioactive compounds. Anti-radicals capacity of the oils were evaluated via 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radicals scavenging assays. The samples were also tested against two important acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) which are among the important drug targets in Alzheimer's disease. Lineweaver-Burk plots were constructed for enzyme inhibition studies which correspond to velocity of enzymes (Vmax) against the reciprocal of substrate concentration (Km) in the presence of test samples and control drugs following Michaelis-Menten kinetics. Docking studies on AChE target were also carried out using Molecular Operating Environment (MOE 2016.0802) software.Results(Gas chromatography-mass spectrometry GC-MS) analysis revealed the presence of thirty-four compounds in Cinnamon oil (Cv.Eo), fourteen in ginger oil (Zo.Eo) and fifty-six in cumin oil (Cc.Eo). In the antioxidant assays, Cv.Eo, Zo.Eo and Cc.Eo exhibited IC50 values of 85, 121, 280 μg/ml sequentially against DPPH radicals. Whereas, in ABTS assay, Cv.Eo, Zo.Eo and Cc.Eo showed considerable anti-radicals potentials with IC50 values of 93, 77 and 271 μg/ml respectively. Furthermore, Cv.Eo was highly active against AChE enzyme with IC50 of 21 μg/ml. Zo.Eo and Cc.Eo exhibited considerable inhibitory activities against AChE with IC50 values of 88 and 198 μg/ml respectively. In BChE assay, Cv.Eo, Zo.Eo and Cc.Eo exhibited IC50 values of 106, 101 and 37 μg/ml respectively. Our results revealed that these oils possess considerable antioxidant and cholinesterase inhibitory potentials. As functional foods these oils can be effective remedy for the prevention and management of neurological disorders including AD. Synergistic effect of all the identified compounds was determined via binding energy values computed through docking simulations. Binding orientations showed that all the compounds interact with amino acid residues present in the peripheral anionic site (PAS) and catalytic anionic site (CAS) amino acid residues, oxyanion hole and acyl pocket via π-π stacking interactions and hydrogen bond interactions.
| S-EPMC8487577 | biostudies-literature
Project description:Bulgaria and North Macedonia have a long history of the production and use of honey; however, there is an obvious lack of systematic and in-depth research on honey from both countries. The oak honeydew honey is of particular interest, as it is highly valued by consumers because of its health benefits. The aim of this study was to characterize honeydew and floral honeys from Bulgaria and North Macedonia based on their NMR profiles. The 1D and 2D 1H and 13C-NMR spectra were measured of 16 North Macedonian and 22 Bulgarian honey samples. A total of 25 individual substances were identified, including quinovose, which was found for the first time in honey. Chemometric methods (PCA-principal component analysis, PLS-DA-partial least squares discriminant analysis, ANOVA-analysis of variance) were used to detect similarities and differences between samples, as well as to determine their botanical and geographical origin. Semiquantitative data on individual sugars and some other constituents were obtained, which allowed for the reliable classification of honey samples by botanical and geographical origin, based on chemometric approaches. The results enabled us to distinguish oak honeydew honey from other honey types, and to determine the country of origin. NMR was a rapid and convenient method, avoiding the need for other more time-consuming analytical techniques.
| S-EPMC7587359 | biostudies-literature