Evaluation of the Chemiluminometric Method for Determination of Polyphenols in Wine

Weingerl, Vesna; Strlič, Matija; Kočar, Drago

doi:10.1080/00032719.2010.512674

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…[21][22][23][24][25] From the scientific literature it is obvious that the most commonly used techniques for the determination of PAs are high-performance liquid chromatography (HPLC) with UV or DAD detection or liquid chromatography coupled with mass spectrometry (LC-MS). [26][27][28][29] Because of the longer sample preparation process for analysis, using gas chromatography with mass spectrometry (GC-MS) in analysis of phenolic compounds is relatively rare, but in comparison with the other methods mentioned, GC-MS offers several advantages, including complete and high-resolution separation, sensitive detection, unambiguous identification and quantitation of a wide range of phenolics (including all isomers) in one chromatographic run. [30][31][32] The aim of our study was to develop a simple and quantitative extraction method of selected PAs to ensure clean extracts in order to obtain a much more sensitive, selective and accurate GC-MS method for identification and quantitation of both free and bound PAs in red wine samples.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous GC-MS Determination of Free and Bound Phenolic Acids in Slovenian Red Wines and Chemometric Characterization

Ivanović

Razboršek

Kolar

2016

ACSi

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Several phenolic acids (PAs), caffeic, vanillic, syringic, p-coumaric and ferulic acid, found in Slovenian red wines were studied using gas chromatography and mass spectrometry. For isolation of the PAs from wine samples, solid phase extraction using hydrophilic modified styrene -HLB cartridges was used. The bound PAs were extracted after basic hydrolysis and o-coumaric acid was used as the internal standard. The developed method was validated and the linear concentration range for all analytes was from 1 to 100 mg L -1 with correlation coefficients above 0.999. We show that the method is repeatable (RSD<2%), recoveries were above 96%, and LOD and LOQ values were acceptable. In all of the wine samples tested, caffeic and p-coumaric acid were determined to be the predominant PAs (17-72 mg L -1 ), while other compounds were found in lower concentrations. Principal Component Analysis and Cluster Analysis were used to study differences between wines related towards varieties and Slovenian wine regions. The results demonstrate that variety has more influence on PAs content than wine regions in Slovenian red wines.

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Section: Introductionmentioning

confidence: 99%

Simultaneous GC-MS Determination of Free and Bound Phenolic Acids in Slovenian Red Wines and Chemometric Characterization

Ivanović

Razboršek

Kolar

2016

ACSi

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“…Wine quality is affected by many physicochemical indicators, such as phenolic compounds (Weingerl, Strlič, & Kočar, ), amines (Manetta et al, ), anthocyanin (Avizcuri, Saenz‐Navajas, Echavarri, Ferreira, & Fernandez‐Zurbano, ), bitterness (Ferrer‐Gallego, Hernández‐Hierro, Rivas‐Gonzalo, & Escribano‐Bailón, ), lees (Dimou et al, ), and food additive content (Amidzic Klaric, Klaric, Mornar, & Nigovic, 2015). These physicochemical wine indicators can be tested with the help of machinery such as an electronic tongue (Yu et al, ), an electronic nose (Prieto et al, ), and a spectroanalysis instrument (Castro Grijalba, Fiorentini, Martinez, & Wuilloud, 2016; Wu et al, ).…”

Section: Resultsmentioning

confidence: 99%

Quality evaluation of Chinese red wine based on cloud model

2019

J Food Biochem

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Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. Practical applications In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.

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“…The detection limit is very low, below that of most chemical methods. The mainly used chemiluminescence reagents are luminol [37, [75][76][77][78][79], lucigenin [39], pholasin (a bioluminescent protein) [80] and peroxyoxalate [81]. Luminol is the main commonly employed aqueous chemiluminescent reagent.…”

Section: Chemiluminescence Methodsmentioning

confidence: 99%

Recent Advances in Antioxidant Capacity Assays

Danet¹

2021

Antioxidants - Benefits, Sources, Mechanisms of Action

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This work presents a survey of the important antioxidant capacity/activity assays applied for a diversity of samples including plant extracts, foods, biological material, etc. The published materials are critically discussed, emphasizing the recent findings in the field. New and emergent antioxidant capacity assays, such as nanoparticles-based assay, are also presented. The discussion includes chemical-based methods as well as biochemical and cellular assays. Chemical methods detailed are radical/ROS-based scavenging assays (the trolox equivalent antioxidant capacity (TEAC/ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), oxygen radical absorbance capacity (ORAC) assays, chemiluminescence methods, total radical-trapping antioxidant parameter (TRAP), total oxy radical scavenging capacity (TOSC), and β-carotene bleaching assays), non-radical redox potential-based assays (ferric reducing antioxidant power (FRAP), cupric reducing antioxidant capacity (CUPRAC), nanoparticle-based methods and electrochemical methods), metal chelation capacity and total phenolic content tests. The biochemical-based assays and in vivo assays discussed include the oxidation of low density lipoprotein (LDL), the thiobarbituric acid reactive substances (TBARS) and the cellular antioxidant activity (CAA) assays. While a direct link between the antioxidant capacity and health benefits is still a matter of debate, the antioxidant testing methodologies presented in this chapter remain valuable for the high efficiency and cost-effective evaluation of antioxidants, from compound discovery to quality control.

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Evaluation of the Chemiluminometric Method for Determination of Polyphenols in Wine

Cited by 9 publications

References 26 publications

Simultaneous GC-MS Determination of Free and Bound Phenolic Acids in Slovenian Red Wines and Chemometric Characterization

Simultaneous GC-MS Determination of Free and Bound Phenolic Acids in Slovenian Red Wines and Chemometric Characterization

Quality evaluation of Chinese red wine based on cloud model

Recent Advances in Antioxidant Capacity Assays

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