Advanced Amperometric Microsensors for the Electrochemical Quantification of Quercetin in Ginkgo biloba Essential Oil from Regenerative Farming Practices.

IF 3.7 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Metabolites Pub Date : 2024-12-31 DOI:10.3390/metabo15010006
Elena Oancea, Ioana Adina Tula, Gabriela Stanciu, Raluca-Ioana Ștefan-van Staden, Jacobus Koos Frederick van Staden, Magdalena Mititelu
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Abstract

In this study, we present a novel approach using amperometric microsensors to detect quercetin in cosmetic formulations and track its metabolic behavior after topical application. This method offers a sensitive, real-time alternative to conventional techniques, enabling the detection of quercetin's bioavailability, its transformation into active metabolites, and its potential therapeutic effects when applied to the skin. Quercetin (Q) is a bioactive flavonoid known for its potent antioxidant properties, naturally present in numerous plants, particularly those with applications in cosmetic formulations. In response to the growing interest in developing novel plant-based dermo-cosmetic solutions, this study investigates the electrochemical detection of quercetin, a ketone-type flavonoid, extracted from Gingko biloba essential oil. Three newly designed amperometric microsensors were developed to assess their efficacy in detecting quercetin in botanical samples. The sensor configurations utilized two forms of carbon material as a foundation: graphite (G) and carbon nanoparticles (CNs). These base materials were modified with paraffin oil, chitosan (CHIT), and cobalt(II) tetraphenylporphyrin (Co(II)TPP) to enhance sensitivity. Differential pulse voltammetry (DPV) served as the analytical method for this investigation. Among the sensors, the CHIT/G-CN microsensor exhibited the highest sensitivity, with a detection limit of 1.22 × 10-7 mol L-1, followed by the G-CN (5.64 × 10-8 mol L-1) and Co(II)TPP/G-CN (9.80 × 10-8 mol L-1) microsensors. The minimum detectable concentration was observed with the G-CN and CoP/G-CN microsensors, achieving a threshold as low as 0.0001 μmol L-1. Recovery rates and relative standard deviation (RSD) values averaged 97.4% ± 0.43, underscoring the sensors' reliability for quercetin detection in botanical matrices.

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先进安培微传感器电化学定量测定再生农业银杏精油中槲皮素。
在这项研究中,我们提出了一种新的方法,使用安培微传感器来检测化妆品配方中的槲皮素,并跟踪其局部应用后的代谢行为。该方法为传统技术提供了一种灵敏、实时的替代方法,可以检测槲皮素的生物利用度、其转化为活性代谢物以及应用于皮肤时的潜在治疗效果。槲皮素(Q)是一种生物活性类黄酮,以其强大的抗氧化特性而闻名,天然存在于许多植物中,特别是那些在化妆品配方中的应用。为了响应人们对开发新型植物性皮肤化妆品解决方案的兴趣,本研究研究了从银杏精油中提取的酮类黄酮槲皮素的电化学检测。研制了三种新型微安培传感器,对其在植物样品中检测槲皮素的效果进行了评价。传感器结构利用两种形式的碳材料作为基础:石墨(G)和碳纳米颗粒(CNs)。用石蜡油、壳聚糖(CHIT)和钴(II)四苯基卟啉(Co(II)TPP)对这些基础材料进行改性以提高灵敏度。差分脉冲伏安法(DPV)作为本研究的分析方法。其中,CHIT/G-CN微传感器灵敏度最高,检测限为1.22 × 10-7 mol L-1,其次是G-CN (5.64 × 10-8 mol L-1)和Co(II)TPP/G-CN (9.80 × 10-8 mol L-1)。G-CN和CoP/G-CN微传感器的最低检测浓度可达0.0001 μmol L-1。回收率和相对标准偏差(RSD)平均为97.4%±0.43,表明传感器检测植物基质中槲皮素的可靠性。
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来源期刊
Metabolites
Metabolites Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
5.70
自引率
7.30%
发文量
1070
审稿时长
17.17 days
期刊介绍: Metabolites (ISSN 2218-1989) is an international, peer-reviewed open access journal of metabolism and metabolomics. Metabolites publishes original research articles and review articles in all molecular aspects of metabolism relevant to the fields of metabolomics, metabolic biochemistry, computational and systems biology, biotechnology and medicine, with a particular focus on the biological roles of metabolites and small molecule biomarkers. Metabolites encourages scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on article length. Sufficient experimental details must be provided to enable the results to be accurately reproduced. Electronic material representing additional figures, materials and methods explanation, or supporting results and evidence can be submitted with the main manuscript as supplementary material.
期刊最新文献
Correction: Song et al. Serum Uric Acid and Bone Health in Middle-Aged and Elderly Hypertensive Patients: A Potential U-Shaped Association and Implications for Future Fracture Risk. Metabolites 2025, 15, 15. Artificial Neural Network Elucidates the Role of Transport Proteins in Rhodopseudomonas palustris CGA009 During Lignin Breakdown Product Catabolism. Metabolic Bone Disease in Captive Flying Foxes: A Conceptual Framework and Future Perspectives. Chemical Attributes of UK-Grown Tea and Identifying Catechin and Metabolite Dynamics in Green and Black Tea Using Metabolomics and Machine Learning. Comparative Analysis of Chemical Constituents in Peppers from Different Regions by Integrated LC-MS and GC-MS Non-Targeted Metabolomics.
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