Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring

IF 10.7 1区 生物学 Q1 BIOPHYSICS Biosensors and Bioelectronics Pub Date : 2024-06-13 DOI:10.1016/j.bios.2024.116498
Muhammad Usman Ur Rehman , Anoud Saud Alshammari , Anam Zulfiqar , Farhan Zafar , Muhammad Ali Khan , Saadat Majeed , Naeem Akhtar , Wajid Sajjad , Sehrish Hanif , Muhammad Irfan , Zeinhom M. El-Bahy , Mustafa Elashiry
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Abstract

The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat quality because, athletes might have positive test for CLN even after consuming very low quantity of CLN contaminated meat. Numerous materials applied for CLN monitoring faced potential challenges like sluggish ion transport, non-uniform ion/molecule movement, and inadequate electrode surface binding. To overcome these shortcomings, herein we engineered bimetallic zeolitic imidazole framework (BM-ZIF) derived N-doped porous carbon embedded Co nanoparticles (CN-CoNPs), dispersed on conductive cellulose acetate-polyaniline (CP) electrospun nanofibers for sensitive electrochemical monitoring of CLN. Interestingly, the smartly designed CN-CoNPs wrapped CP (CN-CoNPs-CP) electrospun nanofibers offers rapid diffusion of CLN molecules to the sensing interface through amine and imine groups of CP, thus minimizing the inhomogeneous ion transportation and inadequate electrode surface binding. Additionally, to synchronize experiments, machine learning (ML) algorithms were applied to optimize, predict, and validate voltametric current responses. The ML-trained sensor demonstrated high selectivity, even amidst interfering substances, with notable sensitivity (4.7527 μA/μM/cm2), a broad linear range (0.002–8 μM), and a low limit of detection (1.14 nM). Furthermore, the electrode exhibited robust stability, retaining 98.07% of its initial current over a 12-h period. This ML-powered sensing approach was successfully employed to evaluate meat quality in terms of CLN level. To the best of our knowledge, this is the first study of using ML powered system for electrochemical sensing of CLN.

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机器学习驱动的 CN 配位钴纳米粒子嵌入纤维素纳米纤维,通过盐酸克伦特罗监测评估肉质
世界反兴奋剂机构(WADA)禁止使用盐酸克伦特罗(CLN),因为它在诱导合成代谢性肌肉生长的同时,还可能导致心悸、焦虑和肌肉震颤等不良反应。因此,对肉类质量进行评估至关重要,因为即使运动员食用了极少量受盐酸克伦特罗污染的肉类,其盐酸克伦特罗检测结果也可能呈阳性。许多应用于 CLN 监测的材料都面临着潜在的挑战,如离子传输缓慢、离子/分子运动不均匀、电极表面结合力不足等。为了克服这些缺点,我们在此设计了双金属沸石咪唑框架(BM-ZIF)衍生的掺杂 N 的多孔碳嵌入钴纳米粒子(CN-CoNPs),并将其分散在导电的醋酸纤维素-聚苯胺(CP)电纺纳米纤维上,用于对 CLN 进行灵敏的电化学监测。有趣的是,巧妙设计的 CN-CoNPs 包裹 CP(CN-CoNPs-CP)电纺纳米纤维可使 CLN 分子通过 CP 的胺和亚胺基团快速扩散到传感界面,从而最大限度地减少离子传输的不均匀性和电极表面结合的不充分性。此外,为了使实验同步进行,还采用了机器学习(ML)算法来优化、预测和验证伏安电流响应。经过 ML 训练的传感器即使在干扰物质中也表现出了高选择性,灵敏度显著(4.7527 μA/μM/cm2),线性范围宽(0.002-8 μM),检测限低(1.14 nM)。此外,该电极还表现出很强的稳定性,在 12 小时内保持了 98.07% 的初始电流。这种以 ML 为动力的传感方法被成功地用于评估肉类质量中的 CLN 含量。据我们所知,这是首次使用 ML 供能系统对 CLN 进行电化学传感的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosensors and Bioelectronics
Biosensors and Bioelectronics 工程技术-电化学
CiteScore
20.80
自引率
7.10%
发文量
1006
审稿时长
29 days
期刊介绍: Biosensors & Bioelectronics, along with its open access companion journal Biosensors & Bioelectronics: X, is the leading international publication in the field of biosensors and bioelectronics. It covers research, design, development, and application of biosensors, which are analytical devices incorporating biological materials with physicochemical transducers. These devices, including sensors, DNA chips, electronic noses, and lab-on-a-chip, produce digital signals proportional to specific analytes. Examples include immunosensors and enzyme-based biosensors, applied in various fields such as medicine, environmental monitoring, and food industry. The journal also focuses on molecular and supramolecular structures for enhancing device performance.
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