分子印迹石英晶体微平衡传感器可靠检测各种松木精油中α -松油醇

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Deepam Gangopadhyay;Sumit Kundu;Mahuya Bhattacharyya Banerjee;Shreya Nag;Panchanan Pramanik;Runu Banerjee Roy
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引用次数: 0

摘要

$\ α $ -松油醇(a -te)是一种具有生物活性的单萜类化合物,因其具有爽口的香味而广泛应用于化妆品和芳香疗法中。最近的研究已经承认它在生物应用和自然疗法方面的巨大潜力。本研究旨在利用高选择性的甲基丙烯酸甲酯(MMA)和丙烯酸(AA)分子印迹聚合物(MIP),利用灵敏的石英晶体微平衡(QCM)传感器开发一种低成本的a - te检测机制。该传感器的频率偏差已被用于四种商业级松树精油(peo)的A-Te检测。这产生了0.149赫兹/ppm的显着灵敏度,线性范围为5-800 ppm。根据再现性和可重复性研究对传感器的可靠性进行了评估,分别显示有希望的值为90.70%和92.32%。检测限(LOD)已达到1.33 ppm。通过傅里叶变换红外光谱(FTIR)和扫描电镜(SEM)对传感器的聚合物表征和表面形貌进行了分析。此外,利用主成分回归(PCR)和随机森林回归(RFR)模型,将PEO样品的响应与传统气相色谱方法进行了相关性分析。值得注意的是,PCR的预测准确率高达96.38%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Molecular Imprinted Quartz Crystal Microbalance Sensor for Reliable Detection of Alpha-Terpineol in Various Pine Essential Oils
$\alpha $ -terpineol (A-Te), a bioactive monoterpenoid, is widely used in cosmetics and aroma therapy for its appeasing fragrance and flavor. Recent studies have acknowledged its immense potential in biological applications and naturopathy. This study aims to develop a low-cost detection mechanism of A-Te using a sensitive quartz crystal microbalance (QCM) sensor, employing a highly selective molecularly imprinted polymer (MIP) of methyl methacrylate (MMA) and acrylic acid (AA). Frequency deviation of the sensor has been utilized for A-Te detection in four commercial-grade pine essential oils (PEOs). This has yielded a remarkable sensitivity of 0.149 Hz/ppm with a wide linear range of 5–800 ppm. Reliability of the sensor has been assessed in terms of a reproducibility and repeatability study, showcasing promising values of 90.70% and 92.32%, respectively. The limit of detection (LOD) has been achieved at 1.33 ppm. Polymer characterization and surface morphology of the sensor have been analyzed through Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM), respectively. Furthermore, responses obtained from PEO samples were correlated with the conventional gas chromatographic method using principal component regression (PCR) and random forest regression (RFR) models. Notably, a high prediction accuracy (96.38%) has been achieved from PCR.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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