SENSORS AND ACTUATORS A-PHYSICAL
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2020
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IF 2.904
Xuefei Su,Daqi Chen,Nan Li,Adrian Carl Stevenson,Guang Li,Ruifen Hu
Wireless sensor technology is a facile way of realizing low cost and miniaturized device for the detection of the volatile organic compounds (VOCs). A wireless electrodeless quartz crystal microbalance with dissipation (WE-QCM-D) sensor array was developed by using a single sensor working in a multi-resonance mode, which obtained an acoustic spectra of the quartz crystal at four harmonics (6 MHz, 18 MHz, 30 MHz and 42 MHz). Multi-responses (frequency shift, and energy dissipation) of WE-QCM-D sensor array were obtained via a ring-down mode and employed as discrimination factors for VOCs. The gas discrimination capability of the WE-QCM-D sensor array has been tested by coating a 100 μ g polyvinylpyrrolidone film on the quartz disc and achieved an optimal recognition rate of 97.5 % with K nearest neighbor algorithm. The results indicate that the single element WE-QCM-D sensor array could be a promising way to realize compact and low-cost VOCs monitoring systems.
研究领域:
传感器与执行器
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环境监测
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材料科学
✓ 已验证
DOI
高影响因子
ANALYTICAL CHEMISTRY
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2019
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IF 6.35
Xiaoding Xu,Lei Zhao,Qilu Xue,Jinkun Fan,Qingqing Hu,Chu Tang,Hongyan Shi,Bo Hu,Jie Tian
Cell detection is of great significance for biomedical research. Surface enhanced Raman scattering (SERS) has been widely applied to the detection of cells. However, there is still a lack of a general, low-cost, rapid, and sensitive SERS method for cell detection. Herein, a dynamic liquid SERS platform, which combines label-free SERS technique with soft tubular microfluidics for cell detection, is proposed. Compared with common static solid and static liquid measurement, the dynamic liquid SERS platform can present dynamical mixing, precise control of the mixing time, and continuous spectra collection. By characterizing the model molecules, the proposed dynamic liquid SERS platform has successfully demonstrated good stability and repeatability with 1.90% and 4.98% relative standard deviation (RSD), respectively. Three cell lines including one normal breast cell line (MCF-10A) and two breast cancer cell lines (MCF-7 and MDA-MB-231) were investigated in this platform. 270 cell spectra were selected as the training set for the classification of the models based on the K-Nearest Neighbor (K-NN) algorithm. In three independent experiments, three types of cells were identified by a test set containing 180 cell spectra with sensitivities above 83.3% and specificities above 91.6%. The accuracy was 94.1 ± 1.14% among three independent cell identifications. The dynamic liquid SERS platform has shown higher signal intensity, better repeatability, less pretreatment, and obtainment of more spectra with less time consumption. It will be a powerful detection tool in the area of cell research, clinical diagnosis, and food safety.
研究领域:
生物医学工程
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分析化学
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细胞生物学
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临床诊断
✓ 已验证
DOI
PubMed
高影响因子
ANALYST
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2024
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IF 3.6
Jian Song,Wenlong Liang,Hongtao Huang,Hongyan Jia,Shouning Yang,Chunlei Wang,Huayan Yang
Typing of bacterial subspecies is urgently needed for the diagnosis and efficient treatment during disease outbreaks. Physicochemical spectroscopy can provide a rapid analysis but its identification accuracy is still far from satisfactory. Herein, a novel feature-extractor-based fusion-assisted machine learning strategy has been developed for high accuracy and rapid strain differentiation using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and Raman spectroscopy. Based on this fusion approach, rapid and reliable identification and analysis can be performed within 24 hours. Validation on a panel of important pathogens comprising Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii showed that the identification accuracies of k-nearest neighbors (KNNs), support vector machines (SVMs) and artificial neural networks (ANNs) were 100%. In particular, when benchmarked against a MALDI-TOF MS spectral dataset, the new approach improved the identification accuracy of Acinetobacter baumannii from 87.67% to 100%. This work demonstrates the effectiveness of combining MALDI-TOF MS and Raman spectroscopy fusion data in pathogenic bacterial subtyping.
研究领域:
微生物学
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生物信息学
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机器学习
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光谱学
✓ 已验证
DOI
PubMed
高影响因子
VIBRATIONAL SPECTROSCOPY
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2015
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IF 2.003
Xiaoxiao Ding,Ying Guo,Yongnian Ni,Serge Kokot
Near-infrared (NIR) spectroscopy is a well-known, rapid and non-destructive technique suitable for analyses of many different food products. In this work, it was used to develop a novel method for the analysis of dehydrated tomato samples, collected from four different producers. The NIR spectra from such samples were discriminated according to the four production sources, with the use of principal component analysis (PCA). Also, hierarchical cluster analysis (HCA), linear discriminant analysis (LDA) and K-nearest neighbors (KNN) were successfully used for pattern recognition. The prediction rates were 100% for assigning producers to samples. Two multivariate calibration models—partial least squares regression (PLSR) and radial basis function neural networks (RBF-NN), were applied for quantitative analysis. NIRS calibration models were established for the determination of lycopene and total acid, sugar, phenols and antioxidant activity in dehydrated tomatoes. These calibrations were then used for prediction of unknown, dehydrated tomato samples with satisfactory results. The RBF-NN results were better than those obtained from the PLSR models, and the better predictions suggested that the novel NIR spectroscopic method supported by chemometrics, is suitable for the discrimination and prediction of the five quality parameters in the dehydrated tomato products.
研究领域:
食品科学
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光谱学
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化学计量学
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）