微含量简单成分气体的人工嗅觉分析方法

提出了由两层径基函数(RBF)网络和两层线性基本函数(LBF)网络组成的串联神经网络,建立了由SnO₂半导体气敏传感器阵列和串联神经网络分类方法组成的人工嗅觉系统.对微含量的甲醇、乙醇、丙酮挥发气的测试与分析结果表明,提高了人工嗅觉系统的灵敏度和识别能力,为人工嗅觉系统对简单与复杂成分气味的实时检测和食品香气质量的人工分析创造了条件.关键词嗅觉模拟,串联神经网络,气敏传感器阵列.     

基于功能化纳米材料的光化学阵列传感器

阵列传感器采用人工模拟嗅觉系统的传感模式,实现多点信息的同时获取,极大地提高了分析效率,在公共安全、环境监测、医学检测等领域具有广阔的应用前景。其中,光化学阵列传感器因灵敏度高、输出信号丰富等优点而备受关注。近年来,为了进一步提高阵列传感器的识别能力和灵敏度,功能化纳米材料被广泛应用于光化学阵列传感器以增加传感材料的种类和发展新的传感方法。本文按照使用的光谱检测技术不同,详细介绍了功能化纳米材料在荧光、比色、催化发光和多通道阵列传感器等4类光化学阵列传感器中的应用。     

改进的人工神经网络在传感器阵列信号处理中的应用

本文提出传感器阵列信号处理的人工神经网络模型，以Cu^ 2/Ca^ 2系统为研究对象．尝试了神经网络方法的效果。其最大相对误差不超过5．％，最大相对预测误差不超过2．4％，结果表明，该方法性能良好，在各种传感器阵列的信号处理方面有广泛的应用前景。     

用于测定空气中甲醛的电子鼻

制作了可定量检测空气中甲醛的便携式电子鼻.该电子鼻由传感器阵列、信号调理电路、模式识别系统以及显示系统等4个部分组成,其中传感器阵列为4个半导体金属氧化物传感器.模式识别系统采用模糊神经网络算法.便携式甲醛电子鼻对甲醛气体响应专一,抗干扰能力强,且定量结果精确,可用于甲醛气体的现场检测.对于0.001～0.25mg/L浓度范围内的甲醛气体,电子鼻定量测报的正确率达到81.3%;对于干扰气体存在下的甲醛气体,未出现错误测报.     

分子印迹阵列式传感器的研究进展

分子印迹阵列式传感器具有识别率高、选择性好、价格低廉等优点,受到研究者们的极大关注,已经在食品分析、环境分析、药物分析、临床诊断等研究领域中得到应用。分子印迹阵列式传感器是以分子印迹聚合物作为识别元素的集成化传感器,通过各传感单元对分析物响应后产生的特征图谱实现对目标化合物的识别,不仅可用于单一目标化合物的选择性识别,还可以用于多种目标化合物同时存在时的测定。分子印迹阵列式传感器的响应信号机制主要划分为光信号、质量敏感信号和电化学信号等。本文简要介绍了分子印迹技术的产生和发展,重点评述了基于三种信号机制的分子印迹阵列式传感器的研究进展,并展望了分子印迹阵列式传感器的应用前景和研究方向。     

VOCs分子的半导体型传感器识别检测研究进展

具有体积小、功耗低、灵敏度高、硅工艺兼容性好等优点的金属氧化物半导体(MOS)气体传感器现已广泛地应用于军事、科研和国民经济的各个领域。然而MOS传感器的低选择性阻碍了其在物联网(IoT)时代的应用前景。为此,本文综述了解决MOS传感器选择性的研究进展,主要介绍了敏感材料性能提升、电子鼻和热调制三种改善MOS传感器选择性的技术方法,阐述了三种方法目前所存在的问题及其未来的发展趋势。同时,本文还对比介绍了机器嗅觉领域主流的主成分分析(PCA)、线性判别分析(LDA)和神经网络(NN)模式识别/机器学习算法。最后,本综述展望了具有数据降维、特征提取和鲁棒性识别分类性能的卷积神经网络(CNN)深度学习算法在气体识别领域的应用前景。基于敏感材料性能的提升、多种调制手段与阵列技术的结合以及人工智能(AI)领域深度学习算法的最新进展,将会极大地增强非选择性MOS传感器的挥发性有机化合物(VOCs)分子识别能力。     

亚微米级叉指型超微带电极阵列的加工和电化学表征

超微电极具有常规电极无法比拟的优良的电化学特性.超微电极包括单超微电极和超微电极阵列,单超微电极响应电流较小,一般仪器难以检测;而超微电极阵列除具有单超微电极的特点外,还能增加测量时的响应电流,有利于仪器检测.其中的叉指型超微带电极阵列(IDA)具有产生-收集效应,可提高检测的灵敏度,实现低浓度测量[1～4].将微电子技术和微细加工技术应用于化学和生物传感技术已引起关注,利用微细加工技术可以实现传感器的微型化、集成化和智能化;减少测量使用的样品量;使传感器的敏感元件具有确定的形状和尺寸,提高测量结果的一致性.本文用多…     

柔性可穿戴式电化学氧气传感器的制备及应用

将金铂合金纳米颗粒(AuPtNPs)修饰单壁碳纳米管(SWCNTs)多孔电极阵列,与琼脂糖水凝胶电解质相结合,构建了柔性可穿戴式电化学氧气传感器。该传感器通过光敏印章技术,在聚偏氟乙烯(PVDF)滤膜表面构筑聚二甲基硅氧烷(PDMS)的高精度电极阵列图案,并利用减压过滤实现SWCNTs和AuPtNPs的依次沉积,从而制备出具有良好导电性、电催化活性和多孔性的柔性电极阵列。在此基础上,采用掺杂了磷酸盐缓冲溶液和表面活性剂的琼脂糖水凝胶作为准固态电解质,制备出可穿戴式的电化学氧气传感器。该传感器对2.5%～30.0%浓度范围内的氧气具有良好的线性响应,可实时动态监测人体呼吸气体中氧气的浓度变化。     

压电晶体微天平阵列传感器识别毒剂的研究

压电晶体微天平(QCM)阵列传感器在毒剂侦检领域具有广泛的应用前景。本研究建立了QCM阵列传感器毒剂检测系统,以氢键酸性共聚硅氧烷(BSP3)、聚表氯醇(PECH)和乙基纤维素(ECEL)为膜材料制备了对毒剂敏感的QCM阵列传感器,对沙林、芥子气、甲基膦酸二甲酯进行了定量检测,并结合模式识别方法对检测结果进行了分析处理,识别率达到98%以上,为探索QCM阵列传感器对毒剂的定性定量分析提供了方法依据。     

荧光传感器阵列

传感器阵列是基于对动物嗅觉系统的认识发展起来的一种有力的分子识别手段,其由一系列传感单元组成,通过各传感单元对样品响应后产生的特征图谱实现对特定物质的识别检测,尤其对混合样品的鉴定具有突出优势。其中,荧光传感器阵列由于具有灵敏度高、无需参照体系、输出信号丰富、能够成像等优点,已成为近年来传感器阵列发展的重点。本综述根据荧光传感单元形式的不同,分别介绍了溶液型、颗粒型、薄膜型荧光传感器阵列的发展情况,并重点阐述了荧光传感器阵列的设计方法、传感机理及其在对金属离子、有机化合物和生物分子识别中的应用。     

Combining genetic algorithm and simulated annealing: a molecular geometry optimization study

We introduce a new hybrid approach to determine the ground state geometry of molecular systems. Firstly, we compared the ability of genetic algorithm (GA) and simulated annealing (SA) to find the lowest energy geometry of silicon clusters with six and 10 atoms. This comparison showed that GA exhibits fast initial convergence, but its performance deteriorates as it approaches the desired global extreme. Interestingly, SA showed a complementary convergence pattern, in addition to high accuracy. Our new procedure combines selected features from GA and SA to achieve weak dependence on initial parameters, parallel search strategy, fast convergence and high accuracy. This hybrid algorithm outperforms GA and SA by one order of magnitude for small silicon clusters (Si₆ and Si₁₀). Next, we applied the hybrid method to study the geometry of a 20-atom silicon cluster. It was able to find an original geometry, apparently lower in energy than those previously described in literature. In principle, our procedure can be applied successfully to any molecular system.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

ANVAS: Artificial Neural Variables Adaptation System for descriptor selection

A new algorithm model-oriented for variable selection is presented in this study. It is based on the combination of genetic algorithms (GA) for hyperspace exploration, and counterpropagation artificial neural network (CP ANN) for deriving the fitness score. The proposed method performed very well on both well defined synthetic data sets and real academic data sets.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Artificial Neural Network-Genetic Algorithm Approach to Optimize Media Constituents for Enhancing Lipase Production by a Soil Microorganism

Results of lipase production by a soil microorganism, expressed in terms of lipolytic activities of the culture were modeled and optimized using artificial neural network (ANN) and genetic algorithm (GA) techniques, respectively. ANN model, developed based on back propagation algorithm, were highly accurate in predicting the system with coefficient of determination (R ²) value being close to 0.99. Optimization using GA, based on the ANN model developed, resulted in the following values of the media constituents: 9.991 ml/l oil, 0.100 g/l MgSO₄ and 0.009 g/l FeSO₄. And a maximum value of 7.69 U/ml of lipolytic activity at 72 h of culture was obtained using the ANN-GA method, which was found to be 8.8% higher than the maximum values predicted by a statistical regression-based optimization technique-response surface methodology.     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

基于支持向量机的药物诱导磷脂质病预测模型

本文应用一种组合遗传算法和共轭梯度法的支持向量机(GA-CG-SVM)方法建立了药物诱导磷脂质病分类预测模型.首先对描述符进行了优化,选出了19个描述符用于模型的构建,所建模型对训练集的预测准确率为81.6%,对测试集的预测精度为87.5%,说明所建SVM分类模型不仅能正确预测训练集药物诱导的磷脂质病,也对其他化合物具...