激光显微热解气相色谱质谱检测系统分析煤中显微有机组分

本文应用激光显微热解气相色谱质谱检测系统（LMPY－GC－MS）分析煤中显微有机质（基质镜质体）的化学组成及结构。LMPY－GC－MS检测系统具有经济、快速、准确、样品不必预处理和分离（原位微束分析）等特点，因此它将具有广阔的应用前景。     

气载、液载颗粒物直接进样ICP-MS技术及其在核工业中的应用

颗粒物分析在核、环境、生命科学等领域具有重要价值,不经复杂化学处理直接将颗粒物引入高灵敏电感耦合等离子体质谱（ICP-MS）分析,具有分析速度快、可获取单颗粒特征信息、化学处理工作量小等优势。本文综述了气载、液载颗粒物直接进样ICP-MS分析技术,介绍了该技术在高效过滤器下游复杂基体气溶胶样品中的超痕量钚检测和悬浮液中单个氧化铒颗粒的高精度同位素分析中的应用,并从技术发展和分析应用角度进行了展望。     

毛细管电泳-质谱联用技术及其在药物和生物分析中的应用

毛细管电泳-质谱（CE-MS）联用技术是在液相色谱-质谱联用技术基础上发展起来的一项新型分析技术,它结合了毛细管电泳具有的分离效率高、分离速度快、样品消耗量少以及质谱检测具有的高灵敏度和强结构解析能力等优点,现已成为倍受分析化学工作者关注的新型微量分析技术。目前,CE-MS联用技术是中药有效成分分析,体内药物分析以及生物样品,如氨基酸、多肽、蛋白质和多糖等分析的重要手段。本文对CE-MS联用技术中同轴鞘流及无鞘流纳流电喷雾等几种接口装置的研究进展,CE-MS技术在中药活性成分分析及多级质谱结构解析以及氨基酸、多肽及蛋白质等生物样品分析中的应用研究进行了综述,并对该技术的发展进行了展望。     

典型环亚胺类毒素大气压化学电离质谱分析

利用离子阱质谱技术分析了两种典型环亚胺类毒素（GYM和SPX1）在大气压化学电离（APCI）条件下的质谱裂解特征,并与电喷雾电离（ESI）质谱法进行了比较。GYM和SPX1在APCI一级质谱分析过程中易形成准分子离子［M+H］⁺峰（基峰）;在二级质谱分析过程中,母离子［M+H］⁺通过丢失H₂O中性碎片形成稳定的特征子离子峰;并结合三级质谱分析,推测了两种毒素的裂解途径。结果表明：大气压化学电离质谱法（APCI-MS）的灵敏度好于电喷雾质谱法（ESI-MS）;液相色谱-大气压化学电离质谱法（LC-APCI-MS²）分析4种不同基质样品中GYM和SPX1的专属性、重复性、稳定性和抗基质干扰能力均好于液相色谱 电喷雾质谱法（LC-ESI-MS²）。综上,APCI-MS法适于典型环亚胺类毒素的分析,本研究可为LC-APCI-MS定性、定量分析不同基质复杂样品中环亚胺类毒素提供参考和依据。     

MALDI-TOF质谱法检测人血清中沙林-丁酰胆碱酯酶加合物

对生物医学样品进行化学毒剂分析检测是禁止化学武器组织（OPCW）核查小组在毒剂指称使用调查中收集事实的方法之一。丁酰胆碱酯酶（BChE）作为有机磷毒剂在体内的作用靶点,是有机磷毒剂染毒检测的最佳生物指示物之一。利用亲和固相萃取（SPE）技术进一步发展了样品预处理方法,建立了血液样品中染毒BChE的分析方法;利用胰蛋白酶酶解方法,采用基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）比较了血液中BChE在沙林染毒前后的肽指纹谱变化。该方法灵敏度高、快速简便,可用于OPCW生物医学样品中毒剂暴露染毒的追溯性检测。     

高效液相色谱-高分辨质谱法研究药物代谢产物的数据处理策略

高效液相色谱-高分辨质谱（HPLC-HRMS）联用技术具有高灵敏度、快速和高质量准确度的特点,是药物代谢产物分析的有力手段。通过使用HPLC-HRMS产生高分辨质谱数据（高分辨质量数、多级质谱数据、同位素分布）,结合多重采集后数据处理技术,可用于药物代谢产物的快速发现和结构鉴定。本工作综述了近年来基于HPLC-HRMS技术应用最为广泛的几种数据挖掘策略及应用,如,提取离子色谱、质量亏损过滤、同位素过滤、本底扣除、产物离子过滤、质谱树状图过滤、SWATH和MS^E以及这些技术的联用等。通过合理地使用其中一种或几种策略对HPLC-HRMS产生的数据进行处理,可以有效地进行药物代谢产物的发现和鉴定。     

光致二溴甲烷离子化学电离质谱分析VOCs

以真空紫外（VUV）灯作为电离源的单光子电离质谱（SPI-MS）分析方法是一种快速分析复杂样品中挥发性有机物（VOCs）的技术,但SPI-MS灵敏度受限于VUV灯较低的光通量及部分VOCs较小的电离截面。本工作自行研制了一种基于VUV Kr灯的新型光致二溴甲烷正离子化学电离源,并将该电离源与飞行时间质谱（TOF MS）联用进行VOCs分析。该电离源以体积分数1000 μL/L的二溴甲烷为试剂气体,利用VUV光电离产生稳定且充足的二溴甲烷正离子,二溴甲烷正离子与样品分子通过电荷转移发生化学电离,大大提高了样品的电离效率。与SPI源相比,该电离源不仅对电离能在10.0 eV附近的VOCs信号强度提升100倍以上（如对2-丙醇、乙酸乙酯和3-氯丙烯分别提高了103、118和126倍）,而且保持着与SPI一致的软电离特性。该电离源10 s内对复杂样品EPA TO-14、TO-15/17校准标气中的42种化合物的最低检测限达到0.06 μg/m³,并且因其具有较好的稳定性,在VOCs的实时在线监测方面有着广泛的应用。     

基于快速蒸发离子化质谱实时检测空气油炸鲳鱼的脂质组学轮廓

快速蒸发离子化质谱（rapid evaporative ionization mass spectrometry, REIMS）是一种无需样品预处理的新型质谱技术，通过手持智能手术刀切割样品采集数据从而获得脂质组学轮廓。本研究采用REIMS技术和主成分分析（principal component analysis, PCA）、隐结构正交投影（orthogonal projection to latent structure, OPLS）等数理统计方法实时监测鲳鱼在空气油炸过程中脂质组学轮廓变化，并探究不同空气油炸温度对鲳鱼肌肉组织脂质组成的影响。通过单因素条件优化得到适宜的REIMS系统参数，电刀输出功率20 W、电刀切割功率1 mm/s、辅助溶剂流速100 μL/min，从而获得稳定可靠的质谱轮廓图。在PCA分析的基础上进行OPLS分析，得到10个重要的特征离子（VIP>1），其中PE（O-16∶1/22∶6）只在原料中检测到，为4.61%；此外，PE（22∶5/22∶6）在10个磷脂离子中的不饱和度最高，其相对含量随油炸温度的升高而降低，同样可见于PA（18∶1/22∶6）和PI（18∶0/22∶6），说明磷脂的饱和度有随油炸温度升高而上升的趋势。经方法验证显示，REIMS检测离子的信噪比范围为49.58~205.30，日内和日间精密度的相对标准偏差为3.10%~7.28%，灵敏度和精密度均较高，因此，该方法将成为脂质组学研究过程中一种简单、高效、准确的新型技术手段。     

Sin-QuEChERS结合UPLC-MS/MS同时检测茶叶中10种有机磷农药残留

基于茶叶样品基质复杂的特点,采用简易的基质分散净化（Sin-QuEChERS）法对样品前处理技术加以改进,并结合超高效液相色谱-串联质谱（UPLC-MS/MS）技术,建立了同时检测茶叶中10种有机磷农药残留的分析方法。与传统的样品预处理方法相比,该方法在保证重复性和稳定性的前提下,具有操作简单、省时省力、普适性好、灵敏度高等优点。在优化的Sin-QuEChERS方法提取净化条件和UPLC-MS/MS检测条件下,可将样品前处理时间控制在10 min以内,使每个样品的分析在0.5 h内完成。结果表明：10种有机磷农药的线性范围为1~500 μg/L,检出限为0.08~1.38 μg/kg,定量限为0.27~4.60 μg/kg;在不同的加标水平下,平均回收率在71.6%~115.0%之间,RSD小于14.5%,与国家标准方法的检测结果接近。将该方法应用于检测空白茶叶加标样品和20种市售茶叶中有机磷农药,验证了方法的可行性。该研究可为快速、简易、准确地评价茶叶中多种有机磷类农药的质量安全提供方法参考。     

高分辨质谱直接分析食用油中脂肪酸甘油酯

食用油中不饱和脂肪酸甘油酯的含量与人体健康密切相关,针对其建立快速有效的检测方法具有重要意义。本研究建立了一种可直接分析食用油中脂肪酸甘油酯的方法,采用大气压化学电离-高分辨质谱技术（APCI-HRMS）对食用油中脂肪酸甘油三酯及脂肪酸甘油二酯分子直接进样分析,在指纹区高分辨质谱数据的基础上,采用分解系数和平均不饱和度（DBE）对不同油品进行比较分析。结果表明,食用油与餐厨回收油及动物油具有明显差别,当分解系数大于10时,或DBE小于3时,可判断样品为餐厨回收油或动物油,利用这两个特征参数可成功地区分食用油与餐厨回收油样品。通过偏最小二乘法的分组验证,以及添加不同比例餐厨回收油的食用植物油样品分解系数和平均不饱和度的比较验证,表明该方法可便捷、准确地分析油品中脂肪酸甘油酯的含量变化,有望成为监测食用油品质的快速、有效的新型分析方法。     

Supervised locally tangent space alignment for machine fault diagnosis

How to deal with the high-dimensional and nonlinear data is a challenging problem for fault diagnosis. An unsupervised locally tangent space alignment (LTSA) has recently proven to be an effective unsupervised manifold learning algorithm for high-dimensional data analysis. In this paper, a supervised expansion of LTSA (named S-LTSA) is proposed, which takes full advantage of class label information to improve classification performance. Based on S-LTSA, a novel machine fault diagnosis approach is proposed to deal with the high-dimensional fault data that contain multiple manifolds corresponding to fault classes. The experiment results with bearing fault data show that the proposed approach outperforms the other fault pattern recognition approaches such PCA, ICA, LDA and LTSA.     

Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.     

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.     

An intelligent instrument for tracking and adaptive filtering of oscillatory signals using Hebbian learning rules

In this paper Hebbian type of learning algorithms using total least squares method is applied for adaptive filtering techniques to remove the noise and undesired oscillatory signals at different systems. Here we have used the generalised Hebbian learning rules for initializing the internal representations of a feedforward neural network, which accelerates the convergence of supervised Hebbian learning rule. In case of constrained anti-Hebbian learning rule, the weight vectors of linear neuron unit is converged to an eigenvector which has the smallest eigenvalue. In the total least squares (TLS) method the noise rejection capability is superior to the least squares method. Here we have applied the initial sets of data for the internal representation of feedforward network which consists of bottom-up unsupervised learning process followed by top-down supervised learning process using total least squares (TLS) algorithm. For faster convergence we have included the momentum term for the updating of weights. An intelligent instrumentation scheme has been developed for on-line measurement of amplitude of oscillatory signals. The undesired oscillations of the signal is also removed by implementing neural network model (using Hebbian rules and total least square algorithm) on a digital signal processor.     

基于机器视觉的表面缺陷检测方法研究进展

在半导体、PCB、汽车装配、液晶屏、3C、光伏电池、纺织等行业中,产品外观与产品性能有着千丝万缕的联系。表面缺陷检测是阻止残次品流入市场的重要手段。利用机器视觉的技术进行检测效率高、成本低,是未来发展的主要方向。本文综述了近十年来基于机器视觉的表面缺陷检测方法的研究进展。首先给出了缺陷的定义、分类以及缺陷检测的一般步骤;然后重点阐述了使用传统图像处理方式、机器学习、深度学习进行缺陷检测的原理,并比较和分析了优缺点,其中传统图像处理方式分为分割与特征提取两个部分,机器学习包含无监督学习和有监督学习两大类,深度学习主要囊括了检测、分割及分类的大部分主流网络;随后介绍了30种工业缺陷数据集以及性能评价指标;最后指出缺陷检测方法目前存在的问题,对进一步的工作进行了展望。     

A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels

Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%.     

Classification decision model for a Hong Kong clothing manufacturing plant locations using an artificial neural network

Clothing manufacturers’ direct investment and joint ventures in developing regions have seen to grow rapidly in the past few decades. Manufacturers face difficulties during the decision-making process in the selection of a plant location due to vague and subjective considerations. Selecting a plant location relies mostly on subjective intuition and assessment as variables to be considered in the decision making process. But these variables cannot always be represented in terms of objective value, such as country risk and community facilities. Though several optimization techniques have been developed to assist decision makers in searching for the optimal sites, it is difficult to rank the sites which display a small difference of scores. Classification is thus more reasonable and realistic. This paper investigates two recent types of classification techniques, namely unsupervised and supervised artificial neural networks, on the site selection problem of clothing manufacturing plants. The limitations of adaptive resonance theory in unsupervised artificial neural networks will be demonstrated. A comparison of the performance of the three types of supervised artificial neural networks – including back propagation, learning vector quantization and probabilistic neural network – is used and the proposed classification decision model will be presented. The experimental results indicate that the supervised artificial neural network is a proven and effective classifier in which a probabilistic neural network performs better than the others in this site selection problem.     

基于一阶导数的中药太赫兹光谱聚类

针对中药等混合物吸收峰重叠导致无明显吸收峰的情况,提出使用K-means、K-me doids和FCM三种无监督聚类算法结合太赫兹吸收谱一阶导数特征,将三七、当归等四种中药品的太赫兹光谱分别与其易混品的太赫兹光谱进行聚类.三种无监督聚类方法补充了监督学习分类方法的适用范围.光谱一阶导数特征可以放大不同物质吸收系数整体或者是局部的微小差异.实验证明,使用原始吸收系数结合其一阶导数作为分类数据,三种聚类算法都取得很好的效果,K-means算法准确率最高,为95.32％.相较于原始吸收系数作为分类数据,聚类准确率提升明显,尤其是对无吸收峰中药易混品的聚类,K-means算法准确率提升了5.38％.三种聚类算法对误差数据都具有很强的抗干扰能力.     

Measurements of volatile organic compounds in the earth's atmosphere using proton-transfer-reaction mass spectrometry

Proton-transfer-reaction mass spectrometry (PTR-MS) allows real-time measurements of volatile organic compounds (VOCs) in air with a high sensitivity and a fast time response. The use of PTR-MS in atmospheric research has expanded rapidly in recent years, and much has been learned about the instrument response and specificity of the technique in the analysis of air from different regions of the atmosphere. This paper aims to review the progress that has been made. The theory of operation is described and allows the response of the instrument to be described for different operating conditions. More accurate determinations of the instrument response involve calibrations using standard mixtures, and some results are shown. Much has been learned about the specificity of PTR-MS from inter-comparison studies as well the coupling of PTR-MS with a gas chromatographic interface. The literature on this issue is reviewed and summarized for many VOCs of atmospheric interest. Some highlights of airborne measurements by PTR-MS are presented, including the results obtained in fresh and aged forest-fire and urban plumes. Finally, the recent work that is focused on improving the technique is discussed.     

Identification of gas-liquid two-phase flow regime in pipelines with low liquid holdup based on ResNet1D-34

Flow pattern identification is an important topic in multiphase flow research. To overcome the subjectivity of manual identification, intelligent identification of flow patterns has attracted much attention in recent years. Both traditional machine learning methods and deep learning methods have been utilized in this field. However, traditional machine learning methods lack accuracy, and existing deep learning methods mostly rely on artificial feature extraction or complex preprocessing. In this paper, we propose a new method with high accuracy and low preprocessing dependency to solve these issues. We modify ResNet, which has proven high performance in computer vision, to fit the data collected by the wire-mesh sensor system (WMS). Due to its outstanding feature extraction ability, the new model can reach high accuracy with simple normalization as the preprocessing step. Additionally, the model can directly process data at various scales without retraining or rebuilding, which gives it high usability and economic value. The experimental results show that the accuracy of this method can reach 99.58% on our dataset.