三七生长初期不同部位微量元素的含量测定

采用电感耦合等离子体发射光谱法(ICP-AES),对文山地区GAP种植及农户常规种植的生长初期三七不同部位中的微量元素Mg、P、Ca、Mn、Na、Fe、Co、Cu、Zn、Mo、Cr、Ni、Ge、Se等14种元素进行了测定分析。结果表明,三七的根、茎、叶中含有丰富的人体必需Mg、P、Ca、Mn、Na、Fe、Co、Cu、Zn、Mo、Ge、Se等有益元素,且P、Ca、Mg、Fe含量较高,Na、Mn、Co、Zn次之,Co、Mo、Cr、Ni、Ge、Se含量均较低。为三七GAP栽培标准和特征制订、三七道地药材的化学特征——化学指纹图谱的建立及研究提供理论依据。     

电感耦合等离子体原子发射光谱法测定钨钢中硅、锰、磷、铬、镍、铜、钼、钒和钨

采用电感耦合等离子体原子发射光谱法测定钨钢中硅、锰、磷、铬、镍、铜、钼、钒和钨的含量。试样用盐酸、柠檬酸铵、硝酸溶解。基体效应采用基体匹配法消除。硅、锰、磷、铬、镍、铜、钼、钒、钨的分析谱线依次为288.158,257.610,177.495,267.716,213.604,327.396,204.598,310.230,239.709nm。9种元素的质量分数在一定的范围内与其发射强度呈线性关系,方法的检出限(3s)在0.000 3%~0.004 8%之间。方法用于两种标准物质的测定,测定结果与认定值相符,测定值的相对标准偏差(n=5)在0.74%~2.1%之间。方法的回收率在95.0%~107%之间。     

电感耦合等离子体发射光谱法测定不锈钢中的铬、镍、锰、铜、钛、铝

建立电感耦合等离子体发射光谱仪同时测定不锈钢中铬、镍、锰、铜、钛、铝6种元素含量的方法。用20mL王水溶解样品,铬、镍、锰、铜、钛、铝的分析谱线分别为283.563,231.604,259.373,324.754,334.941,308.215 nm。铬、镍、锰、铜、钛、铝的质量浓度与其信号强度均呈良好的线性关系,线性相关系数均不小于0.999,检出限分别为0.007,0.009,0.002,0.007,0.002,0.008μg/mL。测定结果的相对标准偏差为0.17%～2.80%(n=6),加标回收率为96.50%～103.70%。用该法测定国家标准物质,测定值与标准值一致,相对误差为0.05%～3.03%。该方法准确、可靠,可用于不锈钢中铬、镍、锰、铜、钛、铝的测定。     

ICP-AES法测定铜精矿中As、Sb、Bi、Ca、Mg、Pb、Co、Zn和Ni

提出采用ICP-AES法同时测定铜精矿中As、Sb、Bi、Ca、Mg、Pb、Zn、Ni、Co的分析方法:样品经王水 HF HClO4溶液后,直接测定。该方法测定As、Sb、Bi、Ca、Mg、Pb、Zn、Ni、Co的回收率在97.9%~102%之间,相对标准偏差在0.23%~2.5%之间。通过和国家标准物质比对及国家标准分析方法的比对,结果准确可靠,现该方法已用于本公司铜精矿的日常分析。     

《分析测试学报》

《分析测试学报》是由中国广州分析测试中心、中国分析测试协会共同主办的全国性学术刊物,中文核心期刊。刊登质谱学、光谱学、色谱学、波谱学、电化学、电子显微学等方面的分析测试新理论、新方法、新技术的研究成果,介绍新仪器装置及在生物、医药、化学化工、商检、食品检验等方     

《中国无机分析化学》征稿征订启事

正技术交流的平台无机分析的阵地企业联系的桥梁同行朋友的纽带《中国无机分析化学》是由北京矿冶研究总院主办的无机分析化学专业性学(技)术期刊。本刊包括岩矿分析、冶金分析、材料分析、环境分析、化工分析、生物医药分析、食品分析、仪器研制、综述评论、技术交流、信息之窗等栏目。读者对象为从事无机分析化学及相关技术的广大科研人员、工程技术人员、管理人员、大专院校师生、相关公司及企事业单位。本刊也是相关图书、情报等部门必不可少的信息来源。     

2010年《中国无机分析化学文摘》征订启事

《中国无机分析化学文摘》1984年创刊,本刊以文摘、简介及题录形式报道国内公开发行的有机无机分析化学的期刊300余种及各种会议论文集、新标准、新书目等,年收录文献4000篇左右。栏目分为:一般问题、重量法与滴定法、光度法、电化学分析、光谱分析、色谱分析、物相分析、气体分析、活化分析、质谱分析、分离方法、贵金属分析等。     

原子吸收光谱法测定水生生物体内铜、锌、镍、铬、铅、镉

用硝酸-高氯酸体系消解螺蛳和水葫芦样品,采用火焰原子吸收光谱法测定铜、锌、镍、铬,用石墨炉原子吸收光谱法测定铅、镉。铜、锌、镍、铬、铅、镉的检出限分别为0.328、0.126、0.271、0.416、0.006 64、0.001 15 mg/kg,线性相关系数不小于0.999 0,测定结果的相对标准偏差为1.1%~3.7%,加标回收率为86.0%~94.2%。     

蔬菜搭配与微量元素

人们都想了解和掌握必需微量元素和宏量元素含量配合较为均衡的蔬菜。根据专家们的研究发现,在人们食用的蔬菜中,含人体必需宏量元素和微量元素适量同时较为均衡的蔬菜有20多种,它们是：白萝卜、胡萝卜、小白菜、小青菜、油菜、蕹菜、白茱苔、蒜苗、油角豆、马兰头、韭菜、苦瓜、芪椰菜、莴苣笋（茎）、山药、芦笋、蒜苔、藕、红辣椒、青辣椒等,这些蔬菜所含的人体必需微量元素如：Fe、Me、Zn、Cu、Se等和人体必需宏量元素如K、Na、Ca、Mg等都是中等量,并且含量都是比较均衡,为人们日常调配饮食,购买、     

《色谱》投稿须知(2022)

《色谱》是中国化学会和中国科学院大连化学物理研究所主办、中国科学技术协会主管、国内外公开发行的专业性学术期刊,月刊。主要报道色谱学科的基础性研究成果、色谱及其交叉学科的重要应用成果及其进展,包括新方法、新技术、新仪器在各个领域的应用,色谱仪器与部件的研制和开发。被Medline、ESCI、CA、CSA、JST、AA、CHI、MSB-S、AJ、IC、SCOPUS等收录。曾入选百种中国杰出学术期刊、中国最具国际影响力学术期刊、中国科学院优秀期刊、中国科协优秀期刊、中文核心期刊、中国科技核心期刊、中国精品科技期刊、中国科协精品科技期刊工程项目。     

小波变换方法的比较──红外光谱数据压缩

介绍了小波变换和多分辨分析的基本理论以及常用小波变换压缩数据的3种方法:(1)只保留模糊信号;(2)全部保留模糊信号及锐化信号中的较大值;(3)保留模糊信号及锐化信号中的较大值.将紧支集小波和正交三次B-样条小波压缩4-苯乙炔基-邻苯二甲酸酐的红外光谱数据进行了对比,计算表明正交三次B-样条小波变换方法效果较好,而在全部保留模糊信号及只保留锐化信号中数值较大的系数时,压缩比大而重建光谱数据与原始光谱数据间的均方差较小.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

SIMPLISMA applied to two-dimensional wavelet compressed ion mobility spectrometry data

A modified SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) algorithm, referred to as real-time (RT) SIMPLISMA has been combined with two-dimensional (2D) wavelet compression (WC²). This tool was evaluated with datasets of drugs and bacteria that were acquired from two different ion mobility spectrometers and published reference data that comprised Raman, FTIR microscopy, near-infrared (NIR) and mass spectral data. RTSIMPLISMA is amenable for real-time modeling and is able to determine the number of components automatically. The 2D wavelet compression, which compresses both acquisition and drift time dimensions of measurement, was applied to the datasets prior to RTSIMPLISMA modeling. RTSIMPLISMA models obtained from the compressed data were wavelet transformed back to the uncompressed representation. The effects of wavelet filter types and compression levels were investigated. The relative root-mean-square errors (RRMSE) of reconstruction, which calculate the relative difference between the extracted models with and without 2D compressions, were used to evaluate the effects of compression on self-modeling. The results showed that satisfactory models could be obtained when a data was compressed to 1/256 of its size.     

A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables

An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.     

基于小波神经网络的新型算法用于化学信号处理

基于紧支集正交小波神经网络的构造思想,用具有紧支集的Ｂ－样条函数的伸缩和平稳替代小波函数,提出了一种新型算法,并将其应用于化学信号的处理,实现了信号的压缩和滤噪,适应小波神经网络相比,学习速度得到了大幅度的提高。     

Alternative common bases and signal compression for wavelets application in chemometrics

Representation or compression of data sets in the wavelet space is usually performed to retain the maximum variance of the original or pretreated data, like in the compression by means of principal components. In order to represent together a number of objects in the wavelet space, a common basis is required, and this common basis is usually obtained by means of the variance spectrum or of the variance wavelet tree. In this study, the use of alternative common bases is suggested, both for classification and regression problems. In the case of classification or class-modeling, the suggested common bases are based on the spectrum of the Fisher weights (a measure of the between-class to within-class variance ratio) or on the spectrum of the SIMCA discriminant weights. In the case of regression, the suggested common bases are obtained by the correlation spectrum (the correlation coefficients of the predictor variables with a response variable) or by the PLS (Partial Least Squares regression) importance of the predictors (the product between the absolute value of the regression coefficient of the predictor in the PLS model and its standard deviation). Other alternative strategies apply the Gram–Schmidt supervised orthogonalization to the wavelet coefficients. The results indicate that, both in classification and regression, the information retained after compression in the wavelets space can be more efficient than that retained with a common basis obtained by variance.     

Wavelet transform applications in analytical chemistry

The wavelet transform has been established with the Fourier transform as a data-processing method in analytical chemistry. The main fields of application in analytical chemistry are related to denoising, compression, variable reduction, and signal suppression. Analytical applications were selected showing prospects and limitations of the wavelet transform. An important aspect consists in showing the advantage of wavelet transform over Fourier transform with respect to dual localization of a signal in both the original and the transformed domain enabling principal new application fields in comparison with Fourier transform.     

Data Compression for a Voltammetric Electronic Tongue Modelled with Artificial Neural Networks

Abstract

In the study of voltammetric electronic tongues, a key point is the preprocessing of the departure information, the voltammograms which form the response of the sensor array, prior to classification or modeling with advanced chemometric tools. This work demonstrates the use of the discrete wavelet transform (DWT) for compacting these voltammograms prior to modeling. After compression, a system based on artificial neural networks (ANNs) was used for the quantification of the electroactive substances present, using the obtained wavelet decomposition coefficients as their inputs. The Daubechies wavelet of fourth order permitted an effective compression up to 16 coefficients, reducing the original dimension by ca. 10 times. The case studied is a mixture of three oxidizable amino acids:tryptophan, cysteine, and tyrosine. With the reduced information, one ANN per specie was trained using the Bayesian regularization algorithm. The proposed procedure was compared with the more conventional treatments of downsampling the voltammogram, or its feature extraction employing principal component analysis prior to ANNs.     

A wavelet‐PCA method saves high mass resolution information in data treatment of SIMS molecular depth profiles

A detailed depth characterization of multilayered polymeric systems is a very attractive topic. Currently, the use of cluster primary ion beams in time‐of‐flight secondary ion mass spectrometry allows molecular depth profiling of organic and polymeric materials. Because typical raw data may contain thousands of peaks, the amount of information to manage grows rapidly and widely, so that data reduction techniques become indispensable in order to extract the most significant information from the given dataset. Here, we show how the wavelet‐based signal processing technique can be applied to the compression of the giant raw data acquired during time‐of‐flight secondary ion mass spectrometry molecular depth‐profiling experiments. We tested the approach on data acquired by analyzing a model sample consisting of polyelectrolyte‐based multilayers spin‐cast on silicon. Numerous wavelet mother functions and several compression levels were investigated. We propose some estimators of the filtering quality in order to find the highest ‘safe’ approximation value in terms of peaks area modification, signal to noise ratio, and mass resolution retention. The compression procedure allowed to obtain a dataset straightforwardly ‘manageable’ without any peak‐picking procedure or detailed peak integration. Moreover, we show that multivariate analysis, namely, principal component analysis, can be successfully combined to the results of the wavelet‐filtering, providing a simple and reliable method for extracting the relevant information from raw datasets. Copyright © 2016 John Wiley & Sons, Ltd.