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三七生长初期不同部位微量元素的含量测定 总被引:6,自引:1,他引:6
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。 相似文献
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ICP-AES法测定铜精矿中As、Sb、Bi、Ca、Mg、Pb、Co、Zn和Ni 总被引:4,自引:0,他引:4
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。 相似文献
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颜卿 《广东微量元素科学》2009,16(4):40-40
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、 相似文献
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颜卿 《广东微量元素科学》2009,(8):60-60
人们都想了解和食用必需微量元素和宏量元素含量配合较为均衡的蔬菜。根据研究发现,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: 相似文献
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建立电感耦合等离子体发射光谱法同时测定固体生物质燃料中钾、钠、钙、镁、砷、铜、铁、锰8种元素的含量。样品采用5 mL硝酸溶液和2 mL过氧化氢溶液进行微波消解,在选定的仪器工作条件下进行测定。钠、钙、镁、砷、铜、铁、锰的质量浓度在0~5.0 mg/L,钾的质量浓度在0~50.0 mg/L范围内与光谱强度具有良好的线性关系,相关系数均大于0.999,方法检出限为0.002~0.022 mg/L。样品的加标回收率为91.9%~108.2%,测定结果的相对标准偏差为2.1%~6.8%(n=6)。该方法简便、快速、高效且准确,适用于固体生物质燃料中钾、钠、钙、镁、砷、铜、铁、锰的测定。 相似文献
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2008年高考理综全国卷(Ⅰ),陕西、湖北、湖南、安徽、福建、浙江、辽宁、江西、广西、河北、山西、河南等省区采用;理综全国卷(Ⅱ),黑龙江、吉林、内蒙古、甘肃、青海、云南、贵州、新疆、西藏等省区采用.这2套试卷的化学试题难度有明显的差异,本文做一对比分析. 相似文献
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小波变换方法的比较──红外光谱数据压缩 总被引:9,自引:0,他引:9
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. 相似文献
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I. Esteban-Díez 《Analytica chimica acta》2006,555(1):84-95
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. 相似文献
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Guoxiang Chen 《Analytica chimica acta》2003,484(1):75-91
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. 相似文献
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A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables 总被引:2,自引:0,他引:2
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. 相似文献
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基于小波神经网络的新型算法用于化学信号处理 总被引:7,自引:0,他引:7
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。 相似文献
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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. 相似文献
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Wavelet transform applications in analytical chemistry 总被引:1,自引:0,他引:1
Ehrentreich F 《Analytical and bioanalytical chemistry》2002,372(1):115-121
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. 相似文献
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《Analytical letters》2012,45(13):2189-2206
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. 相似文献
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A wavelet‐PCA method saves high mass resolution information in data treatment of SIMS molecular depth profiles 下载免费PDF全文
Nunzio Tuccitto Gabriella Zappalà Stefania Vitale Alberto Torrisi Antonino Licciardello 《Surface and interface analysis : SIA》2016,48(6):317-327
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. 相似文献