共查询到18条相似文献,搜索用时 250 毫秒
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南充市表层土壤中多环芳烃的源解析研究 总被引:1,自引:0,他引:1
运用同分异构体比率、聚类分析法和主成分因子载荷法对南充市表层土壤多环芳烃(PAHs)污染源进行了定性和定量分析。研究表明:同分异构体比率分析揭示表层土壤中PAHs污染来源以燃烧源为主;聚类分析将土壤中13种PAHs组分分成3个主群,3个主群分别指示为交通类PAHs污染、煤燃烧类PAHs污染和混合类PAHs污染。主成分因子/多元线性回归分析显示,PAHs主要来源于3大污染源,并定量计算了3种源的贡献量,其中交通燃油污染的贡献率最大(占42.4%),而燃煤燃烧排放、混合污染所占比例分别为32.4%和25.2%。 相似文献
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用局部拟合主成分回归计算光度分析法测定黄连生物碱 总被引:1,自引:0,他引:1
针对具有样本数据非无匀分布和非线性特点的光度分析问题,提聘种局部拟合 主成分回归法,用于中药多组分计算测定。该方法根据待测样本与各已知样本光度 分析数据的欧式距离确定相应的权值,将部分权值较大的样本组成校正集,并用分 段线性拟合算法建立待测样本的校正预测模型,将其用于分析黄连的药根碱、巴巴 亭和小檗碱等三种生物碱,所得预测均方根误差分别为0.023,0.0400和0.052,优 于主成分回归法、偏最小二乘法以及人工神经元网络法所得结果。这表明,本方法 用于中药光度分析能获得较为准确的计算分析结果。 相似文献
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将离散小波变换、小波包变换、傅里叶变换和离散余弦变换与主组分回归方法结合构成4种离散变换主组分回归方法,编制了离散变换主组分回归方法的计算程序。将离散变换主组分回归方法用于处理对硝基甲苯、对硝基酚和对硝基苯胺混合物的重叠紫外吸收光谱数据。结果表明,离散变换主组分回归方法优于主组分回归方法,试样质量浓度的预测值与实际值的相对预测标准误差由3.81%降至约1.11%。 相似文献
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To date, few efforts have been made to take simultaneous advantage of the local nature of spectral data in both the time and frequency domains in a single regression model. We describe here the use of a novel chemometrics algorithm using the wavelet transform. We call the algorithm dual-domain regression, as the regression step defines a weighted model in the time-domain based on the contributions of parallel, frequency-domain models made from wavelet coefficients reflecting different scales. In principle, any regression method can be used, and implementation of the algorithm using partial least squares regression and principal component regression are reported here. The performance of the models produced from the algorithm is generally superior to that of regular partial least squares (PLS) or principal component regression (PCR) models applied to data restricted to a single domain. Dual-domain PLS and PCR algorithms are applied to near infrared (NIR) spectral datasets of Cargill corn samples and sets of spectra collected on batch chemical reactions run in different reactors to illustrate the improved robustness of the modeling. 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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连续小波变换-独立成分回归算法及其在多组分分析中的应用 总被引:1,自引:0,他引:1
采用连续小波变换(CWT)对光谱数据进行处理,用独立成分分析(ICA)进行特征提取,再用回归分析方法对被测组分进行测定,建立了连续小波变换一独立成分回归(CWT-ICR)方法。方法用于肉样品中水分、脂肪和蛋白质多组分的同时测定,所得结果与化学法测得结果相符。 相似文献
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Spline wavelet and orthogonal wavelet are two widely used wavelet methods. In this paper, comparison of these two methodshas been made, including their algorithm, properties and results of signal processing in analytical chemistry signals. It is found that spline wavelet is more effective than orthogonal wavelet in processing high noise signals. The curves obtained from spline wavelet are closer to the theoretical ones than those obtained from orthogonal wavelet and the errors of spline wavelet are smaller than those of orthogonal wavelet. 相似文献
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A new approach to near-infrared spectral data analysis using independent component analysis 总被引:8,自引:0,他引:8
This paper presents a new approach to near-infrared spectral (NIR) data analysis that is based on independent component analysis (ICA). The main advantage of the new method is that it is able to separate the spectra of the constituent components from the spectra of their mixtures. The separation is a blind operation, since the constituent components of mixtures can be unknown. The ICA based method is therefore particularly useful in identifying the unknown components in a mixture as well as in estimating their concentrations. The approach is introduced by reference to case studies and compared to other techniques for NIR analysis including principal component regression (PCR), multiple linear regression (MLR), and partial least squares (PLS) as well as Fourier and wavelet transforms. 相似文献
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Molecular factor computing (MFC) is a new strategy that employs chemometric methods in an optical instrument to obtain analytical results directly using an appropriate filter without data processing. In the present contribution, a method for designing an MFC filter using wavelet functions was proposed for spectroscopic analysis. In this method, the MFC filter is designed as a linear combination of a set of wavelet functions. A multiple linear regression model relating the concentration to the wavelet coefficients is constructed, so that the wavelet coefficients are obtained by projecting the spectra onto the selected wavelet functions. These wavelet functions are selected by optimizing the model using a genetic algorithm (GA). Once the MFC filter is obtained, the concentration of a sample can be calculated directly by projecting the spectrum onto the filter. With three NIR datasets of corn, wheat and blood, it was shown that the performance of the designed filter is better than that of the optimized partial least squares models, and commonly used signal processing methods, such as background correction and variable selection, were not needed. More importantly, the designed filter can be used as an MFC filter in designing MFC-based instruments. 相似文献
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基于独立分量和神经网络的近红外多组分分析方法 总被引:12,自引:2,他引:10
采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。 相似文献