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Independent component analysis algorithms for spectral decomposition in UV/VIS analysis of metal-containing mixtures including multimineral food supplements and platinum concentrates

机译:独立成分分析算法,用于在紫外/可见光分析含金属混合物(包括多种矿物质食品补充剂和铂精矿)中进行光谱分解

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Various independent component analysis (ICA) algorithms (MILCA, JADE, SIMPLISMA, RADICAL) are applied for simultaneous spectroscopic determination of two groups of transition metals: Co(II)-Fe(III)-Cu(II)-Zn(II)-Ni(II) and Pt(iv)-Pd(II)-lr(iv)-Rh(III)-Ru(III)) in complex mixtures. The analysis is based on the decomposition of spectra of multicomponent mixtures in the UV-ViS region based on the natural absorbance of metal salts, or, when a better sensitivity is desirable, based on the absorbance of their complexes with 4-(2-pyridylazo)resorcinol (PAR) and ethylenediaminetetraacetic acid (EDTA). Good quality spectral resolution of up to seven-component mixtures was achieved (correlation coefficients between resolved and experimental spectra are not less than 0.90). In general, the relative errors in the recovered concentrations are at levels of only several percent. While being superior to other ICA algorithms, JV1ILCA is comparable or even outperforms other classical chemometric methods for quantitative analysis that were used for comparison purposes (Partial Least Squares (PLS), Principal Component Regression (PCR), Alternating Least Squares (ALS)). Simultaneous quantitative analysis is possible for mixtures containing up to five metals in the broad concentration ranges even when individual spectra show 99% overlap. A small excess of derivatization reagent (till threefold excess to the sum of metal concentrations) is optimal to obtain good quantitative results. The proposed method was used for analysis of authentic samples (multimineral supplements and platinum concentrates). The resolved ICA concentrations match well with the labelled amounts and the results of other chemometric methods (ALS, PLS). ICA decomposition considerably improves the application range of spectroscopy for metal quantification in mixtures.
机译:各种独立成分分析(ICA)算法(MILCA,JADE,SIMPLISMA,RADICAL)用于同时光谱法测定两组过渡金属:Co(II)-Fe(III)-Cu(II)-Zn(II)-复杂混合物中的Ni(II)和Pt(iv)-Pd(II)-lr(iv)-Rh(III)-Ru(III))。该分析基于对UV-ViS区域中多组分混合物的光谱分解,该分解基于金属盐的自然吸收,或者在需要更高灵敏度的情况下,基于它们与4-(2-吡啶基偶氮)配合物的吸收间苯二酚(PAR)和乙二胺四乙酸(EDTA)。实现了多达七种成分混合物的高质量光谱分辨率(分辨光谱和实验光谱之间的相关系数不小于0.90)。通常,回收浓度的相对误差仅为百分之几。尽管JV1ILCA优于其他ICA算法,但与用于比较目的的定量分析的其他经典化学计量学方法(部分最小二乘(PLS),主成分回归(PCR),交替最小二乘(ALS))相当,甚至优于其他化学计量学方法。即使单个光谱显示出99%的重叠,也可以对在宽浓度范围内包含多达五种金属的混合物进行同时定量分析。为了获得良好的定量结果,少量过量的衍生化试剂(至金属浓度总和的三倍过量)是最佳的。所提出的方法用于分析真实样品(多种矿物质补充剂和铂精矿)。解析出的ICA浓度与标记的量以及其他化学计量方法(ALS,PLS)的结果非常吻合。 ICA分解大大提高了光谱学在混合物中金属定量分析中的应用范围。

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