Synchrotron scanning photoemission microscopy of homogeneous and heterogeneous metal sulfide minerals

Scanning photoemission microscopy (SPEM) has been applied to the investigation of homogeneous and heterogeneous metal sulfide mineral surfaces. Three mineral samples were investigated: homogeneous chalcopyrite, heterogeneous chalcopyrite with bornite, and heterogeneous chalcopyrite with pyrite. Sulfur, copper and iron SPEM images, i.e. surface‐selective elemental maps with high spatial resolution acquired using the signal from the S 2p and Cu and Fe 3p photoemission peaks, were obtained for the surfaces after exposure to different oxidation conditions (either exposed to air or oxidized in pH 9 solution), in addition to high‐resolution photoemission spectra from individual pixel areas of the images. Investigation of the homogeneous chalcopyrite sample allowed for the identification of step edges using the topography SPEM image, and high‐resolution S 2p spectra acquired from the different parts of the sample image revealed a similar rate of surface oxidation from solution exposure for both step edge and a nearby terrace site. SPEM was able to successfully distinguish between chalcopyrite and bornite on the heterogeneous sample containing both minerals, based upon sulfur imaging. The high‐resolution S 2p spectra acquired from the two regions highlighted the faster air oxidation of the bornite relative to the chalcopyrite. Differentiation between chalcopyrite and pyrite based upon contrast in SPEM images was not successful, owing to either the poor photoionization cross section of the Cu and Fe 3p electrons or issues with rough fracture of the composite surface. In spite of this, high‐resolution S 2p spectra from each mineral phase were successfully obtained using a step‐scan approach.     

Optimized fuzzy C-means clustering algorithm for the interpretation of the near-infrared spectra of rocks

The rock near-infrared spectrum contains information of its composition and structure. The interpretation of rock near-infrared spectrum is one of the important approaches in the qualitative and quantitative analyses of the alteration minerals in rock. The rock near-infrared spectra are classified using optimized fuzzy C-means clustering algorithm, and the main mineral composition is obtained for different rock samples through the analysis of cluster centers. The minimum Spectral Correlation Coefficient is used as the objective function to classify the simulation data. In this study, the classification method was first tested for parameter setting using simulation data, which was the mixture of several standard mineral spectra quantified in terms of reflectivity in the near-infrared band. Classification accuracies under different fuzzy index values are compared. When the fuzzy index value is 1.5, the classification accuracy of the simulation samples is 83%. The initial values of different cluster centers were shown to affect the classification result. In the practical application, the initial values of cluster centers need to be rationally chosen based on the knowledge of mineral spectroscopy. This method is applied in the clustering analysis of the rock near-infrared spectra, which were also quantified in terms of reflectivity in the near-infrared band. These actual rock near-infrared spectra were measured by a spectrometer, while the classification results were compared with X-ray diffraction analysis to show the effectiveness of our algorithm. Our study has shown that, with the optimized fuzzy C-means clustering algorithm, the interpretation of rock near-infrared spectra can help us obtain information of the mineral composition and structure more effectively in terms of accuracy and speed. This method is suitable for the rapid processing of massive rock near-infrared spectra and may become an important technology in geological survey and geological prospecting.     

近红外光谱指纹分析在羊肉产地溯源中的应用

为寻求低廉、快速有效地签别羊肉产地来源的方法,对来自内蒙古自治区锡林郭勒盟、呼伦贝尔市和阿拉善盟三个牧区,及重庆市和山东省菏泽市两个农区共99份羊肉样品进行近红外光谱扣描,利用主成分分析结合线性判别分析(PCA+LDA),以及偏最小二乘判别分析法(PLS-DA)对光谱数据进行了分析,建立了羊肉产地来源的定性判别模型.结...     

基于无监督可能模糊学习矢量量化的近红外光谱生菜品种鉴别研究

为解决模糊学习矢量量化(FLVQ)对噪声数据敏感问题,在无监督可能模糊聚类(UPFC)基础上提出一种无监督可能模糊学习矢量量化(UPFLVQ)算法。UPFLVQ用UPFC的隶属度和典型值来更新学习矢量量化网络的学习速率,计算类中心矢量。UPFLVQ 属于无监督机器学习算法,适用于无学习样本情况下的样本分类。研究了UPFLVQ用于近红外光谱生菜品种鉴别的可行性。采用FieldSpec@3型便携式光谱分析仪获取波长范围为350～2 500 nm的三种生菜样本的短波近红外光谱和长波近红外光谱,然后采用主成分分析(PCA)进行近红外光谱的维数压缩,取前三个主成分,累计可信度达97.50%,将2151维的近红外光谱压缩为三维数据。再运行模糊C-均值聚类(FCM)至迭代终止,并以FCM的类中心作为UPFLVQ的初始聚类中心,最后运行UPFLVQ得到隶属度和典型值以实现近红外光谱的生菜品种鉴别。同时,运行UPFC进行近红外光谱的生菜品种鉴别。实验结果表明：UPFLVQ和近红外光谱技术相结合的模型具有检测速度快,鉴别准确率高,对生菜不造成损坏等优点,可实现不同品种生菜的鉴别。UPFLVQ是将UPFC和FLVQ相结合的聚类算法,利用UPFLVQ建立近红外光谱的生菜品种鉴别模型时无需学习样本,适用于线性可分的数据聚类,为快速和无损地鉴别生菜品种提供了一种新的方法。     

FTIR结合统计分析对竹类植物的鉴别分类研究

傅里叶变换红外光谱结合主成分分析和系统聚类分析用于竹类植物鉴别分类研究。六种竹亚科植物54个竹子叶片的红外光谱测试结果显示竹叶光谱主要由蛋白质、碳水化合物、脂类等吸收带组成,竹叶光谱相似,仅在1 800～700 cm-1范围峰数、峰位、峰强上存在较小的差异。六种竹子叶片红外光谱的二阶导数谱在1 800～700 cm-1范围显示明显差异。用1 800～700 cm-1范围二阶导数光谱进行主成分分析,在主成分PC1,PC2,PC3三维空间图中,所测试竹叶样本分类正确率达98%;在PC3-PC4二维投影图显示所有竹叶样本正确分成六个区域;用1 800～700 cm-1范围二阶导数光谱进行聚类分析,所测竹叶样本正确聚为六类。表明FTIR结合统计分析能够在种水平对竹亚科植物鉴别分类。     

ICP-MS测定蜂蜜中多元素结合化学计量学在不同产地荆条蜜溯源中的应用

应用电感耦合等离子体质谱(ICP-MS)测定了北京顺义、河北阜平和河北平山三个地区65个荆条蜜样品中38种元素含量,其中B,Na,Mg,P,K,Ca,Fe和Zn等8种元素的含量处于较高水平,其浓度均高于1 mg·kg-1。比较发现,不同地区荆条蜜中元素含量存在一定差异。以具有显著性差异(p<0.05)的29种元素(Na,Mg,Al,K,Ti,V,Mn,Fe,Ni,Cu,Zn,Ga,As,Sr,Y,Mo,Cd,Ba,La,Ce,Pr,Nd,Sm,Gd,Dy,Ho,Tl,Pb和U)为变量,应用主成分分析(PCA)和反向传输人工神经网络(BP-ANN)两种化学计量学方法按照不同产地对荆条蜜进行了分析。PCA可将所有变量降为4个主成分,并能解释81.6%的变量,结果表明,PCA基本可以将荆条蜜按照不同产地分开。应用BP-ANN建立不同产地荆条蜜溯源模型,用全部样本建立模型时,三个产地荆条蜜分类的正确率均为100%,采用“留一法”进行交叉检验,整体准确率为95.4%;以75%的样本建立模型,25%的样本用于预测,对BP-ANN模型的可靠性进行验证,三个产地整体分类和预测的准确率均为100%。ICP-MS测定蜂蜜中多元素含量结合多变量模型可以实现不同产地荆条蜜溯源。     

可见-近红外高光谱成像结合化学计量学分辨人面部信息

人的面部信息与指纹和虹膜一样可以用于人的身份鉴别,并且相比之下更容易实现远距离的分辨和识别。利用高光谱成像技术可以应用到人脸识别领域并获取丰富的信息和庞大的成像数据量,需要采用化学计量学方法才能充分提取其中包含的有效信息,并为计算机识别奠定基础。研究了可见-近红外高光谱成像技术对人的面部信息进行分析的可行性。结果表明,多元曲线分辨-交替最小二乘方法不同于主成分分析,能够通过主成分纯光谱和相对浓度等具有具体物理化学意义的数据表征人的面部信息,而且可以方便地根据成像数据的特点施加运算中的约束。另外,采用偏最小二乘判别分析的方法实现了对不同肤色的皮肤信号光谱进行分类。白种人和黄种人的面部高光谱信息特征相似,分类难度高于深色皮肤人种。     

主成分分析排序和模糊线性判别分析的生菜近红外光谱分类

贮存时间是影响生菜品质的一项重要因素,传统的贮存时间鉴别方法主要依靠人工经验,但是这种方法的准确率和可信度并不高。研究的目标是建立一种基于模糊识别的模型进行生菜光谱分析以实现生菜贮存时间的鉴别,并与其他鉴别方法作比较。为此,在当地超市购买60份新鲜生菜样品,存放于冰箱中待用。首先,通过AntarisⅡ近红外光谱检测仪采集生菜样品的近红外光谱数据,每隔12小时检测一次,每个样本检测重复三次,并取三次平均值作为实验数据。其次,利用多元散射校正(MSC)减少近红外光谱中的冗余信息。为了进一步去除近红外光谱中的无用信息以及简化随后的数据分类过程,分别运用主成分分析(PCA)和排序主成分分析(PCA Sort)。其中,PCA Sort通过改进对主成分的排序方法能提高分类准确率,同时便于模糊线性鉴别分析(FLDA)进一步提取特征。PCA和PCA Sort的计算仅运用了前15个主成分(能充分反映光谱的主要信息)。最后,利用模糊线性鉴别分析算法(FLDA)和K近邻算法(KNN)进一步分类所得的低维数据。基于PCA和KNN算法的模型鉴别准确率达到43%,而基于PCA, FLDA和KNN算法的模型鉴别准确...     

Structural characteristics of chalcopyrite from a Cu(Au) ore deposit in the Caraj��s Mineral Province, Brazil

Mössbauer spectra and X-ray diffraction data show a chalcopyrite from the Cristalino Cu(Au) deposit in the Carajás Mineral Province in northern Brazil to consist of a single, tetragonal phase. This is in stark contrast to a previously described chalcopyrite from the Camaquã copper mine in southern Brazil, obviously reflecting differences in mineral (and thus ore deposit) genesis.     

激光诱导击穿光谱定量分析锂矿石中锂元素

锂元素具有优良的物理和化学性能,因而在军事、电池、特种合金、受控热核反应等领域具有重要作用。现有的锂矿石分析主要是基于酸分解的原子吸收光谱、电感耦合等离子体质谱或原子发射光谱等离线方法。激光诱导击穿光谱(LIBS)是一种无需样品制备、适用于低原子序数元素(包括锂)的原子发射光谱方法。采用LIBS技术,实验采集了11种锂矿石成分分析标准物质的等离子体发射光谱,分别在610.35和670.78 nm附近观测到了Li的特征峰,但由于谱线的重叠,无法应用单变量线性回归进行建模。在全谱积分强度标准化基础上,分别使用偏最小二乘回归(PLSR)和基于主成份分析的支持向量回归(PCA+SVR)对锂矿石标准物质中的锂含量进行建模。校准模型的相关参数通过留一组交叉验证均方根误差(RMSECV)来确定。结果表明,相较于PCA+SVR校准模型,PLSR的决定系数(R2)更大,校准均方根误差(RMSEC)更小,但预测均方根误差(RMSEP)远大于RMSEC,存在过拟合现象。另一方面,PCA+SVR计算得到的RMSEP和平均相对误差(MRE)相对于PLSR更小,因此认为PCA+SVR模型拥有更好适应度。从而证明,LIBS技术可以实现锂矿石中Li含量的分析,有望应用于位于传送带上锂矿石的原位在线定量分析。     

Quantitative analysis of NMR spectra with chemometrics

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) (1)H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.     

Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging

Chemical imaging method of vibrational spectroscopy, which provides both spectral and spatial information, creates a three‐dimensional (3D) dataset with a huge amount of data. When the components of the sample are unknown or their reference spectra are not available, the classical least squares (CLS) method cannot be applied to create visualized distribution maps. Raman image datasets can be evaluated even in such cases using multivariate (chemometric) methods for extracting the needed hidden information. The capability of chemometrics‐assisted Raman mapping is evaluated through the analysis of pharmaceutical tablets (considered as unknown) with the aim of estimating the pure component spectra based on the collected Raman image. Six chemometric methods, namely, principal component analysis (PCA), maximum autocorrelation factors (MAF), sample–sample 2D correlation spectroscopy (SS2D), self‐modeling mixture analysis (SMMA), multivariate curve resolution–alternating least squares (MCR‐ALS), and positive matrix factorization (PMF), were compared. SMMA was found to be the best choice to determine the number of components. MCR‐ALS and PMF provided the pure component spectra with the highest quality. MCR‐ALS was found to be superior to PMF in the estimation of Raman scores (which correspond to the concentrations) and yielded almost the same results as CLS (using the real reference spectra). Thus, the combination of Raman mapping and chemometrics could be successfully used to characterize unknown pharmaceuticals, identify their ingredients, and obtain information about their structures. This may be useful in the struggles against illegal and counterfeit products and also in the field of pharmaceutical industry when contaminants are to be identified. Copyright © 2011 John Wiley & Sons, Ltd.     

基于近红外光谱技术成品汽油分类方法的研究

在研究成品汽油的分类方法过程中,首先采用判别式聚类分析方法比较了700~1 100和1 100~1 700 nm两个波段范围判别模型的准确性,然后在识别模型准确性较高的波段(1 100~1 700 nm)采用主成分分析法(PCA)结合自组织竞争神经网络方法,对90#,93#和97#成品汽油建立定性识别模型。在建立定性模型前先用PCA法对原始数据进行主成分压缩。主成分分析结果表明,前3个主成分的累积可信度已达97%,取前3个主成分的32个波长点的吸光度作为神经网络的输入,建立三层自组织竞争神经网络模型。神经网络模型的学习参数为0.01,网络训练迭代次数为500。结果表明,基于主成分分析结合自组织竞争神经网络方法建立的近红外光谱鉴别成品汽油的模型鉴别准确率高、方法可行。     

基于高光谱技术的覆盖保鲜膜菠菜货架期预测研究

保鲜膜能提高果蔬保水性,隔绝外界细菌侵染,延长货架期。为了准确估测覆盖保鲜膜果蔬品质的优劣,对其货架期进行预测具有重要意义。应用高光谱技术结合化学计量学方法对同等贮藏条件下覆膜新鲜菠菜叶片的货架期进行了预测。先采集五个不同贮藏时间下75盘共300片菠菜样本在可见-近红外(Vis-NIR,380～1 030 nm)与近红外(NIR,874～1 734 nm)波段的高光谱数据,然后测定不同贮藏时间下菠菜叶片叶绿素含量。提取300片覆膜菠菜叶片的平均光谱(200个为建模集,100个为预测集)后,对建模集光谱进行主成分分析(principal component analysis,PCA),发现不同贮藏期内叶片光谱数据在前3个主成分空间有一定的聚类。根据建模集光谱信息与预先赋予的不同贮藏期虚拟等级分别建立偏最小二乘判别分析(partial least squares discriminant analysis,PLS-DA)模型,得到预测集样本的贮藏期总的判别准确率分别为83%(Vis-NIR)和81%(NIR)。表明,高光谱技术结合化学计量学方法能够实现对新鲜菠菜货架期的分类和预测,为消费者正确评价覆盖保鲜膜的菠菜品质提供了理论指导,也为后期果蔬货架期检测仪器的开发提供了技术支持。     

应用Hilbert变换提取拉曼光谱相位信息进行血液识别分类方法的研究

将拉曼光谱技术和化学计量学方法相结合实现了对人血和动物血种属的区分,并提出了一种基于Hilbert变换的拉曼光谱相位提取方法,提高了人血与动物血区分的准确度。分别对血液光谱数据和它所对应的相位信息进行主成分分析(PCA),通过主成分得分图比较两者对人与动物血液的区分程度,并建立偏最小二乘判别分析(PLS-DA)模型,通过设置合适的分类阈值y,可以实现人与动物血液的有效区分。结果表明在选取第一、第二主成分分析时,利用光谱数据相位信息建立的PCA模型,识别率更高,人与动物血液明显区分开来。其所对应的PLS-DA模型最优主成分数为3,预测标准误差(RMSEP)和决定系数(R2)分别为0.044 3和0.993 2。而用血液原始光谱建立的PLS-DA模型最优主成分数为6,RMSEP和R2分别为0.053 7和0.990 1。说明利用拉曼光谱相位信息建立的PLS-DA模型可以拟合较少的主成分数来获得误差更小的预测结果。进一步观察PLS-DA模型拟合不同主成分数的预测标准误差曲线图,当选取同样多的拟合主成分数时,利用血液拉曼光谱相位信息建立的PLS-DA模型其所对应的预测标准误差均低于原始血液光谱数据。所以,通过提取血液拉曼光谱数据的相位信息,可以降低模型的复杂程度,提高识别准确度。     

Different classification algorithms and serum surface enhanced Raman spectroscopy for noninvasive discrimination of gastric diseases

In this study, surface enhanced Raman spectroscopy (SERS) was used to investigate the spectral characteristics of blood serum for the purpose of diagnosing stomach diseases. SERS spectral data was collected from patients with atrophic gastritis, both pre‐operation and post‐operation gastric cancer, and from healthy individuals. Visual differences in the SERS spectra were observed between the four groups which indicate corresponding biomolecule concentration changes in blood. To further investigate the diagnostic ability of human serum, the spectral data was analyzed with three chemometric processes. These three methods extracted features and classified from the spectral data. Principal component analysis (PCA) was first performed to reduce the dimensionality of the original spectral data. Then, the classification methods support vector machine (SVM), linear discriminant analysis (LDA) and classification and regression tree (CART) were used for the evaluation of diagnostic ability. Accuracies of 96.5%, 88.8% and 87.1% were obtained for PCA‐SVM, PCA‐LDA and PCA‐CART, respectively. Copyright © 2016 John Wiley & Sons, Ltd.     

模糊K-Harmonic-Kohonen网络的FTIR光谱数据聚类分析

食品的品种不同则其含有营养成分和功效存在差异,得到的傅里叶变换红外光谱也存在差异。为了准确的实现品种分类,设计了一种将傅里叶变换红外光谱与模糊聚类分析方法相结合的品种鉴别方法。在模糊Kohonen聚类网络(FKCN)基础上将模糊K调和聚类(FKHM)引入到Kohonen聚类网络的学习速率和更新策略中,提出了模糊K-Harmonic-Kohonen网络(FKHKCN)算法。FKHKCN利用模糊C均值(FCM)聚类的模糊隶属度计算其学习速率,以FKHM的聚类中心为基础通过推导计算得到FKHKCN的聚类中心,可以解决模糊Kohonen聚类网络方法对于初始类中心敏感而导致聚类结果不稳定的问题。FKHKCN作为一种模糊聚类算法,可实现傅里叶变换红外光谱数据的聚类分析。采用三种数据集：(1)采集产自四川的三种茶叶(优质和劣质的乐山竹叶青以及峨眉山毛峰)作为实验样本,样本总数为96。(2)两个品种(robusta和arabica)的咖啡样本。(3)三个品种(鸡肉、猪肉和火鸡)的肉类样本。首先对三个光谱数据集进行预处理,利用多元散射校正降低茶叶样本原始光谱数据集的散射影响,使用Savitzky-Gol...     

土壤碳酸钙中红外光声光谱特征及其应用

测定并分析了碳酸钙(CaCO3)的中红外光声光谱及光谱特征,利用中红外光声光谱并结合主成分回归(PCR)、偏最小二乘回归(PLSR)和人工神经网络(GRNN)三种分析方法建立回归模型,分析了土壤CaCO3的含量。结果表明CaCO3具有丰富的中红外吸收,最强吸收峰波数在1 450cm-1,且干扰少,可以作为土壤CaCO3的特征吸收峰;三种回归建模方法所建模型线性都很好,PLSR和GRNN最好,相关系数(R2)均大于0.9,PCR次之,为0.847;验证样本预测能力PLSR和PCR最佳,R2大于0.9;GRNN次之,为0.882。偏最小二乘回归在校正和预测过程中的结果都非常好,RPD值均大于3.0,具有较强的适用性。     

基于拉曼光谱的复杂硫化矿快速分类方法研究

热液硫化物型脉状矿作为一类复杂硫化矿,其区域特征、成矿规律及矿物成份已有初步研究。由于成矿时期的不同,矿石中有用矿物的特征存在较大差异,导致不同矿物的性质变化较大。在选矿过程中,矿物性质的差异一定程度增加了选矿难度,减少了有用矿物回收率。因此,迫切需要一种快速、简单的对复杂硫化矿进行分类的方法,进而提高选矿指标。激光拉曼光谱技术作为一种能够分析物质结构信息的手段,已被应用于矿物的成份鉴定和结构分析。通过对大量矿物样本的激光拉曼光谱的研究,结合矿物性质深入揭示其光谱差异的原因,提出了一种基于拉曼光谱的复杂硫化矿矿源分类方法。实验结果表明：由于此类复杂硫化矿成矿时期的差异,从而造成矿物结构和性质存在较大差异。荧光主要由原矿中的脉石矿物产生,猝灭矿物中瞒石的荧光背景后可知201.62, 242.54, 288.38和309.77 cm-1处拉曼峰可以作为此类硫化矿的拉曼指纹谱。为此,基于此类硫化矿的荧光强度和代表谱峰强度与荧光背景比值可以将矿源分为三类,并利用工业试验结果进一步验证分类方法的准确性。本研究深入分析了此类复杂硫化矿的激光拉曼光谱与其矿物性质与类别之间的密切关系,提出了矿源快速分类方法,矿样无需经过复杂的化学前处理过程,对提高选矿作业效率具有重要应用价值。