选矿产品矿物自动分析的光片制备

基于扫描电子显微镜的矿物自动分析仪(Quantitative Evaluation of Minerals by Scanning Electronic Microscopy)、MLA(Mineral Liberation Analyser)和AMICS(Advanced Mineral Identification and Characterization System)可用于测定选矿产品中目的矿物的粒度和单体解离度,为确定合理的磨矿细度以及优化选矿工艺流程提供依据。环氧树脂光片的制备是矿物自动识别和测量的最关键的一环,其代表性直接关系到后续数据测量的准确性和真实性。对于金属矿产品来说,由于选矿产品中矿物颗粒粗细不均、密度差异较大,在环氧树脂胶结固化过程中矿物颗粒会产生明显的分异作用并互相黏连,造成分析结果失真。实验证明,把样品与晶质石墨混均,然后加入环氧树脂以及固化剂搅拌混合倒入圆柱状模具进行冷镶嵌,待样品固化后再沿圆柱体的纵向进行切割,并对其切割面进行粗磨、细磨、精磨以及抛光,就可以制备出样品分散性好、分布均匀、表面光滑平整的具有代表性的环氧树脂光片。     

Boosted leave-many-out cross-validation: the effect of training and test set diversity on PLS statistics

It is becoming increasingly common in quantitative structure/activity relationship (QSAR) analyses to use external test sets to evaluate the likely stability and predictivity of the models obtained. In some cases, such as those involving variable selection, an internal test set – i.e., a cross-validation set – is also used. Care is sometimes taken to ensure that the subsets used exhibit response and/or property distributions similar to those of the data set as a whole, but more often the individual observations are simply assigned `at random.' In the special case of MLR without variable selection, it can be analytically demonstrated that this strategy is inferior to others. Most particularly, D-optimal design performs better if the form of the regression equation is known and the variables involved are well behaved. This report introduces an alternative, non-parametric approach termed `boosted leave-many-out' (boosted LMO) cross-validation. In this method, relatively small training sets are chosen by applying optimizable k-dissimilarity selection (OptiSim) using a small subsample size (k = 4, in this case), with the unselected observations being reserved as a test set for the corresponding reduced model. Predictive errors for the full model are then estimated by aggregating results over several such analyses. The countervailing effects of training and test set size, diversity, and representativeness on PLS model statistics are described for CoMFA analysis of a large data set of COX2 inhibitors.     

泛Kennard-Stone算法的数据集代表性度量与分块采样策略

在大数据机器学习时代, 选择更具代表性的数据集对于模型的训练和验证尤为重要. Kennard- Stone(KS)算法及其各种变种(泛KS算法)是一大类优异的数据集分割方法, 但其采样比例或采样数的选择仅能依靠经验或根据建模结果事后评判. KS算法依据原始文献的计算复杂度为\mathrm{O}\left(K^{\mathrm{3}}\right), 难以用于超大数据样本量的计算. 本文基于数据集完备性的讨论, 提出泛KS算法的数据集代表性度量, 以简正振动采样的甲烷分子中碳氢键数据特征分布为例展示采样集代表性效果. 简化KS采样过程的筛选算法, 提高算法效率至\mathrm{O}\text{'}\left(K^{\mathrm{2}}\right). 提出将数据集切分成多个子集分别实施KS采样的分块采样策略, 可进一步提高算法效率至\mathrm{O}″\left(K\right). 偏最小二乘回归测试结果表明， 该方法在提高采样效率的同时仍可保障采样集的代表性.     

Fact or artifact: the representativeness of ESI‐MS for complex natural organic mixtures

Because mass spectrometers provide their own dispersion and resolution of analytes, electrospray ionization mass spectrometry (ESI‐MS) has become a workhorse for the characterization of complex mixtures from aerosols to crude oil. Unfortunately, ESI mass spectra commonly contain multimers, adducts and fragments. For the characterization of complex mixtures of unknown initial composition, this presents a significant concern. Mixed‐multimer formation could potentially lead to results that bare no resemblance to the original mixture. Conversely, ESI‐MS has continually reflected subtle differences between natural organic matter mixtures that are in agreement with prediction or theory. Knowing the real limitations of the technique is therefore critical to avoiding both over‐interpretation and unwarranted skepticism. Here, data were collected on four mass spectrometers under a battery of conditions. Results indicate that formation of unrepresentative ions cannot entirely be ruled out, but non‐covalent multimers do not appear to make a major contribution to typical natural organic matter spectra based on collision‐induced dissociation results. Multimers also appear notably reduced when a cooling gas is present in the accumulation region of the mass spectrometer. For less complex mixtures, the choice of spray solvent can make a difference, but generally spectrum cleanliness (i.e. representativeness) comes at the price of increased selectivity. Copyright © 2014 John Wiley & Sons, Ltd.     

Development of a mobile system based on laser-induced breakdown spectroscopy and dedicated to in situ analysis of polluted soils

Principal Components Analysis (PCA) is successfully applied to the full laser-induced breakdown spectroscopy (LIBS) spectra of soil samples, defining classes according to the concentrations of the major elements. The large variability of the LIBS data is related to the heterogeneity of the samples and the representativeness of the data is finally discussed.