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ASER: Adapted squared error relevance for rare cases prediction in imbalanced regression 不平衡回归中罕见病例预测的自适应平方误差相关性
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-09-08 DOI: 10.1002/cem.3515
Ying Kou, Guang-Hui Fu

Many real-world data mining applications involve using imbalanced datasets to obtain predictive models. Imbalanced data can hinder the model performance of learning algorithms in rare cases. Although there are many well-researched classification task solutions, most of them cannot be directly applied to regression task. One of the challenges in imbalanced regression is to find a suitable evaluation and optimization standard that can improve the predictive ability of the model without severe model bias. Based on the importance of rare cases, this study proposes a new evaluation metric called adapted squared error relevance (ASER) by defining new relevance function and weighting functions. This metric weights data points by defining the importance of rare cases and assigns different weights to losses of the same size at different rare cases, thus enabling the model selected by this evaluation metric to better predict rare cases. ASER is compared with SER on 32 real datasets and 9 simulated datasets to verify the predictive performance of the selected model at rare cases. The experimental results show that the new evaluation metric ASER can obtain a high prediction performance at rare cases, while also not losing too much prediction accuracy in common cases.

许多现实世界的数据挖掘应用涉及使用不平衡数据集来获得预测模型。在极少数情况下,不平衡的数据会阻碍学习算法的模型性能。虽然分类任务的解决方案研究得很好,但大多数都不能直接应用于回归任务。不平衡回归的挑战之一是找到一个合适的评价和优化标准,既能提高模型的预测能力,又不会造成严重的模型偏差。基于罕见案例的重要性,本研究通过定义新的关联函数和加权函数,提出了一种新的评价指标——自适应平方误差相关性(ASER)。该度量通过定义罕见情况的重要性来对数据点进行加权,并对不同罕见情况下相同大小的损失分配不同的权重,从而使该评价度量所选择的模型能够更好地预测罕见情况。将ASER与SER在32个真实数据集和9个模拟数据集上进行了比较,以验证所选模型在极少数情况下的预测性能。实验结果表明,新的评估指标ASER在极少数情况下可以获得较高的预测性能,同时在常见情况下也不会损失太多的预测精度。
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引用次数: 0
On the complementary nature of ANOVA simultaneous component analysis (ASCA+) and Tucker3 tensor decompositions on designed multi-way datasets 关于设计的多维数据集上ANOVA同时分量分析(ASCA+)和Tucker 3张量分解的互补性
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-08-30 DOI: 10.1002/cem.3514
Farnoosh Koleini, Siewert Hugelier, Mahsa Akbari Lakeh, Hamid Abdollahi, José Camacho, Paul J. Gemperline

The complementary nature of analysis of variance (ANOVA) Simultaneous Component Analysis (ASCA+) and Tucker3 tensor decompositions is demonstrated on designed datasets. We show how ASCA+ can be used to (a) identify statistically sufficient Tucker3 models; (b) identify statistically important triads making their interpretation easier; and (c) eliminate non-significant triads making visualization and interpretation simpler. For multivariate datasets with an experimental design of at least two factors, the data matrix can be folded into a multi-way tensor. ASCA+ can be used on the unfolded matrix, and Tucker3 modeling can be used on the folded matrix (tensor). Two novel strategies are reported to determine the statistical significance of Tucker3 models using a previously published dataset. A statistically sufficient model was created by adding factors to the Tucker3 model in a stepwise manner until no ASCA+ detectable structure was observed in the residuals. Bootstrap analysis of the Tucker3 model residuals was used to determine confidence intervals for the loadings and the individual elements of the core matrix and showed that 21 out of 63 core values of the 3 × 7 × 3 model were not significant at the 95% confidence level. Exploiting the mutual orthogonality of the 63 triads of the Tucker3 model, these 21 factors (triads) were removed from the model. An ASCA+ backward elimination strategy is reported to further simplify the Tucker3 3 × 7 × 3 model to 36 core values and associated triads. ASCA+ was also used to identify individual factors (triads) with selective responses on experimental factors A, B, or interactions, A × B, for improved model visualization and interpretation.

在设计的数据集上证明了方差分析(ANOVA)、同步分量分析(ASCA+)和Tucker3张量分解的互补性。我们展示了ASCA+如何用于(a)识别统计上充分的Tucker3模型;(b)识别统计上重要的三联征,使其更容易解释;(c)消除非显著的三和弦,使可视化和解释更简单。对于具有至少两个因素的实验设计的多元数据集,数据矩阵可以折叠成一个多路张量。展开矩阵可用ASCA+建模,折叠矩阵(张量)可用Tucker3建模。据报道,使用先前发表的数据集确定Tucker3模型的统计显著性的两种新策略。逐步在Tucker3模型中加入因子,直到残差中没有ASCA+可检测结构,建立统计上充分的模型。使用Tucker3模型残差的Bootstrap分析来确定负荷和核心矩阵各元素的置信区间,结果表明,3 × 7 × 3模型的63个核心值中有21个在95%置信水平下不显著。利用Tucker3模型中63个三元组的相互正交性,将这21个因素(三元组)从模型中去除。采用ASCA+反向消元策略,进一步简化了Tucker3 3 × 7 × 3模型,得到36个核心值和相关三元组。ASCA+还用于识别对实验因素A、B或相互作用A × B有选择性反应的个体因素(三元组),以改进模型的可视化和解释。
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引用次数: 0
Distributed statistical process monitoring based on block-wise residual generator 基于分块残差发生器的分布式统计过程监控
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-08-23 DOI: 10.1002/cem.3513
Chudong Tong, Xinyan Zhou, Kai Qian, Xin Xu, Jiongting Jiang

The increasing scale of modern chemical plants keeps popularizing investigation as well as application of distributed process monitoring approaches. With a goal of directly quantifying the normal relations between different blocks divided from the whole process, a novel multi-block modeling strategy called block-wise residual generator is proposed, which trains a residual generator for each block through using the partial least squares algorithm with single one output, so that the relation between the corresponding block and the others is quantified as a regression model in a block-wise manner. The deviations caused by the abnormal samples to the normal relations quantified for different blocks could thus be efficiently captured by the residuals generated from the block regression models, which then provide sensitive information for fault detection and contribution-based fault diagnosis. Moreover, the proposed method is applicable for both disjoint and overlapped block divisions, and the direct consideration of individually quantifying relations between different blocks can always guarantee its salient monitoring performance, as validated through comparisons with classical distributed process monitoring methods.

随着现代化工厂规模的不断扩大,分布式过程监控方法的研究和应用不断普及。为了直接量化整个过程中各个分块之间的正常关系,提出了一种新的多分块建模策略——分块残差生成器,该策略利用单输出的偏最小二乘算法对每个分块训练一个残差生成器,从而将相应分块与其他分块之间的关系以分块的方式量化为回归模型。因此,块回归模型产生的残差可以有效地捕获异常样本对不同块量化的正常关系的偏差,从而为故障检测和基于贡献的故障诊断提供敏感信息。此外,该方法既适用于不相交的块划分,也适用于重叠的块划分,并且通过与经典分布式过程监控方法的比较,直接考虑不同块之间的单独量化关系,总能保证其显著的监控性能。
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引用次数: 0
In honour of Edmund R. Malinowski 纪念埃德蒙·马林诺夫斯基
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-08-07 DOI: 10.1002/cem.3499
Marcel Maeder
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引用次数: 0
Methodology adjusting for least squares regression slope in the application of multiplicative scatter correction to near-infrared spectra of forage feed samples 饲料样品近红外光谱乘散校正应用中最小二乘回归斜率调整方法
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-08-03 DOI: 10.1002/cem.3511
Mewa S. Dhanoa, Secundino López, Ruth Sanderson, Sue J. Lister, Ralph J. Barnes, Jennifer L. Ellis, James France

Scatter corrections are commonly applied to refine near-infrared (NIR) spectra. The aim of this study is to assess the impact of measurement errors when using ordinary least squares (OLS) for multiplicative scatter correction (MSC). Any measurement errors attached to the set-mean spectrum may attenuate the OLS slope and that in turn will affect the estimate of the intercept and the adjustment of the spectra when using MSC methods to mitigate scattering. A corrected least squares slope may be used instead to prevent this problem, although the impact of this approach on the final outcome will depend on the relative size of the measurement errors in the individual spectra and the set-mean spectrum. The errors-in-variables or type II regression model (also known as Deming regression) and its special cases, major axis (MA) and reduced major axis (RMA), are discussed and illustrated. The extent of OLS slope bias or attenuation is demonstrated as is the resulting MSC spectral distortion. Further modification to the MSC transformation method is also suggested. The influence of scattering correction (by MSC, standard normal variate (SNV) and detrending) and of using the maximum likelihood estimate of the slope for MSC on the prediction of chemical composition of Lucerne herbage from NIR spectra was assessed. The predictive performance was slightly improved by the use of scattering corrections with fairly minor differences among methods. Nonetheless, it seems well worth considering the use of type II regression models for assessing MSC application aiming at improving the goodness of prediction from NIR spectra.

散射校正通常用于精炼近红外(NIR)光谱。本研究的目的是评估使用普通最小二乘(OLS)进行乘法散点校正(MSC)时测量误差的影响。任何附加在集均值光谱上的测量误差都可能使OLS斜率衰减,进而影响在使用MSC方法减轻散射时对截距的估计和光谱的调整。可以使用修正的最小二乘斜率来防止这个问题,尽管这种方法对最终结果的影响将取决于单个光谱和集平均光谱中测量误差的相对大小。本文讨论并说明了变量误差或II型回归模型(也称为Deming回归)及其特殊情况,即长轴(MA)和缩减长轴(RMA)。OLS斜率偏差或衰减的程度证明了由此产生的MSC光谱失真。本文还提出了进一步改进MSC转化方法的建议。评估了散射校正(通过均方根、标准正态变量(SNV)和去趋势)和均方根斜率的最大似然估计对近红外光谱预测卢塞恩牧草化学成分的影响。使用散射校正,预测性能略有提高,方法之间的差异相当小。尽管如此,似乎很值得考虑使用II型回归模型来评估MSC应用,旨在提高近红外光谱预测的准确性。
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引用次数: 0
Selection of principal variables through a modified Gram–Schmidt process with and without supervision 通过有监督和无监督的改进Gram-Schmidt过程选择主变量
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-07-29 DOI: 10.1002/cem.3510
Joakim Skogholt, Kristian H. Liland, Tormod Næs, Age K. Smilde, Ulf G. Indahl

In various situations requiring empirical model building from highly multivariate measurements, modelling based on partial least squares regression (PLSR) may often provide efficient low-dimensional model solutions. In unsupervised situations, the same may be true for principal component analysis (PCA). In both cases, however, it is also of interest to identify subsets of the measured variables useful for obtaining sparser but still comparable models without significant loss of information and performance. In the present paper, we propose a voting approach for sparse overall maximisation of variance analogous to PCA and a similar alternative for deriving sparse regression models influenced closely related to the PLSR method. Both cases yield pivoting strategies for a modified Gram–Schmidt process and its corresponding (partial) QR-factorisation of the underlying data matrix to manage the variable selection process. The proposed methods include score and loading plot possibilities that are acknowledged for providing efficient interpretations of the related PCA and PLS models in chemometric applications.

在需要从高度多元测量中建立经验模型的各种情况下,基于偏最小二乘回归(PLSR)的建模通常可以提供有效的低维模型解决方案。在无监督的情况下,主成分分析(PCA)可能也是如此。然而,在这两种情况下,确定测量变量的子集对于获得更稀疏但仍然可比较的模型有用,而不会造成信息和性能的重大损失。在本文中,我们提出了一种类似于PCA的稀疏总体方差最大化的投票方法,以及一种类似的替代方法,用于推导与PLSR方法密切相关的稀疏回归模型。这两种情况都为改进的Gram-Schmidt过程及其相应的(部分)底层数据矩阵的QR‐分解提供了pivot策略,以管理变量选择过程。提出的方法包括得分和加载图的可能性,被公认为在化学计量学应用中提供相关PCA和PLS模型的有效解释。
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引用次数: 1
Application of ultramicrotomy and infrared imaging to the forensic examination of automotive paint 超微结构和红外成像技术在汽车油漆法医学检验中的应用
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-07-19 DOI: 10.1002/cem.3509
Haoran Zhong, Elizabeth Donkor, Lisa Whitworth, Collin G. White, Kaushalya Sharma Dahal, Ayuba Fasasi, Thomas M. Hancewicz, Franklin Uba, Barry K. Lavine

In several previously published studies, Lavine and coworkers have demonstrated that infrared (IR) spectra from all layers of an intact multilayered automotive paint chip can be collected in a single analysis by scanning across each layer of a cross sectioned paint chip using a Fourier transform IR imaging microscope. Applying alternating least squares to the spectral data, the IR spectrum of each layer of an original equipment manufacturer paint chip can be extracted from a line map of the spectral image. To further develop this imaging technique for automotive paint analysis, the capability to cross section “small” paint chips (1 mm or less) using an ultramicrotome has been incorporated into our current imaging methodology. An ultramicrotome does not require epoxy or other embedding media for the paint chip and will simplify the analysis. However, extracting the IR spectra for each layer of an original equipment manufacturer paint chip by alternating least squares can be problematic for thin peels (less than one micron thickness), necessitating the use of target testing factor analysis to determine whether a specific layer is present in the line map and modified alternating least squares to recover the IR spectrum of the layer. Using a new sample preparation technique and the appropriate multivariate curve resolution methods, high quality IR spectra of the layers of a modern automotive paint system can be obtained from paint fragments that are smaller than what is practical to analyze by conventional Fourier transform IR spectroscopy.

在之前发表的几项研究中,Lavine及其同事已经证明,通过使用傅里叶变换红外成像显微镜扫描横截面油漆芯片的每一层,可以在一次分析中收集完整多层汽车油漆芯片所有层的红外光谱。将交替最小二乘法应用于光谱数据,可以从光谱图像的线图中提取原始设备制造商油漆芯片的每一层的IR光谱。为了进一步发展这种用于汽车油漆分析的成像技术,横截面“小”油漆碎片的能力(1 mm或更小)已经被纳入我们当前的成像方法中。超微切片机不需要环氧树脂或其他嵌入介质用于油漆芯片,并将简化分析。然而,对于薄皮(小于一微米厚度),通过交替最小二乘法提取原始设备制造商油漆芯片的每一层的IR光谱可能是有问题的,因此需要使用目标测试因子分析来确定线图中是否存在特定层,并修改交替最小二乘法来恢复该层的IR频谱。使用一种新的样品制备技术和适当的多变量曲线分辨率方法,可以从比传统傅立叶变换红外光谱分析实用的更小的油漆碎片中获得现代汽车油漆系统各层的高质量红外光谱。
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引用次数: 0
Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye 利用Tikhonov和分数阶导数工具平滑和区分数据,应用于结晶紫染料表面增强拉曼散射(SERS)光谱
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-07-11 DOI: 10.1002/cem.3507
Nelson H. T. Lemes, Taináh M. R. Santos, Camila A. Tavares, Luciano S. Virtuoso, Kelly A. S. Souza, Teodorico C. Ramalho

All signals obtained as instrumental response of analytical apparatus are affected by noise, as in Raman spectroscopy. Whereas Raman scattering is an inherently weak process, the noise background may lead to misinterpretations. Although surface amplification of the Raman signal using metallic nanoparticles has been a strategy employed to partially solve the signal-to-noise problem, the preprocessing of Raman spectral data through the use of mathematical filters has become an integral part of Raman spectroscopy analysis. In this paper, a Tikhonov modified method to remove random noise in experimental data is presented. In order to refine and improve the Tikhonov method as a filter, the proposed method includes Euclidean norm of the fractional-order derivative of the solution as an additional criterion in Tikhonov function. In the strategy used here, the solution depends on the regularization parameter, λ, and on the fractional derivative order, α. As will be demonstrated, with the algorithm presented here, it is possible to obtain a noise-free spectrum without affecting the fidelity of the molecular signal. In this alternative, the fractional derivative works as a fine control parameter for the usual Tikhonov method. The proposed method was applied to simulated data and to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye in Ag nanoparticles colloidal dispersion.

作为分析设备的仪器响应获得的所有信号都受到噪声的影响,如在拉曼光谱中。尽管拉曼散射是一个固有的弱过程,但噪声背景可能会导致误解。尽管使用金属纳米颗粒对拉曼信号进行表面放大是部分解决信噪比问题的一种策略,但通过使用数学滤波器对拉曼光谱数据进行预处理已成为拉曼光谱分析的一个组成部分。本文提出了一种去除实验数据中随机噪声的Tikhonov改进方法。为了改进和改进作为滤波器的Tikhonov方法,该方法将解的分数阶导数的欧几里得范数作为Tikhonof函数中的附加准则。在这里使用的策略中,解取决于正则化参数λ和分数阶导数α。正如将要证明的那样,利用这里提出的算法,可以在不影响分子信号保真度的情况下获得无噪声频谱。在这种替代方案中,分数导数作为通常的Tikhonov方法的精细控制参数。将所提出的方法应用于模拟数据和银纳米粒子胶体分散体中结晶紫染料的表面增强拉曼散射(SERS)光谱。
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引用次数: 0
Planetary and space science special issue 行星与空间科学特刊
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-07-11 DOI: 10.1002/cem.3508
Emmanuel A. Lalla, Menelaos Konstantinidis

Today more than ever, space science is a vibrant and exciting field. The Mars 2020 Perseverance Rover took off and landed at the height of the Covid-19 pandemic, and the scientific results of its payload are already paying dividends to the scientific community. Meanwhile on Earth, scientific development, far from having halted, remains as active as ever before, albeit with some hiccups over the last 3 years due to restrictions. Nevertheless, the scientific community, and more specifically, the space science community, has remained steadfast in its pursuit of knowledge. And at the core of this pursuit is the ever-growing field of chemometrics.

All in all, the body of work in this special issue represents a tremendous effort on the part of the authors, and we could not be more pleased. We must admit that the continued submissions and forthcoming work made it hard for us to declare a conclusion to this special issue. Indeed, we could have continued receiving submissions indefinitely. However, all good things must come to an end, if for no other reason than to open the door for future endeavors. Whether that means the continuation of methodological work, the inevitable continuation of instrument development for the search of life or other priorities of the space science communities, or simply reflections on where we are headed, there is much to be done and disseminated. But as long as we continue having fun and pushing the proverbial envelope, special issues such as this one should be executed every few years to ensure that the fields of space science and chemometrics benefit from the synergy of our long-standing interdisciplinarity.

For now, enjoy the ride and shoot for the stars, if only to land on the Moon or Mars!

研究人员利用小型激光烧蚀电离质谱仪(LIMS)研究了Gunflint燧石中前寒武纪微化石的化学成分。进行了质谱成像(MSI)和深度剖面分析,获得了68,500个质谱。通过单质量单位光谱分解和多元数据分析技术,研究人员确定了微化石聚集体和周围无机宿主矿物的位置。结果表明,微化石具有独特的特征
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引用次数: 0
Application of stable consistency wavelength in optimizing gasoline RON near-infrared analysis model transfer 稳定一致性波长在优化汽油RON近红外分析模型转移中的应用
IF 2.4 4区 化学 Q1 Mathematics Pub Date : 2023-07-08 DOI: 10.1002/cem.3506
Hong-hong Wang, Hui Yuan, Yun-chao Hu, Zhi-xin Xiong, Zhi-jian Liu, Ying Wang, Hao-ran Huang

The purpose of model transfer is to solve the problem that multivariate calibration models cannot be shared among different near-infrared spectrometers. Taking gasoline as the research object, the transfer analysis of its octane number model was carried out. The gasoline samples collected by two near-infrared spectrometers of the same type were used as the research object. The screening wavelengths with consistent and stable signals (SWCSS) combined with competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projections algorithm (SPA) were used to reduce the adverse effects of invalid wavelengths in the SWCSS method; therefore, the analysis ability of the master model to the slave samples was improved. Partial least squares regression (PLSR) models based on SWCSS-UVE, SWCSS-CARS, and SWCSS-SPA algorithms were established, and comparison was made between their analytical capabilities for slave samples and those of SWCSS, direct standardization (DS) algorithm , piecewise direct standardization (PDS) algorithm, and slope/bias (S/B) algorithm. The results shown that the SWCSS-UVE and SWCSS-CARS methods can be used to establish models from the 231 and 6 wavelengths selected from the consistent wavelengths, respectively. The root mean square error of prediction (RMSEP) of the gasoline octane number (RON) content of the direct analysis of the spectrum measured by the slave machine was reduced from 5.7490 to 0.3226 and 0.3250, respectively, which was better than the single SWCSS and DS, PDS, and SWCSS-SPA methods, and was close to the model transfer accuracy of the S/B algorithm. The transfer accuracy of SWCSS-UVE and SWCSS-CARS was not much different, but the wavelength variable involved in the model transfer of the latter was much smaller than that of the former, and the AIC value of SWCSS-CARS was −59.59, which was much smaller than the akaike information criterion (AIC) value of SWCSS-UVE 398.42.

模型迁移的目的是解决不同近红外光谱仪之间不能共享多变量标定模型的问题。以汽油为研究对象,对其辛烷值模型进行了传递分析。以两台同类型近红外光谱仪采集的汽油样品为研究对象。采用具有一致稳定信号的筛选波长(SWCSS)方法,结合竞争自适应重加权采样(CARS)、无信息变量消除(UVE)和逐次投影算法(SPA)来降低无效波长的不利影响;从而提高了主模型对从样本的分析能力。建立了基于SWCSS‐UVE、SWCSS‐CARS和SWCSS‐SPA算法的偏最小二乘回归(PLSR)模型,并将其对从样本的分析能力与SWCSS、直接标准化(DS)算法、分段直接标准化(PDS)算法和斜率/偏差(S/B)算法进行了比较。结果表明,SWCSS - UVE和SWCSS - CARS方法可以分别从一致性波长中选择231和6个波长建立模型。从机测得的光谱直接分析的汽油辛烷值(RON)含量预测均方根误差(RMSEP)分别从5.7490降低到0.3226和0.3250,优于单一SWCSS方法和DS、PDS和SWCSS‐SPA方法,接近S/B算法的模型传递精度。SWCSS‐UVE与SWCSS‐CARS的模型转移精度差异不大,但后者涉及的波长变量远小于前者,且SWCSS‐CARS的AIC值为- 59.59,远小于SWCSS‐UVE的akaike信息准则(AIC)值398.42。
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引用次数: 0
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Journal of Chemometrics
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