基于主成分分析的彩色扫描仪光谱特性化

为了实现扫描仪在不同光源、不同观察者条件下准确获取颜色信息,最大程度的避免同色异谱现象,本文采用光谱的方法对扫描仪进行特性化处理,通过多项式回归和BP神经网络分别与主成分分析法结合,首先对检测样本的光谱反射率进行主成分分析,提取主成分与主成分系数,通过实验得到主成分系数与多项式回归、BP神经网络结构之间的转换模型,实现了扫描仪低维RGB信号对原始光谱反射率信息的重构,进而实现扫描仪的光谱特性化.实验结果表明,多项式项数为19项时,达到训练样本的均方根误差为1.7%,检测样本的均方根误差为1.9%.而包含15个隐层节点的单隐层BP神经网络结构为比较合理的网络结构,达到训练样本的均方根误差为1.3%,检测样本的均方根误差为1.5%.对彩色扫描仪的特征化处理,采用多项式回归法得到光谱特性化精度较低,采用BP神经网络模型能够实现更高的光谱特性化精度.     

基于平行因子分析法提高近红外无创血糖校正模型稳健性的研究

采用近红外光谱进行无创血糖检测时, 样品背景变动造成的预测集样本与校正集样本量测体系不一致的问题是导致预测精度低的原因之一. 提出一种将母体背景作为变量引入回归建模中, 结合各个母体背景下的样本光谱信息构建三维光谱矩阵以提高校正模型稳健性的分析方法. 将平行因子分析(PARAFAC)与多元线性回归(MLR)相结合, 对人体三层皮肤模型的蒙特卡罗模拟实验和葡萄糖水溶液及其混合物的离体实验进行了验证. 实验结果表明, 与传统的单一母体背景所建立的偏最小二乘模型相比, 将母体背景作为建模元素采用PARAFAC-MLR法所建立的校正模型具有更好的预测能力和稳健性.     

热重量法分析石墨成分

研究了用热重分析仪进行石墨成分分析的方法，摸索了仪器的适宜分析条件，并对试验结果进行了讨论。针对仪器系统误差对挥发分测定的影响，采用一无线性回归对测量数据进行了校正。方法的精密度和准确度试验证明，用热重量法可有效地应用于日常检验工作。     

色温及亮度可调的四基色LED混合白光的方法研究

为满足配置高像素摄像头的成像设备进行成像质量检测时对照明环境的需求,本文研究了色温及亮度可调的LED光源配色技术及光源开发。选取红光、绿光、冷白、暖白LED作为四基色,设计实验装置实现色温及亮度连续可调白光光源的开发。本文研究的配色技术以四基色光源在满功率工作状态下的光谱功率分布、色品坐标和不同功率工作状态下光亮度为输入参数,依据格拉斯曼定律,以目标色温在黑体轨迹上的色坐标为函数值,以参与配光四基色的色坐标为系数、相对光亮度为变量,建立四基色配光模型。模型求解中首先以色差ΔUV≤0.005作为第一优化目标,进行循环校正直至得到的混合光色与同色温黑体颜色的视觉匹配;然后以显色指数Ra≥90作为第二优化目标,进行循环校正直至相关色温误差控制在100K以内。对开发光源的测试结果表明:使用文中开发的色温及亮度可调的混光方法,可在达到目标色温与亮度要求的情况下实现色差ΔUV≤0.003,显色指数R_a≥91。     

基于净信号的混合校正方法用于复杂生物样品光谱的定量分析和物理解析

对于复杂生物样品的光谱定量分析,单独应用偏最小二乘回归(PLSR)不易获得被测量信息的物理解析。为此建立了一种基于被测量净信号(NAP)的混合校正模型(NAP-PLSR),并应用于离体和在体实验,进行了葡萄糖含量的近红外光谱定量分析和物理解析研究。实验结果表明,通过NAP-PLSR模型获得的净信号灵敏度曲线易于分辨,能够提取到与葡萄糖分子吸收有关的1 100~1 300nm和1 500~1 800nm两波段信息,提高了模型精度的同时可获得有效的物理解析。     

消除原子吸收光谱分析中干扰效应的正交多项式回归设计

应用正交多项式回归设计的方法,对原子吸收光谱分析中的干扰效应进行计算消除的研究。通过实验建立了针对高纯 Eu2O3中钙含量测定时消除干扰的最优回归方程,回归方程精度为 1.32× 10－ 2,对高纯 Eu2O3中钙含量测定,计算结果与实际含量基本一致,相对误差为 1％～ 4％。     

面向绘画艺术品复制的多光谱图像数字修复技术研究

绘画艺术品作为人类文明的瑰宝,有着不可估量的艺术价值,然而受人为、意外以及自然环境等因素的影响,会有不同程度的损坏。面向艺术品复制的多光谱图像数字修复,目的是通过采用多光谱技术获取客观准确的艺术品颜色信息并用于彩色图像数字修复,进而用于艺术品的数字典藏、高保真图像复制,满足人们对绘画艺术品颜色准确性与结构完整性的需求。论文研究了多光谱成像技术及其在彩色数字图像修复上的应用,通过多光谱成像系统获取多通道图像,经过图像配准、滤噪、重构与还原,可获得色彩丰富、颜色逼真的多光谱图像,以此作为彩色数字图像进行数字修复,修复质量优于传统获取的RGB图像,取得了较好的修复结果。主要内容： 1.探讨了基于多光谱的绘画艺术品数字修复技术理论框架及面向艺术品复制的需要,对其中的关键问题进行了阐述。基于多光谱成像技术进行图像的获取满足高保真复制对颜色准确性与结构完整性的需求,探讨了其中涉及的关键技术。 2.分析了多光谱图像在颜色复制、图像高保真复制要求方面的优势,探讨了光谱成像的原理,对基于训练样本的多通道图像光谱重构技术进行了研究,提出了针对非线性成像系统基于主成份和多项式回归法的光谱重构算法,对光谱重构精度和色度精度有一定的改善。 3.分析了光谱反射率重构过程中传统训练样本对样本选择的局限性——不能综合反映颜色的光谱特性和色度特性,训练样本的相似性较差。提出基于光谱反射率空间相关性最小以及色度空间相似性聚类的训练样本选择方法,综合考虑了样本的光谱特性和色度特性,实验结果表明,光谱重构精度有一定改善。 4.对面向高保真图像复制的有破损绘画艺术品图像修复问题进行了分析,从3个方面分析了彩色图像的数字修复方法,并针对彩色图像修复时带来的伪彩现象提出了优化方法,提出了基于图像分解的改进算法和彩色图像修复流程,实验结果表明,图像修复的结果明显有所改善,修复图像的均方误差、峰值信噪比和结构相似度统计评价指标明显提高,适合于面向绘画艺术品高保真图像复制的数字修复,为多光谱图像修复奠定了基础。     

主成分回归rFIA同时测定磷和硅的应用研究

以钼酸铵-sb^3^ -抗坏血酸为显色体系,利用rFIA(反相流动注射分析)分光光度法,对磷和硅的同时测定进行了实验研究;根据主成分回归法具有用较少的独立成分说明多个变量所提供的信息,有效降低噪声影响的特点,对实验所得数据进行了主成分回归处理,并建立起校正模型;结果表明,该模型在磷和硅的同时测定中是准确可行的。     

化妆品痕迹物证差异性的比对分析

本文以口红和指甲油为研究对象,共收集了21个同一品牌不同型号颜色相近的口红样本和30个三种颜色、不同品牌不同型号的指甲油样本。使用显微分光分光度计测定样本的色度数据并结合色度学理论计算色差;使用气质联用法对色差很小样本的总离子流图进行比对分析和结构解析,并进一步对总离子流图差异较小的样本选择邻苯二甲酸酯这类特定杂质结构差异进行比对分析。实验结果表明:210份口红样本的色差数据,色差小于1.5的数据为5份,占总比对样本数的2.38%;147份指甲油样本的色差数据,色差小于1.5的数据为6份,占总比对样本数的4.08%。大部分色差很小的样本之间总离子流图谱有较大差别,少部分色差和总离子流图差异均较小样本的特定杂质邻苯二甲酸酯的结构式和质谱图存在明显差异,从而可将口红、指甲油样本分别加以区分,为口红、指甲油的比对分析提供了新思路和新手段。     

多波长数据线性回归法在火焰原子吸收同时测定Fe,Pt中的应用

用火焰原子吸收法在２７１．９０３ｎｍ处测定Ｆｅ时Ｐｔ对其吸收线发生谱线重叠干扰，本实验用多波长数据线性回归法理想地校正了Ｐｔ２７１．９０４ｎｍ，对Ｆｅ的２７１．９０３ｎｍ干扰，实现了用Ｆｅ元素空心阴极灯同进测定Ｆｅ，Ｐｔ两种元素。     

Regularized regression analysis of digitized molecular structures in organic reactions for quantification of steric effects

In organic chemistry, Comparative Molecular Field Analysis (CoMFA) can be defined as a regression analysis between reaction outcomes and molecular fields, wherein we can extract and visualize important structural information from the coefficients of the constructed regression models. In CoMFA, partial least‐squares (PLS) regression, which determines all coefficients in the model, is used for fitting the regression models. However, in organic reactions, steric effects are observed only near the reactive site, indicating that a large number of regression coefficients in the CoMFA of organic reactions should be assigned as 0. The regularized regression method, LASSO/Elastic Net, allows us to fit the regression model while assigning 0 values to unimportant coefficients. Although LASSO/Elastic Net should be suitable for CoMFA, there is no example of its use for organic reaction analysis. Herein, we examine the performance of LASSO/Elastic Net for the quantification of steric effects in CoMFA. We employ digitized molecular structures (the indicator field) as molecular fields that represent steric effects. LASSO/Elastic Net regressions provide highly interpretable models that include less noise than those from PLS regression. © 2017 Wiley Periodicals, Inc.     

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.     

Regression by L1 regularization of smart contrasts and sums (ROSCAS) beats PLS and elastic net in latent variable model

This paper proposes a regression method, ROSCAS, which regularizes smart contrasts and sums of regression coefficients by an L₁ penalty. The contrasts and sums are based on the sample correlation matrix of the predictors and are suggested by a latent variable regression model. The contrasts express the idea that a priori correlated predictors should have similar coefficients. The method has excellent predictive performance in situations, where there are groups of predictors with each group representing an independent feature that influences the response. In particular, when the groups differ in size, ROSCAS can outperform LASSO, elastic net, partial least squares (PLS) and ridge regression by a factor of two or three in terms of mean squared error. In other simulation setups and on real data, ROSCAS performs competitively. Copyright © 2009 John Wiley & Sons, Ltd.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

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.     

Lab分通道直方图的彩色图像分割算法及应用

本文针对传统灰度直方图分割法未综合考虑图像色度及纹理特征、对灰度差异不明显或灰度范围重叠的图像出现过分割或欠分割等问题，提出一种新的基于Lab分通道直方图的彩色图像分割算法，引入具有序列不相关性的亮度L通道、红绿a通道及蓝黄b通道3种分割依据，通过Newton插值法进行拟合运算，可针对不同亮度、色度属性图像进行自由选择，并运用邻域灰度值相匹配原则解决相邻目标区域边缘像素的准确匹配问题，分局部、分形态、分区域实现图像中不同目标的提取。经验证，该法对区域亮度差异较大图像及区域色度差异显著于亮度差异图像的分割效果，均优于传统灰度直方图分割法，极大提升了直方图分割算法的适用性。将其与经典Reinhard色彩迁移算法结合，将源图像感兴趣目标区域经分通道分割后分别进行色彩迁移变换，较好解决了经典Reinhard算法对图像非目标区域的干扰、色彩误传及阶调层次损失严重等问题，突破传统迁移算法只能整体着色的局限性，实现分区域精准着色。     

Data fusion in multivariate calibration transfer

We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.     

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.     

Multivariate calibration maintenance and transfer through robust fused LASSO

This article studies calibration maintenance and transfer to build a statistical model that is able to predict analyte concentrations by a set of spectra. Noticing that the wavelength atoms are naturally ordered in a meaningful way, we propose a novel robust fused LASSO (RFL) based on high‐dimensional sparsity techniques and a recent Θ‐IPOD technique for robustification. This new approach can attain simultaneous wavelength selection and grouping as well as outlier identification, without any human intervention. An efficient and scalable algorithm is developed on the basis of the alternating direction method of multipliers. The obtained RFL model is sparse and shows improved prediction performance over the LASSO and ridge regression. Our results reveal that wavelengths can be combined into blocks, in a smart manner, to enhance the interpretability and reliability for super‐resolution spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.