Random forests for functional covariates

We propose a form of random forests that is especially suited for functional covariates. The method is based on partitioning the functions' domain in intervals and using the functions' mean values across those intervals as predictors in regression or classification trees. This approach appears to be more intuitive to applied researchers than usual methods for functional data, while also performing very well in terms of prediction accuracy. The intervals are obtained from randomly drawn, exponentially distributed waiting times. We apply our method to data from Raman spectra on boar meat as well as near‐infrared absorption spectra. The predictive performance of the proposed functional random forests is compared with commonly used parametric and nonparametric functional methods and with a nonfunctional random forest using the single measurements of the curve as covariates. Further, we present a functional variable importance measure, yielding information about the relevance of the different parts of the predictor curves. Our variable importance curve is much smoother and hence easier to interpret than the one obtained from nonfunctional random forests.     

Parallelizing Nonlinear Least-Squares Regression with Application to Analyses of Microalgae

Nonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000?s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.     

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500?nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345?nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345?nm for carbohydrates, 1180–1590?nm and 1860–2094?nm for fat, and 1700–2345?nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.     

Hierarchy of equations to calculate the linear spectra of molecular aggregates: Time‐dependent and frequency domain formulation

In a recent publication (Ritschel et al., J. Chem. Phys. 2015, 142, 034115) we have derived a hierarchy of coupled differential equations in time domain to calculate the linear optical properties of molecular aggregates. Here, we provide details about issues concerning the numerical implementation. In addition we present the corresponding hierarchy in frequency domain.     

支持向量回归在人体血红蛋白无创检测中的应用

采用线性渐变滤光片(Linear variable filter, LVF),优化设计高性能、便携式的人体血液成分近红外检测设备,研究了支持向量回归(Support vector regression, SVR)模型对人体血红蛋白(Hemoglobin, Hb)的预测能力及稳定性,以实现贫血疾病的无创诊断.无创采集100位志愿者食指前端光谱信息并划分定标集、验证集1和2.应用网格搜索方法优选惩罚参数与核函数参数c=5.28, g=0.33,用以建立稳健的SVR模型.随后,分别对验证集1和2中Hb水平进行定量分析.实验结果表明: 预测标准偏差(RMSEP) 分别为10.20 g/L和10.85 g/L,相对预测标准偏差(R-RMSEP) 为6.85%和7.48%,测量精度较高且SVR模型对不同样品的适应性较强,基本满足临床检测要求.基于SVR算法自行设计的LVF型近红外光谱检测设备在贫血症的无创诊断中有着良好的应用前景.     

在线固相萃取-超高效液相色谱-线性离子阱串联质谱法直接测定水中10种藻类毒素

建立了全自动在线固相萃取-超高效液相色谱-线性离子阱串联质谱法直接测定水中10种藻类毒素的方法.利用程序实现多次进样,通过在线固相萃取对藻类毒素进行富集,然后切换六通阀,将富集的目标物冲洗至分析柱进行分离后,进入线性离子阱质谱检测.10种藻类毒素在相应的浓度范围内线性关系良好,相关系数均大于0.99,检出限在0.0015~0.0050μg/L之间,3个浓度水平(0.02、0.10和1.00μg/L)的加标回收率为83.7%~98.5%.结果表明,在线固相萃取极大简化了前处理过程,线性离子阱串联质谱法提高了痕量藻类毒素测定的灵敏度,增强子离子扫描(EPI)谱库的建立为藻类毒素的确证提供保障.本方法适用于水体中多种藻类毒素的快速确证和定量测定.     

基于拓扑指数对含氧有机化合物闪点的定量构效关系研究

闪点是衡量化合物易燃程度的物理量,也是可燃性液体的一个重要安全性指标。本文对含氧有机化合物(包括醇、酚、醚、醛、酮、羧酸、酯等)的闪点与分子结构之间的关系进行分析,提取并计算了顶点度-距离指数(VDI)、奇偶指数(OEI)、边度-距离指数(EDI)、分子体积参数(MVI)和氢键指示变量(HB)等分子结构参数,并结合分子隐氢图顶点数(N)的2/3次方(N~(2/3)),对训练集中81个含氧有机化合物的闪点进行定量结构-性质相关分析。结果表明,由N~(2/3)、VDI、EDI、OEI、MVI和HB等6个参数对闪点建立的多元线性回归模型的相关系数(R)为0.9909,标准偏差(s)为6.39K,平均相对误差(ARD)为1.60%。用该模型对测试集中20个含氧有机化合物的闪点进行了预测,ARD为1.84%。     

基于支持向量回归机的陀螺漂移预测模型

为了预测某导弹陀螺漂移趋势,以该陀螺漂移角速度时间序列为对象,建立了基于支持向量回归机的预测模型。针对该预测模型的特点,提出了支持向量预选取的模型优化方法。基于ε不敏感损失函数的支持向量回归机具有稀疏性,其结构由支持向量决定。因此从训练样本集中预选出有可能成为支持向量的样本,精简样本规模是提高该类支持向量回归机训练和预测效率的有效方法。针对该类支持向量回归机从分类和回归两个角度分析了支持向量的几何特征,提出了核函数空间免疫聚类的支持向量预选取方法并用于某导弹陀螺漂移预测模型的数据预处理。仿真结果表明优化后的预测模型运算量小、建模速度快,精度高。     

粘塑性损伤模型模拟准超塑性单轴拉伸行为

发展了Chaboche粘塑性本构模型的大变形隐式算法，用损伤(DM)和无损伤(NDM)模型模拟准超塑性单轴拉伸。发现变形过程可分为三个阶段：均匀变形、颈缩发展、断裂破坏阶段。DM可准确模拟前两个阶段变形，NDM只能较好地模拟均匀变形阶段，表明DM可以较精确地描述稳定发展的动态过程。由于有限元方法只能描述连续介质，因此对于断裂破坏阶段，NDM模拟载荷大于试验结果，DM的载荷小于试验结果，这是由高应变速率敏感性造成。DM能够描述试验中出现地多处颈缩现象，局部应变速率分布随时间演化反映了颈缩发展程度。严重颈缩部位的距离代表着超塑性变形能力，距离越大，抗颈缩能力越好。