A new hybrid strategy for constructing a robust calibration model for near-infrared spectral analysis

A new hybrid algorithm is proposed for construction of a high-quality calibration model for near-infrared (NIR) spectra that is robust against both spectral interference (including background and noise) and multiple outliers. The algorithm is a combination of continuous wavelet transform (CWT) and a modified iterative reweighted PLS (mIRPLS) procedure. In the proposed algorithm the spectral interference is filtered by CWT at the first stage then mIRPLS is proposed to detect the multiple outliers in the CWT domain. Compared with the original IRPLS method, mIRPLS does not need to adjust variable parameters to achieve optimum calibration results, which makes it very convenient to perform in practice. The final PLS model is constructed robustly because both the spectral interference and multiple outliers are eliminated. In order to validate the effectiveness and universality of the algorithm, it was applied to two different sets of NIR spectra. The results indicate that the proposed strategy can greatly enhance the robustness and predictive ability of NIR spectral analysis.     

Fluorinating hexagonal boron nitride into diamond-like nanofilms with tunable band gap and ferromagnetism

Cubic boron nitride (c-BN) possesses a number of extreme properties rivaling or surpassing those of diamond. Especially, owing to the high chemical stability, c-BN is desired for fabricating electronic devices that can stand up to harsh environments. However, realization of c-BN-based functional devices is still a challenging task due largely to the subtlety in the preparation of high-quality c-BN films with uniform thickness and controllable properties. Here, we present a simple synthetic strategy by surface fluorination of few-layered hexagonal boron nitride (h-BN) sheets to produce thermodynamically favorable F-terminated c-BN nanofilms with an embedded N-N bond layer and strong inbuilt electric polarization. Due to these specific features, the fluorinated c-BN nanofilms have controllable band gap by thickness or inbuilt and applied electric fields. Especially, the produced nanofilms can be tuned into substantial ferromagnetism through electron doping within a reasonable level. The electron-doping-induced deformation ratio of the c-BN nanofilms is found to be 1 order of magnitude higher than those of carbon nanotubes and graphene. At sufficient high doping levels, the nanofilm can be cleaved peculiarly along the N-N bond layer into diamond-like BN films. As the proposed synthesis strategy of the fluorinated c-BN nanofilms is well within the reach of current technologies, our results represent an extremely cost-effective approach for producing high-quality c-BN nanofilms with tunable electronic, magnetic, and electromechanical properties for versatile applications.     

Multi-Level Robustness Evaluation Approach: From Robustness Criterion to Adapted Algorithm of Dong

This paper proposes an innovative concept for robustness evaluation guided by two crucial aims: indubitable identification of the factors that significantly affect the LC method and avoidance of unnecessary time and money wasting. The first phase of the proposed strategy includes robustness screening during the method optimization. Initial assumptions of the method robustness can be tracked as the rate of the response change while the factors deviate within the expected range. Therefore, partial and total robustness criteria are calculated. If the results obtained are not satisfactory, re-optimization of the method should be considered. Otherwise, extensive robustness testing defined by experimental design and multi-level factors estimation should be performed to confirm the method robustness. Firstly, the important factors are investigated by the standard graphical (normal probability plots) and statistical (algorithm of Dong and error estimation based on a priori declared negligible effects) procedures. Since these approaches have several drawbacks, they can result in the appearance of false negative or false positive results. Thus, the modification of the statistical tests is advised in order to make the final conclusions. Special attention was dedicated to the advantages of the adapted algorithm of Dong (so-called 75 % approach) in the absence of the effect sparsity. The new approach is presented on the optimization and robustness testing of LC method for determination of ramipril and its five impurities. It is proved that the proposed strategy can perform an overall robustness estimation and successfully reveal all important factors.     

Bio‐Inspired Renewable Surface‐Initiated Polymerization from Permanently Embedded Initiators

Herein, we describe a simple and robust approach to repeatedly modify surfaces with polymer brushes through surface‐initiated atomic transfer radical polymerization (SI‐ATRP), based on an initiator‐embedded polystyrene sheet that does not rely on specific surface chemistries for initiator immobilization. The surface‐grafted polymer brushes can be wiped away to expose fresh underlying initiator that re‐initiates polymerization. This strategy provides a facile route for modification of molded or embossed surfaces, with possible applications in the preparation of fluidic devices and polymer‐embedded circuits.     

A robust regression approach for spectrophotometric signal analysis

The effectiveness of a regression method strongly depends on the characteristics of the considered regression problem. As a consequence, this makes it difficult to choose a priori the most appropriate algorithm for a given dataset. This issue is faced in this work through a novel regression approach based on the fusion of an ensemble of different regressors. In order to implement the proposed robust multiple system (RMS), four different fusion strategies are explored. In this context, we propose a novel fusion strategy named selection‐based strategy (SBS) that provides as output the estimate obtained by the regression algorithm (included in the ensemble) characterized by the highest expected accuracy in the region of the feature space associated with the considered model. The SBS is based not on a direct combination of the estimates yielded by all the regressors but on a selection mechanism that identifies the expected best available estimate. For such purpose, it exploits the accuracies of the regressors included in the ensemble in different portions of the input feature space. The experimental assessment of the RMS was carried out on three different datasets: a wine, an orange juice, and an apple datasets. The obtained experimental results suggest that, in general, the fusion of an ensemble of different regression algorithms leads to a regression process that is more robust and sometimes also more accurate than traditional regression methods. In particular, the proposed SBS method represents an effective solution to carry out the fusion process. Copyright © 2012 John Wiley & Sons, Ltd.     

Using Sub-Band Reconstruction in Wavelet Space and Fourier Transform to Extract Local Features from Analytical Signals Exactly and Straightforwardly

To cope with the problem of frequency aliasing in Mallat algorithm, which makes traditional discrete wavelet transform (DWT) inappropriate for feature extraction in some cases, an improved algorithm composed of sub-band reconstruction and Fourier transform is suggested through which the original signal could be split into a series of sub-bands of different frequencies with little distortion both in time and frequency domains. This strategy is developed to extract local features from analytical signals accurately as well as straightforwardly. Some NIR spectra have been selected as examples to demonstrate the availability and application of the proposed method.     

加权最小二乘支持向量机稳健化迭代算法及其在光谱分析中的应用

为克服光谱分析中异常训练样本的影响, 提出了一种加权最小二乘支持向量机(WLS-SVM)的稳健化迭代算法. 针对原始WLS-SVM在收敛性和稳健性方面的不足, 提出了一种新的求取回归误差的方法, 从而从根本上解决了WLS-SVM的收敛性问题; 同时对原始算法求权值的步骤进行了修正, 采用回归误差的中值作为计算加权值的比较基准, 大幅度提高了WLS-SVM的稳健性. 将算法应用于光谱定量分析中, 实验结果证明了该方法是收敛的, 并且崩溃点在35%左右, 是一种有效的稳健建模方法.     

Simultaneous wavelength selection and outlier detection in multivariate regression of near-infrared spectra.

Near-infrared (NIR) spectrometry will present a more promising tool for quantitative measurement if the robustness and predictive ability of the partial least square (PLS) model are improved. In order to achieve the purpose, we present a new algorithm for simultaneous wavelength selection and outlier detection; at the same time, the problems of background and noise in multivariate calibration are also solved. The strategy is a combination of continuous wavelet transform (CWT) and modified iterative predictors and objects weighting PLS (mIPOW-PLS). CWT is performed as a pretreatment tool for eliminating background and noise synchronously; then, mIPOW-PLS is proposed to remove both the useless wavelengths and the multiple outliers in CWT domain. After pretreatment with CWT-mIPOW-PLS, a PLS model is built finally for prediction. The results indicate that the combination of CWT and mIPOW-PLS produces robust and parsimonious regression models with very few wavelengths.     

径向基函数神经网络用于重叠色谱峰解析

 在高斯基径向基函数神经网络 (RBFNN)学习算法中引入了鲁棒性和随机全局寻优的两阶段遗传算法 :结构学习和参数优化。通过两阶段学习算法的交替使用 ,使网络具有结构自学习和参数优化的能力 ,而后将网络应用于组分数未知的重叠色谱峰解析。该方法具有不需人为干预 ,可自动确定网络结构即组分数的优点 ;并且解析精度较高 ,适用于多组分重叠色谱峰的解析 ;对完全重叠色谱峰也具有良好的解析能力。     

基于周期调节的随机共振算法对化学弱信号的检测研究

提出了一种新的随机共振算法,通过对系统的周期进行调节以实现系统的共振.讨论了实现共振的系统参数,并对模拟信号和拉曼光谱实验信号进行了处理.结果表明,该方法不仅可方便地用于弱化学信号的处理,克服检出信号的滞后现象,而且对不同噪声水平的信号均有良好的适用性.     

基于改进排序遗传算法的径向基函数神经网络色谱峰解析

构造了以塔板模型为基函数的径向函数神经网络（P－RBFNN），为了使P－RBFNN具有结构重组能力，又在网络学习算法中引入鲁棒（Rubust)和随机全局最优的两阶段排序遗传算法：结构学习和进化。P－RBFNN结合改进的排序遗传算法很适合组分数未知的色谱（含重叠）峰解析。     

Geometrical features of the protein folding mechanism are a robust property of the energy landscape: a detailed investigation of several reduced models

The concept of a funneled energy landscape and the principle of minimal frustration are the theoretical foundation justifying the applicability of structure-based models. In simulations, a protein is commonly reduced to a C(alpha)-bead representation. These simulations are sufficient to predict the geometrical features of the folding mechanism observed experimentally utilizing a concise formulation of the Hamiltonian with low computational costs. Toward a better understanding of the interplay between energetic and geometrical features in folding, the side chain is now explicitly included in the simulations. The simplest choice is the addition of C(beta)-beads at the center-of-mass position of the side chains. While one varies the energetic parameters of the model, the geometric aspects of the folding mechanism remain robust for a broad range of parameters. Energetic properties like folding barriers and protein stability are sensitive to the details of simulations. This robustness to geometry and sensitivity to energetic properties provide flexibility in choosing different parameters to represent changes in sequences, environments, stability or folding rate effects. Therefore, minimal frustration and the funnel concept guarantee that the geometrical features are robust properties of the folding landscape, while mutations and/or changes in the environment easily influence energy-dependent properties like folding rates or stability.     

Miniature magnetic resonance system for point-of-care diagnostics

We have developed a next generation, miniaturized platform to diagnose disease at the point-of-care using diagnostic magnetic resonance (DMR-3). Utilizing a rapidly growing library of functionalized magnetic nanoparticles, DMR has previously been demonstrated as a versatile tool to quantitatively and rapidly detect disease biomarkers in unprocessed biological samples. A major hurdle for bringing DMR to the point-of-care has been its sensitivity to temperature variation. As an alternative to costly and bulky mechanisms to control temperature, we have implemented an automated feedback system to track and compensate for the temperature drift, which enables reliable and robust DMR measurements in realistic clinical environments (4-50 °C). Furthermore, the new system interfaces with a mobile device to facilitate system control and data sharing over wireless networks. With such features, the DMR-3 platform can function as a self-contained laboratory even in resource-limited, remote settings. The clinical potential of the new system is demonstrated by detecting trace amounts of proteins and as few as 10 bacteria (Staphylococcus aureus) in a short time frame (<30 min).     

A low-cost efficient online derivatization system for the determination of saccharides by high-performance liquid chromatograph-ultraviolet detector

In this study, a low-cost efficient online derivatization system was developed which allows for the detection of various types of mono- and oligo-saccharides only utilizing high-performance liquid chromatography (HPLC)-ultraviolet detector (UV) system. In the proposed method, phenylhydrazine was used as the derivatization reagent and directly spiked in the mobile phase, allowing for the separation and detection of mono- and oligosaccharides in an accessible instrument system (HPLC-UV). And the online derivatization design of the proposed method has significantly reduced the potential harm of derivatization reagents to the analysts. Furthermore, critical chromatographic parameters were optimized via the Box-Behnken design strategy, culminating in the ideal response for saccharides. Finally, the methodology validation of the proposed method was conducted. The proposed method showed satisfactory linear ranges with acceptable correlation coefficients (R² > 0.99), outstanding accuracy (Recovery: 95.3%–105.6%), high intra-day precision (relative standard deviation [RSD]: 1.4%–7.1%) and inter-day precision (RSD: 2.0%–7.4%). The robustness and ruggedness of the proposed method were proved as the recovery values in the range of 95.0%–104.6% and 95.1%–104.8% for robustness and ruggedness, respectively. These satisfactory validation results confirm the applicability and reliability of the proposed method for the analysis of saccharides in various complex real-world samples.     

Asymmetric least squares for multiple spectra baseline correction

In this paper, based on asymmetric least squares smoothing, a new algorithm for multiple spectra baseline correction is proposed. By means of the similarity among the multiple spectra, the algorithm estimates the baselines by penalizing the differences in the baseline corrected signals, which makes the algorithm possible to eliminate scatter effects on the spectra. In addition, a relaxation factor which measures the similarity of the baseline corrected spectra is incorporated into the optimization model and an alternate iteration strategy is used to solve the optimization problem. The proposed algorithm is fast and can output multiple baselines simultaneously. Experimental results on both simulated data and real data demonstrate the effectiveness and efficiency of the algorithm.     

基于GPU计算的并行PLS算法研究与实现

偏最小二乘算法(PLS)是与红外、近红外光谱分析结合使用最为广泛的化学计量学算法,然而当前PLS算法通常采用单线程方式实现,当校正模型数量多或样本数量大、波长点数和主成分数较多,模型需对光谱预处理和波长选择方法反复优化时,计算十分缓慢。为大幅提高建模速度,该文提出了一种基于图形处理器(GPU)的并行计算策略,利用具有大规模并行计算特性的GPU作为计算设备,结合CUBLAS库函数实现了基于GPU并行的PLS建模算法(CUPLS)。利用近红外光谱数据集进行性能对比实验,结果表明CUPLS建模算法较传统单线程实现的PLS算法,加速比可达近42倍,极大地提升了化学计量学算法的建模效率。该方法亦可用于其它化学计量学算法的加速。     

NMR data visualization,processing, and analysis on mobile devices

Touch‐screen computers are emerging as a popular platform for many applications, including those in chemistry and analytical sciences. In this work, we present our implementation of a new NMR ‘app’ designed for hand‐held and portable touch‐controlled devices, such as smartphones and tablets. It features a flexible architecture formed by a powerful NMR processing and analysis kernel and an intuitive user interface that makes full use of the smart devices haptic capabilities. Routine 1D and 2D NMR spectra acquired in most NMR instruments can be processed in a fully unattended way. More advanced experiments such as non‐uniform sampled NMR spectra are also supported through a very efficient parallelized Modified Iterative Soft Thresholding algorithm. Specific technical development features as well as the overall feasibility of using NMR software apps will also be discussed. All aspects considered the functionalities of the app allowing it to work as a stand‐alone tool or as a ‘companion’ to more advanced desktop applications such as Mnova NMR. Copyright © 2015 John Wiley & Sons, Ltd.     

Development and validation of an LC method for the determination of emtricitabine and related compounds in the drug substance

A robust, precise, sensitive, linear, and simple RP LC method coupled with UV for the determination of emtricitabine or 2′,3′‐dideoxy‐5‐fluoro‐3′‐thiacytidine (FTC) and its related substances is described. The method uses an RP C18 column (25 cm×4.6 mm i.d.), 5 μm kept at a temperature of 35°C. The mobile phases for gradient elution consist of ACN, phosphate buffer (pH 4.4), and water. The flow rate is 1.0 mL/min and UV detection is performed at 280 nm. A system suitability test (SST) was developed to verify the adequate performance of the chromatographic system. The developed method was further validated with respect to robustness, precision, sensitivity, and linearity. A central composite design was applied to examine the robustness of the method. The method shows good precision, sensitivity, linearity, and robustness. Three commercial FTC samples were examined using this method. This method is suitable to be used for the determination of related substances and assay of FTC.     

化学谱图重叠峰检测及基线校正

本文提出一种谱峰曲线与其一个阶差分曲线相结合的算法，经宽松变换处理后，用于区分化学谱图中谱峰的终点是否真正落回到基线上，并由此建立了一种具有健壮性的重叠峰检测方法和及时准确的谱图基线校正方法。由于此算法的阈值参数可以固定，从而保证了重叠峰检测较少地受到人为的影响，进一步提高了化学谱图数据处理的自动化水平。