Genetic algorithm‐based wavelength selection method for spectral calibration

In this paper, we propose a genetic algorithm‐based wavelength selection (GAWLS) method for visible and near‐infrared (Vis/NIR) spectral calibration. The objective of GAWLS is to construct robust and predictive regression models by selecting informative wavelength regions. To demonstrate the ability of the proposed method, regression models for soil properties and sugar content of apples are constructed by using GAWLS and other variable selection methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Enhanced genetic operators design for waveband selection in multivariate calibration based on NIR spectroscopy

Nowadays, the techniques employed in data acquisition provide huge amounts of data. Some parts of the information are related to the others, making dimensionality reduction desirable, and losing less information as much as possible, in order to decrease computational times and complexity when applying any ensuing data mining technique. Genetic algorithms offer the possibility of selecting which variables contain the most relevant information to represent all the original ones. The traditional genetic operators seem to be too general, leading to results that could be improved by means of designed genetic operators that employ some available problem‐specific information. Especially, when dealing with calibration by means of near‐infrared spectral data, which use to contain thousands of variables, it is known that not isolated wavelengths but wavebands allow a more robust model design. This aspect should be taken into account when crossing individuals. We propose three crossover operators specifically designed for calibration with near‐infrared spectral data, based on a pseudo‐random two‐point crossover, where the first point is chosen randomly, and the selection of the second point is guided by problem‐specific information. We compare their performance with that of state‐of‐the‐art operators. We combine these new genetic algorithm‐based variable selection designs with partial least squares regression and fuzzy systems based calibration. Our benchmark consists of two real‐world high‐dimensional data sets, corresponding to polyetheracrylat, where hydroxyl number, viscosity, and acidity are on‐line monitored; and melamine resin production, where the chilling point (CP) is considered in order to regulate the condensation. We show that designed operators promote wavebands selection, achieve better‐quality solutions, and converge faster and smoother than state‐of‐the‐art operators. Copyright © 2014 John Wiley & Sons, Ltd.     

Heuristic-based tabu search algorithm for folding two-dimensional AB off-lattice model proteins

The protein structure prediction problem is a classical NP hard problem in bioinformatics. The lack of an effective global optimization method is the key obstacle in solving this problem. As one of the global optimization algorithms, tabu search (TS) algorithm has been successfully applied in many optimization problems. We define the new neighborhood conformation, tabu object and acceptance criteria of current conformation based on the original TS algorithm and put forward an improved TS algorithm. By integrating the heuristic initialization mechanism, the heuristic conformation updating mechanism, and the gradient method into the improved TS algorithm, a heuristic-based tabu search (HTS) algorithm is presented for predicting the two-dimensional (2D) protein folding structure in AB off-lattice model which consists of hydrophobic (A) and hydrophilic (B) monomers. The tabu search minimization leads to the basins of local minima, near which a local search mechanism is then proposed to further search for lower-energy conformations. To test the performance of the proposed algorithm, experiments are performed on four Fibonacci sequences and two real protein sequences. The experimental results show that the proposed algorithm has found the lowest-energy conformations so far for three shorter Fibonacci sequences and renewed the results for the longest one, as well as two real protein sequences, demonstrating that the HTS algorithm is quite promising in finding the ground states for AB off-lattice model proteins.     

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。     

A wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared spectral quantitative analysis

In this paper, we proposed a wavelength selection method based on random decision particle swarm optimization with attractor for near‐infrared (NIR) spectra quantitative analysis. The proposed method was incorporated with partial least square (PLS) to construct a prediction model. The proposed method chooses the current own optimal or the current global optimal to calculate the attractor. Then the particle updates its flight velocity by the attractor, and the particle state is updated by the random decision with the new velocity. Moreover, the root‐mean‐square error of cross‐validation is adopted as the fitness function for the proposed method. In order to demonstrate the usefulness of the proposed method, PLS with all wavelengths, uninformative variable elimination by PLS, elastic net, genetic algorithm combined with PLS, the discrete particle swarm optimization combined with PLS, the modified particle swarm optimization combined with PLS, the neighboring particle swarm optimization combined with PLS, and the proposed method are used for building the components quantitative analysis models of NIR spectral datasets, and the effectiveness of these models is compared. Two application studies are presented, which involve NIR data obtained from an experiment of meat content determination using NIR and a combustion procedure. Results verify that the proposed method has higher predictive ability for NIR spectral data and the number of selected wavelengths is less. The proposed method has faster convergence speed and could overcome the premature convergence problem. Furthermore, although improving the prediction precision may sacrifice the model complexity under a certain extent, the proposed method is overfitted slightly. Copyright © 2015 John Wiley & Sons, Ltd.     

A new and efficient variable selection algorithm based on ant colony optimization. Applications to near infrared spectroscopy/partial least-squares analysis

A new variable selection algorithm is described, based on ant colony optimization (ACO). The algorithm aim is to choose, from a large number of available spectral wavelengths, those relevant to the estimation of analyte concentrations or sample properties when spectroscopic analysis is combined with multivariate calibration techniques such as partial least-squares (PLS) regression. The new algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors. The performance has been tested on a simulated system, where it shows a significant superiority over other commonly employed selection methods, such as genetic algorithms. Several near infrared spectroscopic experimental data sets have been subjected to the present ACO algorithm, with PLS leading to improved analytical figures of merit upon wavelength selection. The method could be helpful in other chemometric activities such as classification or quantitative structure-activity relationship (QSAR) problems.     

基于随机森林结合博弈论的特征选择算法在近红外光谱分类中的应用研究

针对近红外光谱中的噪声和冗余信息导致分类模型识别率低的问题,提出了随机森林结合博弈论的特征选择算法。该算法首先根据随机森林对特征重要性进行度量,优选出对分类具有一定相关性的特征;然后利用改进的夏普利值结合互信息计算优选特征的权重,从加权后的特征集合中去掉冗余得到最优特征子集。为了验证算法的有效性,将其应用于烟叶产地识别模型,实验结果表明,该文所提出的特征选择算法对烟叶产地识别效果较好,分类识别率可达95.88%。     

Bifluoride Ion Mediated SuFEx Trifluoromethylation of Sulfonyl Fluorides and Iminosulfur Oxydifluorides

SuFEx is a new‐generation click chemistry transformation that exploits the unique properties of S?F bonds and their ability to undergo near‐perfect reactions with nucleophiles. We report here the first SuFEx‐based procedure for the efficient synthesis of pharmaceutically important triflones and bis(trifluoromethyl)sulfur oxyimines from sulfonyl fluorides and iminosulfur oxydifluorides, respectively. The new process involves rapid S?F exchange with trifluoromethyltrimethylsilane (TMSCF₃) upon activation by potassium bifluoride in anhydrous DMSO. The reaction tolerates a wide selection of substrates and proceeds under mild conditions without need for chromatographic purification. A tentative mechanism is proposed involving nucleophilic displacement of S?F by the trifluoromethyl anion via a five‐coordinate intermediate. The utility of late‐stage SuFEx trifluoromethylation is demonstrated through the synthesis and selective anticancer properties of a bis(trifluoromethyl)sulfur oxyimine.     

A Stable Room‐Temperature Luminescent Biphenylmethyl Radical

There is only one family of room‐temperature luminescent radicals, the triphenylmethyl radicals, to date. Herein, we synthesize a new stable room‐temperature luminescent radical, (N‐carbazolyl)bis(2,4,6‐tirchlorophenyl)methyl radical (CzBTM), which has improved properties compared to the triphenylmethyl radicals. X‐ray crystallography, electron paramagnetic resonance spectroscopy, and magnetic susceptibility measurements confirmed the radical structure. CzBTM shows room‐temperature deep‐red to near‐infrared emission in various solutions. Both thermal and photo stability were significantly enhanced by the replacement of trichlorobenzene by the carbazole moiety. The electroluminescence results of CzBTM verify its potential application to circumvent the problem of triplet harvesting in traditional fluorescent OLEDs. A new family of stable luminescent radicals based on CzBTM is anticipated.     

Water Confined in the Local Field of Ions

Interionic distances are shorter in concentrated ionic solutions, thus instigating the interaction and overlap of hydration shells, as ions become separated by only one or two layers of water molecules. The simultaneous interaction of water with two oppositely charged ions has, so far, only been investigated by computer simulation studies, because the isolated vibrational spectroscopic signature of these molecules remains undetected. Our combined near‐infrared spectroscopic and molecular dynamics simulation studies of alkali halide solutions present a distinct spectral feature, which is highly responsive to depletion of bulk water and merging of hydration shells. The analysis of this spectral feature demonstrates that absorption trends are in good agreement with the law of matching affinities, thus providing the first successful vibrational spectroscopic treatment of this topic. Combined with commonly observed near‐infrared bands, this feature provides a spectral pattern that describes some relevant aspects of ionic hydration.     

Tuning 2D Light Upconversion Emission by Modulating Phonon Relaxation

The photonic upconversion in rare earth atoms is widely used to convert “invisible” near infrared photons to “visible” photons with continuous wave light. By using a patterned substrate, upconversion become a route for creating new information‐incorporating security codes. The amount of information in the cipher increases in proportion to the number of emission colors as well as the pattern structure. Subsequently, changing the chemical composition of upconversion phosphors on 2 D substrates is required to manufacture information‐rich upconversion cryptography. In this study, we exploited temperature‐controlled thermal reaction on upconversion films deposited on a quartz substrate to prepare security information codes. Multiple color emission was generated from upconversion films as the result of inserting high‐frequency molecular oscillators into the film structures. Fourier‐transform infrared (FTIR) and time‐resolved study corroborated the mechanism of spectral variation of upconversion films.     

Ultrafast algorithm for designing focused combinational arrays

A novel greedy algorithm for the design of focused combinatorial arrays is presented. The method is applicable when the objective function is decomposable to individual molecular contributions and makes use of a heuristic that allows the independent evaluation and ranking of candidate reagents in each variation site in the combinatorial library. The algorithm is extremely fast and convergent and produces solutions that are comparable to and often better than those derived from the substantially more elaborate and computationally intensive stochastic sampling techniques. Typical examples of design objectives that are amendable to this approach include maximum similarity to a known lead (or set of leads), maximum predicted activity according to some structure-activity or receptor binding model, containment within certain molecular property bounds, and many others.     

Structure–electrical property study of anisotropic polyaniline films

The relationships of the structure and electrical properties of anisotropic HCl‐doped polyaniline (PANI) films cast from N,N′‐dimethylpropylene urea (DMPU) solutions and stretched to different draw ratios were studied. The anisotropic structure of the stretched PANI films was examined by X‐ray diffraction, near‐infrared wave‐guide coupling, and polarized infrared measurements. The PANI emeraldine base (EB) films cast from DMPU solutions had a single‐phase noncrystalline structure, and stretching of the films did not cause crystallization to occur. The transition moment angles of two weakly absorbing infrared bands were determined, and the Hermans' orientation functions for the PANI EB films were calculated. The PANI films were then doped with HCl, and the electrical properties were determined by impedance spectroscopy. With a specially designed test fixture, the in‐plane and through‐plane impedance was obtained. The conductivity along the stretch direction increased with orientation. The in‐plane conductivity was significantly higher than the through‐plane conductivity. © 2003 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 41: 823–841, 2003     

Supervised principal components: a new method for multivariate spectral analysis

The supervised principal components (SPC) method was proposed by Bair and Tibshirani for statistics regression problems where the number of variables greatly exceeds the number of samples. This case is extremely common in multivariate spectral analysis. The objective of this research is to apply SPC to near‐infrared and Raman spectral calibration. SPC is similar to traditional principal components analysis except that it selects the most significant part of wavelength from the high‐dimensional spectral data, which can reduce the risk of overfitting and the effect of collinearity in modeling according to a semi‐supervised strategy. In this study, four conventional regression methods, including principal component regression, partial least squares regression, ridge regression, and support vector regression, were compared with SPC. Three evaluation criteria, coefficient of determination (R²), external correlation coefficient (Q²), and root mean square error of prediction, were calculated to evaluate the performance of each algorithm on both near‐infrared and Raman datasets. The comparison results illustrated that the SPC model had a desirable ability of regression and prediction. We believe that this method might be an alternative method for multivariate spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Anomalous Light‐Induced Spin‐State Switching for Iron(II) Spin‐Crossover Molecules in Direct Contact with Metal Surfaces

Light‐induced spin‐state switching is one of the most attractive properties of spin‐crossover materials. In bulk, low‐spin (LS) to high‐spin (HS) conversion via the light‐induced excited spin‐state trapping (LIESST) effect may be achieved with a visible light, while the HS‐to‐LS one (reverse‐LIESST) requires an excitation in the near‐infrared range. Now, it is shown that those phenomena are strongly modified at the interface with a metal. Indeed, an anomalous spin conversion is presented from HS state to LS state under blue light illumination for Fe^II spin‐crossover molecules that are in direct contact with metallic (111) single‐crystal surfaces (copper, silver, and gold). To interpret this anomalous spin‐state switching, a new mechanism is proposed for the spin conversion based on the light absorption by the substrate that can generate low energy valence photoelectrons promoting molecular vibrational excitations and subsequent spin‐state switching at the molecule–metal interface.     

A consensus successive projections algorithm – multiple linear regression method for analyzing near infrared spectra

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.     

Growth of Hydrophilic CuS Nanowires via DNA‐Mediated Self‐Assembly Process and Their Use in Fabricating Smart Hybrid Films for Adjustable Chemical Release

Facile growth of CuS nanowires through self‐assembly and their application as building blocks for near‐infrared light‐responsive functional films have been demonstrated. It is found that DNA is a key factor in preparing the CuS material with defined nanostructure. An exclusive oriented self‐aggregate growth mechanism is proposed for the growth of the nanowires, which might have important implications for preparing advanced, sophisticated nanostructures based on DNA nanotechnology. By employing the hydrophilic CuS nanowire as an optical absorber and thermosensitive nanogel as guest reservoir inside alginate film, a new platform for the release of functional molecules has been set up. In vitro studies have shown that the hybrid film possesses excellent biocompatibility and the release rate of chemical molecules from the film could be controlled with high spatial and temporal precision. Our novel approach and the resulting outstanding combination of properties may advance both the fields of DNA nanotechnology and light‐responsive devices.     

A Low‐Computational‐Cost Strategy to Localize Points in the Slow Manifold Proximity for Isothermal Chemical Kinetics

Dimensionality reduction for the modeling of reacting chemical systems can represent a fundamental achievement both for a clear understanding of the complex mechanisms under study and also for the practical calculation of quantities of interest. To tackle the problem, different approaches have been proposed in the literature. Among them, particular attention has been devoted to the exploitation of the so‐called slow manifolds (SMs). These are lower dimensional hypersurfaces where the slow part of the evolution takes place. In this study, we present a low‐computational‐cost algorithm (based on a previously developed theoretical framework) for the localization of candidate points in the proximity of the SM. A parallel implementation (called DRIMAK) of such an approach has been developed, and the source code is made freely available. We tested the performance of the code on two model schemes for hydrogen combustion, being able to localize points that fall very close to the perceived SM with limited computational effort. The method can provide starting points for other more accurate but computationally demanding strategies; this can be a great help especially when no information about the SM is available a priori, and very many species are involved in the reaction mechanism.     

Partitioned partial least squares regression with application to a batch fermentation process

The performance of Partial Least Squares regression (PLS) in predicting the output with multivariate cross‐ and autocorrelated data is studied. With many correlated predictors of varying importance PLS does not always predict well and we propose a modified algorithm, Partitioned Partial Least Squares (PPLS). In PPLS the predictors are partitioned into smaller subgroups and the important subgroups with high prediction power are identified. Finally, regular PLS analysis using only those subgroups is performed. The proposed Partitioned PLS (PPLS) algorithm is used in the analysis of data from a real pharmaceutical batch fermentation process for which the process variables follow certain profiles during a specific fermentation period. We observed that PPLS leads to a more accurate prediction of the yield of the fermentation process and an easier interpretation, since fewer predictors are used in the final PLS prediction. In the application important issues such as alignment of the profiles from one batch to another and standardization of the predictors are also addressed. For instance, in PPLS noise magnification due to standardization does not seem to create problems as it might in regular PLS. Finally, PPLS is compared to several recently proposed functional PLS and PCR methods and a genetic algorithm for variable selection. More specifically for a couple of publicly available data sets with near infrared spectra it is shown that overall PPLS has lower cross‐validated error than PLS, PCR and the functional modifications hereof, and is similar in performance to a more complex genetic algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

A Comparison of Three Heuristic Algorithms for Molecular Docking

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