Yan-Ping Zhou

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

The present study demonstrated the possibility of utilizing the ytterbium (Yb)‐based internal standard near‐infrared (NIR) spectroscopic measurement technique coupled with multivariate calibration for quantitative analysis of tea, including total free amino acids and total polyphenols in tea. Yb is a rare earth element aimed to compensate for the spectral variation induced by the alteration of sample quantity during the spectral measurement of the powdered samples. Boosting was invoked to be combined with least‐squares support vector regression (LS‐SVR), forming boosting least‐squares support vector regression (BLS‐SVR) for the multivariate calibration task. The results showed that the tea quality could be accurately and rapidly determined via the Yb‐based internal standard NIR spectroscopy combined with BLS‐SVR method. Moreover, the introduction of boosting drastically enhanced the performance of individual LS‐SVR, and BLS‐SVR compared favorably with partial least‐squares regression. Copyright

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright

A robust boosting regression tree with applications in quantitative structure-activity relationship studies of organic compounds.

A regression tree (RT) was extensively utilized in quantitative structure-activity relationship studies (QSAR), due to its inherently promising attributes. The issues of instability and inclination to overfitting and suboptima, however, often occur in RT. In the present study, a robust version of boosting was invoked to simultaneously improve the stability and generalization ability of RT, forming a new method called robust boosting regression tree (RBRT). RBRT works by sequentially employing the RT method to model the robustly reweighted versions of the original training set and then aggregating these resultant predictors via weighted median. The designed RBRT was applied to predict the bioactivities of flavoniod derivatives and the anti-HIV activities of HIV-1 inhibitors, compared with boosting RT (BRT) and RT. The results of these two data sets demonstrated that the introduction of robust boosting drastically enhances the stability and generalization ability of RT, and RBRT is superior to BRT in QSAR studies.

Rapid and non-destructive discrimination of tea varieties by near infrared diffuse reflection spectroscopy coupled with classification and regression trees

The current study attempted to rapidly and non-destructively discriminate the diverse varieties of tea (that is, Biluochun, Longjing, Maojian, Qihong, Tieguanyin, and Yinzhen) via utilizing near infrared (NIR) diffuse reflectance spectroscopy coupled with pattern recognition strategies. Before the recognition analysis, the original NIR spectra were pre-processed by second derivative treatment followed by informative wavenumber interval location. And then, non-linearity detection and outlier diagnosis were performed. When pattern recognition referred, principal component analysis (PCA) was firstly applied to ascertain the discrimination possibility with the NIR spectra. Classification and regression trees (CART), compared with linear discriminant analysis (LDA), and partial squares-discriminant analysis (PLS-DA), was then employed for establishing the discrimination rule. Experimental results showed that the tea quality could be accurately, rapidly, and non-invasively identified via NIR spectroscopy coupled with CART. Key words : Near infrared diffuse reflection spectroscopy, classification and regression trees, and tea variety discrimination.

Partial least-squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with 1H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright

Robust variable selection based on bagging classification tree for support vector machine in metabonomic data analysis

In metabonomics, metabolic profiles of high complexity bring out tremendous challenges to existing chemometric methods. Variable selection (ie, biomarker discovery) and pattern recognition (ie, classification) are two important tasks of chemometrics in metabonomics, especially biomarker discovery that can be potentially used for disease diagnosis and pathology discovery. Typically, the informative variables are elicited from a single classifier; however, it is often unreliable in practice. To rectify this, in the current study, bagging and classification tree (CT) were combined to form a general framework (ie, BAGCT) for robustly selecting the informative variables, based on the advantages of CT in automatically carrying out variable selection as well as measuring variable importance and the properties of bagging in improving the reliability and robustness of a single model. In BAGCT, a set of parallel CT models were established based on the idea of bagging, each CT providing some endowed information such as the splitting variables and their corresponding importance values. The informative variables can be successfully spied via inspecting the variable importance values over all CTs in BAGCT. Taking the promising properties of support vector machine (SVM) into account, we used the informative variables identified by BAGCT as the inputs of SVM, forming a new classification tool abbreviated as BAGCT‐SVM. A metabonomic dataset by hydrogen‐1 nuclear magnetic resonance from the patients with lung cancer and the healthy controls was used to validate BAGCT‐SVM with CT and SVM as comparisons. Results showed that BAGCT‐SVM with less number of variables can give better predictive ability than CT and SVM.

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

Serum Metabolic Profile Alteration Reveals Response to Platinum-Based Combination Chemotherapy for Lung Cancer: Sensitive Patients Distinguished from Insensitive ones

Most lung cancers are diagnosed at fairly advanced stages due to limited clinical symptoms. Platinum-based chemotherapy, either as single regimen or in combination with radiation, is one of the major recommendations for the patients. Earlier evaluation of the effectiveness of the chemotherapies is critical for developing better treatment plan given the toxicity of the chemotherapeutic reagents. Drug efficacy could be reflected in the systemic metabolism characteristics though knowledge about which remains scarce. In this study, serum metabolism influence of three types of commonly used platinum-based combination chemotherapy regimens, namely cisplatin with gemcitabine, vinorelbine or docetaxel, were studied using pattern recognition coupled with nuclear magnetic resonance techniques. The treated patients were divided into sensitive or insensitive subgroups according to their response to the treatments. We found that insensitive subjects can be identified from the sensitive ones with up-regulation of glucose and taurine but reduced alanine and lactate concentrations in serum. The combination chemotherapy of lung cancer is accompanied by disturbances of multiple metabolic pathways such as energy metabolism, phosphatidylcholine biosynthesis, so that the treated patients were marginally discriminated from the untreated. Serum metabolic profile of patients shows potential as an indicator of their response to platinum-based combination chemotherapy.