[Identification of Official Rhubarb Samples by Using PLS and Terahertz Time-Domain Spectroscopy].

The development of terahertz technology is attracting broad intention in recent years. The quality identification is important for the quality control of Chinese medicine production. In the present work, terahertz time-domain spectroscopy (THz-TDS) combined with partial least squares (PLS) were used for the identification model building and studied based on 41 official and unofficial rhubarb samples. First, the THz-TDS spectra of rhubarb samples were collected and were preprocessed by using chemometrics methods rather than transformed to absorption spectra. The identification models were then established based on the processed terahertz time domain spectra. The spectral preprocessing methods include Savitzky-Golay (S-G) first derivative, detrending, standard normal transformation (SNV), autoscaling, and mean centering. The identification accuracy of 90% was accomplished by using proper pretreatment methods, which was higher than the classified accuracy of 80% without any preprocessing for the time domain spectra. The component number of the PLS model was evaluated by leave-one-out cross-validation (LOOCV). The minimum values of the root-mean squared error of cross-validation (RMSECV) and root-mean squared error of prediction (RMSEP) were 0.076 6 and 0.169 0 by using mean centering method, respectively. The results of this work showed that the combination of terahertz time domain spectroscopy technology with chemometrics methods, as well as PLS can be applied for the recognition of genuine and counterfeit Chinese herbal medicines, as well as official and unofficial rhubarbs. The advantage of using terahertz time domain spectra directly with no transformation into absorption spectra is: (1) the thickness of samples could not be considered in the model establishment, and (2) the spectral processing was simplified. The proposed method based on the combination of THz-TDS and chemometrics proved to be rapid, simple, non-pollution and solvent free, suitable to be developed as a promising tool for quality control of many other Chinese herbal medicines.

[Determination of Carbaryl in Rice by Using FT Far-IR and THz-TDS Techniques].

Determination of carbaryl in rice by using Fourier transform far-infrared (FT- Far-IR) and terahertz time-domain spectroscopy (THz-TDS) combined with chemometrics was studied and the spectral characteristics of carbaryl in terahertz region was investigated. Samples were prepared by mixing carbaryl at different amounts with rice powder, and then a 13 mm diameter, and about 1 mm thick pellet with polyethylene (PE) as matrix was compressed under the pressure of 5-7 tons. Terahertz time domain spectra of the pellets were measured at 0.5~1.5 THz, and the absorption spectra at 1.6. 3 THz were acquired with Fourier transform far-IR spectroscopy. The method of sample preparation is so simple that it does not need separation and enrichment. The absorption peaks in the frequency range of 1.8-6.3 THz have been found at 3.2 and 5.2 THz by Far-IR. There are several weak absorption peaks in the range of 0.5-1.5 THz by THz-TDS. These two kinds of characteristic absorption spectra were randomly divided into calibration set and prediction set by leave-N-out cross-validation, respectively. Finally, the partial least squares regression (PLSR) method was used to establish two quantitative analysis models. The root mean square error (RMSECV), the root mean square errors of prediction (RMSEP) and the correlation coefficient of the prediction are used as a basis for the model of performance evaluation. For the R,, a higher value is better; for the RMSEC and RMSEP, lower is better. The obtained results demonstrated that the predictive accuracy of. the two models with PLSR method were satisfactory. For the FT-Far-IR model, the correlation between actual and predicted values of prediction samples (Rv) was 0.99. The root mean square error of prediction set (RMSEP) was 0.008 6, and for calibration set (RMSECV) was 0.007 7. For the THz-TDS model, R. was 0. 98, RMSEP was 0.004 4, and RMSECV was 0.002 5. Results proved that the technology of FT-Far-IR and THz- TDS can be a feasible tool for quantitative determination of carbaryl in rice. This paper provides a new method for the quantitative determination pesticide in other grain samples.

[Studies on Cancer Diagnosis by Using Spectroscopy Combined with Chemometrics].

Studies on cancer diagnosis using various spectroscopic methods combined with chemometrics are briefly reviewed. Elemental contents in serum samples were determined by inductively coupled plasma atomic emission spectroscopy (ICP-AES), bidirectional associative memory (BAM) networks were used to establish diagnosis models for the relationships between elemental contents and lung cancer, liver cancer, and stomach cancer, respectively. Near infrared spectroscopy (NIRS) is a non-destructive detection technology. Near infrared spectra of endometrial carcinoma samples were determined and spectral features were extracted by chemoometric methods, a fuzzy rule-based expert system (FuRES) was used for establishing diagnosis model, satisfactory results were obtained. We also proposed a novel variable selection method based on particle swarm optimization (PSO) for near infrared spectra of endometrial carcinoma samples. Spectra with optimized variable were then modeled by support victor machine (SVM). Terahertz technology is an emerging technology for non-destructive detection, which has some unique characteristics. Terahertz time domain spectroscopy (THz-TDS) was used for cervical carcinoma measurement. Absorption coefficients were calculated from the measured time domain spectra and then processed with derivative, orthogonal signal correction (PC-OSC) to reduce interference components, and then fuzzy rule-based expert system (FuRES), fuzzy optimal associative memory (FOAM), support victor machine (SVM), and partial least squares discriminant analysis (PLS-DA) were used for diagnosis model establishment. The above results provide useful information for cancer occurring and development, and provide novel approaches for early stage diagnosis of various cancers.

[A Terahertz Spectral Database Based on Browser/Server Technique].

With the solution of key scientific and technical problems and development of instrumentation, the application of terahertz technology in various fields has been paid more and more attention. Owing to the unique characteristic advantages, terahertz technology has been showing a broad future in the fields of fast, non-damaging detections, as well as many other fields. Terahertz technology combined with other complementary methods can be used to cope with many difficult practical problems which could not be solved before. One of the critical points for further development of practical terahertz detection methods depends on a good and reliable terahertz spectral database. We developed a BS (browser/server) -based terahertz spectral database recently. We designed the main structure and main functions to fulfill practical requirements. The terahertz spectral database now includes more than 240 items, and the spectral information was collected based on three sources: (1) collection and citation from some other abroad terahertz spectral databases; (2) collected from published literatures; and (3) spectral data measured in our laboratory. The present paper introduced the basic structure and fundament functions of the terahertz spectral database developed in our laboratory. One of the key functions of this THz database is calculation of optical parameters. Some optical parameters including absorption coefficient, refractive index, etc. can be calculated based on the input THz time domain spectra. The other main functions and searching methods of the browser/server-based terahertz spectral database have been discussed. The database search system can provide users convenient functions including user registration, inquiry, displaying spectral figures and molecular structures, spectral matching, etc. The THz database system provides an on-line searching function for registered users. Registered users can compare the input THz spectrum with the spectra of database, according to the obtained correlation coefficient one can perform the searching task very fast and conveniently. Our terahertz spectral database can be accessed at http://www.teralibrary.com. The proposed terahertz spectral database is based on spectral information so far, and will be improved in the future. We hope this terahertz spectral database can provide users powerful, convenient, and high efficient functions, and could promote the broader applications of terahertz technology.

[Application of terahertz technology in medical testing and diagnosis].

Terahertz science and technology is increasingly emphasized in science and industry, and has progressed significantly in recent years. There is an important aspect of attention in the application of terahertz technology to medicine. The overview of the terahertz characters, terahertz spectroscopy and terahertz imaging technology is introduced. This paper focuses on reviewing the use of and research progress in terahertz spectroscopy and terahertz imaging technology in medical testing and diagnosis. Furthermore, the problems to be solved and development directions of terahertz spectroscopy and terahertz imaging technology are discussed.

[Diagnosis of endometrial cancer based on logistic regression and near infrared spectroscopy].

Endometrial carcinoma is one of the most common gynecologic cancers. The present paper reports a new application of Logistic regression to building model of endometrial cancer. Near infrared (NIR) spectra was introduced. In our study, the NIR spectra of 77 specimens were pretreated by principal component-linear discriminant analysis (PC-LDA) and support vector machine discriminant analysis (SVM-DA). Latin partition method for selecting training and test sets was used to determine the significant parameters for Logistic regression model. From the predicted results of logistic regression model, both the categories of samples and the trends of samples belonging to other class were clear and concordant with the clinical result. The proposed procedure proved to be suitable to being developed as a noninvasive diagnosis method for cancer tissue.

[Determination of acidity in oranges based on emphatic orthogonal signal correction and principal component orthogonal signal correction].

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with PC-OSC algorithm, EOSC algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity in 112 orange samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with EOSC algorithm were satisfactory and the EOSC algorithm was not as susceptible to overfitting the data as PC-OSC algorithm. The correlation between actual and predicted values of calibration samples (Rc) obtained by the EOSC acidity model was 0.888 0, and prediction samples (Rp) was 0.885 6. The RMSEP was 0.081 65. The results proved that the portable NIR analyzer combined with EOSC algorithm and GRNN can be a feasible tool for the determination of acidity in oranges.

[Study on artificial neural network combined with near infrared spectroscopy for wood species identification].

In the present article, near infrared spectra of 89 wood samples of different geographical provenances and species were measured, and back propagation artificial neural networks(BPANN) and generalized regression neural network (GRNN) were used for modeling of wood species NIRS identifying. Parameters for two neural networks were chosen via analysis of variance, respectively; and networks were trained with optimum parameters. Considering the difference between spectra, spectra with different levels of white noise and different levels of bias were simulated and predicted by using the models built. It was found that both the two models had satisfactory prediction results, identification correct rates obtained by BPANN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 97%; correct rates obtained by GRNN model applied to spectra with bias level no higher than 2% and noise level no higher than 4% were above 99%.

[Early stage diagnosis of endometrial cancer based on near infrared spectroscopy and support vector machine].

Near-infrared spectroscopy combined with chemometrics methods for diagnosis of cancer has been reported in literatures. In our study, the NIR spectra of 77 specimens of different physiological stages of endometrium were collected. Spectral data were pretreated firstly by multiplicative scatter correction (MSC), orthogonal signal correction (OSC), and both of them, respectively, and then by SG smoothing. Latin partition method was used to select 3/4 samples as a training set, and the other 1/4 samples for test set. Support vector machine (SVM) model was built for classification, and the classification results was compared with that of partial least squares (PLS) model based on the same pretreatment methods. Samples of malignant, hyperplasia and normal endometrium were classified better by SVM (classification accuracy was 92%) than PLS (classification accuracy was 90%). The results suggested that classification accuracy was affected by pretreatment methods and models. SVM combined with endometrial tissue near infrared spectroscopy is expected to develop into a new approach to tumor diagnosis.

[Determination of acidity and vitamin C in apples using portable NIR analyzer].

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (R(c)) obtained by the acidity model was 0.999 4, and for prediction samples (R(p)) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, R(c) was 0.989 1, R(p) was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

[Quality analysis of olive oil and quantification detection of adulteration in olive oil by near-infrared spectrometry and chemometrics].

Discriminant analysis was used to classify 20 olive oil samples based on their near-infrared (NIR) spectra. The samples were successfully classified into two categories which are consistent with extra virgin olive oil and ordinary olive oil defined in the products. The NIR spectra of olive-oil mixtures containing colza oil, corn oil, peanut oil, camellia oil, sunflower oil, and poppy seed oil were collected, respectively. The volume percent of adulterants ranged from 0 to 100%. The best spectrum bands for analysis were selected before developing partial least-squares (PLS) calibration models. The relative errors of prediction ranged from -5.67% to 5.61%. Results showed that the method combined with chemometrics methods and near-infrared spectrometry is simple, fast and credible for qualitative and quantitative analyses of olive oil samples.

[Application of near infrared spectroscopy in screening Cordyceps militaris mutation strains and optimizing their fermentation process].

To fast screen high-yield Cordyceps militaris mutations strains and optimize their fermentation process, near infrared (NIR) spectroscopy technology combined with chemometrics has been applied to establishing models for simultaneous determination of adenosine, protein, polysaccharide and Cordyceps militaris acid contents in Cordyceps militaris powder samples. Fermentations were implemented in Erlenmeyer flask with 468 Cordyceps militaris mutations strains under various fermentation conditions and Cordyceps militaris powder samples were collected. Then their NIR spectra were obtained using UV-VIS-NIR spectrometer and their adenosine, protein, polysaccharide and Cordyceps militaris acid contents were determined using reference methods. Partial least squares (PLS) method was employed to model the relationships between NIR spectra and the above mentioned components' contents in Cordyceps militaris powder samples. Monte Carlo partial least square (MCPLS) was applied to identify the outliers and select suitable number of calibration samples. Moving window partial least square (MWPLS) was applied to select the characteristic wavelength of the components. The degree of the approaching (Da) was employed as criterion for selecting effective pretreatment methods investigated. The optimum models for determination of adenosine, protein, polysaccharide and Cordyceps militaris acid contents in Cordyceps militaris powder samples were obtained with the above mentioned optimization. Their correlation between actual and predictive values of calibration samples (Re) was 0.92943, 0.98479, 0.90785, and 0.85131, respectively. Their root mean square error of prediction set (RMSEP) was 0.66714, 0.02065, 0.01131, and 0.01159, respectively. The obtained results demonstrated that the fitting and the predictive accuracy were satisfactory. It is feasible to apply this method to screen the high yield Cordyceps militaris mutation strains and optimize their fermentation process.

Voltammetric behavior of the MCF-7 cell cytoplasm and the effect of taxol on voltammetric response.

The aims of this study were to find the electroactive species in the human breast cancer (MCF-7) cell cytoplasm causing a voltammetric response of the cells and to establish a simple and rapid measurement method to obtain strong and direct electrochemical responses objectively reflecting the cell viability. Ultrasonication was carried out for the electrochemical detection. The presence of guanine and xanthine in the MCF-7 cell eluent secreted by the living cells and in the MCF-7 cell cytoplasm was verified by HPLC assay with a DAD system and chemometric method. The concentrations of guanine and xanthine in the MCF-7 cell cytoplasm and the voltammetric response of the MCF-7 cell cytoplasm had higher levels than those of intact cell suspensions. Additionally, taxol caused a decrease of the voltammetric response of the cytoplasm and concentrations of xanthine and guanine in the cytoplasm. Therefore, the origin of the voltammetric response of the MCF-7 cytoplasm was driven by the alteration of the levels of xanthine and guanine, which were related to the cell viability. Thus, the voltammetric response of the ultrasonicated MCF-7 cell suspension could be used to monitor the MCF-7 cell growth and to evaluate the effectiveness of antitumor drugs on tumor suppression.

[Quantitative prediction of holocellulose, lignin, and microfibril angle of Chinese fir by BP-ANN and NIR spectrometry].

The amount of holocellulose, lignin, and microfibril angle of Chinese fir was predicted by using back-propagation neural network (BP-ANN) combined with near infrared (NIR) spectrometry. First, the data of original spectra were pretreated by Savitzky-Golay smoothing algorithm and the second derivative, then the data of near infrared spectrometry with 171 points were compressed to 86 points by using wavelet transform, and finally, the models were established by using BP-ANN. The models were validated using leave-n-out cross-validation approach, and the influences of the number of hidden neurons, learning rate, momentum, and epochs were discussed in the present paper. The prediction samples, which were not used in the model generation, were predicted by using the obtained models, the correlation coefficients (R2) of holocellulose, lignin and microfibril angle were 0.91, 0.90 and 0.87, respectively. The root mean square errors of prediction (RMSEP) of the established models were 0.86%, 0.33%, and 4.9%, respectively. The obtained results showed that the method is fast and nondestructive and can basically satisfy the requirement of quantitative analysis.

[Determination of hesperidin content in guogongjiu medicinal wine based on NIR spectrometry and least squares support vector machines].

Near-infrared spectroscopy (NIRS) combined with least squares support vector machines (LS-SVM) was used to establish a new method for the determination of the hesperidin content in guogongjiu medicinal wine. Firstly, training set was partitioned by Kernard-Stone (KS) algorithm. Secondly, spectral pretreatment methods were discussed in detail, comparing smoothing, rangescaling, autoscaling, first derivative, second derivative, along with those methods combined. Smoothing, first derivative and rangescaling were used for the pretreatment of the NIR spectra of guogongjiu medicinal wine. Thirdly, the effective interval was selected for 8211-8312 and 9712-9808 cm(-1) by synergy interval partial least squares (siPLS). Finally, the model was established by LS-SVM, the root mean square error of cross validation (RMSECV) is 0.001, root mean square error of prediction (RMSEP) is 0.004, and relative deviation of predicting set is less than 5%. It was compared with siPLS, radial basis function neural network (RBF-NN), and SVM, The result shows that the method is rapid, non-destructive, and credible. It is an effective measurement for determining the hesperidin content in guogongjiu medicinal wine.

[Determination of hesperidin in tangerine leaf by near-infrared spectroscopy with SPXY algorithm for sample subset partitioning and Monte Carlo cross validation].

It is very crucial that a representative training set can be extracted from a pool of real samples. Moreover, it is difficult to determine the adapted number of latent variables in PLS regression. For comparison, PLS models were constructed by SPXY, as well as by using the random sampling, duplex and Kennard-Stone methods for selecting a representative subset during the measurement of tangerine leaf. In order to choose correctly the dimension of calibration model, two methods were applied, one of which is leave-one-out cross validation and the other is Monte Carlo cross validation. The results present that the correlation coefficient of the predicted model is 0.9969, RMSECV is 0.7681, and RMSEP is 0.7369, which reveal that SPXY is superior to the other three strategies, and Monte Carlo cross validation can successfully avoid an unnecessary large model, and as a result decreases the risk of over-fitting for the calibration model.

A preliminary study on constructing a high-dimensional asynchronous spectrum to analyze bilinear data.

A novel approach to constructing high-dimensional asynchronous spectra (nD-Asyn) is proposed. Three theorems relevant to 1D slices of nD-Asyn are revealed. nD-Asyn is used to analyze bilinear data from mixtures containing multiple components obtained via hyphenated techniques. The spectral contribution of different components can be removed in a stepwise manner by increasing the dimensions of asynchronous spectra. As a result, the spectra of different components can be faithfully recovered even if the time-related profiles of different components severely overlap. Moreover, correct results can still be obtained via the nD-Asyn even if a considerable level of noise and baseline drift are present. The nD-Asyn approach is compared with MCR-ALS using different constraints in analyzing the data for a simulated and also for a real system. The nD-Asyn produced correct spectrum of every component. Only when complete constraints obtained from nD-Asyn method is utilized in the MCR-ALS calculation, correct spectra of all the components can be obtained. Thus, nD-Asyn can be used alone or in conjunction with MCR-ALS to analyze bilinear data containing contributions of multiple components.

Synthesis, Anti-Varicella-Zoster Virus and Anti-Cytomegalovirus Activity of 4,5-Disubstituted 1,2,3-(1H)-Triazoles.

BACKGROUND: Clinical drugs for herpesvirus exhibit high toxicity and suffer from significant drug resistance. The development of new, effective, and safe anti-herpesvirus agents with different mechanisms of action is greatly required. OBJECTIVE: Novel inhibitors against herpesvirus with different mechanisms of action from that of clinical drugs. METHODS: A series of novel 5-(benzylamino)-1H-1,2,3-triazole-4-carboxamides were efficiently synthesized and EC50 values against Human Cytomegalovirus (HCMV), Varicella-Zoster Virus (VZV) and Herpes Simplex Virus (HSV) were evaluated in vitro. RESULTS: Some compounds present antiviral activity. Compounds 5s and 5t are potent against both HCMV and VZV. Compounds 5m, 5n, 5s, and 5t show similar EC50 values against both TK+ and TK- VZV strains. CONCLUSION: 5-(Benzylamino)-1H-1, 2,3-triazole-4-carboxamides are active against herpesviruses and their activity is remarkably affected by the nature and the position of substituents in the benzene ring. The results indicate that these derivatives are independent of the viral thymidine kinase (TK) for activation, which is indispensable for current drugs. Their mechanisms of action may differ from those of the clinic anti-herpesvirus drugs.

A novel systematic absence of cross peaks-based 2D-COS approach for bilinear data.

A novel approach to use two-dimensional correlation spectroscopy (2D-COS) to analyze bilinear data is proposed. A phenomenon called Systematic Absence of Cross Peaks (SACPs) is observed in a 2D asynchronous spectrum. Two theorems relevant to SACPs have been derived. The SACP-based 2D-COS method has been successfully applied on analyzing bilinear data from mixed samples (including one model system and two real systems). Implicit isolated peaks can be identified and assigned to different components based on characteristic pattern of SACPs even if the time-related profiles of different components are severely overlapped. Based on the results of SACPs, spectra of pure components can be retrieved. Identification of SACPs can still be achieved in the presence of artifacts. Thus, neither noise nor baseline drift can produce significant influence on the results obtained from the approach described in this paper. We have used several well-established chemometric methods, including N-Findr, VCA, and MCR with various initial settings, on two systems that can be successfully solved using the 2D-COS method. The chemometric methods mentioned above cannot provide correct spectra of pure components because of severe problem of rotational ambiguity derived from severe overlapping of the time-related profiles. Only when the information from SACPs in 2D-COS is used as additional constraints in MCR calculation, correct spectra can be obtained. That is to say, the SACP-based 2D-COS method provides intrinsic information which is crucial in the analysis of chromatographic-spectroscopic and analogous data even if the time-related profiles of different components overlap severely.