[Quantitative Analysis of Heave Mental Ion Based on Portable NIR Spectrometer].

By virtue of the characters of rapid analysis and simple pretreatment, near infrared reflectance spectroscopy technique is widely used in agriculture, medicine, environment, petrochemical and other fields. To satisfy the rapid on-site identification and analysis, portable near-infrared spectrometers have gained more and more attention. Because near infrared reflectance spectroscopy technique also is a green tool for multi-component analysis, the paper aims at investigating the feasibility for simultaneous quantitative analysis of various heavy metal ions in dilute solution using portable near infrared spectrometer. First, amino modified polymerized starch was used as adsorbent to enrich nickel ions and copper ions in diluted solution. Then, the diffuse reflectance spectra of amino modified polymerized starch samples were measured directly by portable near-infrared spectrometer. Furthermore, with the help of spectral preprocessing methods and partial least-squares regression, quantitative models were built from the near infrared diffuse reflectance spectra of amino modified polymerized starch enriched with heavy metal ions and reference concentrations. At last, the stability of the models was proved through cross validation and external validation. The results show that nickel ions and copper ions in diluted solution can be efficiently enriched by amino modified polymerized starch in the presence of other interfering ions. The adsorption rates for nickel ions and copper ions are 99.5% and 99.8%, respectively. Two robust models can be achieved after spectra processing and partial least squares regression. The spectra processing methods contains Continuous wavelet transform and multiplicative scatter correction combined with Savitzky-Golay. The obtained corresponding correlation coefficients of the two robust models are 0.981 9 and 0.965 4, respectively. Thus, simultaneous quantitative analysis of nickel ions and copper ions in the mixed diluted solution was achieved, and the detectable concentrations of nickel ions and copper ions are both as low as 3.0 mg·L(-1). The method not only improves the sensitivity of near infrared reflectance spectroscopy technique, but also demonstrates the feasibility for simultaneous quantitative determination of various heavy metal ions by using portable near infrared spectrometer. Moreover, the method may be a useful exploration to further broaden the application of near infrared reflectance spectroscopy technique.

[Near infrared spectroscopy and multivariate statistical process analysis for real-time monitoring of production process].

Near infrared diffusive reflectance spectroscopy has been applied in on-site or on-line analysis due to its characteristics of fastness, non-destruction and the feasibility for real complex sample analysis. The present work reported a real-time monitoring method for industrial production by using near infrared spectroscopic technique and multivariate statistical process analysis. In the method, the real-time near infrared spectra of the materials are collected on the production line, and then the evaluation of the production process can be achieved by a statistic Hotelling T2 calculated with the established model. In this work, principal component analysis (PCA) is adopted for building the model, and the statistic is calculated by projecting the real-time spectra onto the PCA model. With an application of the method in a practical production, it was demonstrated that a real-time evaluation of the variations in the production can be realized by investigating the changes in the statistic, and the comparison of the products in different batches can be achieved by further statistics of the statistic. Therefore, the proposed method may provide a practical way for quality insurance of production processes.

A multidimensional sensing device for the discrimination of proteins based on manganese-doped ZnS quantum dots.

Lab-on-a-nanoparticle: the triple-channel optical properties of Mn-doped ZnS quantum dots (fluorescence, phosphorescence, and light scattering) are explored to develop a multidimensional sensing device for the discrimination of proteins in a lab-on-a-nanoparticle approach.

[Classification of oils by attenuated total reflectance-Fourier transform infrared spectrometry combined with pattern recognition techniques].

In the present work, the combination of attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR) and pattern recognition, including principal components analysis (PCA) and hierarchical cluster analysis (HCA), is used as a fast and convenient analytical tool to classify oil samples. Twenty five samples including crude oils and fuel oils with different total contents of n-alkanes were analyzed. It was found that multiplicative scatter correction (MSC) and continuous wavelet transform (CWT) as a pretreatment method could improve the classification results of pattern recognition. The classification results were proved to be in agreement with the origin of the oil samples. The oils with high content of n-alkanes and those with low content were classified clearly by this developed method, but it still had some constraint to differentiating oils with little difference. The present work provides a feasible method for quick classification of oils, which can be used for the initial identification of spill oils and afford useful information for the further identification of the oils.

[A local regression method for near-infrared spectral quantitative analysis of tobacco samples].

A local regression method based on distance criterion in principal component (PC) space for near-infrared (NIR) spectral quantitative analysis was proposed. In this method, principal component analysis (PCA) is firstly utilized to extract the information of the NIR spectra, and then, the calibration subsets are individually selected for each prediction sample according to the distance between the sample and calibration samples in the PCs space. Finally, the PLS local model for every prediction sample is established individually and the prediction of the sample is done with the local model. It was found that the Euclidean distance can more effectively measure the similarity of the samples than Mahalanobis distance. With an application of the local regression method to the quantitative determination of chlorine and nicotine in tobacco samples, it is proved that the prediction precision of local regression method is better than that of global regression methods, especially in the situation of predicting the low concentration components.

[Application of near-infrared spectra in the determination of water soluble chloride ion in plant samples].

A new method was proposed to extract relevant information from near-infrared (NIR) spectra for multivariate calibration of water soluble chloride ion in complex plant samples. The method is a combination of discrete wavelet transform (DWT) and least squares support vector regression (LSSVR). After data compression and background removal in NIR spectra by DWT, the LSSVR approach was used to build NIR spectra regression models on the retained wavelet coefficients. Compared with partial least square regression (PLSR) and LSSVR, the proposed method is superior both in calculation speed and prediction accuracy.

[A novel method for the determination of inorganic ions in complex plant samples by near infrared spectroscopy].

Inorganic ions in plant samples can be determined by near-infrared (NIR) spectral technique, because they are combined with organic groups with NIR absorption. A method for the determination of inorganic ions in complex plant samples was established based on the combination of discrete wavelet transform (DWT) and NIR technique. In the proposed method, the raw NIR data and their wavelet coefficients are used for modeling and prediction of the contents of potassium in tobaccos by partial least square method (PLS). It is shown that there is almost no loss of spectral information with the NIR data compressed to 3.3% of its original size. The model based on wavelet coefficients is better than that based on the full NIR spectral range. With the improved method, accurate prediction can be achieved.

[Research on the nonlinear model of near infrared spectroscopy and the total sugar of tobacco samples].

Near infrared spectroscopy (NIR) is an instrumental method applied for rapidly measuring the NIR spectra of pulverized tobacco samples and computing the chemical compositions from the spectral data. In the present paper, a mixed algorithm was employed for building the nonlinear model of NIR and the total sugar of tobacco samples. The mixed algorithm was combined with Partial Least Squares (PLS) method and Artificial Neural Network (ANN). The model based on the mixed algorithm was divided into two parts: linear part and nonlinear part, and the corresponding model of each part was built respectively. Compared with the classical multivariate calibration methods such as Principle Component Regression (PCR), PLS and nonlinear PLS (NPLS), the proposed procedure performed much better. The results showed that the mixed algorithm could be used for the quantitative analysis of the total sugar in tobacco samples.

Wavelet: a new trend in chemistry.

Since 1989, wavelet transform (WT) has attracted much interest of chemists working on signal and image processing, and the WT-based techniques have been successfully applied to the chemical signal processing. This approach has been demonstrated as fast in computation with localization and having quick decay properties, in contrast to the popular methods existing, especially to the fast Fourier transform. More than 370 papers have been published up to the year 2002 which covered applications of WT in various fields of chemistry, including analytical chemistry, chemical physics, and quantum chemistry. In this paper, we report on applications of WT to data compression, data smoothing and denoising, baseline and background correction, resolution of multicomponent overlapping signals, regression and classification, and analytical images processing in analytical chemistry. Through this report we wish to induce greater interest of chemists in WT and to obtain greater benefits from using the WT-based techniques.

[Determination of tetracyclines antibiotics by reversed-phase high performance liquid chromatography].

A method for the determination of 7 tetracycline antibiotics (TCs) by reversed-phase high performance liquid chromatography is described. TCs were successfully separated on a Diamonsil C18 column (250 mm x 4.6 mm i.d., 5 microns) using methanol-acetonitrile-0.01 mol/L oxalic acid (pH 2.0) (11:22:67, volume ratio) as mobile phase at a flow rate of 0.8 mL/min and detected at 267 nm within 22 min. Effects of the pH value of the mobile phase, concentration of the mobile phase buffer, elution composition and detection wavelength on the response and retention were studied. Tetracycline (TC) and oxytetracycline(OTC) in medicinal tablets were quantitated by standard added method. It has been proven that the method is fast, accurate and suitable for routine analysis.