[Algae identification research based on fluorescence spectral imaging technology combined with cluster analysis and principal component analysis].

In order to explore rapid real-time algae detection methods, in the present study experiments were carried out to use fluorescence spectral imaging technology combined with a pattern recognition method for identification research of different types of algae. The fluorescence effect of algae samples is obvious during the detection. The fluorescence spectral imaging system was adopted to collect spectral images of 40 algal samples. Through image denoising, binarization processing and making sure the effective pixels, the spectral curves of each sample were drawn according to the spectral cube. The spectra in the 400-720 nm wavelength range were obtained. Then, two pattern recognition methods, i.e., hierarchical cluster analysis and principal component analysis, were used to process the spectral data. The hierarchical cluster analysis results showed that the Euclidean distance method and average weighted method were used to calculate the cluster distance between samples, and the samples could be correctly classified at a level of the distance L=2.452 or above, with an accuracy of 100%. The principal component analysis results showed that first-order derivative, second-order derivative, multiplicative scatter correction, standard normal variate and other pretreatments were carried out on raw spectral data, then principal component analysis was conducted, among which the identification effect after the second-order derivative pretreatment was shown to be the most effective, and eight types of algae samples were independently distributed in the principal component eigenspace. It was thus shown that it was feasible to use fluorescence spectral imaging technology combined with cluster analysis and principal component analysis for algae identification. The method had the characteristics of being easy to operate, fast and nondestructive.

[Spectra modulated surface plasmon resonance sensor based on side polished multi-mode optical fiber].

Surface plasmon resonance, which utilizes the resonance of optical evanescent wave with the metal surface plasmon wave, has been developed into a high sensitivity, rapid, label-less measurement method for chemical and biological analysis. In order to improve the spectral sensitivity in refractive index for a side polished fiber surface plasmon resonance sensor, the whole cladding layer and part of core of a multimode fiber was polished off. Additionally, an extra chrome layer with relatively high refractive index was coated on the polished zone before a gold film. The results showed that the sensor can measure the refractive index range from 1.333 to 1. 431 RIU, with the average spectral sensitivity of 4.11 x 10(3) nm RIU(-1), which is better than the reported results. Especially, in the refractive index range of 1. 417 1. 431 RIU, the sensitivity reaches to 1.09 x 10(4) nm RIU(-1). The minimum resolution of approximately 3.6 x 10(-5) RIU was estimated by a combination analysis with the sensor sensitivity and stability. The superiorities possessed by the proposed sensor in high sensitivity, wide detection range, small size and good stability and reproducibility, etc., make it a good candidate for food testing, environmental monitoring, biomedical testing and other related fields.

[Study on the application of empirical mode decomposition to noninvasive hemoglobin measurement by NIRS].

To increase the signal-to-noise ratio (SNR) of human near infrared (NIR) spectra, so as to improve the stability and precision of calibration model, the empirical mode decomposition (EMD) method was applied. Eighty-one fingertip absorption curves were collected, with the corresponding clinical examination results obtained immediately. By means of outliers detection and removal, finally 78 samples were determined as the research objects. A three-layer back-propagation artificial neutron network (BP-ANN) model was established and worked for prediction. The results turned out that, through EMD method, the prediction correlation coefficient increased greatly from 0.74 to 0.87. RMSEP was reduced from 12.85 to 8.08 g x L(-1). Other indexes were also obviously improved. The overall results sufficiently demonstrate that it is feasible to use EMD method forhigh SNR pulse wave signals, thus improving the performance of noninvasive hemoglobin calibration models. The application of EMD method can help promote the development of noninvasive hemoglobin monitoring technology.

[Rapid detection of alanine aminotransferase with near-infrared spectroscopy].

Near infrared transmission spectroscopy of Whole blood are investigated with different thickness (0.5, 1, 2, 4 mm) in order to explore the feasibility of detecting alanine aminotransferase rapidly by near-infrared spectra. The results show that the whole blood sample with 0.5 mm thickness is more suitable for spectral analysis. And then Near infrared spectroscopy of 176 samples were collected. Multiplicative scatter correction and second-order differential method have been used to spectral pretreatment. Stepwise multiple linear regression method and partial least squares regression method have been employed to establish quantitative detection model to predict content of alanine aminotransferase in whole blood. The alanine aminotransferase measured presents best result in calibration and prediction by Near-Infrared Spectroscopy with partial least squares regression calibration model, and the calibration correlation coefficient, the standard error of calibration and the standard error of prediction are 0.98, 2.42 and 7.22 respectively.

[Progress in noninvasive biochemical examination by near infrared spectroscopy].

In the early nineties of last century, great importance had been gradually attached to the potential of near-infrared spectroscopy (NIRS) in the human body noninvasive biochemical examination. However, the human body is extremely complex. Although research teams have made some achievements in experimental simulations and in-vitro analysis, there is still no substantive breakthrough in clinical application now. The present paper discusses the key problems which prevent NIRS from achieving human noninvasive clinical biochemical examination, such as weak signal, the interference of human tissue background and the problem of blood volume change. The thoughts of noninvasive biomedical examination using NIRS are divided into two categories in terms of analytical method, that is classical near-infrared analysis and issue background interference elimination analysis. This paper also introduces in detail the current status of the two categories in the world, and believes that the second category is more promising to be successful in clinical application under the existing conditions.

[Research on the effective signal extraction in the noninvasive blood glucose sensing by near infrared spectroscopy].

Diabetes seriously endanger human health, and noninvasive glucose sensing is the expectation of both doctors and patients. Physiological background is complicated, volatile and mixed with a variety of tissue information, resulting in direct measurement of the body's near infrared spectra difficult to truly reflect the concentration change in glucose. As a matter of fact, blood volume is always changing, but human tissue background and the concentration of blood components are constant in a short period. Taking advantage of this, subtracted blood volume spectrometry is propounded, which could eliminate the interference of human tissue background and obtain effective spectrum information of blood. To verify the effectiveness of the method, a experimental system was developed. The system noise is better than 20 microAU, and the signal to noise ratio of the effective spectrum signal at 1250 nm is 20,000:1. Finally, the feasibility and advantages of subtracted blood volume spectrometry are clarified in clinical application of near infrared non-invasive glucose sensing.

[Investigation on the quality indicators of beers using NIR].

Near infrared spectroscopy (NIR) has been used to determine important indicators of the quality of undeaired beers by a partial least squares (PLS) regression and stepwise multiple linear regression (SMLR). The indicators are original, real extract and alcohol contents. Absorbance spectra in transmission mode of 83 samples were obtained with 1 mm and 5 mm path-length quartz cell. The selected resolving powers are 8, 16 and 32 cm(-1). Air and water were used as background respectively. It was concluded that the calibration and prediction results are similar with different background, pathlength and resolving power. The SMLR method seems to be better than PLS method. The results of this paper provide a foundation for the application and further development of NIR on-line beer analyzer.

[Near infrared determination of the content of caffeine in tea polyphenol].

In the present paper the caffeine in the tea polyphenol was analysed spectrally and quantitatively by using near infrared spectroscopy. From the original absorbance of caffeine in the tea polyphenol an obvious and strong peak can be viewed. By using second derivative, MSC (multiple scatter correction) and correlation analysis the spectral characteristics of caffeine in the near infrared region can be seen very clearly, thus the robust calibration model can be set up easily. The result obtained shows that through this technique the absorptive characteristic of those primary fundamentals of caffeine can be looked through easily, meanwhile, calibration test was performed to quantitatively measure the weight percent of caffeine in the tea polyphenol, and fine precision of the result was obtained in a comparatively very large range of concentration. The SEC(standard error of calibration) is 0.49%, and the correlation coefficient r is 0.993. The result shows that NIR is feasible and superior in analyzing the content of caffeine in tea polyphenol.

[Study on temperature correctional models of quantitative analysis with near infrared spectroscopy].

Effect of enviroment temperature on near infrared spectroscopic quantitative analysis was studied. The temperature correction model was calibrated with 45 wheat samples at different environment temperaturs and with the temperature as an external variable. The constant temperature model was calibated with 45 wheat samples at the same temperature. The predicted results of two models for the protein contents of wheat samples at different temperatures were compared. The results showed that the mean standard error of prediction (SEP) of the temperature correction model was 0.333, but the SEP of constant temperature (22 degrees C) model increased as the temperature difference enlarged, and the SEP is up to 0.602 when using this model at 4 degrees C. It was suggested that the temperature correctional model improves the analysis precision.

[Development of soft X-ray and vacuum ultraviolet spectrum sources].

The soft X-ray and vacuum ultraviolet sources developed in CIOMP are presented. The wall-stabilized argon arc source with spectrum stability and repeatability of +/-0.3% is applied to the calibration of spectrum intensity distribution of the vacuum ultraviolet instruments as an absolute standard source. The Penning source, duobplasma source and hollow cathode source are able to produce atomic and ionic line spectra as a wavelength standard source, which covers a few nanometers to several tens nanometers with spectrum radiation stability and repeatability of +/-1.0%. In particular, the low debris laser produced plasma source with liquid aerosol spray target recently developed can emit stronger soft X-ray for soft X-ray lithography and metrology, which has a transfer efficiency as high as 0.75%/2pi x sr/2% bandwidth.

[The PLS analysis with short wave near-infrared of whole wheat for protein].

Short wave near-infrared spectrum of whole wheat was obtained by diffusion reflection. PLS method was used to analyze protein content of whole wheat. Different wavelength ranges were chosen for regression and information abstraction. The 3D curves were shown for different factors, prediction residual sum of squares (PRESS), and RMSECV. The best wavelength ranges and factors were determined. Analysis results for protein content of whole wheat were shown in three wavelength regions, and the predicted results were compared and discussed. The method of selecting advantageous wavelength ranges is feasible to obtain high prediction precision.

[Influence of smooth, 1st derivative and baseline correction on the near-infrared spectrum analysis with PLS].

This paper studied the influence of using pre-procession such as smooth, 1st derivative and baseline correction on the analysis of near-infrared spectrum. Comparing the analysis results by the pre-procession methods, and using PLS arithmetic, the best pre-procession was determined. In smooth pre-procession method, the best smooth points were proposed for regression using PLS. The analysis result is satisfactory.

[Research on model and wavelength selection of near infrared spectral information].

Based on stepwise linear regression, and according to the theory of near infrared absorbption, spectrum (1000-2500 nm) obtained by detector was divided into three ranges, which were I (1000-1400 nm) and II (1400-1860 nm) and III (1860-2500 nm). In each range the regression wavelengths of different wavelength gaps were picked up stepwise. Regression coefficients and parameters were calculated by Matlab5.3 Program. Regression models were built up in different ranges with different wavelength gaps. Best models could be determined. Prediction results of protein content of ground wheat were displayed in scatter plots. Different results were discussed and compared, which has referencemeaning for application.