[Study on detection methods of interstitial fluid glucose concentration based on infrared attenuated total reflection].

Measuring the glucose concentrations in the interstitial fluid is currently the main method to achieve the continuous blood glucose monitoring. The MIR-ATR(Mid-infrared, Attenuated Total Reflection)Spectroscopy has prominent advantage on the analysis of small biological molecule for composition information like the glucose, but it is still an unresolved problem that how to detect the subcutaneous glucose concentration by using the MIR-ATR Spectroscopy. In the present paper, we carry out the experiment based on MIR-ATR for the detection of subcutaneous glucose information on both the natural state and the penetration state based on the theoryanalysis of MIR penetration depth. Firstly, collect spectral data of the subcutaneous glucose concentration of human finger on the natural state were collected as the light shined the skin directly, and it was discussed whether the MIR can penetrate the skin to get the information of subcutaneous glucose. On this basis, collect spectral data of the subcutaneous glucose concentration of human finger at the penetration state were collected when the Interstitial fluid is permeated to the surface layer by using low-frequency ultrasound and vacuum, then it analyzed that whether it can detect the glucose-specific information or not. As the two-dimensional correlation spectroscopy has high resolution and good versatility, it is widely used to analyze the inter-molecular reaction and judge the absorption peaks information in many fields including the MIR spectroscopy field, so we choose the Two-dimensional correlation spectroscopy to analyze the information of subcutaneous glucose concentration at the natural state and the penetration state. The experiment result shows that the MIR-ATR spectroscopy can't be applied in the detection of subcutaneous glucose concentrationdirectly, and it is a promising direction to make the Interstitial fluid permeated to the surface layer by the physical methods or chemical methods.

[Identification of adulterated milk based on two-dimensional correlation near-infrared spectra parameterization and BP neural network].

Discriminant models of adulterated milk and pure milk were established using BP neural network combined with two-dimensional (2D) correlation near-infrared spectra parameterization. Forty pure milk samples, 40 adulterated milk samples with urea (1-20 g x L(-1)) and 40 adulterated milk samples with melamine (0.01-3 g x L(-1)) were prepared respectively. Based on the characteristics of 2D correlation near-infrared spectra of pure milk and adulterated milk, 5 apparent statistic parameters were calculated based on the parameterization theory. Using 5 characteristic parameters, discriminant models of urea adulterated milk, melamine adulterated milk and two types of adulterated milk were built by BP neural network The prediction rate of unknown samples were 95%, 100% and 96.7%, respectively. The results show that this method can extract effectively feature information of adulterant, reduce the input dimensions of BP neural network, and better realize qualitative analysis of adulterant in milk.

[Study on specificity of noninvasive glucose measurements based on two-dimensional correlation mid-infrared spectroscopy].

Glucose specificity is the premise of spectroscopic measurements of blood glucose concentration, and it is also paramount for feasibility study of a spectral measurement method. Two-dimensional correlation spectroscopy technology is widely used in many fields such as inter-/intra-molecular reaction, material phase transition and information extraction because of its high resolution and effective Noda's rule. In the present paper, the glucose specificity for noninvasive glucose measurements by mid-infrared spectra based on the 2D correlation spectroscopy was investigated. First, the feasibility of this method was validated by a series of in vitro experiments of glucose. Then the in vivo experiments of four volunteers were conducted and the characteristic information of glucose by mid-infrared spectra collected from human fingers was confirmed by 2D correlation spectroscopy analysis.

[Study of chance correlation in blood glucose sensing].

In the noninvasive blood glucose sensing by the near-infrared spectroscopy, chemometrics is applied to achieve the quantitative analysis of unknown samples. In modeling and validation process, however, there usually introduces a certain degree of chance correlation, thus affecting the stability of the model. In the present paper, normally distributed random numbers were used to simulate spectral data and reference concentration. In this way, it can investigate the probability level of chance correlation from the number of selected modeling wavelengths and different probable cross validation methods. Chance correlation exists in the process of modeling. In this paper, there has also given the best level of modeling wavelengths and the optimal cross validation method to reduce the chance correlation. In addition, the in vitro experiment of glucose aqueous solution at different temperature is conducted. In this experiment, the relationship between the temperature and the glucose concentration was obtained, according to which the temperature effect in practice was reduced.

Simultaneous fluorometric resolution of pyridoxal, pyridoxamine and 4-pyridoxic acid using chemometric methods.

An alternatively minimizing covariant matrix error (AMCME) algorithm, newly proposed by the present authors, was applied to the simultaneous fluorometric determination of pyridoxal, pyridoxamine and 4-pyridoxic acid without loss of sensitivity. The experimental results illustrate that the profiles of spectra and concentration can be accurately resolved using the AMCME algorithm with a high sensitivity and stable repeatability. That is to say, the closely overlapping problem of the spectra could be resolved owing to the characteristic features of the AMCME algorithm.

[Memory-assessing methods which help the clinical diagnosis of early Alzheimer's disease].

OBJECTIVE: To evaluate distribution and influence factors of logic memory (LM) modified in assessing and scoring method in normal population and Alzheimer's disease (AD) patients, and definite the cut-off point of the modified scale. METHODS: Totally 183 AD patients, including 118 mild and 65 moderate in degree, 1,417 controls, including 1,283 normal individuals and 134 individuals suffered from other diseases, were recruited in this study. Modified LM was conducted. RESULTS: Educational level (F=354.36, STB=0.46, P=0.0001) was the most obvious factor in demographic data to influence total score in normal control group by a fitting of multiple regression models. The total score increased with the rising of educational level in normal controls (P=0.0001) and other diseases controls (P=0.0001), but not in AD cases (P=0.1365). The total scores were significantly different among normal controls (20.2 +/- 0.2), other diseases controls (17.5 +/- 0.5), mild AD patients (9.6 +/- 0.5) and moderate AD patients (7.1 +/- 0.7) (P=0.0001, P=0.0059), after adjusted educational level, age, sex and rural/urban status by multiple analysis covariance. The sensitivity of cut-off points using modified methods to diagnose AD reasonably increased to 71.98%, while the specificity was 94.11%. According to the sum of long-delayed recall and long-delayed recognition, the sensitivity increased with the rising of educational levels. For education levels at illiteracy, elementary school, junior middle school, senior middle school and above senior middle school, the cut-off points for total score of modified method were 6.5, 9.5, 10.8, 13 and 15.8, respectively, and for sum of long-delayed recall and long-delayed recognition the cut-off points were 5, 6, 8, 9, 10. CONCLUSIONS: When modified LM used as a neuropsychological assessment, it is with high specificity, high accuracy and reasonable sensitivity. It is suitable for the diagnosis of AD in early stages, especially for individuals with high educational levels.

An algorithm for three-way data analysis that alternatively minimizes coupled vector (COV) resolution error and PARAFAC error.

A novel algorithm, alternatively minimizing coupled vector (COV) resolution error and PARAFAC error algorithm, is proposed in this paper. This algorithm can overcome the problem of slow convergence and is insensitive to the estimation of component number, such problems are unavoidable while using the traditional parallel factors analysis (PARAFAC) algorithm. In other words, this algorithm is capable of improving the computing speed and providing accurate resolutions provided that the number of factors used in the computation is no less than that of the actual underlying ones. The characteristic performances were demonstrated with a novel fluorescence data array.

Spectrofluorometric resolution of closely overlapping drug mixtures using chemometrics methods.

A modified parallel factors analysis (PARAFAC) algorithm with a penalty diagonalization error (PDE), newly proposed by the present authors, was utilized to simultaneously resolve drug mixtures of propranolol (PRO), dipyridamole (DIP) and amiloride (AMI) without any loss of sensitivity. The analyses were performed in aqueous solution. The experimental results demonstrated that the profiles of the spectra and the concentrations could be accurately resolved using the PDE algorithm with a high sensitivity and stable repeatability. That is to say, the closely overlapping problem of the spectra could be easily solved. Furthermore, simultaneous determinations of three kinds of tablets, which contain PRO, AMI and DIP, respectively, were successfully performed with satisfactory results.