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Traffic accidents, high-altitude drops, mechanical shock and other accidents may lead to human spinal cord injury, interrupting the transmission channel of human nerve signals, resulting in loss of some limb function. Since there is no method of spinal cord injury site of nerve cell activity, while the conventional testing instruments such as X ray, CT and other traditional instruments cannot provide any nerve cell activity, doctors couldn’t have a clearer understanding of the patient’s condition, which may delay the best timing of treatment, thus causing lifelong paralysis. Spectroscopy method can be used in the detection of cell organization change while near infrared spectrum technology is a kind of fast measurement and simple operation of nondestructive measuring. Therefore, aiming at the shortcomings of the existing medical testing equipment, on the basis of animal experiment, this paper uses near infrared spectrum technology, based on the absorption characteristics of the material in near infrared wave band, and combined with near infrared spectrum analysis technique of qualitative determination and quantitative analysis of the characteristics. Using clustering algorithm for spinal cord injury site of neuron-specific nuclear protein and neurotransmitters classify and simulation, partial least squares (PLS) algorithm is used to calculate the content to achieve a spinal cord accurate detection of the site of injury nerve cell activity. This method provides theoretical basis for the detection of spinal cord injury, bringing hopes to patients with limb function reconstruction and rehabilitation, for clinical provides a method to detect the activity of nerve cells in the noninvasive.

In this paper, two-dimensional (2D) correlation spectroscopy analysis was applied to investigate the influence of the main component in blood and the systematic drift during the measurement on the specificity of glucose in the near-infrared (NIR) spectroscopy. First, the NIR transmittance of glucose aqueous solutions was measured and the 2D correlation NIR spectra were calculated under the perturbation of glucose concentration. Based on the comparative analysis for synchronous and asynchronous 2D correlation spectra, the characteristic absorption peaks of glucose in the combination band and the overtone band were determined. Then a small amount of albumin was added into glucose aqueous solutions, and the transmittance was recorded to perform 2D correlation spectroscopy analysis under the perturbation of glucose concentration. However, the absorption of glucose in the first overtone band (1590nm) and second overtone band (1195nm) was no longer homologous in the 2D correlation spectra, which means that the albumin may reduce the specificity of glucose. Further, the oral glucose tolerance test of healthy volunteer was conducted and the NIR diffuse reflectance of left palm was collected in vivo. The 2D correlation analysis results showed that, the homology of glucose in the diffuse reflectance was also destroyed. Moreover, as the spectral variation from the glucose concentration change is too low to be covered by that induced by systematic drift easily, some background correction methods were usually required. For the transmittance experiment of glucose aqueous solutions and the diffuse reflectance experiment of human body, the pure water sample and 5% diffuse reflectance standard were used as the reference, respectively. Then 2D correlation spectroscopy was developed under the perturbation of measurement time. Results showed that, smaller band shift was observed in the slice spectra of 2D correlation synchronous spectra after the corresponding background correction, and the specificity of glucose was improved both in the in vitro and in vivo experiments. So for the non-invasive glucose sensing by NIR spectroscopy, the wavelengths should be chosen carefully to avoid the absorption band of some interfering components which may destroy the homology of glucose and make spectral interpretation more complicated. And the selection of reference samples for relative measurement is also important to improve the specificity of glucose.

In order to improve the technical level of the rapid detection of liquor fermented grains, in this paper, use near infrared spectroscopy technology to quantitative analysis moisture, starch, acidity and alcohol of liquor fermented grains. Using CARS, iPLS and no information variable elimination method (UVE), realize the characteristics of spectral band selection. And use the multiple scattering correction (MSC), derivative and standard normal variable transformation (SNV) pretreatment method to optimize the models. Establish models of quantitative analysis of fermented grains by PLS, and in order to select the best modeling method, using R2, RMSEP and optimal number of main factors to evaluate models. The results showed that the band selection is vital to optimize the model and CARS is the best optimization of the most significant effect. The calculation results showed that R2 of moisture, starch, acidity and alcohol were 0.885, 0.915, 0.951, 0.885 respectively and RMSEP of moisture, starch, acidity and alcohol were 0.630, 0.519, 0.228, 0.234 respectively. After optimization, the model prediction effect is good, the models can satisfy the requirement of the rapid detection of liquor fermented grains, which has certain reference value in the practical.

Near-infrared reflectance spectroscopy (NIRS) is an inexpensive, rapid, environment-friendly and non-invasive analytical technique that has been extensively applied in the analysis of the dietary attributes and the animal products. Acquisition of dietary attributes is essential for nutritional diagnoses to provide animals with reasonable diet. Traditionally, the calibration equations for the prediction of dietary attributes (e. g. crude protein) are developed from feed NIR spectra and the results of conventional chemical analysis (i. e. reference data). It is difficult to obtain the NIR spectra of forages consumed by grazing animals, so the method of this calibration is inappropriate for free-grazing herbivores. Feces, as the animal's metabolites, contain the information about both the animal's diet and the animal itself. Recently, Fecal-NIRS (F. NIRS) has been directly used to monitor diet information (botanical composition, chemical composition and digestibility), based on correlation between reference data and fecal NIR profile. Subsequently, some additional application (such as sex and species discrimination, reproductive and parasite status) of F. NIRS also is outlined. In the last, application of NIRS in animal manure is summarized. NIRS was shown to be an alternative to conventional wet chemical methods for analyzing some nutrient concentrations in animal manure rapidly. Overall, this paper proves that F. NIRS is a rapid and valid tool for the determination of the dietary attributes and of the physiological status of animal, although more efforts need to be done to improve the accuracy of the F. NIRS technique. Several researchers in English have reviewed the applications of F. NIRS. In China, however, there is a paucity of research and application regarding F. NIRS. We expect that this paper in Chinese will be helpful to the development of F. NIRS in China. At the same time, we propose NIRS as a simple and rapid analytical method for predicting the main chemical composition (dry matter, organ matter, total solid, volatile solid, total nitrogen, total Kjeldahl nitrogen and ammonium nitrogen) in animal manure.

As an indispensable drink of people's daily life, milk's quality has been also increasingly concerned by consumers. Rapid and accurate detection of milk and its products is the indispensable step for improving the quality of milk and daily products in production. However, traditional methods cannot meet the need. In this paper, rapid quantitative detection of true protein in pure milk was studied by using visible/near-infrared (VIS/NIR) reflectance spectroscopy (350-2500 nm). The spectral data and the protein content data of the pure milk samples were collected by ASD spectrometer and CEM rapid protein analyzer, respectively. Based on the analysis and comparison of different spectrum preprocessing methods and band selection methods, the feature bands were determined. Finally, using the Principle Component Regression (PCR) and Least Squares Support Vector Machine (LS-SVM) model, the regression models between the reflectance spectroscopy and the protein content in milk were presented for pure milk samples and the predictive ability was also analyzed. In this way, the optimal inversion model for true protein content in milk was established. The results were shown as follows: (1) In the process of spectral pretreatment, the combination of multiple scatter correction and second derivative achieved a better result; (2) Compared with the modeling of whole spectral, appropriate variable optimization models had the ability to improve the accuracy of the inversion results and reduce the modeling time; (3) The analysis results between PCR model and LS-SVM model demonstrated that the prediction accuracy of LS-SVM model was better than PCR model. The coefficient of determination (R(P)²) of PCR and LS-SVM were 0.952 2 and 0.958 0 respectively, and the root mean square error of prediction (RMSEP) of PCR and LS-SVM were 0.048 7 and 0.048 2 respectively. The result of this research is expected to provide a novel method for nondestructive and rapid detection of true protein in milk.

This paper explored the relationship among genetic distances, NIR spectra distances and NIR-based identification model performance of the seeds of maize inbred lines. Using 3 groups (total 15 pairs) of maize inbred lines whose genetic distaches are different as experimental materials, we calculates the genetic distance between these seeds with SSR markers and uses Euclidean distance between distributed center points of maize NIR spectrum in the PCA space as the distances of NIR spectrum. BPR method is used to build identification model of inbred lines and the identification accuracy is used as a measure of model identification performance. The results showed that, the correlation of genetic distance and spectra distancesis 0.9868, and it has a correlation of 0.9110 with the identification accuracy, which is highly correlated. This means near-Infrared spectrum of seedscan reflect genetic relationship of maize inbred lines. The smaller the genetic distance, the smaller the distance of spectrum, the poorer ability of model to identify. In practical application, near infrared spectrum analysis technology has the potential to be used to analyze maize inbred genetic relations, contributing much to genetic breeding, identification of species, purity sorting and so on. What's more, when creating a NIR-based identification model, the impact of the maize inbred lines which have closer genetic relationship should be fully considered.

The whole spectrum usually contains a lot of redundant information in the near-infrared spectroscopy model, the presence of redundant information will increase the model resolution time and increase the difficulty of parsing model, Therefore, how to select the characteristic wavelength quickly and effectly is very crucial. In this paper, we combined the algorithm based on SPA (successive projections algorithm ) with IPLS (interval partial least squares ) to selec the characteristic wavelength in the fermentation of wheat straw microbial biomass, A total of 85 samples prepared by measuring microbial biomass using glucosamine method, 68 samples are chosen as calibration set and 17 simples are chosen as verification set. First, the whole spectral region 520 points are segmented modeling according to the interval wavelength point size 10, 20, 30, 40 and 4 450~4 925 cm-1, 9 194~9 993 cm-1 two-band range are selected as the characteristic wavelength band, then pick out the new feature wavelength points by Successive Projections Algorithm band and Genetic Algorithm (GA), comprehensive analysis and comparison the result of model. The experimental results show that the using of IPLS-SPA algorithm to select the combination band 4 450~4 925 cm-1 & 9 194~9 993 cm-1 has the best modeling effect, compared with the modeling of whole spectrum, the wavelength points decrease from 520 to 10, the correction coefficient of determination R2 rised from 0. 884 9 to 0. 945 28, root mean square error (RMSE) dropped from 11. 104 9 to 8. 203 3, although the genetic algorithm model achieved the better accuracy, but the results are instable and have a strong randomness , while IPLS combined SPA method can select characteristic wavelength information stability and accurately, which can improve the model calculation speed and reduce the fitting difficulty of the model, it can be used as a new reference method for band selection. The results show that using near infrared spectroscopy method for straw biomass rapid detection is feasible.

Soil phosphorus provides nutrient elements for plants, is one of important parameters for evaluating soil quality. The traditional method for soil total phosphorus content (STPC) measurement is not effective and time-consuming. However, remote sensing (RS) enables us to determine STPC in a fast and efficient way. Studies on the estimation of STPC in near-infrared spectroscopy have been developed by scholars, but model accuracy is still poor due to the low absorption coefficient and unclear absorption peak of soil phosphorus in near-infrared. In order to solve the deficiency which thermal-infrared emissivity estimate desert soil total phosphorus content, and could improve precision of estimation deserts soil total phosphorus. In this paper, characteristics of soil thermal-infrared emissivity are analyzed on the basis of laboratory processing and spectral measurement of deserts soil samples from the eastern Junggar Basin. Furthermore, thermal-infrared emissivity based RS models for STPC estimation are established and accuracy assessed. Results show that: when STPC is higher than 0.200 g x kg(-1), the thermal-infrared emissivity increases with the increase of STPC on the wavelength between 8.00 microm and 13 microm, and the emissivity is more sensitive to STPC on the wavelength between 9.00 and 9.6 microm; the estimate mode based on multiple stepwise regression was could not to estimate deserts soil total phosphorus content from thermal-infrared emissivity because the estimation effects of them were poor. The estimation accuracy of model based on partial least squares regression is higher than the model based on multiple stepwise regression. However, the accuracy of second-order differential estimation model based on partial least square regression is higher than based on multiple stepwise regression; The first differential of continuous remove estimation model based on partial least squares regression is the best model with R2 of correction and verification are up to 0.97 and 0.82 respectively, and RMSE of correction and verification are only 0.0106 and 0.015 7 respectively, RPD is 2.62. Research results provide optimized models for remotely sensed analysis on deserts soil total phosphorus content and could realize timeliness and effective monitoring on the space-time dynamic of deserts soil total phosphorus content for future regional ecological restoration.

Near infrared spectroscopy (NIRS) technology developed fast in recent years, due to its rapid speed, less pollution, high-efficiency and other advantages. It has been widely used in many fields such as food, chemical industry, pharmacy, agriculture and so on. The seed is the most basic and important agricultural capital goods, and seed quality is important for agricultural production. Most methods presently used for seed quality detecting were destructive, slow and needed pretreatment, therefore, developing one kind of method that is simple and rapid has great significance for seed quality testing. This article reviewed the application and trends of NIRS technology in testing of seed constituents, vigor, disease and insect pests etc. For moisture, starch, protein, fatty acid and carotene content, the model identification rates were high as their relative contents were high; for trace organic, the identification rates were low as their relative content were low. The heat-damaged seeds with low vigor were discriminated by NIRS, the seeds stored for different time could also been identified. The discrimination of frost-damaged seeds was impossible. The NIRS could be used to identify health and infected disease seeds, and did the classification for the health degree; it could identify parts of the fungal pathogens. The NIRS could identify worm-eaten and health seeds, and further distinguished the insect species, however the identification effects for small larval and low injury level of insect pests was not good enough. Finally, in present paper existing problems and development trends for NIRS in seed quality detection was discussed, especially the single seed detecting technology which was characteristic of the seed industry, the standardization of its spectral acquisition accessories will greatly improve its applicability.

With the development in last twenty years, functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique which widely used in cognitive neuroscience studies. Based on mechanism of neurovascular coupling, increased functional neural activities in brain induce higher regional cerebral blood flow, which will cause relative concentration change of oxygenated and deoxygenated hemoglobin. In this paper, a single channel continuous wave fNIRS system based on multi-function data acquisition board was proposed. With the benefits of narrow spectral peaks and low divergence, laser diodes provided a better accuracy for measurement with optimal dual-wavelength of 690 and 830 nm. Frequency multiplexing technique was used to distinguish light sources from different emitters, and remove environmental stable interference sources such as ambient light and line power noise as well. LabVIEW was used to design graphical user interface with functionalities including source sequence schedule, auto gain setting, digital inhase and quadrature demodulation, data visualization and storage. The experimental results during holding breath and mental arithmetic task indicated that our system was capable of monitoring regional concentration change of hemoglobin in real time, and detecting activation associated with advanced brain functions.

The appropriate algorithm for calibration set selection was one of the key technologies for a good NIR quantitative model. There are different algorithms for calibration set selection, such as Random Sampling (RS) algorithm, Conventional Selection (CS) algorithm, Kennard-Stone(KS) algorithm and Sample set Portioning based on joint x-y distance (SPXY) algorithm, et al. However, there lack systematic comparisons between two algorithms of the above algorithms. The NIR quantitative models to determine the asiaticoside content in Centella total glucosides were established in the present paper, of which 7 indexes were classified and selected, and the effects of CS algorithm, KS algorithm and SPXY algorithm for calibration set selection on the accuracy and robustness of NIR quantitative models were investigated. The accuracy indexes of NIR quantitative models with calibration set selected by SPXY algorithm were significantly different from that with calibration set selected by CS algorithm or KS algorithm, while the robustness indexes, such as RMSECV and |RMSEP-RMSEC|, were not significantly different. Therefore, SPXY algorithm for calibration set selection could improve the predicative accuracy of NIR quantitative models to determine asiaticoside content in Centella total glucosides, and have no significant effect on the robustness of the models, which provides a reference to determine the appropriate algorithm for calibration set selection when NIR quantitative models are established for the solid system of traditional Chinese medcine.

In this paper, total of 5170 flue-cured tobacco samples collected from 2003 to 2012 in the domestic and foreign origin by Shanghai Tobacco Group Technical Center were tested by near infrared spectroscopy, including the typical upper leaves 1394, central 2550, the lower part of 1226. Using projection model of based on principal component and Fisher criterion (PPF), follow the projected results to get no statistically significant differences at adjacent principal components, and the number of principal components as little as possible, in this paper, four main components to build projection analysis model, the model results show that: the near-infrared spectral characteristics of the upper and lower leaves have a significant difference that can be achieved almost entirely distinguished; while the middle leaves with upper and lower have a certain degree of overlap, which is consistent to the actual situation of the continuity of tobacco leaf. At the same time, Euclidean distance between the predicted sample projection values and the mean projection values of each class in the model, a description is given for the prediction samples to quantify the extent of the site features, and its first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typically upper, upper to central, central to upper, typical central, central to the lower, the lower to central, typically the lower, or super-model range. The model was validated by 34 tobacco samples obtained from the re-drying process in 2012 with different origins and parts. This kind of analysis methods, not only can achieve discriminant analysis, and get richer feature attribute information, can provide guidance on the raw tobacco processing and formulations.

In this paper, the 7 different origin before redrying raw tobacco & after redrying sheet tobacco's online near infrared spectroscopy were collected from sorting & redrying production line specifically for "ZHONGHUA" brand. By using the projection model bulit by different origin tobacco's online spectroscopy and the method of variance and correlation analysis, we studied the uniformity and similarity quality characteristics change before and after the redrying of tobacco, which can provide support for understanding the quality of the tobacco material and cigarette product formulations. This study show that selecting about 10,000 by equally spaced sampling time from a huge number of online near infrared spectroscopy, for modeling are feasible, and representative. After manual sorting, threshing, and redrying, the uiformity of each origin tobacco near-infrared spectroscopy can be increased by 10%~35%, homogeneity of the tobacco leaf has been significantly improved. After redrying, the similar relationship embodied in the origin also have significant changes, overall it reduce significantly, that shows the quality differences embodied by origin significantly improve, which can provide greater space for formulations, it shows the need for high-quality Chinese cigarette production requires large amounts of financial and human resources to implement cured tobacco processing. The traditional means of chemical analysis, it takes a lot of time and effort, it is difficult to control the entire processing chain, Near Infrared Spectroscopy with its rapid, non-destructive advantage, not only can achieve real-time detection and quality control, but also can take full advantage of near-infrared spectroscopy information created in the production process, which is a very promising online analytical detection technology in many industries especially in the agricultural and food processing industries.

Soil is a much complicated substance, because animals, plants and microbes live together, organic and inorganic exist together. So soil contains a large amount of information. The traditional method in laboratory is a time-consuming effort. But the technology of near infrared reflectance spectroscopy (NIRS) has been widely used in many areas, owing to its rapidness, high efficiency, no pollution and low cost, NIRS has become the most important method to detect the composition of soil. This paper mainly introduce some traditional methods in laboratory, the basic processes of soil detection by NIRS, some algorithms for data preprocessing and modeling. Besides, the present paper illustrates the latest research progress and the development of portable near infrared instruments of the soil. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.

As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM' s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near in- frared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper. Experiments for binary classification and multi-label classification were conducted, and the conclusion proved that the proposed method has more stable performance, higher classification accuracy and lower sensitivity to training samples than the existing ones, such as the BP neural network, ELM and-ELM by particle swarm optimization.

In the present paper we discussed the methods of classification of citrus greening and extraction of spectral features based on the spectral reflectance of four different statuses of citrus leaves (healthy, HLB, iron deficiency and nitrogen deficiency). Between two classes of classification, the values of discriminability of different spectra were calculated to extract spectral features. The greater value of discriminability showed a bigger difference of the two spectra, which means it would be easier to distinguish the two classes. By the Fisher linear discriminant analysis, three classification models (HLB & healthy, HLB & iron deficiency and HLB & nitrogen deficiency) based on the spectral features yielded more than 90% accuracies, which were better than expected. And at last, we discussed the application of the classification tree in multi-class discriminant analysis and spectral features extraction. The models trained based on the original reflectance spectra, first derivative and selected spectral features yielded more than 88% average accuracy, and especially the model based on the spectral features yielded more than 94% average accuracies, which verified the feasibility of detection of citrus greening in multi-class discriminant analysis and the importance of the spectral feature extraction. The results were compared based on classification tree, k-NN and Bayesian classifiers. Adoption of spectral features as input variables was significantly superior to using the original spectrum, which confirmed the validity of spectral feature selection. Spectral features could be used well for developing a multi-spectral imaging system to detect the citrus greening.

In the present paper, a total of 4,733 flue-cured tobacco samples collected from 2003 to 2012 in 17 provincial origins and 5 ecological areas were tested by near infrared spectroscopy, including the NONG(Luzhou) flavor 1,580 cartons, QING (Fen) flavor 2004 cartons and Intermediate flavor 1 149 cartons. Using projection model based on principal component and Fisher criterion (PPF), Projection analysis models of tobacco ecological regions and style characteristics were established. Reasonableness of style flavor division is illustrated by the model results of tobacco ecological areas. With the Euclidean distance between the predicted sample projection values and the mean projection values of each class in style characteristics model, a description is given for the prediction samples to quantify the extent of the style features, and their first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typical NONG, NONG to Intermediate, Intermediate to NONG, typical Intermediate, Intermediate to QING, QING to Intermediate, typical QING, QING to NONG, NONG to QING, or super-model range. The model was validated by 35 tobacco samples obtained from the re-dryingprocess in 2012 with different origins and parts. This kind of analysis methods not only can achieve discriminant analysis, but also can get richer feature attribute information and provide guidance to raw tobacco processing and formulations.

The present paper firstly reviewed the research progress and main methods of NIR spectral assignment coupled with our research results. Principal component analysis was focused on characteristic signal extraction to reflect spectral differences. Partial least squares method was concerned with variable selection to discover characteristic absorption band. Two-dimensional correlation spectroscopy was mainly adopted for spectral assignment. Autocorrelation peaks were obtained from spectral changes, which were disturbed by external factors, such as concentration, temperature and pressure. Density functional theory was used to calculate energy from substance structure to establish the relationship between molecular energy and spectra change. Based on the above reviewed method, taking a NIR spectral assignment of chlorogenic acid as example, a reliable spectral assignment for critical quality attributes of Chinese materia medica (CMM) was established using deuterium technology and spectral variable selection. The result demonstrated the assignment consistency according to spectral features of different concentrations of chlorogenic acid and variable selection region of online NIR model in extract process. Although spectral assignment was initial using an active pharmaceutical ingredient, it is meaningful to look forward to the futurity of the complex components in CMM. Therefore, it provided methodology for NIR spectral assignment of critical quality attributes in CMM.

In the present paper, 152 vinegar samples with four different brands were chosen as research targets, and their near infrared spectra were collected by diffusion reflection mode and transmission mode, respectively. Furthermore, the brand traceability models for edible vinegar were constructed. The effects of the collection mode and pretreatment methods of spectrum on the precision of traceability models were investigated intensively. The models constructed by PLS1-DA modeling method using spectrum data of 114 training samples were applied to predict 38 test samples, and R2, RMSEC and RMSEP of the model based on transmission mode data were 0.92, 0.113 and 0.127, respectively, with recognition rate of 76.32%, and those based on diffusion reflection mode data were 0.97, 0.102 and 0.119, with recognition rate of 86.84%. The results demonstrated that the near infrared spectrum combined with PLS1-DA can be used to establish the brand traceability models for edible vinegar, and diffuse reflection mode is more beneficial for predictive ability of the model.

The present paper discusses how to monitor and analyze the relative change in muscle oxygen content in quadriceps tissue, and measures and records the change in blood lactate acid concentration, blood volume and heart rate when eight players who are good at middle-distance races perform grade incremental intensity exercise on cycle ergometer by using noninvasive tissue oximeter with near infrared spectroscopy produced by China independently. The results show that muscle oxygen content has a close relationship (p < 0.01)with exercise load, blood lactic acid, blood volume and heart rate. When determined muscle oxygen content and blood lactate acid concentration was determined for many times to the same person, the test proved regular falling and rising. There was no significant changes when analyzed each set of the data was analyzed through horizontal comparison. It verifies we can judge the subjects's endurable exercise intensity and the upward inflection point of blood lactic acid corresponding to the decreasing inflection point of blood lactate acid concentration & muscle oxygen content according to the muscle oxygen content change of skeletal muscle while exercising. This paper shows NIRS research status and present situation in sports field through investigation, and analyzes the main trouble and research tendency in the future. By understanding NIRS technology gradually, the authors can realize that the muscle oxygen content which measured by noninvasive tissue oximeter using near infrared spectroscopy produced by China independently is a sensitive, nondestructive, up-to-date and reliable index, it has irreplaceable advantages when compared with traditional invasive, excised and fussy test methods.

From the perspective of calibration, the present paper studies the model stability problem in qualitative analysis of NIR. Aiming at the issue of model failure caused by different data acquisition time, 13 varieties of corn were used as experimental material, and learning from the idea of model calibration transfer between the two instruments in quantitative analysis of NIR, the DS (direct standardization) algorithm was used to calibrate the spectra acquired at different times with the same instrument, that made the varieties identification model established one time able to be applied to identify the test data at different acquisition time. First, transfer set was selected from the master spectrum set by Kennard/Stone algorithm, the corresponding number spectrums in slave spectrum set were selected, and then DS algorithm was applied to transfer set to calculate the transformation function between the two sets of data. Finally, the remaining slave spectrums were transformed so that they could apply to the model. This study does some experiment to discuss the impact of the number of transfer set and the location of calibration on the calibration results. Respectively, the experiment results were analyzed from two aspects, one is the correct discrimination rate in qualitative analysis, and the other is the distribution distance between master spectrums and slave spectrums before and after calibration. The experiment results indicate that this approach is effective to solve the spectra drift produced by sampling over time, can bring higher recognition rate on different sampling time test sets, also improves the robustness and application scope of the identification model, and the experiment results also indicate that the best result can be obtained with calibration locating after feature extraction.

Drugs are special goods that are directly related to public health, so their quality should be monitored in any link of their whole lifecycle. With nondestructive, rapid and environmentally friendly characteristics, near infrared technique is highly suitable for monitoring drug quality in the open market as well as the distribution channels. The present paper reviewed the current situation (analytical objects, methods and instruments) about the application of near infrared spectroscopy in monitoring the quality of the final drug products, Chinese crude drug or decoction pieces in domestic circulation since 1997, expounded the unsolved problems and future prospects.

The present paper studies the best detector-distance to improve the near-infrared spectrum signal intensity of the dermis layer and eliminate the interference of the epidermis and subcutaneous layer. First, we analyzed the organizational structure of the skin and calculated the tissue optical parameters of different layers. And we established the Monte Carlo model with the example of glucose absorption peak at 2 270 nm. Then, we used the Monte Carlo method to simulate the light transmission rules in the skin, obtaining the average path length, the average visit depth and the fractions of absorbed energy at each layer with the change in critical angle and detector-distance. The results show that when the photons are incident at an angle less than 45 degrees, you can ignore the effect of the incident angle on photon transmission path, and when the detector-distance is 1 mm, the fraction of absorbed photon energy by the dermis layer is the largest, while it can ensure more energy received by detector. We determined that the best detector-distance is 1mm, which successfully avoids the interference of the epidermis spectral information and obtains large amounts of blood in the dermis layer, which is conducive to the near-infrared non-invasive measurement of biochemical components and the subsequent experiments.

The present paper presented a fast and non-destructive method for the discrimination of minnan oolong tea varieties by near-infrared spectroscopy technology. Two hundred ten samples including Tieguanyin, Huangjingui, Benshan, Maoxie and Meizhan were collected in different tea plantations of Minnan. NIR spectra of 1,100-1,300 nm and 1,640-2,498 nm were successfully obtained. Prediction model was built by principal component analysis (PCA), and the effects of multiplicative scatter correction (MSC) and standard normal variate (SNV) on the model were observed and compared. It was indicated that the effect of MSC on the model was superior for the effect of SNV because the classification accuracy of model for the calibration samples reached 96%, and this number to the prediction samples was about 90%. These results demonstrated that the near-infrared spectroscopy method established could be an efficient and accurate way for the discrimination of minnan oolong teas and would have a strong practical value.

Near-infrared spectroscopy (NIRS) is a reliable, rapid, and non-destructive analytical method widely applied in as a number of fields such as agriculture, food, chemical and oil industry. In order to suit different applications, near-infrared spectrometers are now varied. Portable near-infrared spectrometers are needed for rapid on-site identification and analysis. Instruments of this kind are rugged, compact and easy to be transported. In this paper, the current states of portable near-infrared spectrometers are reviewed. Portable near-infrared spectrometers are built of different monochromator systems: filter, grating, Fourier-transform methods, acousto-optic tunable filter (AOTF) and a large number of new methods based on micro-electro-mechanical systems (MEMS). The first part focuses on working principles of different monochromator systems. Advantages and disadvantages of different systems are also briefly mentioned. Descriptions of each method are given in turn. Typical spectrometers of each kind are introduced, and some parameters of these instruments are listed. In the next part we discuss sampling adapters, display, power supply and some other parts, which are designed to make the spectrometer more portable and easier to use. In the end, the current states of portable near-infrared spectrometers are summarized. Future trends of development of portable near-infrared spectrometers in China and abroad are discussed.

A rapid nondestructive method for identifying intact foods containing trans fatty acids (TFA) using diffuse near infrared spectroscopy (NIR) was proposed in the present paper. The diffuse Fourier transform near infrared (FT-NIR) spectra of intact samples were collected by fiber probe, and the reference data of TFA content were determined by Chinese standard method GB/T 22110-2008 (gas chromatography (GC) method). In this work, all the samples were classified into two categories: foods with TFA and foods without TFA according to the TFA content of the foods. The identification models were established by different supervised pattern recognition algorithms including partial least square discriminant analysis (PLSDA), support vector machine (SVM), soft independent modeling of class analogy (SIMCA) and K-nearest neighbor method (KNN) etc. The performances of the established models employing different algorithms, data pretreatments and wavelength bands were compared. The results show that PLSDA and SVM algorithms have the ability of identifying intact foods with TFA, and the performance of identification models established by PLSDA is better than that of SVM. The PLSDA models established by the wavelength bands of 4 138-4 428, 5 507-5 963 and 7 794-8 960 cm(-1) which were pretreated with pretreatment methods of auto scaling and second derivative have the best performance. The correct classification percentages of its calibration and validation set are 96.4% and 88%, respectively, which indicates that this method is feasible for the identification of foods with TFA. This NIR method above mentioned has the characteristics of rapidness, non-destruction and easy operation due to the elimination of sample pretreatment such as oil extraction and grinding, therefore it is very suitable for on-line and in-site detection application.

In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular, lack of homogeneity of the meat samples influenced the accuracy of estimation of chemical components. In general the predicting results of intramuscular fat, fatty acid and moisture are best, the predicting results of crude protein and myoglobin are better, while the predicting results of ash and collagen are less accurate.

Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.07%. Milk powder principal component regression model based on SIMCA was established, and used to classify the milk powder samples in prediction sets. The results showed that the recognition rate of Guangming milk powder, Netherlands milk powder and Nestle milk powder was 78%, 75% and 100%, the rejection rate was 100%, 87%, and 88%, respectively. Therefore, the near infrared spectroscopy combined with SIMCA model can classify milk powder with high accuracy, and is a promising identification method of milk powder variety.

Multivariate detection limits (MDLs) of different types of near-infrared instruments were investigated to guide the selection of device type for TCM NIR analysis. In this paper, near-infrared spectroscopy of Qingkailing injection was performed in transmission mode on four near-infrared spectrometers named a, b, c and d, respectively. High performance liquid chromatography (HPLC) was used as the reference method to determine the content of baicalin in Qingkailing injection. Partial least squares (PLS) and interval partial least squares (iPLS) quantitative models of baicalin in Qingkailing injection were established and MDLs of quantitative models based on different types of instruments were calculated. The determination coefficient of prediction (R2(pre))and the root mean square errors of prediction (SEP) of PLS models of four different near-infrared spectrometers are 0.9762 and 230.4 microg x mL(-1) (a), 0.9561 and 246.4 microg x mL(-1) (b), 0.9662 and 264.4 microg x mL*-1) (c), 0.9985 and 71.5 microg x mL(-1) (d). And the model of instrument d shows a better prediction performance than the other three types. There are no remarkable superiorities in predictability in iPLS models of instruments a and b after variable selection, since the R2(pre) and SEP values for instruments a and b are 0.9771 and 218.4 microg x mL(-1), and 0.9754 and 219.4 microg x mL(-1), respectively. Models c and d show no results of variable selection. MDLs (delta(0.05, 0.05) of different instruments are all less than 250 microg x mL(-1), and the MDLs of instruments c and d reach to 58 and 2.9 microg x mL(-1) respectively. The results reveal that the predictability of models and corresponding MDLs are different for different detection equipments. This paper innovatively used the theory of MDL to investigate the detection performance of different types of NIR instruments. The results demonstrated the feasibility of the approach. And it is expected that in actual applications, choosing the right type of instrument should be based on the characteristics of the study carrier to ensure quantitative accuracy.

Based on the method of kernet Orthogonal Projection to Latent Structure Discriminant Analysis, discrimination models for adulterated milk were established in the present paper. Forty adulterated milk samples with melamine (0.01-3 g x L(-1)) and 40 adulterated milk samples with urea (1-20 g x L(-1)) were prepared, respectively. Then the near-infrared absorption spectra of all samples were measured. The spectra in the range of 4 200-4 800 cm(-1) were selected to construct the KOPLS-DA models for milk adulterated with melamine, milk adulterated with urea and milk adulterated with both melamine and urea. The results showed that, compared with PLS-DA and OPLS-DA models, KOPLS-DA model had better discriminant ability for the adulterated milk, and its classification accuracy rate (CAR) for milk adulterated with melamine, milk adulterated with urea and milk adulterated with both melamine and urea were 95%, 100% and 97.5%, respectively.

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.

The present paper is to study and develop a method for online monitoring of the column separation and purification process of active components that are madecassoside and asiaticoside of Centella asiatica L. Urban using near-infrared (NIR) spectroscopy technology. After collecting 50%-ethanol eluant, we detected their NIR spectra and developed the high performance liquid chromatography (HPLC) assay method of active components. Then, partial least square (PLS) was used to develop linear correlation between their NIR spectra and contents. During modeling, correlation coefficient (R2) and root mean square errors of cross-validation (RMSECV) were regarded as the indexes to select optimal wavenumbers and preprocessing methods. The optimal wavenumbers of madecassoside and asiaticoside were in the range of 12 000.8-7 499.8 cm(-1) and 12 000.8-9 750.3 cm(-1), respectively; R2 were 96.44 and 96.07, respectively, and RMSECV were 0.084 80 and 0.000 99, respectively. The above developed model was used for online monitoring of the contents of madecassoside and asiaticoside during the column separation and purification process of Centella asiatica L. Urban. The predicted results were satisfactory. This method was proved to be fast, convenient and precise. It can be used in online monitoring and quality control of the manufacturing of madecassoside and asiaticoside.

The present paper studies the near-infrared spectroscopy of soluble solids content in navel orange that was influenced by different placement positions of the navel orange. According to the different angles between incident light and the straight line composed by navel orange stems and pit, the authors chose three different placement positions,vertical (90 degrees), parallel (0 degrees) and random(not including 0 degrees and 90 degrees). The authors acquired the semi-transmission spectrum of the navel orange placed in different positions in the wavelength range of 465-1 150 nm by the miniature fiber spectrometer USB4000, there were 336 navel orange samples in the experiment, 228 samples weree used as calibration set, and the rest 108 samples were used as prediction set. The authors used partial least-square regression combined with different pre-processing methods to establish the prediction model of SSC in navel orange with different placement positions. The result shows that when the angle is vertical the prediction models of SSC in navel orange are good, and the best correlation coefficient of the model is r = 0.93, RMSEC = 0.37%, r, = 0.88, and RMSEP = 0.49%.

Intra-operation monitoring depth of anesthesia is an important method to insure the quality and safety of clinical anesthesia. As a noninvasive brain function monitoring technology, functional near-infrared spectroscopy can provide objective and reliable brain activity monitoring and imaging in real time. The characteristic of this technique is highly suitable for interrelated research on depth of anesthesia monitoring. The present paper briefly introduced the fundamental and instruments of functional near-infrared spectroscopy, reviewed the current situation about the application of functional near-infrared spectroscopy in research on depth of anesthesia monitoring, pointed out the possible way of using functional near-infrared spectroscopy in depth of anesthesia monitoring research, and expounded the unsolved problems and future prospects.

In the present paper, a kind of noninvasive determination for human serum protein concentration of albumin, globulin and total protein was explored based on the technology of near-infrared reflectance spectra. Reflectance spectra on the tongue tip of 58 volunteers were collected. Because these is an nonlinear mapping relationship induced by the individual differences between these spectra data and serum protein concentration, SVM was used to establish quantitative regression models of 3 kinds of protein concentration respectively after the normalized spectral reflectance was calculated and the protein content statistics distribution of the sample set was analyzed. In addition, results of SVM were compared with that of PLS. The results show that the predictive effect for calibrated model of SVM is obviously better than that of PLS. Using SVM model to predict the prediction set, the correlation coefficients of ALB, GLB and TP are respectively 0.894, 0.931 and 0.863, and root mean square errors are 2.19, 1.93 and 4.38. So SVM can resist the impact of nonlinear factors among in-vivo determinations, and enhance the robustness of the models. Meanwhile it was also showed that the near infrared spectral information for tongue can relatively objectively reflect changes in human physiological and biochemical indexes, and this technology for noninvasive determination of serum protein concentration is highly feasible.

Aiming to differentiate 13 varieties of corn, present paper proposes an effective approach to solving the parameter drift problem of spectrum instruments. Remarkable drift has been found among the inter-day data when using the identical spectrum instrument to acquire sample data at different times, modeling with the intra-day data, and testing with the rest. The correct recognition rate is reduced to only 7.69% in the condition of severe drift. To tackle this problem, this paper proposes a supervised feature-based inter-day combination modeling approach, at first, the representative sample data acquired at multiple times will be selected to make up the modeling set, and then the PLS+LDA algorithm will be applied to extract the feature of varieties which is independent on instrument parameter drift, and finally BPR will be used to identify the varieties. The experiment results indicate that this approach is effective to rectify the data drift at different times, can bring higher recognition rate, and also shows its stability in practice.

In order to improve the accuracy of spectroscopy analysis and reduce the modeling wavelength numbers, empirical mode decomposition (EMD) and successive projections algorithm (SPA) were applied together in the measurement of benzoyl peroxide (BPO) additive amount in flour by near infrared spectroscopy. Spectra of flour samples into which BPO were added were collected. Firstly, EMD was implemented to eliminate the noise of original spectra, and then SPA was employed to select the characteristic wavelengths. The precision of the model based on the processed spectra by EMD was greatly improved compared with the model based on the original spectra, with the calibration determination coefficient Rcal2 increased from 0.81 to 0.899 and the prediction determination coefficient R2pred increased from 0. 755 4 to 0. 86. Seven characteristic variables were selected from 512 wavelength variables by SPA. And the performance of the model built by the selected characteristic variables (Rcal2 is 0.863, Rpred2 is 0.86) was as good as full-spectrum model's, while the number of modeling variables was greatly reduced by 96.4%. The results indicated that empirical mode decomposition and successive projections algorithm can be effectively used to denoise the spectra and selected characteristic wavelengths for the detection of BPO addition in flour. The seven selected wavelengths in this paper can be a reference for designing portable BPO detection meter.

Rosewood is a kind of precious wood which include many wood species. It's difficult to most people to identify rosewood species. Near infrared spectroscopy (NIR) of eight rosewood species was investigated in the present paper. The results showed that (1) there was significant correlation between near infrared spectroscopy and color parameters expressed by L*, a* and b* values of rosewood, the correlation coefficients between NIR predicted and laboratory measured L* , a* and b* values were 0.988, 0.991 and 0.993, respectively; (2) The eight rosewood samples can be distinctly divided into eight categories by principal component analysis (PCA), the differences in the NIR among the eight rosewood species were more distinctly revealed by the three-dimensional PCA score plot than that of the two-dimensional. The results illustrated that it was feasible to identify rosewood species by near infrared spectroscopy coupled with chemometrics, and also provided a new method to rapidly identify or classify rosewood.

In the present paper, the distribution of sugar level within the mini-watermelon was studied, a new sugar characterization method of mini-watermelon using average sugar level, the highest sugar level and the lowest sugar level index is proposed. Feasibility of nondestructive determination of mini-watermenlon sugar level using diffuse reflectance spectroscopy information was investigated by an experiment. PLS models for measuring the 3 sugar levels were established. The results obtained by near infrared spectroscopy agreed with that of the new method established above.

A prediction model of paddy storage time was established based on near infrared reflectance (NIRS) and chemometrics. A spectroradiometer was used for collecting spectra in the wavelength range from 950 to 1 650 nm. The NIR spectra were collected from 90 samples of paddy. The best pretreatment method was obtained while choosing the total spectra area combined with PLS using the UNSCRAMBLER 9.7. The best pretreatment method is first derivative combined with S. Golay, and the number of principal components is 7. The model is feasible, because the r2 is 0.9679, RMSEP is 54.51 and the result of T-test is passable while validation method is cross validation In this paper, a feasible method is established to measure the storage time of paddy based on near infrared reflectance(NIRS)and chemometrics.

The feasibility of wood identification of softwood and hardwood by near infrared spectroscopy (NIR) coupled with partial least squares discriminant analysis (PLS-DA) was investigated in the present paper. The near infrared spectra (780 - 2 500 nm) were collected from wood cross-section from one softwood species (Chinese fir) and one hardwood species (eucalyptus). The results show that: (1) The identification accuracy of the calibration samples predicted by the model based on NIR coupled the PLS-DA was 100%. The correlation coefficient between the NIR predicted category variable value and the true value was 0.990, and the SEC was 0.071; (2) The identification accuracy by the model based on the spectra with 780-1 100 nm wavelengths also was 100%, and the correlation coefficient and SEC were 0. 990 and 0. 070, respectively; (3) The identification accuracy for the test samples was 100%. It was suggested that NIR can be used to rapidly and accurately identify softwood and hardwood samples. It also provides a new approach to identifying wood species.

The majority of nutrients in ruminants and other herbivores come from forages. Forage quality not only affects the growth and production efficiency of livestock, but also determines the final output and quality of livestock products. Forage quality mainly depends on nutrient concentrations and their digestibility, palatability and the level of presence of antiquality factors and mycotoxins in forage. Near infrared reflectance spectroscopy (NIRS) has been widely used in many research areas because it is a inexpensive, rapid, simple and nondestructive technique offering the potential for qualitative and quantitative analysis. The present paper briefly introduces the principle and characteristics of NIRS, detailedly expounds the application of NIRS in forage quality. In addition, other applications of near infrared spectroscopy technique in forage are also discussed, including forage breeding, identification of variety and classification by kind. This paper comprehensively reviews the status quo of application of NIRS in forage filed, in order to contribute to promoting development of NIRS in this field in China.

In the present paper, the spectral measurements of maize population samples were researched so as to identify their authenticity. Diffuse reflectance and transmittance measure modes were used to collect spectral data of 8 maize varieties. DPLS-DA was used to compress pretreated data. The recognition models of eight maize varieties were built based on biomimetic pattern recognition (BPR). The average correct recognition rate and average correct rejection rate of identification models built by two modes were calculated. The average recognition rate and rejection rate of diffuse reflectance method reached 94.50% and 96.71%, and those of transmittance method reached 98.5% and 98.00%, respectively. Both of them met the requirements of maize preliminary screening, and the recognition rate and rejection rate of transmittance method are higher than diffuse reflectance method by 4% and 1.3% respectively.

The spectral subtraction approach with different flow blood volume in near infrared noninvasive biochemical sensing can eliminate human tissue background interference, but at the same time the pathlength of subtracted spectrum is unknown. Thus, the model will be insufficiently effective if established directly with pathlength-unknown spectra, which is a big problem for subtracted blood volume spectrometry. In the present paper, a simulated experiment was designed to simulate this issue. The orthogonal signal correction method was proposed to eliminate the influence brought by pathlength. Compared with the PLS model before and after orthogonal signal correction, the root mean square error of cross validation (RMSECV) was from 90.17 mg x dL(-1) down to 31.62 mg x dL(-1), and the correlation coefficient was improved from 0.9787 to 0.9968. The experimental results show that using the orthogonal signal correction method can effectively restrain the interference information of path-length, and improve the prediction precision of calibration model. The conclusion in this paper sets the stage for the practical application of the spectral subtraction approach with different flow blood volume.

Near infrared spectroscopy (NIRS) is a process analysis and monitoring tool with many advantages, while it needs to set up quantitative or discriminative calibration models in advance, and needs to adjust these models when the process conditions are varied, which makes it difficult for ordinary user to take its full advantage of it. To tackle this problem, this paper presented a novel, simple and model-free methodology for online process monitoring based on two reciprocal viewpoints of measuring the variability of spectroscopy-both the similarity and dissimilarity of process spectrum, i.e., the adaptive moving window standard deviation function(AMWSW) and similarity function(S). The methodology was validated by a column chromatography process of traditional Chinese medicine using near infrared spectroscopy. The online trend curves of AMWSW and S obtained by proposed method were validated by a comparison with the content variation curves of multiple indicative components analyzed by high performance liquid chromatography (HPLC), and these trend curves demonstrated their potential for real-time process status monitoring, accurately determining the beginning point, the peak point, the end point of the elution, and the phase change from water solution to ethanol solution. The proposed methodology can also be used to other process analysis techniques, such as ultraviolet/visible, infrared, Raman, fluorescence, chromatograph and mass spectrum.

The research on near infrared spectroscopy of sodium in biological and medicine is significant. Sodion is the main component of electrolytes in human blood and electrolytes help maintain the body's acid-base balance. In the present paper the concentration of sodium was determined with the use of NIR spectra. On the basis of NIR spectroscopic measurement mechanism of sodion, prediction models of the concentration of sodium were developed with linear regression using the absorbance at selected wavelengths. In order to reduce temperature perturbations to water bands with the measurement of sodium, Partial least squares regression (PLS) was adopted using select spectra area. The result shows that the determination coefficients (R2) = 99.82%, the root mean square error of cross validation (RMSECV) = 14.5, and the residual prediction deviation (RPD) = 23.7, for the calibration model. It meets the daily requirements of biochemical detection accuracy. This technique can be applied to quantitative analysis of sodion in the hospital laboratory.

Goose down and duck down have very similar appearance but the quality of goose down is better than that of duck down in general. There is a highest allowable limit as specified by the various national standards of feather and down for the percentage of duck feather or down mixed in goose feather or down. Traditional detection method, manual inspection with a high-scale microscope, is labor intensive and not suitable for large-volume samples analysis and on-site rapid testing. In the present paper, visible/near-infrared (NIR) spectroscopy combined with successive projection algorithm (SPA) for characteristic wavelengths selection was used to determinate the content of duck down mixed in goose down. In the range of 450-930 nm, the multiple linear regression (MLR) model established with the 8 characteristic wavelengths selected by SPA achieved good prediction, the correlation coefficient of 0.983, root mean square error of calibration (RMSEC) of 5.44%, and root mean square error of prediction (RMSEP) of 5.75%. Therefore, it is expected to be used for rapid detection of feather and down quality in future.

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.

In the present paper, both the physical characteristics and spectral signatures of southern Xinjiang jujube were studied. With the methods of repetitive adjustment and stepwise refinement, the analysis diagram of jujube quality subdivision and characteristic factors were obtained. In actual practice, the spectra repeatability and quality can be improved by setting an optimum acquisition parameter according to different experimental requirements. Through experiments the characteristic factors of crack browning, luster and compositions were obtained, which demonstrates a distinguishing downing characteristic spectral line at the wave number of 10 170 cm(-1). Through characteristic analysis of jujube NIR spectra, the correspondence between the jujube spectra and their qualities was established, which lays the foundation for jujube qualities' characteristic coding in the future. The application of near-infrared topology method in the quality analysis of southern Xinjiang jujube is cost saving, which has a broad application prospect in establishing the NIR analytical standard and model database sharing of the jujube's quality in the future.

Weak signal, low instrument signal-to-noise ratio, continuous variation of human physiological environment and the interferences from other components in blood make it difficult to extract the blood glucose information from near infrared spectrum in noninvasive blood glucose measurement. The floating-reference method, which analyses the effect of glucose concentration variation on absorption coefficient and scattering coefficient, gets spectrum at the reference point and the measurement point where the light intensity variations from absorption and scattering are counteractive and biggest respectively. By using the spectrum from reference point as reference, floating-reference method can reduce the interferences from variation of physiological environment and experiment circumstance. In the present paper, the effectiveness of floating-reference method working on improving prediction precision and stability was assessed through application experiments. The comparison was made between models whose data were processed with and without floating-reference method. The results showed that the root mean square error of prediction (RMSEP) decreased by 34.7% maximally. The floating-reference method could reduce the influences of changes of samples' state, instrument noises and drift, and improve the models' prediction precision and stability effectively.