Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new materials.Hemicellulose is an important component in corn stalks,and it is very important to determine its content in corn stalks.In this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was studied.In order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of NIRS.Its modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

Classification of Guizhou Aspidistra Plants by Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study was conducted to establish a near-infrared diffuse reflectance spectroscopy of Guizhou Aspidistra plants. [Method] Twenty three batch- es of Guizhou Aspidistra plants including A. chishuiensis, A. spinula, A. Caespitosa, A. sichuanensis, A. ebianensis, A. retusa, A. guizhouensis and A. liboensis were subjected to drying, pulverization and sieving and then directly determined for near- infrared reflectance spectrums; and the plants in this genus were classified by clus- ter analysis and principal component analysis （PCA）. [Result] The near-infrared re- flectance spectrums of the 23 batches of Guizhou Aspidistra plants showed very high similarity. The spectrums were processed by first derivative method, and the spectral range of 4 000-7 500 cm-1 was selected as the analytical range. Cluster analysis and PCA were employed to mass spectrum variables of plants in Aspidis- tra, fewer new variables became the linear combination of primary variables, and small differences between different varieties were enlarged, thereby facilitating intu- itive classification of plants in this genus. [Conclusion] Near-infrared diffuse re- flectance spectroscopy is nondestructive and rapid for determination of solid sam- pies, and provides a new method for the classification of Guizhou Aspidistra plants combined by information processing techniques.

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Fructus cnidii （Chinese name shechuangzi） is the fruit produced by Cnidium monnieri （L.） Cusson （Umbelliferae）. It is a perennial herb that is used to treat skin-related diseases and gynecopathyell. Recent pharmacological studies have revealed crude extracts or components isolated from fructus cnidii possess antiallergic, antipruritic, antidermatophytic, antibacterial, antifungal, and antiosteoporotic activities. Osthole and imperatorin are the major compounds present in shechuangzi. They are often used as standards for the evaluation of the quality of shechuangzi products.

Rapid Identification for Drought Resistance of Wheat Using Near-infrared Diffuse Reflectance Spectroscopy

[Objective] This study aimed to establish an identification system for drought-resistance in wheat by using near-infrared diffuse reflectance spectroscopy. [Method] In 2006-2007, 36 wheat varieties with different drought resistance were selected and were classified according to their drought resistance grades determined by the Technical Specification of Identification and Evaluation for Drought Resistance in Wheat （GB/T 21127-2007）. In addition, the harvested wheat seed samples were spectrally analyzed with FOSS NIRSystems5000 near-infrared spectrum analyzer for grain quality （full spectrum analyzer） and then the forecasted regression equations were established. [Result] After the establishment of a database and validation, dis- criminated functions were obtained. The determination coefficient （RSQ） and coeffi- cients of determination for cross validation （1-VR） in the discriminant function built with seed samples from water stress area were 0.846 0 and 0.781 8, respectively, which indicated that the consistency between drought resistance and spectral charac- teristics in wheat varieties was good, and there was high correlation between the near-infrared diffuse reflectance spectra of seeds and the drought resistance in wheat. [Conclusiou] Under water stress condition, it is feasible to establish a conve- nient, rapid and no-damage identification system for the drought resistance in wheat by using the near-infrared diffuse reflectance spectrum technique to scan wheat seeds.

Use of near-infrared reflectance spectroscopy for the rapid determination of the digestible energy and metabolizable energy content of corn fed to growing pigs

Background： The ability of near-infrared reflectance spectroscopy（NIRS） to determine the digestible energy（DE）and metabolizable energy（ME） content of corn fed to growing pigs was tested. One hundred and seventeen corn samples, comprising different planting regions and varieties were collected from all over China in a three-year period. The samples were randomly split into a calibration set（n = 88） and a validation set（n = 29）. The actual and calculated DE and ME content of the corn samples was determined by digestion-metabolism experiments and the prediction equations of Noblet and Perez（J Anim Sci. 71：3389–98,1993）. The samples were then subjected to NIRS scanning and calibrations were performed by the modified partial least square（MPLS） regression method based on77 different spectral pre-treatments. The NIRS equations based on the actually determined and calculated DE and ME were built separately and then validated using validation samples.Results： The NIRS equations obtained from actually determined DE, the coefficient of determination for calibration（RSQcal）, cross-validation（R^2CV）, and validation（RSQv） were 0.89, 0.87 and 0.86, and these values for determined ME were 0.87, 0.86 and 0.86. For the NIRS equations built from calculated DE, the RSQcal, R^2CV, and RSQvvalues were 0.88, 0.85 and 0.84, and these values for calculated ME were 0.86, 0.84 and 0.82. Except for the equation based on calculated ME（RPD_v= 2.38, 〈 2.50）, the other three equations built from actually determined energy and calculated DE produced good prediction performance（RPD_vranging from 2.53 to 2.69, 〉 2.50） when applied to validation samples.Conclusion： These results indicate that NIRS can be used as a quantitative method for the rapid determination of the available energy in corn fed to growing pigs, and the NIRS equations based on the actually determined energy produced better predictive performance than those built from calculated energy values.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Soft independent modeling of class analogy (SIMCA) was successful in classifying a large library of 758 commercially available, non-blended samples of acetate, cotton, polyester, rayon, silk and wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance level). In the present study, cotton and silk had a 62% and 24% chance, respectively, of being classified with their own group and also with rayon. SIMCA correctly identified a counterfeit “silk” sample as polyester. When coupled with diffuse NIR reflectance spectroscopy and a large sample library, SIMCA shows considerable promise as a quick, non-destructive, multivariate method for fiber identification. A major advantage is simplicity. No sample pretreatment of any kind was required, and no adjust-ments were made for fiber origin, manufacturing process residues, topical finishes, weave pattern, or dye content. Increasing the sample library should make the models more robust and improve identification rates over those reported in this paper.

RAPID DETERMINATION OF PROTEIN IN MILLET BY FOURIER TRANSFORM NEAR-INFRARED(FTNIR)DIFFUSE REFLECTANCE SPECTROSCOPY

In this paper,the Fourier transform near-infrared(FTNIR)diffuse reflectance spectroscopy is applied for the rapid determination of protein in millet.The partial least-squares(PLS)regression is successfully used as an effective multivariate calibration technique.The calibration set is composed of 20 standard millet samples that the protein contents were determined by the traditional Kjeldahl method.The optimal model dimension is found to be 5 by cross-validation.22 millet samples were determined by the proposed FTNIR-PLS method.The correlation coefficient between the concentration values obtained by the FTNIR-PLS method and the traditional Kjeldahl method is 0.9805.The standard error of prediction(SEP)is 0.28% and the mean recovery is 100.2%.The proposed method has been successfully applied for the routine analysis of protein in about 10,000 grain samples.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

Evaluation of Expression Recognition Function in Autism Spectrum Disorder Using Near-Infrared Spectroscopy

One of the characteristics of Autism Spectrum Disorder (ASD) is social disorder. The specificity of facial and expression recognition for people with ASD is gathering attention as a factor of this social disorder. The study examined the hemodynamic activities in the prefrontal cortex using near-infrared spectroscopy (NIRS) when a person with ASD performed an expression recognition task. The subjects were twenty males (18 - 22 years old) with ASD and without intellectual disabilities. Forty-five healthy males matched for age and sex were included as a control group. In both groups, the degree of autistic tendencies was evaluated using the Autism-Spectrum Quotient (AQ). Using eight standard emotional expressions of Japanese people, two expression recognition tasks were set. An NIRS was used to measure the prefrontal cortex blood mobilization during the expression-processing process. The AQ was significantly higher in the ASD group, while the rate of overall correct expression response was significantly lower (p &rho;= &minus;0.40 p < 0.001). In the automatic expression-processing task, no activation in the prefrontal cortex was found in either the ASD or the control group. In the conscious expression-processing task, the activation of the left and right lateral prefrontal cortex was weaker in the ASD group compared to the control group. Unlike in the control group, a mild activation of posterior prefrontal cortex was found in the ASD group. The expression-processing process of the ASD group was found to be different from that of the control group. NIRS was effective in detecting a brain function disorder in people with ASD during an expression-processing process.

Diagnosis of Nitrogen Status in Rice Leaves with Canopy Spectral Reflectance

The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under different nitrogen treatments. The results showed there was a close correlation between the canopy spectral reflectance and total leaf nitrogen accumulation. Ratio of near infrared to green band (R810/R560) was linearly related with total leaf nitrogen accumulation. independent of nitrogen levels and development stages. Different datasets were used to test the linear regression equation, with average estimation accuracy of 91. 22%, RMSE of 1.09 and average relative error of 0. 026. Thus, the ratio index R810/R560 of canopy spectral reflectance should be useful for non-destructive monitoring and diagnosis of nitrogen status in rice plants.

Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

High-entropy rare earth stannate ceramics:Acid corrosion resistant radiative cooling materials with high atmospheric transparency window emissivity and high near-infrared solar reflectivity

In response to the development of the concepts of“carbon neutrality”and“carbon peak”,it is critical to developing materials with high near-infrared(NIR)solar reflectivity and high emissivity in the atmospheric transparency window(ATW;8–13μm)to advance zero energy consumption radiative cooling technology.To regulate emission and reflection properties,a series of high-entropy rare earth stannate ceramics(HE-RE_(2)Sn_(2)O_(7):(Y_(0.2)La_(0.2)Nd_(0.2)Eu_(0.2)Gd_(0.2))_(2)Sn_(2)O_(7),(Y_(0.2)La_(0.2)Sm_(0.2)Eu_(0.2)Lu_(0.2))_(2)Sn_(2)O_(7),and(Y_(0.2)La_(0.2)Gd_(0.2)Yb_(0.2)Lu_(0.2))_(2)Sn_(2)O_(7))with severe lattice distortion were prepared using a solid phase reaction followed by a pressureless sintering method for the first time.Lattice distortion is accomplished by introducing rare earth elements with different cation radii and mass.The as-synthesized HE-RE_(2)Sn_(2)O_(7)ceramics possess high ATW emissivity(91.38%–95.41%),high NIR solar reflectivity(92.74%–97.62%),low thermal conductivity(1.080–1.619 W·m^(−1)·K^(−1)),and excellent chemical stability.On the one hand,the lattice distortion intensifies the asymmetry of the structural unit to cause a notable alteration in the electric dipole moment,ultimately enlarging the ATW emissivity.On the other hand,by selecting difficult excitation elements,HE-RE_(2)Sn_(2)O_(7),which has a wide band gap(Eg),exhibits high NIR solar reflectivity.Hence,the multi-component design can effectively enhance radiative cooling ability of HE-RE_(2)Sn_(2)O_(7)and provide a novel strategy for developing radiative cooling materials.

Evaluation of TiAl and 40Cr diffusion bonding quality using ultrasonic reflection spectrum

Combined with ultrasonic pulse-echo technique, reflection spectrum analysis was introduced to evaluate TiAl and 40Cr diffusion bonding quality. Frequency dependence of reflection coefficient was used to distinguish perfect bonding from imperfect bonding. It is found that the reflection coefficient from perfect bonding interface does not vary with frequency. When the size of imperfections is much smaller than the wavelength of ultrasound, the reflection coeffwient depends on frequency. When the size of imperfections is the same order of or even larger than the wavelength of ultrasound, the reflection coeffwient does not exhibit frequency dependence. However the amplitude of imperfect interface is higher than the amplitude of perfect bonding interface. A combination of reflection spectrum analysis and ultrasonic pulse-echo technique provides more accurate information about the bonding quality of dissimilar materials.

Determination of Rice Amylose Content by Near-infrared Spectroscopy

Amylose contents in 298 rice samples were determined by conventional method, and a near-infrared spectral model of rice amylose content was established by partial least squares. The calibration determination coefficient （RC） was 0.95; the standard error of calibration （SEC） was 1.58; and the determination coefficient of cross validation （RP） was 0.91, and the standard error of prediction SEP was 1.92. External validation was performed with 20 samples, the predicted values and the determined values were not significantly different, and the correlation coefficient between them was over 95%. The calibration model has good prediction perfor- mance, and could rapidly determine rice amylose content instead of chemical ana- lytical method.

A Preliminary Stndy on Identification of Clay Minerals in Soils with Reference to Reflectance Spectra

The characteristics of the reflectance spectra of clay minerals and their influences on the reflectancespectra of soils are dealt with in the paper. The results showed that dominant clay minerals in soils couldbe distinguished in light of the spectral-form parameters of the reflectance spectra of soils, thus making itpossible to develop a quick method to determine clay minerals by means of reflectance spectra of soils in thelab. and providing a theoretic basis for retnote sensing of clay minerals in soils with a high resolution imagingspectrometer.

Canopy Spectral Reflectance Characteristics of Rice with Different Cultural Practices and Their Fuzzy Cluster Analysis

The influence of major cultural practices including different nitrogen application rates, population densities, transplanting leaf ages of seedling, and water regimes on rice canopy spectral reflectance was investigated. Results showed that increased nitrogen rates, water regimes and population densities and decreased seedling ages could enhance reflectance at NIR （near infrared） bands and reduce reflectance at visible bands. Using reflectance of green, red and NIR band and ratio index of 810-560 nm could distinguish the different type of rice by fuzzy cluster analysis,

Development and implementation of incident response near-infrared models for analyzing contaminated medicines containing diethylene glycol solvent

Samples of preparations contaminated by diethylene glycol(DEG),diethylene glycol raw materials and laboratory prepared solutions were measured to get NIR spectra.Then the iden-tification models were developed using the collected spectra and the spectra of distilled water,propylene glyool and the preparations without diethylene glyool.Besides,the quantification model was also established for determining the concentration of diethylene glyool in the pre-parations.V alidation results show the identification and quantification models have ideal pre-diction performance.The emergency NIR models are rapid,easy to use and accurate,and can be implemented for identifying diethylene glycol raw material,screening the preparations contam-inated by diethylene glycol in the markets and analyzing the concentrations of DEG.

Quantification of the chemical composition of lunar soil in terms of its reflectance spectra by PCA and SVM

In the second phase of the Chang'E Program an unmanned lunar rover will be launched onto the Moon. When ground scientists get a full understanding of the chemical composition of lunar soil around the rover, they can make more detailed survey plans for the rover and various payloads onboard so as to satisfy their scientific objectives. There is an obvious relationship between the reflectance of lunar soil and its chemical characteristics. Both principal component analysis (PCA) and support vector machine (SVM) models were applied to establishing the relationship between the reflectance spectra and chemical compositions of lunar highland and mare soil samples sent back by Apollo missions 11, 12, 14, 15, 16 and 17 and measured by Lunar Soil Characterization Consortium (LSCC). PCA was used to reduce and select the features of the reflectance spectra of lunar soil samples. Then, these features were put into SVM to estimate the abundances of various chemical components in lunar soil. We also compared the results of our measurement with those obtained by the SVM model [partial least squares (PLS)] and the principal component regression (PCR) model reported in literature. Our studies showed that with the exception of TiO2, the results of prediction of the abundances of chemical compounds in lunar soil by our model are much more reliable than those reported in literature. The reflectance spectra of lunar soil are closely related to the materials from which it was derived.

Wave Reflection Coefficient Spectrum

The wave reflection coefficient frequency spectrum and directional spectrum for concrete face slope breakwaters and rubble mound breakwaters are investigated through physical model tests in the present study. The reflection coefficients of oblique irregular waves are analyzed by the Modified Two-Point Method (MTPM) proposed by the authors. The results show that the wave reflection coefficient decreases with increasing wave frequency and incident angle or decreasing structure slope. The reflection coefficient frequency spectrum and its variation with Iribarren number are given in this paper. The paper also suggests an empirical 3-dimensional reflection coefficient spectrum, i.e. reflection coefficient directional spectrum, which can be used to illustrate quantitatively the variation of reflection coefficient with the incident angle and the Iribarren number for oblique irregular waves.