Dual-Domain Calibration Transfer Using Orthogonal Projection.

To improve the quality of input data of multivariate calibration model, a new hybrid preprocessing algorithm (WPT-GA-OSC) was proposed, which is the combination of wavelet packet transform (WPT), genetic algorithm (GA) and orthogonal signal correction (OSC) algorithm. At first, WPT algorithm is applied to split the raw spectra into different frequency components. Then, based on the root mean square error of prediction models, the genetic algorithm is employed to select the WPT components related to analyte as the input data of regression model. At last, to further improve the quality of input data, OSC algorithm is applied to each GA-filtered component to eliminate the information irrelevant to analyte information. To validate the WPT-GA-OSC algorithm, it was applied to develop the calibration model for oil concentration measurement of corn. Compared with the conventional preprocessing algorithm, the WPT-GA-OSC algorithm can take full advantages of multiscale property of near infrared (NIR) spectra, and also can significantly decrease the prediction error by up to 48.3%, indicating that it is a promising way for filtering the spectral data to develop the NIR calibration model.

Near-infrared determination of active substance content in intact low-dosage tablets

Fast determination of lipid and protein content in green coffee beans from different origins using NIR spectroscopy and chemometrics

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) spectroscopy, the transfer of predictive models between Fourier transform near-infrared (FT-NIR) and scanning–grating-based instruments has been accomplished on relatively dry samples (< 10% water) using various chemometric techniques—for example, slope and bias correction (SBC), direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT), and application of neural networks. In this study, seven well-known techniques [SBC, DS, PDS, double-window PDS (DWPDS), OSC, FIR, and WT], a photometric response correction and wavelength interpolative method, and a model updating method were assessed in terms of root mean square error of prediction (RMSEP) (using Fearn's significance testing) for calibration transfer (standardization) between pairs of spectrometers from a group of four spectrometers for noninvasive prediction of soluble solid content (SSC) of melon fruit. The spectrometers were diffraction grating-based instruments incorporating photodiode array photodetectors (MMS1, Carl Zeiss, Jena, Germany), used with a standard optical geometry of sample, light source, and spectrometer. A modified WT method performed significantly better than all other standardization methods and on a par with model updating.

The qualitative determination of acetone is performed by passive Fourier transform infrared (FT-IR) spectrometry with data spanning two spectrometers. Digital filtering and piecewise linear discriminant analysis techniques are optimized and applied directly to short interferogram segments to eliminate background and instrument variation and then perform pattern recognition. Once optimized, this methodology classifies remote sensing data into categories representing the presence or absence of the analyte in an automated fashion. The addition to the training set of small numbers of interferogram data from a second spectrometer is evaluated in the creation of qualitative models robust with respect to differences between the instruments. Results of these experiments show that classification percentages averaged across all tested interferogram segments are improved from 76.2 ± 6.4% to 95.1 ± 2.2% with the addition of as few as 10 background interferograms collected in the field with the secondary instrument. The results also demonstrate that a broader, more optimal range of segments in the interferogram can be utilized when these background data are added from the secondary instrument. It is also found possible to standardize the data from the secondary instrument with blackbody background interferograms collected in the laboratory.

Single kernel wheat hardness estimation using near infrared hyperspectral imaging

The principal aim of this work was to compare the ability of measurement technique among interactance and reflectance in predicting soluble solids content (SSC), ascorbic acid (ASC) and firmness of intact persimmon fruit by using visible/short-wave near-infrared (Vis/SWNIR) spectroscopy. Calibration models were developed by partial least square (PLS) regression that related near-infrared (NIR) spectra to reference values. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the correlation coefficients of calibration (Rcal) and prediction (Rval) and the ratio of performance to deviation (RPD) were used to consider the model accuracy. The PLS models from interactance showed satisfactory performance, providing better prediction results than reflectance technique in all parameters. ASC and firmness presented the best calibration models. ASC, Rval = 0.92, RMSEP = 5.56 (mg kg-1 FW) and RPD = 2.54 were attained when using orthogonal signal correction (OSC) pretreatment. Firmness, Rval = 0.89, RMSEP = 4.21 N and RPD = 2.14 were also achieved using OSC pretreatment. These findings highlighted the potential of Vis/SWNIR spectroscopy with the multivariate calibration technique to be applied for evaluating ASC and firmness of fresh persimmon fruit.

Multivariate calibration models for predicting the initial reactant and an impurity in a synthesis of contrast media have been developed using near‐infrared (NIR) spectroscopy. The reaction is presently monitored off‐line by HPLC. The calibration models are based on spectra measured on‐line. The process solution is chemically complex and the intention of this study was to enhance knowledge about the predictive ability of the models. Different data‐preprocessing and variable selection methods reported in the literature have been tested in order to enhance chemical information and reduce irrelevant variability due to physical influences on measured spectra such as scattering and temperature. In addition to standard preprocessing methods such as normalization, differentiation and multiplicative scatter correction, orthogonal signal correction and optimized scaling were tested. The modelling was performed by partial least squares (PLS), except for optimized scaling which uses principal component regression (PCR). Optimized scaling on differentiated data gave the lowest prediction error. The NIR region from 1100 to 1900 nm gave better models than variable selection and fewer variables. Models with correlation coefficients for best fit equal to 0.998 and 0.961 were obtained for the initial reactant and the impurity, respectively. Copyright © 2002 John Wiley & Sons, Ltd.

The global utilization of nuclear energy has come a long way from its humble beginnings in the first sustained nuclear reaction at the University of Chicago in 1942. Today, there are over 440 nuclear reactors in 31 countries producing approximately 16% of the electrical energy used worldwide. In the United States, 104 nuclear reactors currently provide 19% of electrical energy used nationally. The International Atomic Energy Agency projects significant growth in the utilization of nuclear power over the next several decades due to increasing demand for energy and environmental concerns related to emissions from fossil plants. There are 28 new nuclear plants currently under construction including 10 in China, 8 in India, and 4 in Russia. In the United States, there have been notifications to the Nuclear Regulatory Commission of intentions to apply for combined construction and operating licenses for 27 new units over the next decade. The projected growth in nuclear power has focused increasing attention on issues related to the permanent disposal of nuclear waste, the proliferation of nuclear weapons technologies and materials, and the sustainability of a once-through nuclear fuel cycle. In addition, the effective utilization of nuclear power will require continued improvements in nuclear technology, particularly related to safety and efficiency. In all of these areas, the performance of materials and chemical processes under extreme conditions is a limiting factor. The related basic research challenges represent some of the most demanding tests of our fundamental understanding of materials science and chemistry, and they provide significant opportunities for advancing basic science with broad impacts for nuclear reactor materials, fuels, waste forms, and separations techniques. Of particular importance is the role that new nanoscale characterization and computational tools can play in addressing these challenges. These tools, which include DOE synchrotron X-ray sources, neutron sources, nanoscale science research centers, and supercomputers, offer the opportunity to transform and accelerate the fundamental materials and chemical sciences that underpin technology development for advanced nuclear energy systems. The fundamental challenge is to understand and control chemical and physical phenomena in multi-component systems from femto-seconds to millennia, at temperatures to 1000?C, and for radiation doses to hundreds of displacements per atom (dpa). This is a scientific challenge of enormous proportions, with broad implications in the materials science and chemistry of complex systems. New understanding is required for microstructural evolution and phase stability under relevant chemical and physical conditions, chemistry and structural evolution at interfaces, chemical behavior of actinide and fission-product solutions, and nuclear and thermomechanical phenomena in fuels and waste forms. First-principles approaches are needed to describe f-electron systems, design molecules for separations, and explain materials failure mechanisms. Nanoscale synthesis and characterization methods are needed to understand and design materials and interfaces with radiation, temperature, and corrosion resistance. Dynamical measurements are required to understand fundamental physical and chemical phenomena. New multiscale approaches are needed to integrate this knowledge into accurate models of relevant phenomena and complex systems across multiple length and time scales.

Surface-wave-based methods involving active or passive sources are used in investigations spanning a wide range of scales from ultrasonic non-destructive evaluation of civil infrastructure to global seismic imaging of the Earth’s mantle. Near-surface geophysical applications with active sources, probing to depths ~ 30 m, are experiencing steady growth (Socco et al ., 2010). For the most part, the key information is embedded in high-amplitude, low-frequency Rayleigh waves, i.e. the ground roll that is normally regarded as a source of noise in seismic body wave reflection and refraction studies. Typically, surface or interface waves of various types (e.g. Rayleigh, Love, Scholte, Lamb, and Stoneley waves) are guided and highly dispersive. Recognition of these properties drove the development in the 1980s of the spectral analysis of surface wave (SASW) method (Nazarian and Stokoe, 1986) and, later, the multichannel analysis of surface wave (MASW) method (Park et al ., 1999). In these and other related techniques, apparent Rayleigh phase velocity versus frequency curves are first constructed, and then inverted to obtain shear-wave depth profiles. The resulting estimates of shear-wave speed in the shallow subsurface can be interpreted in terms of physical properties such as stiffness, liquefaction potential, and moisture content. These properties are of great interest to geotechnical and construction engineers, soil scientists, and others. In addition, the magnitude of ground shaking in response to a nearby earthquake is highly dependent on the subsurface shear-velocity structure. Rayleigh waves Consider a mechanical disturbance within an infinite homogeneous elastic medium. Both compressional (P-) and shear (S-) body waves are generated, as described in the previous chapter. Suppose now the elastic medium occupies only the lower halfspace, such that a free surface is present. In this case there exists also a Rayleigh wave (R-wave) solution to the elastic wave equations (Richart et al ., 1970). If the medium is heterogeneous, with spatially varying elastic moduli, the R-wave packet is dispersive. The wave packet can be decomposed by Fourier analysis into its individual frequency components. Each frequency component of the wave packet travels at its own characteristic phase velocity. The shape of the phase velocity versus frequency curve is sometimes called the dispersion characteristic . Note that an R-wave traveling in a homogeneous elastic medium is not dispersive.

There is an increasing need for using a set of near infrared (NIR) instruments in industrial activities. In this study, three methodologies of working with a set of NIR instruments are reviewed: the standardisation of the predicted values, the standardisation of the absorbances and the modelling of the instrument differences. Modelling the NIR differences is performed by including in the calibration/validation sets spectra recorded in any of the different instruments of the set without any pre-treatment or after transfer by orthogonal projection (TOP) pre-transformation. In the latter case, TOP calculations from different sets of products are assayed. The methods are briefly presented and discussed through the results of a set of four InfraAlyzer 2000 instruments, 19-filter spectrometers which are used in the quality control of fresh alfalfa. Although the optimal procedure depends on the particular conditions of the industrial work (number of products, number of properties, number of instruments, etc.), all the above methods have been found to be suitable for operating with a set of NIR instruments. The present results also indicate an independence of the TOP results from the product used to compute the TOP factors, at least for the present set of NIR instruments which all belong to the same model and company.

This quantitative study aimed to explore the relationships among music performance anxiety (MPA), teacher anxiety (TA), and self-efficacy (SE) through a survey study of Chinese preservice music teachers (N = 237). We also examined gender, grade, primary instrument, secondary instrument, music learning time length, and time spent in four activities: peer teaching, practicum, internship, and private teaching as potential predictors of MPA, TA, and SE. Results indicated that the higher the self-efficacy, the lower music performance anxiety and teaching anxiety; Simultaneously, the increased music performance anxiety was associated with an increased teaching anxiety. Partial correlation results indicated a significant but negative correlation between TA and SE with MPA controlled. Teaching anxiety, followed by primary piano, was the strongest predictor of MPA. MPA, followed by SE and peer teaching, was the strongest predictor of TA. TA, followed by grade level, was the strongest predictor of SE. The results from the multivariate analysis of variance revealed that the SE of male preservice music teachers were significantly higher than their female counterparts. As a study implication, music teacher educators may consider interventions and support mechanisms that address both types of anxiety simultaneously to improve overall teacher preparedness.

본 논문은 외국인관광객의 의료관광 만족을 측정하기 위한 지표와 척도를 개발하는데 목적이 있었다. 우선 문헌고찰을 통하여 1차 에비항목으로 63개를 선정하였고, 다음으로 전문가를 대상으로 한 면담조사를 통하여 부적합한 9개 문항을 제외한 54개 문항을 2차 예비지표 항목으로 설정하였다. 이를 국내관광을 위해 방문한 외국인관광객을 대상으로 2012년 7월 1일부터 2012년 10월 20일까지 조사하였다. 외국인관광객 589명을 대상으로 요인분석을 한 결과 7개의 요인이 추출되었으며, 이 요인들의 누적 설명분산은 60.9%이었다. 의료관광 만족을 측정하기 위한 지표는 '의료서비스의 질', '대응성', '관광연계성', '공감성', '편리성', '신뢰성', '경제성'으로 명명하였다. 결론적으로 한국의 의료관광에 관심과 의도를 가지고 있는 영어, 일본어, 중국어 등 다양한 언어권의 관광객을 대상으로 의료관광 만족에 대한 신뢰도와 타당도를 검증한 척도개발 연구라는 점에서 의의가 있다. The purpose of this study was a methodological research to develop an instrument to assess the medical tourism satisfaction of foreign tourist. A primary instrument with 63 questions was developed based on literature review. A group of experts revised individual questions and removed 9 irrelevant questions. This secondary instrument, then, was distriuted from Jury 1, 2012 to October 20, 2012. It was tested with 589 members of foreign tourist. After factor analysis on the final 42 questions, 7 factors were identified; quality of medical service, correspondance, tourism connection, sympathy, convenience, believability, and economical efficiency. The total variance explained was 60.9%. This instrument was statistically reliable and valid to measure medical tourism satisfaction of foreign tourist.

A sequential algorithm for multiblock orthogonal projections to latent structures