Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment&#x2019;s results demonstrated its advantages in both visual fidelity and numerical measures.

Wenjian Chen

Yi Wang

Xuan Li

Wei Gao

Shiwei Ma

Yuanyuan Duan

Xiaopeng Shao

Hazardous Gas Emission Monitoring Based on High-Resolution Images

The mineral chemistry of twenty chlorite samples from the United States Geological Survey (USGS) spectral library and two other regions, having a wide range of Fe and Mg contents and relatively constant Al and Si contents, was studied via infrared (IR) spectroscopy, near-infrared (NIR) spectroscopy, and X-ray fluorescence (XRF) analysis. Five absorption features of the twenty samples near 4525, 4440, 4361, 4270, and 4182 cm−1 were observed, and two diagnostic features at 4440 and 4280 cm−1 were recognized. Assignments of the two diagnostic features were made for two combination bands (<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-4.67947pt" id="M1" height="13.9678pt" version="1.1" viewBox="-0.0498162 -9.28833 86.3438 13.9678" width="86.3438pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-41" d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"/></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path id="g185-47" d="M213 67C239 136 257 225 255 300C253 353 242 447 164 447C112 447 54 389 41 319L67 309C90 362 117 377 134 377C184 377 189 277 190 239C193 153 164 43 146 -12H176C278 73 450 253 450 390C450 439 410 463 396 463C384 463 380 459 382 449C385 440 389 415 389 402C389 284 312 146 216 67H213Z"/></g><g transform="matrix(.013,0,0,-0.013,13.306,0)"><path id="g117-36" d="M535 230V280H323V490H265V280H52V230H265V-3H323V230H535Z"/></g><g 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311V109C104 41 97 35 33 30V0H257Z"/></g><g transform="matrix(.0091,0,0,-0.0091,45.163,3.132)"><use xlink:href="#g58-63"/></g><g transform="matrix(.0091,0,0,-0.0091,51.56,3.132)"><use xlink:href="#g58-106"/></g><g transform="matrix(.0091,0,0,-0.0091,53.926,3.132)"><use xlink:href="#g50-42"/></g><g transform="matrix(.0091,0,0,-0.0091,57.184,3.132)"><use xlink:href="#g58-77"/></g><g transform="matrix(.0091,0,0,-0.0091,64.073,3.132)"><use xlink:href="#g54-33"/></g><g transform="matrix(.0091,0,0,-0.0091,69.633,3.132)"><use xlink:href="#g58-77"/></g><g transform="matrix(.0091,0,0,-0.0091,76.458,3.132)"><use xlink:href="#g58-70"/></g></svg>) by regression with IR fundamental absorptions. Furthermore, the determinant factors of the NIR band position were found by comparing the band positions with relative components. The results showed that Fe/(Fe + Mg) values are negatively correlated with the two NIR combination bands. The findings provide an interpretation of the NIR band formation and demonstrate a simple way to use NIR spectroscopy to discriminate between chlorites with different components. More importantly, spectroscopic detection of mineral chemical variations in chlorites provides geologists with a tool with which to collect information on hydrothermal alteration zones from hyperspectral-resolution remote sensing data.

Min Yang

Meifang Ye

Haihui Han

Guangli Ren

Ling Han

Zhuan Zhang

Near-Infrared Spectroscopic Study of Chlorite Minerals

Near-infrared (NIR) spectroscopy provides a rapid alternative to traditional methods of wood property assessment. For organizations who assess wood properties on a large scale, multisite, multispecies calibrations are of practical interest. We examined NIR spectroscopy for the estimation of density (at 12&#x0025; moisture content), modulus of elasticity (MOE), and modulus of rupture (MOR) using clear wood samples obtained from several pine species (Pinus caribaea var. bahamensis, var. hondurensis, and var. caribaea, P. chiapensis, P. maximinoi, P. oocarpa, P. taeda, and P. tecunumanii). We compared different methodologies for collecting spectra, that is, benchtop instrument versus benchtop fiber-optic probe and field portable fiber-optic probe, and different wood surfaces (radial and transverse). Calibrations based on the benchtop instrument were superior to those obtained using the fiber-optic probe systems. Difficulty with adequately representing the sample when collecting spectra using a fiber-optic probe and lower quality spectra explain the differences among the data sets. Spectra collected from radial and transverse surfaces provided similar calibration statistics. The calibrations obtained for density (R2&#x2009;=&#x2009;0.81, SECV&#x2009;=&#x2009;38.5&#x2009;kg/m3) and MOE (R2&#x2009;=&#x2009;0.81, SECV&#x2009;=&#x2009;1124&#x2009;GPa) using benchtop instrument spectra demonstrate that it is possible to obtain general calibrations for estimating the wood properties of a number of tropical, subtropical, and temperate pine species.

L. R. Schimleck

J. L. M. Matos

R. Trianoski

J. G. Prata

Comparison of Methods for Estimating Mechanical Properties of Wood by NIR Spectroscopy

Consumers concern about food adulteration. Pork meat is the principal adulterated species of beef and mutton. The conventional detection methods have their own limitations; therefore, we sought to develop an efficient and economical identification method using an infrared spectroscopy technique for meat. The Mahalanobis distance method was used to remove outliers in spectrum data. Interferences were eliminated using multiple scatter correction, standard normal variate, Savitzky-Golay smoothing, and normalization. The partial least square discriminant analysis (PLS-DA) and support vector machine (SVM) were used to establish identification models. In the Mahalanobis distance method, the coefficient of test sets was increased from 0.93 to 0.99; the RMSEC and RMSECV were decreased from 0.17 to 0.09 and 0.21 to 0.11 accordingly. The coefficient of determination in-between the calibration and testing sets in PLS-DA reached 0.99 and 0.99, RMSEC was 0.06, and both the RMSECV and RMSEP were 0.08. In contrast, in SVM, methods were 0.97 and 0.96. The RMSEC, RMSECV, and RMSEP were 0.15, 0.17, and 0.24, respectively. In summary, using a combination of infrared spectroscopy technology with PLS-DA was a better identification method than the SVM method that can be used as an effective method to identify pork, beef, and mutton meat samples.

Ling Yang

Ting Wu

Yun Liu

Juan Zou

Yunmao Huang

Sarath Babu V.

Li Lin

Rapid Identification of Pork Adulterated in the Beef and Mutton by Infrared Spectroscopy

The objective of this paper is to apply planar laser-induced fluorescence (PLIF) technology to flow field visualization. This experiment was carried out in a one-meter wind tunnel to study the wake flow field around a circular cylinder. This experiment studied the method of injecting tracer into the flow field; the frequency of the vortex in the wake field and the vortex speed are quantitatively analyzed. This paper gives the correspondence between the speed of the flow field and the frequency of the laser, which could be used as a rough reference standard for future wind tunnel visualization experiments. The result shows that PLIF diagnostic technology has great potential in visualization of flow field.

Jiang Bo Peng

Ze Fang Lv

Xin Yu

Song He Meng

Hao Dong

Fan Deng

Lin Chen

De Ying Chen

Rong Wei Fan

Zhi Wei Dong

Xu Dong Li

Visualization of Flow Field: Application of PLIF Technique

Quantifying the decomposition of soil organic carbon (SOC) fractions under climate change is essential to predict carbon-climate feedbacks. The accuracy and utility of a combustion module coupled with cavity ring-down spectroscopy (CM-CRDS) system were assessed for simultaneously determining SOC and &#x03B4;13C-SOC. Using a range of standard materials as well as soil samples, we compared the results of the CM-CRDS system with those from other systems for determining C content and &#x03B4;13C value. The CM-CRDS system can determine a vast range of &#x03B4;13C values from &#x2212;7.639&#x2030; to &#x2212;34.318&#x2030;. The &#x03B4;13C values measured at C content&#x2009;&#x003E;&#x2009;0.2&#x2009;mg C, corresponding to 1000&#x2009;ppmv of CO2, were relatively stable. However, below a content of 0.2&#x2009;mg C, the &#x03B4;13C values appeared unsteady and seemed to be affected by background signal. We found that, with the increase of C content, the recovery rates (RRs) for soil samples also increased. On the contrary, the RRs for inorganic materials were much lower than organic material and soil samples. Overall, the CM-CRDS system provides a valid alternative method to determine SOC and &#x03B4;13C-SOC for a sample simultaneously.

Dan Liu

Zhiguo Yu

Junjie Lin

Application of Combustion Module Coupled with Cavity Ring-Down Spectroscopy for Simultaneous Measurement of SOC and &#x03B4;13C-SOC

A wavelength modulation spectroscopy- (WMS-) based gas sensing system was established to measure concentration of carbon monoxide (CO) in the range 0&#x2013;100&#x0025;. The CO absorption line at 1563.06&#x2009;nm was scanned with a tunable distributed feedback (DFB) laser, and two InGaAs photodiodes were applied to perform optic-electric conversion. Without using commercial instruments, essential electrical circuits were self-developed and integrated, including laser temperature controller, laser current driver, signal generator, and digital lock-in amplifier. The gas cell deployed in the system was fiber coupled with a total effective optical path length of 50&#x2009;cm. The second-order harmonic signal was extracted, and experiments of gas detection were carried out to investigate the performance of the sensor, including detection repeatability, detection accuracy, response time, and limit of detection (LoD). Experiment results show that the sensor is reliable and has acceptable probing performance. The maximum relative detection error is less than 3.8&#x0025;, suggesting good detection stability. Benefiting from the self-developed sensor, the whole CO detection system has small size, affordable expense, and application potential.

Bin Li

Shuo-Cheng Zhang

Yao-Dan Chi

Development and Integration of a CO Detection System Based on Wavelength Modulation Spectroscopy Using Near-Infrared DFB Laser

Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.

Xiaoyan Ma

Yanbin Zhang

Hui Cao

Shiliang Zhang

Yan Zhou

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

The laser-induced breakdown spectroscopy (LIBS) combined with multivariate regression analysis of measured data were utilised for determination of the heating value and the chemical composition of pellets made from Estonian oil shale samples with different heating values. The study is the first where the oil shale heating value is determined on the basis of LIBS spectra. The method for selecting the optimal number of spectral lines for ordinary multivariate least squares regression model is presented. The correlation coefficient between the heating value predicted by the regression model, and that measured by calorimetric bomb, was <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="24.361pt" style="vertical-align:-0.2063999pt" id="M1" height="11.7978pt" version="1.1" viewBox="-0.0498162 -11.5914 24.361 11.7978"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-83" d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"/></g><g transform="matrix(.0091,0,0,-0.0091,8.151,-5.741)"><path id="g50-51" d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"/></g><g transform="matrix(.013,0,0,-0.013,16.73,0)"><path id="g117-34" d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"/></g></svg><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.924pt" style="vertical-align:-0.2063999pt" height="11.7978pt" version="1.1" viewBox="27.942183800000002 -11.5914 21.924 11.7978"><g transform="matrix(.013,0,0,-0.013,27.992,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,34.232,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,37.196,0)"><path id="g113-58" d="M244 635C114 635 38 519 38 422C38 317 111 240 217 240C236 240 255 244 277 256L345 292C311 140 203 39 59 15L64 -15C89 -15 150 -5 204 17C339 72 440 202 440 386C440 521 368 635 244 635ZM228 602C326 602 352 479 352 390C352 370 351 347 348 324C327 308 293 296 258 296C174 296 124 369 124 458C124 517 152 602 228 602Z"/></g><g transform="matrix(.013,0,0,-0.013,43.438,0)"><path id="g113-57" d="M249 635C141 635 70 555 70 471C70 401 114 353 179 316C143 294 106 267 90 252C68 231 45 202 45 157C45 50 130 -12 237 -12C322 -12 435 52 435 169C435 256 372 304 303 343C349 374 375 398 383 407C401 429 411 458 411 487C411 569 344 635 249 635ZM238 603C285 603 337 567 337 482C337 422 310 385 276 358C205 393 145 426 145 500C145 552 179 603 238 603ZM248 20C183 20 125 70 125 163C125 218 158 268 206 300C284 261 355 217 355 143C355 66 308 20 248 20Z"/></g></svg>. The standard deviation of prediction was 0.24 MJ/kg. Concentrations of oil shale components predicted by the regression model were compared with those measured by ordinary methods.

M. Aints

P. Paris

M. Laan

K. Piip

H. Riisalu

I. Tufail

Determination of Heating Value of Estonian Oil Shale by Laser-Induced Breakdown Spectroscopy

About this journal

Journal of Spectroscopy publishes research into the theory and application of spectroscopy across all disciplines, including biology, chemistry, engineering, earth sciences, medicine, materials science, physics, and space science.

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Chief Editor&nbsp;Dr Daniel Cozzolino&nbsp;is based at the University of Queensland, Australia. His research focuses on the developments of chemometric and spectroscopic methods for use in agriculture and food applications.

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Journal of Spectroscopy - Table of contents 2018

title

Journal of Spectroscopy publishes research into the theory and application of spectroscopy across all disciplines, including biology, chemistry, engineering, earth sciences, medicine, materials science, physics, and space science.

Journal of Spectroscopy