Monitoring the Temperature of Dilute Aqueous Solutions Using Near-Infrared Water Absorption

2003 ◽  
Vol 57 (6) ◽  
pp. 661-666 ◽  
Author(s):  
Eugenio H. Otal ◽  
Fernando A. Iñón ◽  
Francisco J. Andrade
Keyword(s):  
Aqueous Solutions ◽  
Water Absorption ◽  
Prediction Error ◽  
Near Infrared ◽  
Matrix Effects ◽  
Signal To Noise Ratio ◽  
Predictive Ability ◽  
Multilinear Regression ◽  
Multilinear Regression Analysis ◽  
Dissolved Ions

An alternative spectroscopic approach for monitoring the temperature of aqueous solutions is presented. The method is based upon the temperature-induced spectral changes undergone by the second overtone (around 960 nm) of the near-infrared (NIR) water absorption band. Single and multilinear regression analysis are tested in order to evaluate the predictive ability of temperature. A linear dependence is found when measurements are performed at a single wavelength, but a lower prediction error is obtained when multilinear models are applied. No matrix effects produced by moderately concentrated common dissolved ions are found in a broad range of pH. A signal-to-noise ratio allows a precision of 0.5 °C for temperature monitoring. A prediction error of 0.77 °C (single linear regression) and 0.25 °C (multilinear approach) are achieved in a range from 15 to 90 °C. Advantages in terms of instrumentation and data analysis required are discussed.

scholarly journals Establishment of Prediction Equations of Lean Body Mass Suitable for Chinese Adults

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinhua Li ◽  
Jingjie Shang ◽  
Bin Guo ◽  
Jian Gong ◽  
Hao Xu
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Predictive Ability ◽  
Female Group ◽  
Chinese Adults ◽  
Multilinear Regression ◽  
Predictive Equations ◽  
Large Sample ◽  
Multilinear Regression Analysis ◽  
Southern Chinese

Aim. To develop predictive equations of lean body mass (LBM) suitable for healthy southern Chinese adults with a large sample. LBM measured by dual-energy X-ray absorptiometry (DXA) are considered as the standard ones. Methods. Retrospective analysis was conducted on the consecutive people who did total body measurement with DXA from July 2005 to October 2015. People with diseases that might affect LBM were excluded and overall 12,194 subjects were included in this study. Information about the 10,683 subjects (2,987 males and 7,696 females) from July 2005 to November 2014 was used to establish equations. These subjects were grouped by sex and then subdivided according to their body mass index (BMI). The female group was divided into another two subgroups: the premenopausal and postmenopausal subgroups. Equations were developed through stepwise multilinear regression analysis of height, weight, age, and BMI. Information about the 1,511 subjects (395 males and 1116 females) from December 2014 to October 2015 was used to verify the established equations. Results. BMI, height, weight, and age were introduced into the equations as independent variables in the male group, while age was proved to have no influence on LBM in the female group. Regrouping according to BMI or menopause did not increase the predictive ability of equations. Good agreement between LBM evaluated by equation (LBM_PE) and LBM measured by DXA (LBM_DXA) was observed in both the male and female groups. Conclusion. Predictive equations of LBM suitable for healthy southern Chinese adults are established with a large sample. BMI was related to LBM content; however, there is no need for further group based on BMI or menopause while developing LBM questions.

scholarly journals Near-Infrared Transmission Spectroscopy of Aqueous Solutions: Influence of Optical Pathlength on Signal-To-Noise Ratio

2002 ◽  
Vol 56 (12) ◽  
pp. 1600-1606 ◽  
Author(s):  
Peter Snoer Jensen ◽  
Jimmy Bak
Keyword(s):  
Aqueous Solutions ◽  
Noise Level ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Pure Water ◽  
Optimal Choice ◽  
Infrared Transmission ◽  
Signal To Noise ◽  
Infrared Spectrometer ◽  
Noise Ratio

The optimal choice of optical pathlength, source intensity, and detector for near-infrared transmission measurements of trace components in aqueous solutions depends on the strong absorption of water. In this study we examine under which experimental circumstances one may increase the pathlength to obtain a measurement with higher signal-to-noise ratio. The noise level of measurements at eight different pathlengths from 0.2 to 2.0 mm of pure water and of 1 g/dL aqueous glucose signals were measured using a Fourier transform near-infrared spectrometer and a variable pathlength transmission cell. The measurements demonstrate that the noise level is determined by the water transmittance. The noise levels in the spectral region from 5000 to 4000 cm−1 show that the optimal pathlength (0.4 mm) is the same for pure water and 1 g/dL aqueous glucose solutions. When detector saturation occurs it is favorable to increase the pathlength instead of attenuating the light source. The obtained results are explained by an analytical model.

Using near infrared measurements to evaluate NaCl and KCl in water

2019 ◽  
Vol 27 (2) ◽  
pp. 147-155
Author(s):  
Reisha D Peters ◽  
Scott D Noble
Keyword(s):  
Aqueous Solutions ◽  
Near Infrared ◽  
Average Error ◽  
Spectral Measurements ◽  
Salt Solutions ◽  
Saturation Point ◽  
Test Set ◽  
Solution Type ◽  
All Solutions ◽  
Infrared Measurements

Spectral differences between aqueous solutions of NaCl and KCl have received minimal attention in previous research due to strong similarities between the two salts and the lack of motivation to differentiate between them. Correlations between salinity and absorbance have been developed previously with varying degrees of linearity but have not been tested to saturation. This work will demonstrate that correlating spectral measurements and the concentration of NaCl and KCl in water can be extended up to the saturation point of both salts and that solutions of these salts with unknown concentrations can be distinguished. Spectral data for samples of NaCl and KCl in single-salt solutions were collected up to saturation and correlations were developed for differentiating between solutions of the two species. These correlations were able to correctly identify the solution type for all solutions in the test set and estimate their concentrations with an average error of 0.9%.

scholarly journals The Improvement on the Performance of DMD Hadamard Transform Near-Infrared Spectrometer by Double Filter Strategy and a New Hadamard Mask

Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 149 ◽  
Author(s):  
Zifeng Lu ◽  
Jinghang Zhang ◽  
Hua Liu ◽  
Jialin Xu ◽  
Jinhuan Li
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Nonuniform Distribution ◽  
Stray Light ◽  
Spectral Energy ◽  
Hadamard Transform ◽  
Digital Micromirror Device ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
The Impact

In the Hadamard transform (HT) near-infrared (NIR) spectrometer, there are defects that can create a nonuniform distribution of spectral energy, significantly influencing the absorbance of the whole spectrum, generating stray light, and making the signal-to-noise ratio (SNR) of the spectrum inconsistent. To address this issue and improve the performance of the digital micromirror device (DMD) Hadamard transform near-infrared spectrometer, a split waveband scan mode is proposed to mitigate the impact of the stray light, and a new Hadamard mask of variable-width stripes is put forward to improve the SNR of the spectrometer. The results of the simulations and experiments indicate that by the new scan mode and Hadamard mask, the influence of stray light is restrained and reduced. In addition, the SNR of the spectrometer also is increased.

Combined Near-Infrared Raman Microprobe and Laser Trapping System: Application to the Analysis of a Single Organic Microdroplet in Water

1998 ◽  
Vol 52 (3) ◽  
pp. 339-342 ◽  
Author(s):  
Katsuhiro Ajito
Keyword(s):  
Raman Spectrum ◽  
Near Infrared ◽  
Laser Light ◽  
Signal To Noise Ratio ◽  
Peak Height ◽  
Laser Spot ◽  
Laser Trapping ◽  
Raman Microprobe ◽  
A Charge ◽  
Nir Laser

A combined Raman microprobe and laser trapping system using near-infrared (NIR) laser light was developed for the investigation of single organic microdroplets. The NIR laser light is noninvasive and reduces fluorescence interference in the Raman spectrum for organic molecules. The focused laser beam used for the laser trapping of a microdroplet serves simultaneously as the laser microprobe for Raman measurement. With this system, the focused laser spot is about 1 μm in diameter, which is small enough for the laser trapping of a single toluene microdroplet in water. The system also makes it possible to visualize a focused laser spot together with a laser-trapped microdroplet by using holographic notch filters. The Raman spectrum for a single laser-trapped toluene microdroplet can be obtained from below 100 cm−1 to above 3000 cm−1 with a charge-coupled device (CCD) detector. Fluorescence interference in the Raman spectrum is completely removed by using NIR laser light. The signal-to-noise ratio (SNR), defined as the ratio of the peak height to the standard deviation of the baseline noise in the spectrum, exceeded 250 for the 1003 cm−1 band of a toluene microdroplet at 1 s, which is sufficient to allow identification of the molecular species of a microdroplet.

scholarly journals Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

2021 ◽  
Vol 13 (8) ◽  
pp. 1519
Author(s):  
Kensuke Kawamura ◽  
Tomohiro Nishigaki ◽  
Andry Andriamananjara ◽  
Hobimiarantsoa Rakotonindrina ◽  
Yasuhiro Tsujimoto ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Soil Phosphorus ◽  
Predictive Ability ◽  
Available P ◽  
Accurate Estimation ◽  
One Dimensional ◽  
P Content ◽  
Soil Available P

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

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