Temperature-Insensitive Near-Infrared Spectroscopic Measurement of Glucose in Aqueous Solutions

1994 ◽  
Vol 48 (4) ◽  
pp. 477-483 ◽  
Author(s):  
Kevin H. Hazen ◽  
Mark A. Arnold ◽  
Gary W. Small
Keyword(s):  
Spectral Range ◽  
Near Infrared ◽  
Spectroscopic Measurement ◽  
Calibration Model ◽  
Pls Regression ◽  
Absorption Bands ◽  
Single Beam ◽  
Fourier Filtering ◽  
Experimental Parameters ◽  
Analytical Utility

A digital Fourier filter is combined with partial least-squares (PLS) regression to generate a calibration model for glucose that is insensitive to sample temperature. This model is initially created by using spectra collected over the 5000 to 4000 cm−1 spectral range with samples maintained at 37°C. The analytical utility of the model is evaluated by judging the ability to determine glucose concentrations from a set of prediction spectra. Absorption spectra in this prediction set are obtained by ratioing single-beam spectra collected from solutions at temperatures ranging from 32 to 41°C to reference spectra collected at 37°C. The temperature sensitivity of the underlying water absorption bands creates large baseline variations in prediction spectra that are effectively eliminated by the Fourier filtering step. The best model provides a mean standard error of prediction across temperatures of 0.14 mM (2.52 mg/dL). The benefits of the Fourier filtering step are established, and critical experimental parameters, such as number of PLS factors, mean and standard deviation for the Gaussian shaped Fourier filter, and spectral range, are considered.

scholarly journals The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared – Part 3: Quantification of the mid- and near-infrared water vapor continuum in the 2500 to 7800 cm<sup>−1</sup> spectral range under atmospheric conditions

2016 ◽  
Vol 16 (18) ◽  
pp. 11671-11686 ◽  
Author(s):  
Andreas Reichert ◽  
Ralf Sussmann
Keyword(s):  
Water Vapor ◽  
Spectral Range ◽  
Near Infrared ◽  
Atmospheric Conditions ◽  
Water Vapor Absorption ◽  
Absorption Bands ◽  
Continuum Absorption ◽  
Upper Limits ◽  
Vapor Absorption ◽  
Spectral Ranges

Abstract. We present a first quantification of the near-infrared (NIR) water vapor continuum absorption from an atmospheric radiative closure experiment carried out at the Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.). Continuum quantification is achieved via radiative closure using radiometrically calibrated solar Fourier transform infrared (FTIR) absorption spectra covering the 2500 to 7800 cm−1 spectral range. The dry atmospheric conditions at the Zugspitze site (IWV 1.4 to 3.3 mm) enable continuum quantification even within water vapor absorption bands, while upper limits for continuum absorption can be provided in the centers of window regions. Throughout 75 % of the 2500 to 7800 cm−1 spectral range, the Zugspitze results agree within our estimated uncertainty with the widely used MT_CKD 2.5.2 model (Mlawer et al., 2012). In the wings of water vapor absorption bands, our measurements indicate about 2–5 times stronger continuum absorption than MT_CKD, namely in the 2800 to 3000 cm−1 and 4100 to 4200 cm−1 spectral ranges. The measurements are consistent with the laboratory measurements of Mondelain et al. (2015), which rely on cavity ring-down spectroscopy (CDRS), and the calorimetric–interferometric measurements of Bicknell et al. (2006). Compared to the recent FTIR laboratory studies of Ptashnik et al. (2012, 2013), our measurements are consistent within the estimated errors throughout most of the spectral range. However, in the wings of water vapor absorption bands our measurements indicate typically 2–3 times weaker continuum absorption under atmospheric conditions, namely in the 3200 to 3400, 4050 to 4200, and 6950 to 7050 cm−1 spectral regions.

scholarly journals Near-Infrared Spectroscopy for Measuring Urea in Hemodialysis Fluids

2001 ◽  
Vol 47 (7) ◽  
pp. 1279-1286 ◽  
Author(s):  
Christopher V Eddy ◽  
Mark A Arnold
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Spectral Range ◽  
Near Infrared ◽  
Factor Model ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Calibration Models ◽  
Sensitivity And Selectivity

Abstract Background: Near-infrared spectroscopy is proposed as a method for providing real-time urea concentrations during hemodialysis treatments. The feasibility of such noninvasive urea measurements is evaluated in undiluted dialysate fluid. Methods: Near-infrared spectra were collected from calibration solutions of urea prepared in dialysate fluid. Spectra were collected over three distinct spectral regions, and partial least-squares calibration models were optimized and compared for each. Selectivity for urea was demonstrated with two-component samples composed of urea and glucose in the dialysate matrix. The clinical significance of this approach was assessed by measuring urea in real hemodialysate samples. Results: Urea absorptions within the combination and short-wavelength, near-infrared spectral regions provided sufficient spectral information for sound calibration models in the dialysate matrix. The combination spectral region had SEs of calibration (SEC) and prediction (SEP) of 0.38 mmol/L and 0.26 mmol/L, respectively, over the 4720–4600 cm−1 spectral range with 5 partial least-square factors. A second calibration model was established over the combination region from a series of solutions prepared with independently variable concentrations of urea and glucose. The best calibration model for urea in the presence of variable glucose concentrations had a SEC of 0.6 mmol/L and a SEP of 0.4 mmol/L for a 5-factor model over the 4600–4350 cm−1 spectral range. There was no significant decrease in SEP when the 4720–4600 cm−1 calibration model was used to measure urea in real samples collected during actual hemodialysis. Conclusions: Urea can be determined with sufficient sensitivity and selectivity for clinical measurements within the matrix of the hemodialysis fluid.

Near-Infrared Spectroscopy for Monitoring Starch Hydrolysis

2000 ◽  
Vol 54 (2) ◽  
pp. 277-283 ◽  
Author(s):  
Hoeil Chung ◽  
Mark A. Arnold
Keyword(s):  
Near Infrared ◽  
Physical Property ◽  
Nir Spectroscopy ◽  
Starch Hydrolysis ◽  
Calibration Model ◽  
Calibration Models ◽  
Fourier Filtering ◽  
On Line ◽  
Acid Catalyzed ◽  
Better Than

Near-infrared (NIR) spectroscopy has been evaluated for monitoring the acid-catalyzed hydrolysis (thinning) of starch. In practice, the extent of starch hydrolysis is measured in fluidity units, which correspond to a physical property of the hydrolyzed starch material. NIR spectra of samples taken periodically during a series of starch-thinning reactions were used to predict fluidity. The standard error of prediction (SEP) was 1.06 mL with the use of partial least-squares (PLS) regression in conjunction with digital Fourier filtering. This SEP was significantly better than that reported before with a univariate calibration model based on the integrated area of the 4400 cm−1 (2272 nm) absorption band for carbohydrates. The improved SEP meets the industry demands for real-time monitoring. Although these calibration models were developed from samples prepared in the laboratory, no spectroscopic differences were apparent between spectra collected from these laboratory samples and spectra collected from samples taken directly from plant starch slurries during actual thinning reactions. This similarity in spectral features, and hence chemical matrix, supports the potential of NIR spectroscopy for on-line monitoring of industrial starch-thinning processes.

Comparison of Near-Infrared, Infrared, and Raman Spectroscopy for the Analysis of Heavy Petroleum Products

2000 ◽  
Vol 54 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Hoeil Chung ◽  
Min-Sik Ku
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Petroleum Products ◽  
American Petroleum Institute ◽  
Visible Range ◽  
Pls Regression ◽  
Absorption Bands ◽  
Heavy Petroleum

Near-infrared (NIR) spectroscopy has been successfully applied to the determination of API (American Petroleum Institute) gravity of atmospheric residue (AR), which is the heaviest fraction in crude oil. This fraction is completely dark and very viscous. Preliminary studies involving Raman and infrared (IR) spectroscopies were also evaluated along with NIR spectroscopy. The Raman spectrum of AR was completely dominated by strong fluorescence from polycyclic aromatic hydrocarbons, called asphaltenes. IR spectroscopy provided reasonable spectral features; however, its spectral reproducibility was poorer and noisier than that of NIR. Although absorption bands in the NIR region were broad and less characterized, NIR provided better spectral reproducibility with higher signal-to-noise ratio (which is one of the most important parameters in quantitative calibration in comparison to Raman and IR spectroscopies). Partial least-squares (PLS) regression was utilized to develop calibration models. NIR spectra of AR samples were broad, and baselines were varying due to the strong absorption in the visible range. However, the necessary information was successfully extracted and correlated to the reference API gravity with the use of PLS regression. API gravities in the prediction set were accurately predicted with an SEP (standard error of prediction) of 0.22. Additionally NIR showed approximately three times better repeatability compared to the ASTM reference method, which directly influences the process control performance.

scholarly journals Understanding the root cause(s) of nonlinearities in near infrared spectroscopy

NIR news ◽  
2021 ◽  
Vol 32 (1-2) ◽  
pp. 20-26
Author(s):  
Harald Martens
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Predictive Ability ◽  
Calibration Model ◽  
Pls Regression ◽  
Root Cause ◽  
Sources Of Variation ◽  
Set Up ◽  
Physical And Chemical ◽  
Absorbance Spectra

NIR process monitoring and NIR hyperspectral video generates a deluge of non-selective spectral data, information-rich but per se useless. This paper demonstrates how interpretable data modelling can lead to simpler and better use of such NIR Big Data: A set of simple powder mixtures of the main constituents in wheat flour were measured by NIR transmission under different measurement conditions. Their absorbance spectra were submitted to multivariate calibration for predicting the protein content, by standard chemometric calibration by PLS regression. A reasonable calibration model was obtained, but it was unexpectedly complex and not robust. However, closer inspection the PLS regression subspace showed a surprising structure. This allowed us to identify the problem: Non-additive, strongly overlapping light scattering and light absorption effects in the NIR absorbance spectra. Based on this insight, a pragmatic, but causal preprocessing model was set up and iteratively optimized for predictive ability. This nonlinear optimized extended signal correction (OEMSC) separated and quantified the main physical and chemical sources of variation in the spectra. The preprocessing greatly simplified the NIR spectra and their quantitative calibration and prediction.

scholarly journals The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared. Part III: Quantification of the near-infrared water vapor continuum under atmospheric conditions

2016 ◽  
Author(s):  
Andreas Reichert ◽  
Ralf Sussmann
Keyword(s):  
Water Vapor ◽  
Spectral Range ◽  
Near Infrared ◽  
Atmospheric Conditions ◽  
Water Vapor Absorption ◽  
Absorption Bands ◽  
Continuum Absorption ◽  
Upper Limits ◽  
Vapor Absorption ◽  
Spectral Ranges

Abstract. We present a first quantification of the near-infrared (NIR) water vapor continuum absorption from an atmospheric radiative closure experiment carried out at Mt. Zugspitze (47.42° N, 10.98° E, 2964 m a.s.l.). Continuum quantification is achieved via radiative closure using radiometrically calibrated solar FTIR absorption spectra covering the 2500 to 7800 cm−1 spectral range. The dry atmospheric conditions at the Zugspitze site (IWV 1.4 to 3.3 mm) enable continuum quantification even within water vapor absorption bands, while upper limits for continuum absorption can be provided in the centers of window regions. Throughout 75 % of the 2500 to 7800 cm−1 spectral range, the Zugspitze results are agree within our estimated uncertainty with the widely used MT_CKD 2.5.2-model (Mlawer et al., 2012). Notable exceptions are the 2800 to 3000 cm−1 and 4100 to 4200 cm−1 spectral ranges, where our measurements indicate about 5 times stronger continuum absorption than MT_CKD. The measurements are consistent with the laboratory measurements of Mondelain et al. (2015), which rely on cavity ring-down spectroscopy (CDRS), and the calorimetric-interferometric measurements of Bicknell et al. (2006). Compared to the recent FTIR laboratory studies of Ptashnik et al. (2012) and (2013), our measurements indicate 2–5 times weaker continuum absorption under atmospheric conditions in the wings of water vapor absorption bands, namely in the 3200 to 3400 cm−1, 4050 to 4200 cm−1, and 6950 to 7050 cm−1 spectral regions.

A chemometric approach for moisture control in stingless bee honey using near infrared spectroscopy

2018 ◽  
Vol 26 (6) ◽  
pp. 379-388 ◽  
Author(s):  
Suelen Ávila ◽  
Polyanna Silveira Hornung ◽  
Gerson Lopes Teixeira ◽  
Márcia Regina Beux ◽  
Marcelo Lazzarotto ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Stingless Bee ◽  
Average Error ◽  
Calibration Model ◽  
Moisture Determination ◽  
Absorption Bands ◽  
Melipona Marginata

Honey is a product that is often adulterated by the addition of water. Stingless bee honey naturally has a higher moisture content than that produced by the traditional Apis mellifera. In most countries, there is a lack of quality standards and methods to characterise and assure the authenticity of stingless bee honey, which demands for the development of fast methods to assess its main properties, avoiding potential fraud. Thus, this work aimed to develop a non-destructive moisture determination method for stingless bee honey based on diffuse reflectance near infrared spectroscopy combined with chemometrics. Thirty-two honey samples from four stingless bee species ( Melipona quadrifasciata, Melipona marginata, Melipona bicolor and Scaptotrigona bipuncata) were used to develop calibration models using partial least squares regression analyses. Results revealed intense absorption bands in C–H, O–H and C–O vibrations in the spectra of stingless bee honey. The calibration model was used to predict the moisture content in honey from an external group. The prediction of the honey’s moisture showed good correlation (r2 = 0.93) with the refraction index method and an average error of 2.14%. The statistics variables for the calibration ( R2 = 0.947, SEP = 1.005 and RPD = 4.3) revealed that this model can be used to predict the moisture from stingless bee honey and that near infrared spectroscopy is a reliable tool to be applied in quality control with rapid, simple and accurate results.

Near-Infrared Spectroscopy for Monitoring the p-Diethylbenzene Separation Process

2000 ◽  
Vol 54 (5) ◽  
pp. 715-720 ◽  
Author(s):  
Hoeil Chung ◽  
Min-Sik Ku ◽  
Jaebum Lee ◽  
Jaebum Choo
Keyword(s):  
Aromatic Hydrocarbons ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Separation Process ◽  
Molecular Structures ◽  
Calibration Model ◽  
Spectral Features ◽  
Excellent Correlation ◽  
Absorption Bands ◽  
Combination Bands

Near-infrared (NIR) spectroscopy has been successfully used for the monitoring of important components in the p-diethylbenzene (PDEB) separation process. The process is composed of mostly diethylbenzene isomers ( ortho, meta, and para) and extractant ( p-xylene), as well as other C9–C11 aromatic hydrocarbons. Therefore, the major concern in using NIR spectroscopy in this process was the spectral resolution of NIR spectra among diethylbenzene isomers, since the molecular structures of each isomer were very similar. NIR spectral features of o-diethylbenzene (ODEB), m-diethylbenzene (MDEB), and PDEB showed considerable spectral differences in the 2100–2220 nm range. These combination bands originated from the combination of the =C–H stretch at 3100–3000 cm−1 and C=C ring stretch at 1600–1450 cm−1. Characteristic C=C ring stretches of each isomers in the IR region result in selective and identifiable spectral features in the NIR region. Partial least-squares (PLS) regression was used to build each calibration model for ODEB, MDEB, PDEB, and p-xylene (PX). PLS calibration results of the four components showed excellent correlation with gas chromatography data. The combination region (2100–2500 nm) provided the important isomeric spectral information for PLS calibration since the absorption bands in this region were the most sensitive and selective.

Quality Analysis of Stored Bell Peppers Using Near-Infrared Hyperspectral Imaging

2018 ◽  
Vol 61 (4) ◽  
pp. 1199-1207 ◽  
Author(s):  
Anisur Rahman ◽  
Mohammad Akbar Faqeerzada ◽  
Rahul Joshi ◽  
Santosh Lohumi ◽  
Lalit Mohan Kandpal ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Bell Pepper ◽  
Soluble Solids ◽  
Quality Analysis ◽  
Calibration Model ◽  
Pls Regression ◽  
Quality Properties ◽  
Bell Peppers

Abstract. The objective of this study was to predict the moisture content (MC), soluble solids content (SSC), and titratable acidity (TA) content in bell peppers during storage (18°C, 85% relative humidity) over 12 days, based on near-infrared hyperspectral imaging (NIR-HSI) in the 1000-1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares (PLS) regression with different preprocessing spectra techniques. The most effective wavelengths were selected using the variable importance in projection (VIP) technique, which selected optimal variables for the target quality parameters of bell peppers from a full set of variables. Subsequently the selected variables were used to develop a PLS-VIP model for simplifying the prediction model. The MC, SSC, and TA content in bell peppers during storage changed from 90.7% to 93.0%, from 6.1%Brix to 7.3%Brix, and from 0.222% to 0.334%, respectively. The PLS regression model with MC, SSC, and TA content resulted in coefficients of determination (R2pred) of 0.83, 0.85, and 0.7, with standard errors of prediction (SEP) of 0.08%, 0.075%Brix, and 0.013%, respectively, using SNV preprocessed spectra for MC and TA content and Savitzky-Golay (S-G) second-order derivatives preprocessed spectra for SSC of bell peppers. By contrast, the prediction results yielded R2pred of 0.69, 0.75, and 0.68, respectively, with SEP values of 0.103%, 0.107%Brix, and 0.011% when the PLS-VIP model was employed. The PLS-VIP model simplified the calibration model by selecting the most important variables in terms of their responsiveness to bell pepper quality properties. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers. Keywords: Fruit quality, Hyperspectral imagery, Image analysis, Spectral analysis, Stored bell pepper.

Metastructures for the Giant Enhancement of Raman Scattering in the Near Infrared Spectral Range

JETP Letters ◽  
2020 ◽  
Vol 112 (1) ◽  
pp. 31-36
Author(s):  
V. I. Kukushkin ◽  
V. E. Kirpichev ◽  
E. N. Morozova ◽  
V. V. Solov’ev ◽  
Ya. V. Fedotova ◽  
...  
Keyword(s):  
Raman Scattering ◽  
Spectral Range ◽  
Near Infrared ◽  
Infrared Spectral Range ◽  
Infrared Spectral
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