A Brief Review of near Infrared in Petroleum Product Analysis

1996 ◽  
Vol 4 (1) ◽  
pp. 69-74 ◽  
Author(s):  
Jerome Workman
Keyword(s):  
Functional Groups ◽  
Petroleum Product ◽  
Octane Number ◽  
Near Infrared ◽  
Scientific Literature ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Hydrocarbon Fuel ◽  
Chemical Information ◽  
Product Analysis

The use of infrared spectroscopy [including near infrared (NIR) spectroscopy] for the analysis of petroleum product analysis has become an essential component of hydrocarbon processing and refining since the mid-1940s. Early scientific literature identified absorption band positions for a variety of hydrocarbon functional groups from pure compounds to complex mixtures. The short wavelength NIR region (generally designated as 750–1100 nm), and the long-wavelength NIR region (1100–2500 nm) have been explored for their relative chemical information content with respect to hydrocarbon fuel mixtures. The functional groups of methyl, methylene, carbon–carbon, carbon–oxygen (including carbonyl), and aromatic (C–H) are measured directly using NIR spectroscopy. NIR spectroscopy combined with multivariate calibration has resulted in the reported analysis of numerous fuel components. The scientific literature has reported varied success for the measurement of RON (research octane number), MON (motor octane number), PON (pump octane number), cetane, cloud point, MTBE ( tert-Butyl methyl ether), RVP (Reid vapour pressure), ethanol, API, bromine number, lead, sulphur, aromatics, olefins and saturates content in such products as gasoline, diesel fuels, and jet fuels. This review paper summarises the foundational work using near-infrared for hydrocarbon fuels measurement beginning in 1938.

scholarly journals Investigation of NIR spectroscopy for identifying and sorting wood with respect to species, moisture content, and weathering

2021 ◽  
Author(s):  
Ekaterina Tounis
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Surface Preparation ◽  
Nir Spectroscopy ◽  
Species Group ◽  
Chemical Information ◽  
Calibration Techniques

Near-infrared spectroscopy can characterize wood surfaces fast and without significant surface preparation. It is based on molecular overtone and combination vibrations which are typically very broad, leading to complex spectra. Multivariate calibration techniques are often employed to extract the desired chemical information. This study focused on the potential of near-infrared spectroscopy combined with partial least squares for identifying and sorting wood with respect to species and physical properties and on the effects of wood preparation and weathering on the precision of analysis. It was shown that a range of moisture content values and artificial weathering periods could be well predicted indepenedently of wood species analyzed. Species within the spruce-pine-fir species group could be predicted reasonably well when tested under similar conditions. When different moisture contents and weathering exposure periods were introduced, species prediction was still possible, but, with decreased prediciton ability.

scholarly journals Investigation of NIR spectroscopy for identifying and sorting wood with respect to species, moisture content, and weathering

2021 ◽  
Author(s):  
Ekaterina Tounis
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Surface Preparation ◽  
Nir Spectroscopy ◽  
Species Group ◽  
Chemical Information ◽  
Calibration Techniques

Near-infrared spectroscopy can characterize wood surfaces fast and without significant surface preparation. It is based on molecular overtone and combination vibrations which are typically very broad, leading to complex spectra. Multivariate calibration techniques are often employed to extract the desired chemical information. This study focused on the potential of near-infrared spectroscopy combined with partial least squares for identifying and sorting wood with respect to species and physical properties and on the effects of wood preparation and weathering on the precision of analysis. It was shown that a range of moisture content values and artificial weathering periods could be well predicted indepenedently of wood species analyzed. Species within the spruce-pine-fir species group could be predicted reasonably well when tested under similar conditions. When different moisture contents and weathering exposure periods were introduced, species prediction was still possible, but, with decreased prediciton ability.

Industrial Applications of Near-Infrared Chemical Imaging Microscopy

2001 ◽  
Vol 7 (S2) ◽  
pp. 162-163
Author(s):  
EN Lewis ◽  
LH Kidder ◽  
KS Haber
Keyword(s):  
Spatial Distribution ◽  
Near Infrared ◽  
Imaging System ◽  
Raw Materials ◽  
Single Point ◽  
Nir Spectroscopy ◽  
Chemical Imaging ◽  
Industrial Applications ◽  
Chemical Information ◽  
Spatially Resolved

Single point near-infrared (NIR) spectroscopy is used extensively for characterizing raw materials and finished products in a wide variety of industries: polymers, paper, film, pharmaceuticals, paintings and coatings, food and beverages, agricultural products. As advanced industrial materials become more complex, their functionality is often determined by the spatial distribution of their discrete sample constituents. However, conventional single point NIR spectroscopy cannot adequately probe the interrelationship between the spatial distribution of sample components with the physical properties of the sample. to fully characterize these samples, it is necessary to probe simultaneously spatial and chemical heterogeneity and correlate these properties with sample characteristics.Recently, we have developed a novel NIR imaging spectrometer that can deliver spatially resolved chemical information very rapidly. in contrast to conventional, single point NIR spectrometers, the imaging system uses an infrared focal-plane array (FPA) to collect up to 76,800 complete spectra, one for each pixel on the array, in approximately one minute.

scholarly journals Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yong-Dong Xu ◽  
Yan-Ping Zhou ◽  
Jing Chen
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Extraction Process ◽  
Sesame Oil ◽  
Aqueous Extraction ◽  
Oil Yield ◽  
Support Vector ◽  
Calibration Model ◽  
Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

Prediction of Yellow-Poplar (Liriodendron Tulipifera) Veneer Stiffness and Bulk Density Using near Infrared Spectroscopy and Multivariate Calibration

2008 ◽  
Vol 16 (5) ◽  
pp. 487-496 ◽  
Author(s):  
Oluwatosin Emmanuel Adedipe ◽  
Ben Dawson-Andoh
Keyword(s):  
Bulk Density ◽  
Spectral Range ◽  
Predictive Models ◽  
Spectral Region ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Yellow Poplar ◽  
First Derivative ◽  
Calibration Models

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

Investigation of Noninvasive in vivo Blood Hematocrit Measurement Using NIR Reflectance Spectroscopy and Partial Least-Squares Regression

2000 ◽  
Vol 54 (2) ◽  
pp. 294-299 ◽  
Author(s):  
Songbiao Zhang ◽  
Babs R. Soller ◽  
Shubjeet Kaur ◽  
Kristen Perras ◽  
Thomas J. Vander Salm
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Study Groups ◽  
Patient Specific ◽  
Least Squares Regression ◽  
Fiber Optic Probe

Hematocrit (Hct), the volume percent of red cells in blood, is monitored routinely for blood donors, surgical patients, and trauma victims and requires blood to be removed from the patient. An accurate, noninvasive method for directly measuring hematocrit on patients is desired for these applications. The feasibility of noninvasive hematocrit measurement by using near-infrared (NIR) spectroscopy and partial least-squares (PLS) techniques was investigated, and methods of in vivo calibration were examined. Twenty Caucasian patients undergoing cardiac surgery on cardiopulmonary bypass were randomly selected to form two study groups. A fiber-optic probe was attached to the patient's forearm, and NIR spectra were continuously collected during surgery. Blood samples were simultaneously collected and reference Hct measurements were made with the spun capillary method. PLS multivariate calibration techniques were applied to investigate the relationship between spectral and Hct changes. Single patient calibration models were developed with good cross-validated estimation of accuracy (∼ 1 Hct%) and trending capability for most patients. Time-dependent system drift, patient temperature, and venous oxygen saturation were not correlated with the hematocrit measurements. Multi-subject models were developed for prediction of independent subjects. These models demonstrated a significant patient-specific offset that was shown to be partially related to spectrometer drift. The remaining offset is attributed to the large spectral variability of patient tissue, and a significantly larger set of patients would be required to adequately model this variability. After the removal of the offset, the cross-validated estimation of accuracy is 2 Hct%.

scholarly journals Identification and Quantification of Hydrocarbon Functional Groups in Gasoline Using 1H-NMR Spectroscopy for Property Prediction

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6989
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Chemical Composition ◽  
Nmr Spectroscopy ◽  
Functional Groups ◽  
Octane Number ◽  
Cetane Number ◽  
Hydrocarbon Fuel ◽  
Fuel Properties ◽  
Finite Set ◽  
Si Engines ◽  
Identification And Quantification

Gasoline is one of the most important distillate fuels obtained from crude refining; it is mainly used as an automotive fuel to propel spark-ignited (SI) engines. It is a complex hydrocarbon fuel that is known to possess several hundred individual molecules of varying sizes and chemical classes. These large numbers of individual molecules can be assembled into a finite set of molecular moieties or functional groups that can independently represent the chemical composition. Identification and quantification of groups enables the prediction of many fuel properties that otherwise may be difficult and expensive to measure experimentally. In the present work, high resolution 1H nuclear magnetic resonance (NMR) spectroscopy, an advanced structure elucidation technique, was employed for the molecular characterization of a gasoline sample in order to analyze the functional groups. The chemical composition of the gasoline sample was then expressed using six hydrocarbon functional groups, as follows: paraffinic groups (CH, CH2 and CH3), naphthenic CH-CH2 groups and aromatic C-CH groups. The obtained functional groups were then used to predict a number of fuel properties, including research octane number (RON), motor octane number (MON), derived cetane number (DCN), threshold sooting index (TSI) and yield sooting index (YSI).

scholarly journals SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 770 ◽  
Author(s):  
Ying Tian ◽  
Xinyu You ◽  
Xiuhui Huang
Keyword(s):  
Real Time ◽  
Octane Number ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Soft Sensor ◽  
Training Time ◽  
Sensor Model ◽  
Blending Process

As the most important properties in the gasoline blending process, octane number is difficult to be measured in real time. To address this problem, a novel deep learning based soft sensor strategy, by using the near-infrared (NIR) spectroscopy obtained in the gasoline blending process, is proposed. First, as a network structure with hidden layer as symmetry axis, input layer and output layer as symmetric, the denosing auto-encoder (DAE) realizes the advanced expression of input. Additionally, the stacked DAE (SDAE) is trained based on unlabeled NIR and the weights in each DAE is recorded. Then, the recorded weights are used as the initial parameters of back propagation (BP) with the reason that the SDAE trained initial weights can avoid local minimums and realizes accelerate convergence, and the soft sensor model is achieved with labeled NIR data. Finally, the achieved soft sensor model is used to estimate the real time octane number. The performance of the method is demonstrated through the NIR dataset of gasoline, which was collected from a real gasoline blending process. Compared with PCA-BP (the dimension of datasets of BP reduced by principal component analysis) soft sensor model, the prediction accuracy was improved from 86.4% of PCA-BP to 94.8%, and the training time decreased from 20.1 s to 16.9 s. Therefore, SDAE-BP is proposed as a novel method for rapid and efficient determination of octane number in the gasoline blending process.

Effect of Random Noise on the Performance of NIR Calibrations

1998 ◽  
Vol 6 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Jing Lu ◽  
W.F. McClure ◽  
F.E. Barton ◽  
D.S. Himmelsbach
Keyword(s):  
Least Squares ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Simple Procedure ◽  
Random Noise ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Optical Data ◽  
Spectra Analysis ◽  
Calibration Methods

The proliferation of applications for near infrared (NIR) spectroscopy has been fostered by advances in instrumentation and statistics. NIR analytical instrumentation is becoming more stable and reliable. Chemometrics is playing an important role in qualitative and quantitative NIR spectra analysis. The objective of this study was to evaluate the performances of four commonly used calibration models: (1) stepwise multiple linear regression (SMLR); (2) classical least-squares (CLS); (3) principal component regression (PCR); and (4) partial least-squares (PLS) in NIR spectroscopy analysis when random noise is present in the optical data. A conceptually simple procedure for comparing the performance of the four calibration methods in the presence of different levels of random noise in spectra data has been introduced here. This procedure, using the computer simulation data and real spectra of tobacco, has provided useful information for understanding the effects of random noise on the performance of multivariate calibration methods. Both numerical and graphical results will be shown.

Comparison of Three near Infrared Spectrophotometers for Infestation Detection in Wild Blueberries Using Multivariate Calibration Models

2009 ◽  
Vol 17 (4) ◽  
pp. 203-212 ◽  
Author(s):  
Boyan N. Peshlov ◽  
Floyd E. Dowell ◽  
Francis A. Drummond ◽  
Darrell W. Donahue
Keyword(s):  
Wavelength Range ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
System Development ◽  
Nir Spectroscopy ◽  
Insect Infestation ◽  
Signal To Noise ◽  
Wild Blueberries ◽  
Technical Specifications ◽  
Imaging Spectrograph

A near infrared (NIR) spectroscopy system for rapid, automated and non-destructive detection of insect infestation in blueberries is desirable to ensure high quality fruit for the fresh and processed markets. The selection of suitable instruments is the first step in system development. Three diode array spectrophotometers were evaluated based on technical specifications and capacity for larva detection in wild blueberries ( Vaccinium angustifolium) using discriminant partial least squares (PLS) regression models. These instruments, differing mainly in wavelength range and detector type, comprised two spectrophotometers with scanning wavelength ranges of 650–1100 nm and 600–1700 nm and an imaging spectrograph with the scanning range of 950–1400 nm. The assessed factors affecting predictions included signal-to-noise ratio, wavelength range, resolution, measurement configuration, spectral pre-processing and absorbance bands related to infestation. The scanning spectrophotometers demonstrated higher signal-to-noise ratios with infestation prediction accuracies of 82% and 76.9% compared to the imaging spectrograph with 58.9% accuracy. Resolution, spectral pre-processing and measurement configuration had a lesser effect on model accuracy than wavelength range. The 950–1690 nm bands were identified as important for infestation prediction. In general, NIR spectroscopy should be a feasible technique for rapid classification of insect infestation in fruit.

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