scholarly journals Fault Isolation for Desalting Processes Using Near-Infrared Measurements

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhiguo Wang ◽  
Yawen Gao ◽  
Yuan Ge ◽  
Fei Liu
Keyword(s):  
Infrared Spectroscopy ◽  
Crude Oil ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Molecular Vibration ◽  
Infrared Measurements ◽  
Selection Operator

Due to the important role of crude oil desalting for the whole petroleum refining process, the near-infrared spectroscopy resulting from molecular vibration is used to detect and isolate potential faults of the desalting process in this paper. With the molecular spectral data reflected by the near-infrared spectroscopy, the principal component analysis is adopted to monitor the process to see if it is in a normal operating condition or not. Considering the feature that the dimension of near-infrared spectroscopy is much larger than the sample size, the least absolute shrinkage and selection operator is employed to achieve an automatic variable selection procedure of the observed spectral data. Simultaneously, if some faults occur, the least absolute shrinkage and selection operator can be used to locate the spectral region affected by the failure. In such a way, the roots of faults can be tracked according to the change of the wavelength numbers. Performances of the proposed fault detection and isolation approaches are evaluated based on the near-infrared spectroscopy sampled for the crude oil desalting process to show the effectiveness.

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

2019 ◽  
Vol 52 (18) ◽  
pp. 2914-2930 ◽  
Author(s):  
Karla Pereira Rainha ◽  
Júlia Tristão do Carmo Rocha ◽  
Rayza Rosa Tavares Rodrigues ◽  
Betina Pires de Oliveira Lovatti ◽  
Eustáquio Vinicius Ribeiro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Crude Oil ◽  
Nitrogen Content ◽  
Near Infrared Spectroscopy ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Basic Nitrogen ◽  
Selection Tools

scholarly journals Use of Mitochondrial Inhibitors to Demonstrate That Cytochrome Oxidase Near-Infrared Spectroscopy Can Measure Mitochondrial Dysfunction Noninvasively in the Brain

1999 ◽  
Vol 19 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Chris E. Cooper ◽  
Mark Cope ◽  
Roger Springett ◽  
Philip N. Amess ◽  
Juliet Penrice ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Cytochrome Oxidase ◽  
Near Infrared Spectroscopy ◽  
Redox State ◽  
Near Infrared ◽  
Blood Oxygenation ◽  
Concentration Addition ◽  
Pig Brain ◽  
Infrared Measurements ◽  
Neonatal Pig

The use of near-infrared spectroscopy to measure noninvasively changes in the redox state of cerebral cytochrome oxidase in vivo is controversial. We therefore tested these measurements using a multiwavelength detector in the neonatal pig brain. Exchange transfusion with perfluorocarbons revealed that the spectrum of cytochrome oxidase in the near-infrared was identical in the neonatal pig, the adult rat, and in the purified enzyme. Under normoxic conditions, the neonatal pig brain contained 15 μmol/L deoxyhemoglobin, 29 μmol/L oxyhemoglobin, and 1.2 μmol/L oxidized cytochrome oxidase. The mitochondrial inhibitor cyanide was used to determine whether redox changes in cytochrome oxidase could be detected in the presence of the larger cerebral hemoglobin concentration. Addition of cyanide induced full reduction of cytochrome oxidase in both blooded and bloodless animals. In the blooded animals, subsequent anoxia caused large changes in hemoglobin oxygenation and concentration but did not affect the cytochrome oxidase near-infrared signal. Simultaneous blood oxygenation level-dependent magnetic resonance imaging measurements showed a good correlation with near-infrared measurements of deoxyhemoglobin concentration. Possible interference in the near-infrared measurements from light scattering changes was discounted by simultaneous measurements of the optical pathlength using the cerebral water absorbance as a standard chromophore. We conclude that, under these conditions, near-infrared spectroscopy can accurately measure changes in the cerebral cytochrome oxidase redox state.

scholarly journals Detection of deltamethrin in cabbages using visible shortwave near-infrared spectroscopy

Food Research ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 273-280
Author(s):  
C.D.M. Ishkandar ◽  
N.M. Nawi ◽  
R. Janius ◽  
N. Mazlan ◽  
T.T. Lin
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Spectral Data ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

Pesticides have long been used in the cabbage industry to control pest infestation. This study investigated the potential application of low-cost and portable visible shortwave near-infrared spectroscopy for the detection of deltamethrin residue in cabbages. A total of sixty organic cabbage samples were used. The sample was divided into four batches, three batches were sprayed with deltamethrin pesticide whereas the remaining batch was not sprayed (control sample). The first three batches of the cabbages were sprayed with the pesticide at three different concentrations, namely low, medium and high with the values of 0.08, 0.11 and 0.14% volume/volume (v/v), respectively. Spectral data of the cabbage samples were collected using visible shortwave near-infrared (VSNIR) spectrometer with wavelengths range between 200 and 1100 nm. Gas chromatography-electron capture detector (GC-ECD) was used to determine the concentration of deltamethrin residues in the cabbages. Partial least square (PLS) regression method was adopted to investigate the relationship between the spectral data and deltamethrin concentration values. The calibration model produced the values of coefficient of determination (R2 ) and the root mean square error of calibration (RMSEC) of 0.98 and 0.02, respectively. For the prediction model, the values of R2 and the root mean square error of prediction (RMSEP) were 0.94 and 0.04, respectively. These results demonstrated that the proposed spectroscopic measurement is a promising technique for the detection of pesticide at different concentrations in cabbage samples.

scholarly journals Use of near-infrared spectroscopy for prediction of chemical composition of Tifton 85 grass

2021 ◽  
Vol 42 (3) ◽  
pp. 1287-1302
Author(s):  
Camila Cano Serafim ◽  
◽  
Geisi Loures Guerra ◽  
Ivone Yurika Mizubuti ◽  
Filipe Alexandre Boscaro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Spectral Data ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Neutral Detergent Fiber ◽  
Mineral Matter ◽  
Data Set

The reduction in the quality, consumption, and digestibility of forage can cause a decrease in animal performance, resulting in losses to the rural producer. Thus, it is important to monitor these characteristics in forage plants to devise strategies or practices that optimize production systems. The aim of this study was to develop and validate prediction models using near-infrared spectroscopy (NIRS) to determine the chemical composition of Tifton 85 grass. Samples of green grass, its morphological structures (whole plant, leaf blade, stem + sheath, and senescent material) and hay, totaling 105 samples were used. Conventional chemical analysis was performed to determine the content of oven-dried samples (ODS), mineral matter (MM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose (CEL), hemicellulose (HEM), and in vitro dry matter digestibility (IVDMD). Subsequently, all the samples were scanned using a Vis-NIR spectrometer to collect spectral data. Principal component analysis (PCA) was applied to the data set, and modified partial least squares was used to correlate reference values to spectral data. The coefficients of determination (R2) were 0.74, 0.85, 0.98, 0.75, 0.85, 0.71, 0.82, 0.77, and 0.93, and the ratio of performance deviations (RPD) obtained were 1.99, 2.71, 6.46, 2.05, 2.58, 3.84, 1.86, 2.35, 2.09, and 3.84 for ODS, MM, CP, NDF, ADF, ADL, CEL, HEM, and IVDMD, respectively. The prediction models obtained, in general, were considered to be of excellent quality, and demonstrated that the determination of the chemical composition of Tifton 85 grass can be performed using NIRS technology, replacing conventional analysis.

Crude Oil Model Emulsion Characterised by means of Near Infrared Spectroscopy and Multivariate Techniques

2000 ◽  
Vol 21 (3) ◽  
pp. 245-262 ◽  
Author(s):  
Harald Kallevik ◽  
Susanne Brunsgaard Hansen ◽  
Øystein Sæther ◽  
Olav Martin Kvalheim ◽  
Johan Sjöblom
Keyword(s):  
Infrared Spectroscopy ◽  
Crude Oil ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Multivariate Techniques ◽  
Model Emulsion
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