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.

Noninvasive Measurement of Oxygenation of Hemoglobin by Direct Transmission of Near-Infrared Energy (700–1000 nm) from an Electronically Tuned Ti: Sapphire Laser Driven by a Dual Radio-Frequency Driving Method

2000 ◽  
Vol 54 (8) ◽  
pp. 1163-1167 ◽  
Author(s):  
Hidetoshi Sato ◽  
Satoshi Wada ◽  
Mingyi Ling ◽  
Hideo Tashiro
Keyword(s):  
Radio Frequency ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
High Reliability ◽  
Nir Spectroscopy ◽  
Detection Sensitivity ◽  
Human Hand ◽  
Transmission Spectra ◽  
Absorption Bands ◽  
Good Signal

We have applied an electronically tuned Ti: sapphire (ETT) laser controlled with a dual radio-frequency driving (DRD) method to noninvasive measurements of the transmission spectra of a whole human hand in which the bands due to hemoglobin and lipid were identified. Spectra of a human hand that is more than 3 cm thick were obtained in the spectral range from 700 to 1000 nm, allowing one to probe the oxygenation of hemoglobin (Hb). Our study demonstrates that an ETT laser may be used as a potential light source for near-infrared (NIR) spectroscopy in biomedical applications. For the acquisition of NIR transmission spectra of Hb with a good signal-to-noise ratio, the laser emission was programmed electronically to fill up strong absorption bands due to water, and baseline undulation due to detection sensitivity and light scattering. The present study required more stable control of the ETT laser, so we developed the DRD method, achieving high reliability in wavelength (< 0.1 nm) and in intensity (< ± 2% per average in 10 pulses).

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

scholarly journals Biodiesel Synthesis Monitoring using Near Infrared Spectroscopy

2018 ◽  
Vol 12 (1) ◽  
pp. 95-110 ◽  
Author(s):  
Estela Kamile Gelinski ◽  
Fabiane Hamerski ◽  
Marcos Lúcio Corazza ◽  
Alexandre Ferreira Santos
Keyword(s):  
Fossil Fuels ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Transesterification Reaction ◽  
Biodiesel Production ◽  
Molar Ratios ◽  
External Data ◽  
Biodiesel Synthesis ◽  
Biodiesel Production Monitoring

Objective: Biodiesel is a renewable fuel considered as the main substitute for fossil fuels. Its industrial production is mainly made by the transesterification reaction. In most processes, information on the production of biodiesel is essentially done by off-line measurements. Methods: However, for the purpose of control, where online monitoring of biodiesel conversion is required, this is not a satisfactory approach. An alternative technique to the online quantification of conversion is the near infrared (NIR) spectroscopy, which is fast and accurate. In this work, models for biodiesel reactions monitoring using NIR spectroscopy were developed based on the ester content during alkali-catalyzed transesterification reaction between soybean oil and ethanol. Gas chromatography with flame ionization detection was employed as the reference method for quantification. FT-NIR spectra were acquired with a transflectance probe. The models were developed using Partial Least Squares (PLS) regression with synthetic samples at room temperature simulating reaction composition for different ethanol to oil molar ratios and conversions. Model predictions were then validated online for reactions performed with ethanol to oil molar ratios of 6 and 9 at 55ºC. Standard errors of prediction of external data were equal to 3.12%, hence close to the experimental error of the reference technique (2.78%), showing that even without using data from a monitored reaction to perform calibration, proper on-line predictions were provided during transesterification runs. Results: Additionally, it is shown that PLS models and NIR spectra of few samples can be combined to accurately predict the glycerol contents of the medium, making the NIR spectroscopy a powerful tool for biodiesel production monitoring.

Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

2005 ◽  
Vol 13 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Roland Welle ◽  
Willi Greten ◽  
Thomas Müller ◽  
Gary Weber ◽  
Hartwig Wehrmann
Keyword(s):  
Grain Yield ◽  
Dry Matter ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Successful Implementation ◽  
Grain Moisture ◽  
Yield Determination ◽  
Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

scholarly journals Quantitative Characterization of Arnicae flos by RP-HPLC-UV and NIR Spectroscopy

Foods ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Daniela Ivanova ◽  
Vera Deneva ◽  
Dimitrina Zheleva-Dimitrova ◽  
Vesela Balabanova-Bozushka ◽  
Daniela Nedeltcheva ◽  
...  
Keyword(s):  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Sesquiterpene Lactones ◽  
Nir Spectroscopy ◽  
Spectral Distortion ◽  
Active Components ◽  
Quantitative Characterization ◽  
Convolution Window ◽  
Spectra Processing

The possibility of applying near-infrared (NIR) spectroscopy to monitor 13 active components (phenolic acids, flavonoids, and sesquiterpene lactones) in Arnicae flos was studied. The preprocessing of the spectra were performed by using the conventional Golay-Savitzky procedure and the newly developed step-by-step filter. The results obtained show that the step-by-step filter derivatives provide a better signal-to-noise ratio at a lower convolution window. Better calibration for the content of protocatechuic acid, chlorogenic acid, caffeic acid, p-cumaric acid, ferulic acid, isoquercitrin, and quercetin were obtained by step-by-step filter derivatives, compared to the direct raw spectra processing and the Golay-Savitzky approach. Although the step-by-step filter substantially reduces the spectral distortion, the convolution procedure leads to loss of spectral points in the red end of the spectral curve. Probably for this reason this approach shows better calibration only in seven of the monitored 13 active components.

Application of near-infrared spectroscopy for the detection of internal insect infestation inPiceaabiesseed lots

2004 ◽  
Vol 34 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén ◽  
Tong Yun Shen
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Absorption Bands ◽  
Classification Rate ◽  
Data Set ◽  
Storage Lipids ◽  
Nir Absorption ◽  
Seed Origin ◽  
Single Seeds

The use of near-infrared (NIR) spectroscopy to discriminate between uninfested seeds of Picea abies (L.) Karst and seeds infested with Plemeliella abietina Seitn (Hymenoptera, Torymidae) larva is sensitive to seed origin and year of collection. Five seed lots collected during different years from Sweden, Finland, and Belarus were used in this study. Initially, seeds were classified as infested or uninfested with X-radiography, and then, NIR spectra from single seeds were collected with a NIR spectrometer from 1100 to 2498 nm with a resolution of 2 nm. Discriminant models were derived by partial least squares regression using raw and orthogonal signal corrected spectra (OSC). The resulting OSC model developed on a pooled data set was more robust than the raw model and resulted in 100% classification accuracy. Once irrelevant spectral variations were removed by using OSC pretreatment, single-lot calibration models resulted in similar classification rates for the new samples irrespective of origin and year of collection. Dis criminant analyses performed with selected NIR absorption bands also gave nearly 100% classification rate for new samples. The origin of spectral differences between infested and uninfested seeds was attributed to storage lipids and proteins that were completely depleted in the former by the feeding larva.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

Estimation of Wood Stiffness and Strength Properties of Hybrid Larch by Near-Infrared Spectroscopy

2007 ◽  
Vol 61 (8) ◽  
pp. 882-888 ◽  
Author(s):  
Takaaki Fujimoto ◽  
Hiroyuki Yamamoto ◽  
Satoru Tsuchikawa
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Strength Properties ◽  
Density Variation ◽  
Modulus Of Rupture ◽  
Cellulose Content ◽  
Hybrid Larch ◽  
Absorption Bands ◽  
Wood Stiffness ◽  
Predicted Values

This work was undertaken to investigate the feasibility of near-infrared (NIR) spectroscopy for estimating wood mechanical properties, i.e., modulus of elasticity (MOE) and modulus of rupture (MOR) in bending tests. Two sample sets having large and limited density variation were prepared to examine the effects of wood density on estimation of MOE and MOR by the NIR technique. Partial least squares (PLS) analysis was employed and it was found that the relationships between laboratory-measured and NIR-predicted values were good in the case of sample sets having large density variation. MOE could be estimated even when density variation in the sample set was limited. It was concluded that absorption bands due to the OH group in the semi-crystalline or crystalline regions of cellulose strongly influenced the calibrations for bending stiffness of hybrid larch. This was also suggested from the result that both α-cellulose content and cellulose crystallinity showed moderate positive correlation to wood stiffness.

Lipid Analysis by near Infrared Spectroscopy to Evaluate Inhibitory Effects of Swine Waste Compost on Plant Growth

2008 ◽  
Vol 16 (5) ◽  
pp. 481-486 ◽  
Author(s):  
Takayuki Fujiwara ◽  
Keiichi Murakami
Keyword(s):  
Plant Growth ◽  
Lipid Content ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Swine Waste ◽  
Inhibitory Effects ◽  
Absorption Bands ◽  
Freeze Dried ◽  
Calibration Equations

The lipid content of swine manure decreases during the process of composting, and inhibitory effects of compost on root growth in germination tests are strongly correlated to lipid content. Therefore, we tested whether the determination of the lipid content of swine waste compost by near infrared (NIR) spectroscopy provided a measure by which the degree of inhibition of plant growth by immature compost could be predicted. Reflectance spectra of untreated compost samples, as well as freeze-dried and milled samples, were taken using a scanning monochromator. Second derivative spectra from 700 nm to 2500 nm and multiple regression analysis were used to develop calibration equations for lipid content and moisture. A pronounced absorption peak of lipid was found at 2310 nm, attributable to the absorption bands of the CH2 stretching–bending combination. However, calibration equations containing this absorption band were inappropriate for lipid determination, because sawdust and rice husk, which were added to the compost, influenced the spectra in this band. The standard error of prediction ( SEP) of the best calibrations for lipids in dry and untreated samples was 6.0 g kg−1 and 3.2 g kg−1, while the ratios of the standard deviation and the range in the prediction set to SEP (RPD and RER) were 5.5 and 2.8, and 13.5 and 5.0, respectively. The main wavelengths of these calibration equations were 1700 nm for dry samples and 1764 nm for untreated samples, which were attributed to the absorption bands of the CH2 stretching first overtone. In conclusion, the determination of lipid content in dry compost samples by NIR spectroscopy provided an indirect estimate of the maturity of swine waste compost. Moreover, NIR spectroscopy was found useful for the rough assessment of the maturity of untreated swine waste compost.

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