Monitoring the Chemistry and Monosaccharide Ratio of Eucalyptus Dunnii Wood by near Infrared Spectroscopy

2016 ◽  
Vol 24 (6) ◽  
pp. 537-548 ◽  
Author(s):  
Chengfeng Zhou ◽  
Wei Jiang ◽  
Brian K. Via ◽  
P.M. Chetty ◽  
Tammy Swain
Keyword(s):  
High Performance ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Nir Spectroscopy ◽  
Forest Products ◽  
Chemical Components ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Wet Chemistry ◽  
Eucalyptus Dunnii

Determination of wood chemical components such as extrac tives, lignin and car bohydrate content by conventional wet chemistry is time consuming and sometimes hazardous. Near infrared reflectance (NIR) spectroscopy coupled with multivariate calibration was utilised to offer a fast alternative to wet chemistry methods. In this study, 70 Eucalyptus dunnii wood samples were collected to investigate the correlation and modelling potential of using NIR spectra to predict extractives, lignin, carbohydrate content and ash which were determined with classical methods (extractives, ash and lignin) and high-performance liquid chromatography (sugars). Partial least squares regression was used for multivariate calibration. An evaluation of the results found that ash, extractives and lignin could be predicted with the strongest prediction diagnostics while mannose and glucose-to-mannose ratio models exhibited the lowest performance. The robust ability to predict glucose-to-xylose ratio ( r2 = 0.87) provided a unique way to utilise NIR to monitor biomass quality and could be helpful for the improvement of ethanol and other forest products. The large range in glucose-to-xylose ratio (2.0 to 4.0), as determined through NIR, suggests that using xylose content to estimate total hemicellulose content may be unsuitable, though this type of ratio assumption and analysis is common for softwoods.

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 Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3394
Author(s):  
Lu Xu ◽  
Yongsheng Hong ◽  
Yu Wei ◽  
Long Guo ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Jianghan Plain ◽  
Model Accuracy ◽  
Near Infrared Reflectance ◽  
Soc Estimation ◽  
Anthropogenic Soil

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

Monitoring Biodiesel Fuel Quality by near Infrared Spectroscopy

2007 ◽  
Vol 15 (2) ◽  
pp. 97-105 ◽  
Author(s):  
Pedro Felizardo ◽  
Patrícia Baptista ◽  
Margarida Sousa Uva ◽  
José C. Menezes ◽  
M. Joana Neiva Correia
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Analytical Data ◽  
Nir Spectroscopy ◽  
Laboratory Scale ◽  
Biodiesel Fuel ◽  
Fuel Quality ◽  
Least Squares Regression ◽  
Animal Fats ◽  
Biodiesel Quality

Biodiesel is produced mainly by a transesterification reaction which involves the reaction of vegetable oils, animal fats or waste oils with an alcohol (such as methanol) in the presence of a catalyst (such as sodium hydroxide or methoxide). Since the presence of contaminants can cause severe engine problems, the assessment of the biodiesel quality is very important. This work reports the use of near infrared (NIR) spectroscopy to determine the content of water and methanol in industrial and laboratory-scale biodiesel samples. A qualitative analysis of the spectra by principal components analysis was carried out and partial least squares regression was used to develop calibration models between spectral and analytical data. The results indicate that the use of NIR spectroscopy, in combination with multivariate calibration, is a promising technique to assess the biodiesel quality in both laboratory-scale and industrial-scale samples.

scholarly journals Analysis of amino acid composition of sheep colostrum by near-infrared spectroscopy

2011 ◽  
Vol 49 (No. 5) ◽  
pp. 177-182 ◽  
Author(s):  
S. Kráčmar ◽  
R. Jankovská ◽  
K. Šustová ◽  
J. Kuchtík ◽  
L. Zeman
Keyword(s):  
Infrared Spectroscopy ◽  
Amino Acid ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Near Infrared Reflectance

This paper deals with changes in the basic composition of sheep colostrum within the first 72 hours after parturition on the one hand and with the possibility of determining the major components of sheep colostrum by near-infrared spectroscopy on the other. Levels of essential, nonessential and total amino acids in sheep colostrum were determined by near-infrared reflectance spectroscopy (NIRS). ). For each component, sets of 90 samples were used to calibrate the instrument by means of a modified partial least-squares regression. The values of correlation coefficients (r) were as follows: 0.979 for Thr; 0.954 for Val; 0.968 for Leu; 0.918 for Ile; 0.946 for Lys; 0.908 for Arg; 0.845 for His; 0.999 for Trp; 0.915 for Phe; 0.909 for Met; 0.939 for Cys; 0.911 for Σmet + Cys; 0.933 for Tyr; 0.945 for Asp; 0.935 for Glu; 0.986 for Ser; 0.985 for Pro; 0.957 for Gly; 0.949 for Ala; 0.940 for ΣEAA; 0.958 for ΣNEAA and 0.977 for ΣAA. Partial least-squares (PLS) regression was used to develop calibration models for examined samples of sheep colostrum. When using the NIRS method, the following correlation coefficients were calculated: Thr (0.959), Val (0.912), Leu (0.936), Ile (0.855), Lys (0.903), Arg (0.853), His (0.717), Trp (0.667), Phe (0.854), Met (0.867), Cys (0.895), Σmet + Cys (0.868), Tyr (0.886), Asp (0.910), Glu (0.882), Ser (0.968), Pro (0.968), Gly (0.923), Ala (0.916), ΣEAA (0.901), ΣNEAA (0.923) and ΣAA (0.943). Calibration was tested using the same set of samples.NIRS results were compared with reference data and no significant differences between them were found (P = 0.05). Calibration and validation models were constructed in the same way.Results of this study indicate that NIR spectroscopy can be used for a rapid analysis of amino acid contents in sheep colostrum.  

Multivariate Classification of Sound and Insect-Infested Seeds of a Tropical Multipurpose Tree, Cordia Africana, with near Infrared Reflectance Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Multipurpose Tree ◽  
Cordia Africana

Near infrared (NIR) spectroscopy was used to classify insect-infested and sound seeds of a tropical multipurpose tree, Cordia africana Lam. A calibration model derived by partial least squares regression of orthogonal signal corrected spectra resulted in a 100% classification rate. Difference spectrum and partial least squares weight indicated that absorbance differences between insect-infested and sound seeds might have been due to differences in composition of chitin and cuticular lipid components as well as moisture content. The result shows the possibility of using NIR spectroscopy in the seed cleaning process in the future provided that appropriate sorting instruments are developed.

Investigating the total antioxidant capacity of gluten-free grains with miniaturized near-infrared spectrometer

NIR news ◽  
2019 ◽  
Vol 30 (5-6) ◽  
pp. 35-38
Author(s):  
Verena Wiedemair ◽  
Christian Wolfgang Huck
Keyword(s):  
Antioxidant Capacity ◽  
Total Antioxidant Capacity ◽  
High Performance ◽  
Near Infrared ◽  
Least Squares Regression ◽  
Test Set ◽  
Total Antioxidant ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Infrared Instruments

The use of ever smaller near-infrared instruments is becoming more and more prevalent, since they are cheaper, more versatile and often advertised as high-performance spectrometer. The last claim is rarely verified by independent researchers, which is why the presented work evaluates the performance of three hand-held spectrometers in comparison to a benchtop instrument. Seventy-seven samples comprising buckwheat, millet and oat were investigated for their total antioxidant capacity using Folin–Ciocalteu and near-infrared spectroscopy. Partial least squares regression models were established using cross- and test set validation. Results showed that all instruments were able to predict total antioxidant capacity to some extent. The coefficients of determinations ranged from 0.823 to 0.951 for cross-validated and from 0.849 to 0.952 for test set validated models. Errors for cross-validated models ranged from 1.11 to 2.08 mgGAE/g and for test set validated models from 1.02 to 1.86 mgGAE/g.

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 A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

<p>Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.</p><p>The study is performed in Mediterranean Croatia (44° 05’ N; 15° 22’ E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed <em>Quercus ssp.</em> and <em>Juniperus ssp.</em> forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22<sup>nd</sup> 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31<sup>st</sup> and September 5<sup>th </sup>2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE<sub>p</sub>) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.</p><p>The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE<sub>p </sub>(Spectroscopy dataset RMSE<sub>p</sub> = 1.91; Sentinel-2 dataset RMSE<sub>p</sub> = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.</p><p><strong>Keywords:</strong> remote sensing, soil spectroscopy, wildfires, soil organic matter</p><p><strong>Acknowledgment: </strong>This work was supported by the Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO). Aleksandra Perčin is acknowledged for her cooperation during the laboratory work.</p>

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