The Use of near Infrared Spectroscopy to Quantify Lignite-Derived Carbon in Humus-Lignite Mixtures

2007 ◽  
Vol 15 (3) ◽  
pp. 195-200 ◽  
Author(s):  
Marcin Chodak ◽  
Maria Niklińska ◽  
Friedrich Beese
Keyword(s):  
Near Infrared ◽  
Regression Coefficient ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Mine Soils ◽  
Organic Horizons ◽  
Predicted Values ◽  
Calibration Equations ◽  
Mixed Samples ◽  
Forest Humus

Assessment of the percentage of lignite-derived C (lign-C%) in mine soils may be achieved only by using time-consuming and expensive methods. The objectives of this study were (1) to compare near infrared (NIR) spectra of forest humus and lignite and (2) to test whether NIR spectroscopy may assess lign-C% in artificial mixtures of humus and lignite. The experiment consisted of three trials (T1, T2 and T3). In T1 the mixed samples ( n = 75) were produced from one humus sample and one lignite sample, in T2 (n = 74) from 74 different humus samples and one lignite sample and in T3 (n = 74) from 74 different humus samples and 15 lignite samples. In each trial, 35 samples were used to develop calibration equations and the remaining samples were used for validation. The humus and the lignite samples used to produce the mixed samples were analysed for C, H, N and S and their NIR spectra were recorded. The lignite samples contained more C, H and S and less N than the humus samples. Principal component analysis revealed significant differences between NIR spectra of the humus and the lignite samples. The prediction of lign-C% in T1 [regression coefficient (b) of linear regression (measured against predicted values) = 0.99, correlation coefficient ( r2) = 1.00, standard error of prediction (SEP) = 1.2%] and T2 ( b = 0.99, r2 = 0.99, SEP = 1.9%) was very good and in T3 satisfactory ( b = 0.83, r2 = 0.92, SEP = 4.0% ). The calibration equations of T2 predicted lign-C% satisfactorily and also in the validation samples of T3 (b = 0.88, r2 = 0.93, SEP = 4.0% ). The results indicate the ability of NIR spectroscopy to predict lign-C% in the mixed humus and lignite samples and suggest usefulness of NIR spectroscopy for the assessment of the percentage of lignite-derived C in the organic horizons of mine soils.

scholarly journals Prediction of wheat milling characteristics by near-infrared reflectance spectroscopy

2011 ◽  
Vol 23 (No. 4) ◽  
pp. 145-151 ◽  
Author(s):  
J. Blažek ◽  
O. Jirsa ◽  
M. Hrušková
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Milling Process ◽  
Near Infrared Reflectance ◽  
Independent Validation ◽  
Milling Characteristics ◽  
Predicted Values ◽  
Calibration Equations

The aim of this study was to explore the use of NIR spectroscopy of laboratory milled flour to predict the milling characteristics of wheat. Quantitative traits of the milling process of wheat were predicted by analyses of NIR spectra of six sets consisting of 94 samples. Reference data were obtained by grinding the samples on the laboratory mill Chopin CD1-auto (France), spectral data were measured on spectrograph NIRSystem 6500. Commercial spectral analysis software WINISI II was used to collect spectra, develop calibration equations and evaluate calibration performance. The quality of prediction was evaluated by coefficients of correlation between the measured and the predicted values from cross and independent validation. MPLS/PLS regression and ANN methods were used. A statistically significant dependence (at the probability level of 99%) was determined for all traits studied in the case of cross-validation. Satisfactory accuracy of the prediction models by independent validation was achieved only for semolina extraction rate, models for other characteristics did not show acceptable precision.  

Prediction of Ethylene Content in Melt-State Random and Block Polypropylene by Near-Infrared Spectroscopy and Chemometrics: Influence of a Change in Sample Temperature and its Compensation Method

2005 ◽  
Vol 59 (5) ◽  
pp. 600-610 ◽  
Author(s):  
Masahiro Watari ◽  
Yukihiro Ozaki
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Compensation Method ◽  
Sample Temperature ◽  
Calibration Model ◽  
Laboratory System ◽  
Difference Spectra ◽  
The Difference ◽  
Predicted Values

This paper reports on the influence of a change in sample temperature, and a method for its compensation, for the prediction of ethylene (C2) content in melt-state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. Near-infrared (NIR) spectra of RPP in the melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and an FT-NIR laboratory system. There are some significant differences between the solid- and melt-state RPP spectra. Moreover, we investigated the predicted values of the C2 content from the RPP or BPP spectra measured at 190 °C and 250 °C using the calibration model for the C2 content developed using the RPP or BPP spectra measured at 230 °C. The errors in the predicted values of the C2 content depend on the pretreatment methods for each calibration model. It was found that multiplicative signal correction (MSC) is very effective in compensating for the influence of the change of temperature for the RPP or BPP samples on the predicted C2 content. From the suggestion of principal component analysis (PCA) and difference spectrum analysis, we propose a new compensation method for the temperature change that uses the difference spectra between two spectra sets measured at different temperatures. We achieved good results using the difference spectra between the RPP/BPP spectra sets measured at 190 °C and 250 °C after correction and the calibration model developed with the spectra measured at 230 °C. The comparison between the method using MSC and the proposed method showed that the predicted error in the latter is slightly better than those in the former.

Fourier Transform near Infrared Spectroscopy Applied to Analysis of Chocolate

2000 ◽  
Vol 8 (4) ◽  
pp. 251-257 ◽  
Author(s):  
Jolana Tarkošová ◽  
Jana Čopíková
Keyword(s):  
Fourier Transform ◽  
Reference Values ◽  
Near Infrared ◽  
Cross Validation ◽  
Nir Spectroscopy ◽  
Sufficient Accuracy ◽  
Standard Methods ◽  
Calibration Models ◽  
Predicted Values ◽  
Calibration Equations

Fourier transform near infrared (FT-NIR) spectroscopy was used to establish calibration equations with the aim of determining sucrose, lactose, fat and moisture in chocolate. The possibility of using FT-NIR spectroscopy for evaluating rheological properties (viscosity and yield) of chocolate was also investigated. The concentrations of individual components and the values of viscosity and yield obtained by standard methods were used as reference values. The spectra of 96 chocolate samples were recorded in reflectance mode in the range of 910–2500 nm using an FT-NIR Nicolet Avatar 360N spectrometer equipped with the UpDRIFT accessory. The first or second derivative transformation of the original NIR spectra gave the best accuracy. A partial least squares (PLS) algorithm was used to create calibration models relating reference values to spectral data. The models were validated using cross-validation. The validation results proved that fat, sucrose and lactose can be predicted with sufficient accuracy, while predicted values for moisture, viscosity and yield are less reliable.

scholarly journals Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Elise A. Kho ◽  
Jill N. Fernandes ◽  
Andrew C. Kotze ◽  
Glen P. Fox ◽  
Maggy T. Sikulu-Lord ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Haemonchus Contortus ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Diagnostic Methods ◽  
Calibration Models ◽  
Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

Near infrared spectroscopy as an alternative method for rapid determination of the solidification point of 2,4,6-trinitrotoluene in production

2019 ◽  
Vol 27 (4) ◽  
pp. 286-292
Author(s):  
Chongchong She ◽  
Min Li ◽  
Yunhui Hou ◽  
Lizhen Chen ◽  
Jianlong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Determination ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Quality Parameter ◽  
Solidification Point ◽  
Fast Analysis ◽  
Online Application

The solidification point is a key quality parameter for 2,4,6-trinitrotoluene (TNT). The traditional solidification point measurement method of TNT is complicated, dangerous, not environmentally friendly and time-consuming. Near infrared spectroscopy (NIR) analysis technology has been applied successfully in the chemical, petroleum, food, and agriculture sectors owing to its characteristics of fast analysis, no damage to the sample and online application. The purpose of this study was to study near infrared spectroscopy combined with chemometric methods to develop a fast and accurate quantitative analysis method for the solidification point of TNT. The model constructed using PLS regression was successful in predicting the solidification point of TNT ([Formula: see text] = 0.999, RMSECV = 0.19, RPDCa = 33.5, [Formula: see text] = 0.19, [Formula: see text] = 0.999). Principal component analysis shows that the model could identify samples from different reactors. The results clearly demonstrate that the solidification point can be measured in a short time by NIR spectroscopy without any pretreatment for the sample and skilled laboratory personnel.

Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths

2020 ◽  
Vol 28 (4) ◽  
pp. 224-235
Author(s):  
Irina M Benson ◽  
Beverly K Barnett ◽  
Thomas E Helser
Keyword(s):  
Discriminant Analysis ◽  
Spatial Variability ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Otolith Chemistry ◽  
K Nearest Neighbor ◽  
Fisheries Science

Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

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