scholarly journals Analysis of Selected Properties of Fibreboard Panels Manufactured from Wood and Leather Using the Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Kerstin Wagner ◽  
Thomas Schnabel ◽  
Marius-Catalin Barbu ◽  
Alexander Petutschnigg
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Bending Strength ◽  
Raw Materials ◽  
Nir Spectroscopy ◽  
Physical And Mechanical Properties ◽  
Principal Component ◽  
Least Squares Regression

This paper deals with the characterization of the properties of wood fibres leather shavings composite board by using the near infrared spectroscopy (NIRS) and multivariate data analysis. In this study fibreboards were manufactured with different leather amounts by using spruce fibres, as well as vegetable and mineral tanned leather shavings (wet white and wet blue). The NIR spectroscopy was used to analyse the raw materials as well as the wood leather fibreboards. Moreover, the physical and mechanical features of the wood leather composite fibreboards were determined to characterize their properties for the further data analysis. The NIR spectra were analysed by univariate and multivariate methods using the Principal Component Analysis (PCA) and the Partial Least Squares Regression (PLSR) method. These results demonstrate the potential of FT-NIR spectroscopy to estimate the physical and mechanical properties (e.g., bending strength). This phenomenon provides a possibility for quality assurance systems by using the NIRS.

Application of visible and near-infrared spectroscopy for evaluation of ewes milk with different feeds

2019 ◽  
Vol 59 (6) ◽  
pp. 1190 ◽  
Author(s):  
A. Bahri ◽  
S. Nawar ◽  
H. Selmi ◽  
M. Amraoui ◽  
H. Rouissi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Random Forest Regression ◽  
Milk Samples

Rapid measurement optical techniques have the advantage over traditional methods of being faster and non-destructive. In this work visible and near-infrared spectroscopy (vis-NIRS) was used to investigate differences between measured values of key milk properties (e.g. fat, protein and lactose) in 30 samples of ewes milk according to three feed systems; faba beans, field peas and control diet. A mobile fibre-optic vis-NIR spectrophotometer (350–2500 nm) was used to collect reflectance spectra from milk samples. Principal component analysis was used to explore differences between milk samples according to the feed supplied, and a partial least-squares regression and random forest regression were adopted to develop calibration models for the prediction of milk properties. Results of the principal component analysis showed clear separation between the three groups of milk samples according to the diet of the ewes throughout the lactation period. Milk fat, protein and lactose were predicted with good accuracy by means of partial least-squares regression (R2 = 0.70–0.83 and ratio of prediction deviation, which is the ratio of standard deviation to root mean square error of prediction = 1.85–2.44). However, the best prediction results were obtained with random forest regression models (R2 = 0.86–0.90; ratio of prediction deviation = 2.73–3.26). The adoption of the vis-NIRS coupled with multivariate modelling tools can be recommended for exploring to differences between milk samples according to different feed systems, and to predict key milk properties, based particularly on the random forest regression modelling technique.

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.

Determination of soil organic carbon and nitrogen at the field level using near-infrared spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 413-422 ◽  
Author(s):  
P D Martin ◽  
D F Malley ◽  
G. Manning ◽  
L. Fuller
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analytical Data ◽  
Principal Component ◽  
Soil Samples ◽  
Organic N ◽  
Least Squares Regression ◽  
Total N ◽  
Organic C

This study explored the use of near-infrared spectroscopy (NIRS) for the rapid analysis of organic C (Corg) and organic N (Norg) in the A horizon of soil within a single field. Soil was sampled throughout a field in Manitoba, Canada to capture soil variability associated with topography. The soil samples were oven-dried and treated with acid to remove carbonates, after which C and N were determined by dry combustion. In this study, portions of the dried soil samples not treated with acid were scanned with a near-infrared scanning spectrophotometer between 1100 and 2500 nm. Correlating the spectral and the chemical analytical data using multiple linear regression or principal component analysis/partial least squares regression gave useful correlations for Corg. Over the range of 0–40 mg g-1 Corg, NIR-predicted values explained 75–78% of the variance in the chemical results. Results were improved to 80% for calibrations developed for the 0–20 mg g-1 organic C range. Useful results were not obtained for Norg although the literature shows that total N in soil is predictable using NIRS. It is likely that the acid treatment altered the composition of the samples in an inconsistent manner such that the chemically analyzed samples and those scanned by NIRS were different from each other in Norg concentration or composition. Extrapolation of these Corg results to the landscape scale implies that NIRS has potential to be a suitable method for mapping C for the purposes of monitoring C sequestration. Key words: Near-infrared spectroscopy, soil, carbon, nitrogen, topography, soil monitoring

Rapid Discrimination of Fermented Cordyceps Mycelium Powder by Near-Infrared Spectroscopy (NIRS)

2013 ◽  
Vol 302 ◽  
pp. 189-193 ◽  
Author(s):  
Ning Xu ◽  
Wei Qiang Luo ◽  
Hai Qing Yang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Clustering Analysis ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Recognition Method ◽  
Hyper Spectral ◽  
Rapid Discrimination

The potential of near-infrared spectroscopy (NIRS) was investigated for its ability to rapidly discriminate the various brands of fermented Cordyceps mycelium powder. Relationship between mycelium powder varieties and the absorbance spectra was well established with the spectra region of 12500-4000 cm-1. Spectra preprocessing was performed using 1st derivative. Principal component analysis (PCA) was adopted for the clustering analysis and re-expressing of the hyper spectral data, and then, the obtained principal components (PCs) were used as the input of back-propagation artificial neural network (BPANN) to build PCA-BPANN model for the variety discrimination. The unknown samples in prediction set were precisely identified with the correlation coefficient R of 0.9959 and root-mean-square error of prediction (RMSEP) of 0.1007, which suggests that the NIR spectroscopy, if coupled with appropriate pattern recognition method, is very promising for rapid and nondestructive discrimination of fermented Cordyceps mycelium powder.

Estimation of Crystallinity of Poplar Cellulose Using Near Infrared Spectroscopy

2012 ◽  
Vol 622-623 ◽  
pp. 1532-1535
Author(s):  
Zhen Bo Liu ◽  
Wen Yang Kong ◽  
Yi Xing Liu ◽  
Zhan Chuan Xue ◽  
Xiao Yan Shen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Wood Properties ◽  
Least Squares Regression ◽  
Cellulose Crystallinity ◽  
Test Models

Many studies have successfully applied near infrared (NIR) spectroscopy to estimate important wood properties. In this paper, the use of NIR (350–2500 nm) spectroscopy to predict the cellulose crystallinity of Poplar (Populus nigra var.) was investigated. The calibration and test models were constructed using partial least squares regression (PLS). The correlations were significant both the calibration and the test samples using six factors, and the correlation coefficients (R2) were 0.9367, 0.9472 respectively. The results suggest that NIR spectroscope may provide a useful tool for rapid and accurate prediction of the cellulose crystallinity of Poplar.

scholarly journals Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden

Agroteknika ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 67-74
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Principal Component ◽  
Least Squares Regression ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Cucumis Melo L

Kadar air merupakan salah satu atribut kualitas yang penting pada komoditas hortikultura. Penetapan kadar air buah melon dengan metode konvensional memakan waktu yang lama dan perlu merusak sampel buah. Penelitian ini bertujuan untuk memprediksi kadar air buah melon golden menggunakan teknologi visible-near infrared spectroscopy (Vis-NIRS). Metode koreksi spektra orthogonal signal correction (OSC) diterapkan pada spektra original untuk meningkatkan kehandalan model kalibrasi. Partial least squares regression (PLSR) digunakan sebagai metode pendekatan regresi untuk mengekstraksi data spektra Vis-NIRS. Hasil penelitian membuktikan bahwa Vis-NIRS dapat diandalkan untuk memprediksi kadar air buah melon golden. Metode koreksi spektra OSC mampu memperkecil jumlah principal component (PC) pada spektra original. Linieritas pada model kalibrasi menggunakan spektra OSC tercatat memperoleh nilai tertinggi sebesar 0,92. Ratio of performance to deviation (RPD) pada spektra OSC menampilkan nilai tertinggi pula yaitu 3,63. Model kalibrasi yang diperoleh pada penelitian ini dapat ditransfer ke dalam spektrometer Vis-NIRS untuk prediksi kadar air melon golden secara cepat dan simultan.

scholarly journals Use of near infrared spectroscopy to distinguish carbonization processes and charcoal sources

CERNE ◽  
2010 ◽  
Vol 16 (3) ◽  
pp. 381-390 ◽  
Author(s):  
Thiago Campos Monteiro ◽  
Renato Vieira da Silva ◽  
José Tarcísio Lima ◽  
Paulo Ricardo Gherardi Hein ◽  
Alfredo Napoli
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Native Species ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Native Vegetation ◽  
Biological Origin ◽  
First Derivative ◽  
Eucalyptus Wood

The near infrared spectroscopy (NIRS) has shown a rapid and accurate technique for evaluation of materials of biological origin. The objective of this study was to evaluate the ability of the near infrared (NIR) spectroscopy associated to the Principal Component Analysis (PCA) for the separation of carbonization processes and identification of the origin of the woods used in the carbonizations. Hence, the charcoal of seven species of Eucalyptus and twenty native species from the Cerrado (savannah) of Minas Gerais, Brazil were investigated. The Eucalyptus wood was carbonized in a laboratory furnace and in a 190 m³ industrial rectangular kilns while the wood of native vegetation was carbonized only under laboratory conditions. The samples were grinded for NIR spectra acquirement. The NIR spectra were analyzed by PCA but no cluster were identified allowing discrimination between charcoal produced from native and from Eucalyptus wood. However, the cluster formed in the PCA when using the first derivative NIR spectra permitted to distinguish charcoal produced in different processes of carbonization. Two groups of data for charcoal produced in the industrial rectangular kilns were also observed, suggesting heterogeneity in the carbonization process.

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.  

Discrimination of Chinese patent medicines using near-infrared spectroscopy and principal component accumulation method

2014 ◽  
Vol 6 (13) ◽  
pp. 4692-4697 ◽  
Author(s):  
Ruifeng Shan ◽  
Zhiyi Mao ◽  
Lihui Yin ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Patent Medicines ◽  
Chinese Patent ◽  
Accumulation Method

NIR spectroscopy combined with PCAcc was used to identify 12 classes of Chinese patent medicines.

The Use of Visible and near Infrared Spectroscopy to Classify the Floral Origin of Honey Samples Produced in Uruguay

2005 ◽  
Vol 13 (2) ◽  
pp. 63-68 ◽  
Author(s):  
E. Corbella ◽  
D. Cozzolino
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Sample Preparation ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Models ◽  
Linear Discriminant

This study reports the use of visible (vis) and near infrared (NIR) spectroscopy as a tool to classify honey samples from Uruguay, according to their floral origin. Classification models were developed using principal component analysis, discriminant partial least squares (DPLS) regression and linear discriminant analysis (LDA). Honey samples ( n = 50) from two floral origins, namely Eucalyptus spp. and pasture, were split randomly into even calibration ( n = 25) and validation sets ( n = 25). Both LDA and DPLS models correctly classified, on average, more than 75% of the honey samples belonging to pasture and more than 85% of the honey samples belonging to Eucalyptus spp. These results showed that vis-NIR might be a suitable and alternative method that can easily be implemented by both the industry and retailers to classify samples according their floral origin. Vis-NIR analysis requires little sample preparation and is rapid. However, the relatively limited number of samples involved in the present work led us to be cautious in terms of extrapolating the results of this work to other floral types.

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