scholarly journals Simultaneous Quantification of 14 Compounds in Achillea millefolium by GC-MS Analysis and Near-Infrared Spectroscopy Combined with Multivariate Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lan-Ping Guo ◽  
Jian Yang ◽  
Li Zhou ◽  
Sheng Wang ◽  
Chuan-Zhi Kang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Achillea Millefolium ◽  
Multivariate Techniques ◽  
Preparation Technique ◽  
Least Squares Regression ◽  
Plant Materials ◽  
Analytical Technique ◽  
Simultaneous Quantification

The proposed work is focused on the simultaneous quantification of 14 compounds in the medicinal plant Achillea millefolium based on Near-Infrared Spectroscopy (NIR). The regression model of single-compound models (SCMs) and multicompound model (MCM) were created by partial least-squares regression (PLSR). Also, these models were calibrated by gas chromatographic mass spectroscopy (GC-MS). The results showed that the averaged standard errors of prediction (SEP) for the SCMs and MCM were 0.49 and 0.62, respectively, and most of the 14 compounds were significantly correlated. 43 correlations were significant at the 0.01 level (47.25% of the total), and 11 correlations were significant at the 0.05 level (12.09% of the total). The first three principal components (PCs) of principal component analysis (PCA) can explain >78% of the total variance. According to the component matrix and the communality table, octadecanoic acid has the largest influence on PC 1 (extraction squared = 46.72%), whose extraction was 0.932. The communality of neophytadiene, Z,Z,Z-9,12,15-octadecatrienoic acid, and oleic acid was also found to be large, whose extractions were 0.955, 0.937, and 0.859, respectively. These results indicate that if one compound shows a linear relationship with the NIR absorbance signal (SCM) also, an MCM can be created due to the close interrelations of these compounds. In this context, the present work highlights a suitable sample preparation technique to perform NIR analysis of raw plant material to benefit from robust and precise calibrations. To sum up, this NIR spectroscopic approach offers a precise, rapid, and cost-effective high-throughput analytical technique to simultaneously and noninvasively perform quantitative analysis of raw plant materials.

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.

Evaluation of the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 301-310 ◽  
Author(s):  
Jetsada Posom ◽  
Panmanas Sirisomboon
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Development ◽  
Volatile Matter ◽  
Ash Content ◽  
Least Squares Regression ◽  
Fixed Carbon ◽  
Bomb Calorimetry ◽  
Heating Value

This research aimed to determine the higher heating value, volatile matter, fixed carbon and ash content of ground bamboo using Fourier transform near infrared spectroscopy as an alternative to bomb calorimetry and thermogravimetry. Bamboo culms used in this study had circumferences ranging from 16 to 40 cm. Model development was performed using partial least squares regression. The higher heating value, volatile matter, fixed carbon and ash content were predicted with coefficients of determination (r2) of 0.92, 0.82, 0.85 and 0.51; root mean square error of prediction (RMSEP) of 122 J g−1, 1.15%, 1.00% and 0.77%; ratio of the standard deviation to standard error of validation (RPD) of 3.66, 2.55, 2.62 and 1.44; and bias of 14.4 J g−1, −0.43%, 0.03% and −0.11%, respectively. This report shows that near infrared spectroscopy is quite successful in predicting the higher heating value, and is usable with screening for the determination of fixed carbon and volatile matter. For ash content, the method is not recommended. The models should be able to predict the properties of bamboo samples which are suitable for achieving higher efficiency for the biomass conversion process.

scholarly journals Rapid Diagnosis of HIV-1 virus by Near Infrared Spectroscopy: based on Partial least squares regression

2021 ◽  
Vol 271 ◽  
pp. 03067
Author(s):  
Xiaohong He ◽  
Zhihong Song ◽  
Haifei Shang ◽  
Silang Yang ◽  
Lujing Wu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
False Negative ◽  
Cost Effective ◽  
Rapid Diagnosis ◽  
Enzyme Linked Immunosorbent Assay ◽  
Least Squares Regression ◽  
Early Screening ◽  
Hiv 1

Currently, the laboratory diagnostic tests available for HIV-1 viral infection are mainly based on serological testing which relies on enzyme-linked immunosorbent assay (ELISA) for blood HIV antigen detection and reverse transcription polymerase chain reaction (RT-PCR) for HIV specific RNA sequence identification. However, these methods are expensive and time-consuming, and suffer from false positive and/or false negative results. Thus, there is an urgent need for developing a cost effective, rapid and accurate diagnostic method for HIV-1 infection. In order to reduce the barriers for effective diagnosis, a near-infrared spectroscopy (NIR) method was used to detect the HIV-1 virus in human serum, specifically, three absorption peaks with dose-dependent at 1582nm, 1810nm and 2363nm were found by multiple FBiPLSR test analysis for HIV-nano and HIV-EGFP, but not for MLV. Therefore, we recommend the use of 1582nm, 1810nm and 2363nm as the characteristic spectrum peak, for early screening and rapid diagnosis of serum HIV.

scholarly journals Hemodialysis monitoring using mid- and near-infrared spectroscopy with partial least squares regression

2018 ◽  
Vol 11 (7) ◽  
pp. e201700365 ◽  
Author(s):  
Raphael Henn ◽  
Christian G. Kirchler ◽  
Zora L. Schirmeister ◽  
Andreas Roth ◽  
Werner Mäntele ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Least Squares Regression

scholarly journals OTIMIZAÇÃO DE CALIBRAÇÕES BASEADAS EM ESPECTROSCOPIA NO INFRAVERMELHO PRÓXIMO PARA ESTIMATIVA DE PROPRIEDADES DA MADEIRA DE Eucalyptus

FLORESTA ◽  
2010 ◽  
Vol 40 (3) ◽  
Author(s):  
Paulo Ricardo Gherardi Hein ◽  
José Tarcísio Lima ◽  
Gilles Chaix Gilles Chaix
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Laboratory Data ◽  
Wood Properties ◽  
Coefficient Of Determination ◽  
Mathematical Treatment ◽  
Least Squares Regression ◽  
Eucalyptus Wood ◽  
Prediction Density

A espectroscopia no infravermelho próximo (NIRS) é uma técnica não-destrutiva, rápida e utilizada para avaliação, caracterização e classificação de materiais, sobretudo de origem biológica. A obtenção de informações contida nos espectros NIR é complexa e requer a utilização de métodos quimiométricos. Assim, por meio de regressão multivariada, os espectros de absorbância podem ser associados às propriedades da madeira, tornando possível a sua predição em amostras desconhecidas. Existem algumas ferramentas quimiométricas que melhoram o ajuste dos modelos preditivos. Assim, o objetivo deste trabalho foi simular regressões dos mínimos quadrados parciais baseados nas informações espectrais e de laboratório e estudar a influência da aplicação de tratamentos matemáticos, do descarte de amostras anômalas e da seleção de comprimentos de onda no ajuste dos modelos para estimativa da densidade básica e do módulo de elasticidade em ensaio de compressão paralela às fibras da madeira de Eucalyptus. A aplicação da primeira e segunda derivada nos espectros, o descarte de amostras anômalas e a seleção de algumas das variáveis espectrais melhorou significativamente o ajuste do modelo, reduzindo o erro padrão e aumentando o coeficiente de determinação e a relação de desempenho do desvio.Palavras-chave:  Espectroscopia no infravermelho próximo; predição; densidade básica; MOE; madeira; Eucalyptus. AbstractOptimization of calibrations based on near infrared spectroscopy for estimation of Eucalyptus wood properties. Near infrared spectroscopy (NIRS) is a non-destructive technique used for rapid evaluation, characterization and classification of biological materials. The extraction of the information contained in the NIR spectrum is complex and requires the use of chemo metric methods. Thus, by means of multivariate regression, the absorbance spectra are correlated to wood properties, making possible the prediction in unknown samples. There are some chemo metric tools that can improve the adjustment of the predictive models. The aim of this work was to simulate partial least squares regression based on NIR spectra and laboratory data and to study the influence of the application of mathematical treatment, the removal of outliers and the wavelengths selection in the adjustment of models to estimate the density and modulus of elasticity in Eucalyptus wood. The use of the first and second derivative spectra, the disposal of outliers, and the variables selection improved significantly the model fit, reducing the standard error and increasing the coefficient of determination and the ratio of performance to deviation.Keywords: Near infrared; spectroscopy; prediction; density; MOE; wood; Eucalyptus.

Near infrared spectroscopy to evaluate change in color and chemical composition in heat-treated bamboo

2020 ◽  
Vol 28 (4) ◽  
pp. 214-223
Author(s):  
Junqian Mo ◽  
Wenbo Zhang ◽  
Xiaohui Fu ◽  
Wei Lu
Keyword(s):  
Infrared Spectroscopy ◽  
Chemical Composition ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Chemical Components ◽  
Outer Side ◽  
Heating Process ◽  
Least Squares Regression ◽  
Heat Treated

This study investigated the feasibility of using near infrared spectroscopy technology to predict color and chemical composition in the heat-treated bamboo processing industry. The quantitative presentations of the changes in the chemical components were discussed using the difference spectra method of the 2nd derivative NIR spectra of the heat-treated bamboo samples. Then, the relationships between the color changes of the heat-treated bamboo and its near infrared spectra were constructed using the changes in the chemical components of the bamboo samples during the heating process. The prediction of color and chemical composition of both the outer and inner sides of the heat-treated bamboo surface were constructed using partial least squares regression method combined with a leave-one-out cross-validation process. Then, the results were validated by independent sample sets. The proposed prediction models were found to produce high r2P (above 0.93), RPD (above 3.13), and low RMSEP for both the outer and inner sides of the heat-treated bamboo samples. These studies’ results confirmed that the proposed models, especially outer side models, were perfectly suitable for the in-process inspections of the color and chemical content changes of heat-treated bamboo.

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