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.

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.

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.

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.

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.

scholarly journals Rapid and Green Method Forensic Authentication of Rice Using Near-Infrared Spectroscopy (NIRS)

2020 ◽  
Vol 12 (7) ◽  
pp. 105
Author(s):  
Francisco S. Panero ◽  
Pedro S. Panero ◽  
João S. Panero ◽  
Fernando S. E. D. V. Faria ◽  
Anselmo F. R. Rodriguez
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Dna Fingerprint ◽  
Raw Material ◽  
Geographical Discrimination

Rice is one of the most consumed cereals in the world. Currently, techniques for the authentication and geographical origin of rice is known not to be objective because to depend on the naked eye of a well-trained inspector. DNA fingerprint methods have been shown to be inappropriate for on-site application because the method needs a lot of labor and skilled expertise. Rice consumers want to confirm cultivation origin because they believe price or eating score has a high correlation according to them. Considering rice as a raw material of economic and social value and the recent use of NIR spectroscopy coupled with chemometric methods to authentication and discrimination of geographical origin as an alternative to classical methods in the search for a methodology in line with Green Chemistry, this work investigates the potential of NIR spectroscopy combined with multivariate analysis: PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis) for rapid and non-destructive forensic authentication of rice grains from Brazil and Venezuela. This study investigated the potential of near-infrared spectroscopy, combined with PCA and HCA chemometric technique to the authenticity of rice. It was verified that is feasible and advantageous to implement authenticity detection of different brands, typology and geographical discrimination (Brazil and Venezuela) rice.

Fast and Non-Destructiveness Discrimination of Varieties of Fragrant Mushroom Using Near Infrared Spectroscopy

2011 ◽  
Vol 460-461 ◽  
pp. 159-164
Author(s):  
Peng Cheng Nie ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Di Wu ◽  
Yong He
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
High Speed ◽  
Near Infrared ◽  
Recognition Rate ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Hidden Layer

Visible/near-infrared spectroscopy (NIRS) is the millimeter wave ,It is the high speed and non-destructiveness method, high precision and reliable detection data, is a rapid and non-destructiveness method for discrimination varieties of Fragrant mushrooms by means of VIS/NIR spectroscopy was developed in this study. The relationship between the reflectance spectra and Fragrant mushrooms varieties was established. The spectral data was compressed by the wavelet transform (WT). The features from WT can be visualized in principal component (PC) space, appeared to provide a reasonable clustering of the varieties of Fragrant mushrooms. The fivet principal components computed by PCA had been applied as inputs to a back propagation neural network(BP) with one hidden layer. The 220 samples of four varieties were selected randomly to build BP model. This model was used to predict the varieties of 40 unknown samples. The predict recognition rate has achieved 99.5%. This model was reliable and practicable.

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.

Discrimination of plant samples using near-infrared spectroscopy with a principal component accumulation method

2012 ◽  
Vol 4 (9) ◽  
pp. 2893 ◽  
Author(s):  
Yi Wang ◽  
Xiang Ma ◽  
Yadong Wen ◽  
Jingjing Liu ◽  
Wensheng Cai ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Plant Samples ◽  
Accumulation Method

scholarly journals Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy

Recycling ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Kirsti Cura ◽  
Niko Rintala ◽  
Taina Kamppuri ◽  
Eetta Saarimäki ◽  
Pirjo Heikkilä
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Automated Recognition ◽  
Textile Materials ◽  
Input Material ◽  
Adequate Quality ◽  
Automated Line

In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.

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