scholarly journals Textile Fiber Identification Using Near-Infrared Spectroscopy and Pattern Recognition

2019 ◽  
Vol 19 (2) ◽  
pp. 201-209 ◽  
Author(s):  
Jinfeng Zhou ◽  
Lingjie Yu ◽  
Qian Ding ◽  
Rongwu Wang
Keyword(s):  
Pattern Recognition ◽  
Near Infrared ◽  
Raw Materials ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rejection Rate ◽  
Chemical Compositions ◽  
Target Fibers ◽  
Materials Used

Abstract Fibers are raw materials used for manufacturing yarns and fabrics, and their properties are closely related to the performances of their derivatives. It is indispensable to implement fiber identification in analyzing textile raw materials. In this paper, seven common fibers, including cotton, tencel, wool, cashmere, polyethylene terephthalate (PET), polylactic acid (PLA), and polypropylene (PP), were prepared. After analyzing the merits and demerits of the current methods used to identify fibers, near-infrared (NIR) spectroscopy was used owing to its significant superiorities, the foremost of which is it can capture the tiny information differences in chemical compositions and morphological features to display the characteristic spectral curve of each fiber. First, the fibers’ spectra were collected, and then, the relationships between the vibrations of characteristic chemical groups and the corresponding wavelengths were researched to organize a spectral information library that would be beneficial to achieve quick identification and classification. Finally, to achieve intelligent detection, pattern recognition approaches, including principal component analysis (PCA) (used to extract information of interest), soft independent modeling of class analogy (SIMCA), and linear discrimination analysis (LDA) (defined using two classifiers), assisted in accomplishing fiber identification. The experimental results – obtained by combining PCA and SIMCA – displayed that five of seven target fibers, namely, cotton, tencel, PP, PLA, and PET, were distributed with 100% recognition rate and 100% rejection rate, but wool and cashmere fibers yielded confusing results and led to relatively low recognition rate because of the high proportion of similarities between these two fibers. Therefore, the six spectral bands of interest unique to wool and cashmere fibers were selected, and the absorbance intensities were imported into the classifier LDA, where wool and cashmere were group-distributed in two different regions with 100% recognition rate. Consequently, the seven target fibers were accurately and quickly distinguished by the NIR method to guide the fiber identification of textile materials.

scholarly journals Near Infrared Spectroscopy Based on Supervised Pattern Recognition Methods for Rapid Identification of Adulterated Edible Gelatin

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Haifeng Sun ◽  
Ling Wang ◽  
Shun Wang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Near Infrared ◽  
Scatter Correction ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Support Vector ◽  
Linear Discriminant ◽  
Pattern Recognition Methods ◽  
Gelatin Gels

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Use of NIR Spectroscopy Combined with Recognition Methods for the Identification of Black Teas from Different Regions

2012 ◽  
Vol 503-504 ◽  
pp. 1601-1604 ◽  
Author(s):  
Jing Ming Ning ◽  
Sheng Peng Wang ◽  
Zheng Zhu Zhang ◽  
Xiao Chun Wan
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Black Tea ◽  
Rapid Identification ◽  
K Nearest Neighbor ◽  
Identification Rate

Near-infrared (NIR) spectroscopy, combined with pattern recognition, was applied in this study for the rapid identification of Black tea from different origins.The K-Nearest Neighbor model recognition method was used for the establishment of a tea origin recognition model, which involved optimization of the principal component factors (PCs) and the identification rate using a cross-validation method. The experimental results showed that, after standard normal variant spectral preprocessing, an optimized model was obtained when the PCs were equal to three, with the cross-validation recognition rate and the predicted recognition rate reaching 98.1% and 93.3%, respectively.

Rapid identification of plant- and chemical-dyed cotton fabrics using the near-infrared technique

2020 ◽  
Vol 90 (19-20) ◽  
pp. 2275-2283
Author(s):  
Mingxia Li ◽  
Guangting Han ◽  
Wei Jiang ◽  
Chengfeng Zhou ◽  
Yuanming Zhang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Recognition Rate ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Rapid Identification ◽  
Cotton Fabrics ◽  
Calibration Model ◽  
Pattern Recognition Methods ◽  
Validation Set

Plant dye is a promising dyestuff to be used in textiles due to its unique environmental compatibility. However, currently there is no effective method for the identification of plant-dyed and chemical-dyed textiles. In this study, near-infrared (NIR) spectroscopy combined with three kinds of pattern recognition methods, namely soft independent modeling of class analogy (SIMCA), partial least squares (PLS) regression and principal component regression (PCR), were applied to identify cotton fabrics dyed with plant and chemical dyes. A total of 336 plant dye and chemical dye dyed cotton fabrics were prepared and the NIR spectra were collected; 267 samples were used as the calibration set, while the remaining 69 samples were used as the validation set. After pretreatment with the Savitzky–Golay first derivative, the calibration model was constructed. In the SIMCA model, the correct recognition rate values of the calibration and prediction sets were 100% and 98.55%, respectively. The PLS model showed that the number of principal components (PCs) and the correlation coefficient ( R2) were 8 and 0.9978, respectively, and the results of PCR were PC = 10, R2 = 0.9937. Both methods were satisfactory for the predicted results. The overall results indicated that NIR spectroscopy could be used for rapid and nondestructive identification of plant-dyed cotton fabrics and chemical-dyed cotton fabrics.

scholarly journals Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

2008 ◽  
Vol 26 (No. 5) ◽  
pp. 360-367 ◽  
Author(s):  
Q. Chen ◽  
J. Zhao ◽  
M. Liu ◽  
J. Cai
Keyword(s):  
Pattern Recognition ◽  
Camellia Sinensis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Ann Model ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Non Invasive ◽  
Artificial Neural Network Ann

Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea (<I>Camellia sinensis</I> L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.

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.

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.

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