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.

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.

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.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

1988 ◽  
Vol 42 (7) ◽  
pp. 1273-1284 ◽  
Author(s):  
Tomas Isaksson ◽  
Tormod Næs
Keyword(s):  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Data Sets ◽  
Prediction Errors ◽  
Regression Methods ◽  
Multiplicative Scatter Correction ◽  
Linearity Improvement

Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7–68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

scholarly journals Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1551
Author(s):  
Annalisa De Girolamo ◽  
Salvatore Cervellieri ◽  
Erminia Mancini ◽  
Michelangelo Pascale ◽  
Antonio Francesco Logrieco ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Linear Discriminant ◽  
Production And Consumption ◽  
High Prediction ◽  
Validation Set ◽  
Performance Results ◽  
Prediction Capability

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

Beyond Prediction: Extracting Relevant Information from near Infrared Spectra

1996 ◽  
Vol 4 (1) ◽  
pp. 75-84 ◽  
Author(s):  
P. Robert ◽  
M.-F. Devaux ◽  
D. Bertrand
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Relevant Information ◽  
Correlated Data ◽  
Spectral Interpretation ◽  
Highly Correlated ◽  
Prediction Techniques ◽  
Spectral Patterns

With the increase of near infrared (NIR) applications, numerous chemometric methods have been developed. Among the mathematical treatments available, principal comoponent analysis (PCA) is certainly the most well-known when considering highly correlated data. In the field of near infrared spectroscopy, it allows the study of spectra without deleting wavelengths and without making any preliminary assumptions on the data. One advantage of PCA lies in the graphical displays obtained and, more precisely, on the similarity maps and spectral patterns. While the maps reveal clusters of the samples, the spectral patterns make a spectral interpretation possible. The present paper reviews our contribution to the development and application of PCA to NIR spectroscopy. It shows that PCA is the core of various mathematical treatments such as principal component regression (PCR), factorial discriminant analysis (FDA) and canonical correlation analysis (CCA). One advantage of using PCA in the prediction techniques lies in the use of all the wavelengths in the predictive model. The extraction of relevant and comprehensive wavelengths can be guided by CCA which allows the description of the samples by taking both mid- and near infrared data into account. Besides a comprehensive presentation of the mathematical treatements, examples are given.

Use of Near-Infrared Spectroscopy to Determine the Composition of High-Density/Low-Density Polyethylene Blend Films

1993 ◽  
Vol 47 (2) ◽  
pp. 222-228 ◽  
Author(s):  
Charles E. Miller
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
High Density ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Blend Films ◽  
Polyethylene Blend

The ability of near-infrared (NIR) spectroscopy, combined with principal component regression (PCR), to nondestructively determine the blend ratio of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) in extruded films is demonstrated. Results indicate that the NIR spectrum in the region 2100 to 2500 nm can be used to determine the HDPE mass percentage of 60–80- μm-thick film samples to within 2.5%, over a range of 0 to 100%. NIR spectral effects from scattering are important for the determination of the HDPE % for HDPE contents above 50%, and spectral effects from changes in the methyl group concentration and perhaps the PE crystallinity are important for the determination of the HDPE % for HDPE contents below 50%. In addition, a large variation between the spectra of replicate samples, probably caused by variations in the degree or direction of molecular orientation in the samples, was observed.

scholarly journals Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
Lu Xu ◽  
Xian-Shu Fu ◽  
Cui-Wen Fan ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Standard Normal Variate ◽  
Pattern Recognition Methods ◽  
Standard Normal ◽  
Cotton Genotypes ◽  
Rapid Discrimination

This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal component analysis (rPCA) was applied to each separate sample group to detect and exclude outliers. The influence of different data preprocessing methods on model prediction performance was also investigated. The results demonstrate that rPCA can effectively detect outliers and maintain the efficiency of discriminant analysis. Moreover, the classification accuracy can be significantly improved by second-order derivative and standard normal variate (SNV). The best partial least squares discriminant analysis (PLSDA) models obtained total classification accuracy of 100% and 97.6% for seeds and leaves, respectively.

Nondestructive Characterizations of Polyethylene/Nylon Laminates by Near-Infrared Spectroscopy

1993 ◽  
Vol 47 (3) ◽  
pp. 346-356 ◽  
Author(s):  
Charles E. Miller ◽  
Svend A. Svendsen ◽  
Tormod Næ
Keyword(s):  
Layer Thickness ◽  
Food Packaging ◽  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Polyamide 6 ◽  
Chemical Modeling ◽  
Ethylene Vinyl Alcohol ◽  
Laminate Thickness

The use of near-infrared (NIR) spectroscopy for the rapid and nondestructive analysis of food packaging laminates containing polyethylene (PE), polyamide-6 (PA-6), and ethylene vinyl alcohol (EVOH) layers is demonstrated. The method of Pathlength Correction with Chemical Modeling (PLC-MC) is used to estimate the total laminate thickness, and Principal Component Regression (PCR), is used to estimate the thickness percentages of PE and EVOH in the laminates, from NIR reflective-transmission spectra in the region 1500–2500 nm. Results indicate that the NIR method can be used to determine the total laminate thickness within 2–4 μm, the PE layer thickness percentage within 0.7–1.8%, and the EVOH layer thickness within 0.7–0.8 μm. In addition, detailed observation of the PCR models indicates that the NIR method is also sensitive to the absorbed water content, the morphology of the polymers, and perhaps the amount of polyurethane adhesive in the laminates. The usefulness of PCR outlier detection, for identification and characterization of strange samples, and principal component rotation, for improvement of PCR model interpretability, is also demonstrated.

Depth-Resolved Near-Infrared Spectroscopy

1996 ◽  
Vol 50 (2) ◽  
pp. 285-291 ◽  
Author(s):  
Nadhamuni G. Nerella ◽  
James K. Drennen
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Principal Component ◽  
Depth Resolution ◽  
New Drugs ◽  
Penetration Enhancers ◽  
Calibration Model ◽  
Near Ir ◽  
Hydrogel Matrix

While there is substantial evidence proving the success of transdermal drug delivery, there have been few accomplishments in the area of depth-resolved prediction of drug concentration during diffusion through a matrix. Such a method for noninvasive quantification of a diffusing species could assist in the development of new drugs, dosage forms, and penetration enhancers. Near-infrared depth-resolved measurements were accomplished by strategically controlling the amount of reflected light reaching the detectors using a combination of diaphragms with different-diameter apertures. Near-IR spectra were collected from a set of cellulose and Silastic® membranes to prove the possibility of depth-resolved near-IR measurements. Principal component regression was used to estimate the depth resolution of this method, yielding an average resolution of 31 μm. Further, to demonstrate depth-resolved near-IR spectroscopy in a practical in vitro system, we determined concentrations of salicylic acid (SA) in a hydrogel matrix during diffusion experiments carried out for up to three hours. An artificial-neural-network-based calibration model was developed which predicted SA concentrations accurately ( R2 = 0.993, SEP = 123 μg/mL).

Comparison of Multivariate Calibration and Discriminant Analysis in Evaluating NIR Spectroscopy for Determination of Meat Tenderness

1997 ◽  
Vol 51 (3) ◽  
pp. 350-357 ◽  
Author(s):  
Tormod Næs ◽  
Kjell Ivar Hildrum
Keyword(s):  
Discriminant Analysis ◽  
Reference Values ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Meat Tenderness ◽  
Intermediate Group ◽  
Calibration Methods

Often the primary goal of analytical measurement tasks is not to find good estimates of continuous reference values but rather to determine whether a sample belongs to one of a number of categories or subgroups. In this paper the potential of different statistical techniques in the classification of raw beef samples in tenderness subgroups was studied. The reference values were based on sensory analysis of beef tenderness of 90 samples from bovine M. longissimus dorsi muscles. The sample set was divided into three categories—very tough, intermediate, and very tender—according to degree of tenderness. A training set of samples was used to find the relationship between category and near-infrared (NIR) spectroscopic measurements. The study indicates that classical discriminant analysis has advantages in comparison to multivariate calibration methods [i.e., principal component regression (PCR)], in this application. One reason for this observation seems to be that PCR underestimates high measurement values and overestimates low values. In this way most samples are assigned to the intermediate group of samples, causing a small number of erroneous classifications for the intermediate subgroup, but a large number of errors for the two extreme groups. With the use of PCR the number of correct classifications in the extreme subgroups was as low as 23%, while the use of discriminate analysis increased this number to almost 60%. The number of classifications in correct or neighbor subgroup for the two extreme subgroups was equal to 97%. A “bias-correction” was also attempted for PCR, and this gave results comparable to the best results obtained by discriminant analysis methods. Test sets used NIR analysis of fresh, raw beef samples with different processing. While this spectroscopic approach had previously been shown to be useful with frozen products, it appears unsuitable at this time for fresh beef. However, its marginal analytical utility proved useful in evaluating the two classification approaches employed in this study.

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