scholarly journals Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

2020 ◽  
Author(s):  
Mohammad Al Ktash ◽  
Otto Hauler ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Near Infrared ◽  
Dominant Role ◽  
Principal Component ◽  
Industrial Applications ◽  
Acquisition Time ◽  
Hydroxyl Groups ◽  
Short Acquisition Time ◽  
Different Types

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

scholarly journals Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 603
Author(s):  
Lukáš Krauz ◽  
Petr Páta ◽  
Jan Kaiser
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Hyperspectral Image Processing ◽  
Different Types ◽  
The Difference ◽  
Hyperspectral Camera

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

scholarly journals Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid Line to Line Analysis Tool

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4436
Author(s):  
Mohammad Al Ktash ◽  
Mona Stefanakis ◽  
Barbara Boldrini ◽  
Edwin Ostertag ◽  
Marc Brecht
Keyword(s):  
Hyperspectral Imaging ◽  
Large Scale ◽  
Solid Phase ◽  
Ultra Violet ◽  
Principal Component ◽  
Ccd Camera ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Analysis Tool ◽  
Sample Set

A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application.

scholarly journals Separation of Cultivars of Soybeans by Chemometric Methods Using Near Infrared Spectroscopy

2018 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
João S. Panero ◽  
Henrique E. B. da Silva ◽  
Pedro S. Panero ◽  
Oscar J. Smiderle ◽  
Francisco S. Panero ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Relevant Information ◽  
Whole Grains ◽  
Signal Correction ◽  
Different Types ◽  
Linear Fit

Near Infrared (NIR) Spectroscopy technique combined with chemometrics methods were used to group and identify samples of different soy cultivars. Spectral data, collected in the range of 714 to 2500 nm (14000 to 4000 cm-1), were obtained from whole grains of four different soybean cultivars and were submitted to different types of pre-treatments. Chemometrics algorithms were applied to extract relevant information from the spectral data, to remove the anomalous samples and to group the samples. The best results were obtained considering the spectral range from 1900.6 to 2187.7 nm (5261.4 cm-1 to 4570.9 cm-1) and with spectral treatment using Multiplicative Signal Correction (MSC) + Baseline Correct (linear fit), what made it possible to the exploratory techniques Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to separate the cultivars. Thus, the results demonstrate that NIR spectroscopy allied with de chemometrics techniques can provide a rapid, nondestructive and reliable method to distinguish different cultivars of soybeans.

A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses

2003 ◽  
Vol 11 (4) ◽  
pp. 269-281 ◽  
Author(s):  
Kurt C. Lawrence ◽  
William R. Windham ◽  
Bosoon Park ◽  
R. Jeff Buhr
Keyword(s):  
Real Time ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Ccd Camera ◽  
Sample Population ◽  
Multivariate System ◽  
Ratio Image ◽  
On Line

A method and system for detecting faecal and ingesta contaminants on poultry carcasses were demonstrated. A visible/near infrared monochromator, which measured reflectance and principal component analysis were first used to identify key wavelengths from faecal and uncontaminated skin samples. Measurements at 434, 517, 565 and 628 nm were identified and used for evaluation with a hyperspectral imaging system. The hyperspectral imaging system, which was a line-scan (pushbroom) imaging system, consisted of a hyperspectral camera, fibre-optic line lights, a computer and frame grabber. The hyperspectral imaging camera consisted of a high-resolution charge coupled device (CCD) camera, a prism-grating-prism spectrograph, focusing lens, associated optical hardware and a motorised controller. The imaging system operated from about 400 to 900 nm. The hyperspectral imaging system was calibrated for wavelength, distance and percent reflectance and analysis of calibrated images at the key wavelengths indicated that single-wavelength images were inadequate for detecting contaminants. However, a ratio of images at two of the key wavelengths was able to identify faecal and ingesta contaminants. Specifically, the ratio of the 565-nm image divided by the 517-nm image produced good results. The ratio image was then further processed by masking the background and either enhancing the image contrast with a non-linear histogram stretch, or applying a faecal threshold. The results indicated that, for the limited sample population, more than 96% of the contaminants were detected. Thus, the hyperspectral imaging system was able to detect contaminants and showed feasibility, but was too slow for real-time on-line processing. Therefore, a multivariate system operating at 565 and 517 nm, which should be capable of operating at real-time on-line processing speed, should be used. Further research with such a system needs to be conducted.

scholarly journals Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Leandro da Conceição Luiz ◽  
Maria José Valenzuela Bell ◽  
Roney Alves da Rocha ◽  
Nayara Lizandra Leal ◽  
Virgílio de Carvalho dos Anjos
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Medicinal Products ◽  
Statistical Tool ◽  
Experimental Procedure ◽  
Milk Samples ◽  
Antimicrobial Residues ◽  
Maximum Residue Limits ◽  
Different Types ◽  
Ceftiofur Hydrochloride

This study focuses on detection of antimicrobial residues in milk through Fourier transform near-infrared spectroscopy. Simulated and real samples were considered. The simulated ones take into account veterinary drugs added in milk samples in the following concentrations: enrofloxacin 100 μg/L, terramycin 100 μg/L, and penicillin 4 μg/L. The statistical tool used to discriminate the samples was the principal component analysis (PCA). Our results show that, with this experimental procedure, it is possible to discriminate different types of antimicrobials dissolved in milk. Moreover, the methodology was able to detect real sample milked on different days after the injection of ceftiofur hydrochloride which is in principle a zero grace period antimicrobial. The methodology proved to be fast and accurate within the maximum residue limits allowed by European Agency for Medicinal Products and Ministry of Agriculture Livestock and Food Supply from Brazil.

Prediction of water content in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics

2020 ◽  
Vol 28 (3) ◽  
pp. 140-147
Author(s):  
Eloïse Lancelot ◽  
Philippe Courcoux ◽  
Sylvie Chevallier ◽  
Alain Le-Bail ◽  
Benoît Jaillais
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Content ◽  
Hyperspectral Imaging ◽  
Analysis Of Variance ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Water Contents

The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

Non-Destructive Assessment of Edible Seed Chemical Content by Near-Infrared Hyperspectral Imaging for the Improvement of Nutritional Traits in Bottle Gourd (Lagenaria Siceraria)

2021 ◽  
Vol 7 (5) ◽  
pp. 75-81
Author(s):  
Nadège Aurelie N’dri-Aya ◽  
◽  
Irié Vroh-Bi ◽  
◽  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Bottle Gourd ◽  
Lagenaria Siceraria ◽  
Absorption Bands ◽  
Tropical Regions ◽  
Nutrition Security ◽  
Chemical Content ◽  
Non Destructive

The edible seeds of bottle gourd [Lagenaria siceraria (Molina) Standl.] are rich in oils, proteins and minerals of high nutritional quality. They are highly prized in pan tropical regions where they constitute valuable resources for food and nutrition security. In this study, near-infrared hyperspectral imaging (NIR-HSI) was combined with chemometrics to assess the variability of seed chemical content of African cultivars for the selection of nutritional traits. Six hundred seeds of four accessions belonging to two cultivars were collected from the Ivory Coast (West Africa) and analysed. The NIR-HSI spectra collected on whole seeds in the 1100-2400 nm range revealed that the main absorption bands of the seed chemical content were associated with water, lipids and proteins. The absorbance values between seeds of the same accession in these spectral regions varied up to 1.8 folds. Among the two chemometric tools used, principal component analysis (PCA) did not separate the accessions while Partial Least Squares Discriminant Analysis (PLS-DA) discriminated the accessions with 87.33 % to 94.67 %, and the cultivars with 90 % to 92 % correct classification. Seed oils from bottle gourd are for instance rich in linoleic acid which is an essential fatty acid for human health. The non-destructive and qualitative determination of the content of single seeds was demonstrated in the study and provides the opportunity to select superior seeds for the improvement of key nutritional traits in bottle gourd. Lagenaria siceraria, near-infrared hyperspectral imaging, seed chemical content, PCA, PLS-DA, nutrition security

Comprehensive evaluation of the industrial processing effects on the fiber properties of the pulps from wood residues

2020 ◽  
Vol 35 (4) ◽  
pp. 631-640
Author(s):  
Yuan Chen ◽  
Kuizhong Shen ◽  
Gaiyun Li
Keyword(s):  
Thermal Stability ◽  
Wood Species ◽  
Large Scale ◽  
Fiber Quality ◽  
Paper Industry ◽  
Negative Influence ◽  
Industrial Applications ◽  
Hydroxyl Groups ◽  
Wood Processing ◽  
Fiber Separation

AbstractWood processing residue, as an essential forest resource, is underestimated in the industrial applications. With the development of pulping technology, fiber quality and cost control have become the important prospects. It is very crucial to investigate the influencing factors of pulping, designed to reveal the relationship between fiber separation and physical performances. In this study, a range of effect factors of large-scale pulping process, including wood species, pretreatment, mechanical conditions, and post-bleaching treatment, were comprehensively investigated. The results illustrated that the industrial pulping process affected the dimensions of the fibers, their surface morphology, chemical structure, and thermal stability. The separated positions of the fibers were determined collectively by the mechanical temperature, time, and wood species. In addition, mild bleaching could effectively increase the crystallinity index, accessibility of the hydroxyl groups, and water retention values. However, it exerted a negative influence on the thermal stability. Further, chemical-assisted-refinement can replace higher temperature and prolonged ones to obtain pulp fibers with high quality while consuming less energy. Improved knowledge of how the industrial affects the fiber separation of a wood matrix and pulp quality is required as a basis for the development of the pulp and paper industry.

NIR hyperspectral imaging (NIR-HI) and μXRD for determination of the transition between juvenile and mature wood of Pinus sylvestris L.

Holzforschung ◽  
2019 ◽  
Vol 73 (7) ◽  
pp. 621-627 ◽  
Author(s):  
Antonio Ruano ◽  
Andreas Zitek ◽  
Barbara Hinterstoisser ◽  
Eva Hermoso
Keyword(s):  
Pinus Sylvestris ◽  
Hyperspectral Imaging ◽  
Transition Point ◽  
Near Infrared ◽  
Juvenile Wood ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Mature Wood ◽  
X Ray

AbstractThe ratio of juvenile wood (jW) to mature wood (mW) is relevant for structural wood applications because of their different properties. Near infrared-hyperspectral imaging (NIR-HI) indicates after calibration, the spatial distribution of jW and mW, and this approach is less time consuming than the established micro X-ray densitometry (μXRD). In the present study, a comparative detection of the jW and mW ofPinus sylvestrisL. was performed by NIR-HI and μXRD and the NIR-HI results were evaluated in combination with three chemometric approaches, namely, the principal component analysis (PCA), partitional k-means unsupervised classification (p-k-mUC) and partial least squares discriminant analysis (PLS-DA) in the range of 900–1700 nm. The best NIR-HI results can be obtained when the transition point of earlywood (EW) and latewood (LW) are assessed separately by PLS-DA. The presented results are useful for an automating data evaluation and simplified data collection.

Sign in / Sign up

Export Citation Format

Share Document