Hyperspectral image analysis. A tutorial

Amigo, José Manuel; Babamoradi, Hamid; Elcoroaristizabal, Saioa

doi:10.1016/j.aca.2015.09.030

Cited by 266 publications

(172 citation statements)

References 79 publications

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“…To satisfy the UK Border Force requirements of a maximum scanning time of 5 min, improvements to the system design, signal processing or calibration model are required and will be the focus of future work. Recent methodology developed for the analysis of hyperspectral images will be explored to motivate improvements in drug detection performance (Amigo 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The research introduces continuous hyperspectral imaging of parcel cross sections and employs the well-established partial least squares regression method on the resulting hypercubes to predict illicit drug concentration locally through the parcel volume. The results of this study motivate further research using spectroscopic image analysis and chemometric methods to advance the exploration and prediction methodology from hyperspectral diffraction images in security applications (Amigo et al 2015). The research presented here is a proof of concept of full-volume parcel screening and future work is required to bring down the current 30 min procedure time to meet the time constraints set by UK Border Force.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate analysis of energy dispersive X-ray diffraction data for the detection of illicit drugs in border control

et al. 2017

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A system using energy dispersive X-ray diffraction has been tested to detect the presence of illicit drugs concealed within parcels typical of those which are imported into the UK via postal and courier services. The system was used to record diffraction data from calibration samples of diamorphine (heroin) and common cutting agents and a partial least squares regression model was established between diamorphine concentration and diffraction spectra. Parcels containing various crystalline and amorphous materials, including diamorphine, were then scanned to obtain multiple localised diffraction spectra and to form a hyperspectral image. The calibration model was used for the prediction of diamorphine concentration throughout the volume of parcels and enabled the presence and location of diamorphine to be determined from the visual inspection of concentration maps. This research demonstrates for the first time the potential of an EDXRD system to generate continuous hyperspectral images of real parcels from volume scanning in security applications and introduces the opportunity to explore hyperspectral image analysis in chemical and material identification. However, more work must be done to make the system ready for implementation in border control operations by bringing down the procedure time to operational requirements and by proving the system's portability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate analysis of energy dispersive X-ray diffraction data for the detection of illicit drugs in border control

et al. 2017

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“…Para que seja gerada a imagem, a área da amostra deve ser subdividida em unidades menores e, para cada um desses pixels, deve ser obtido um espectro de absorção, emissão ou reflectância, por exemplo. 6,7 Na literatura, são encontrados muitos exemplos dessa abordagem, seja na discriminação de nanopartículas magnéticas dispersas em polímero, 8 na identificação de bactérias 9 ou na análise de produtos alimentícios como carne e presunto cru. 10,11 Para a geração das imagens hiperespectrais, dentre várias técni-cas analíticas, podem ser empregadas a espectroscopia Raman 12 ou a espectroscopia no infravermelho médio, 13 por exemplo.…”

Section: Introductionunclassified

Experimento Didático De Quimiometria Para O Mapeamento De Pellets Farmacêuticos Utilizando Espectroscopia De Imagem Na Região Do Infravermelho Próximo E Resolução Multivariada De Curvas Com Mínimos Quadrados Alternantes: Um Tutorial, Parte Iv

2017

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A TUTORIAL, PART IV. The aim of this manuscript is to present a didactic experiment based on the combination of Near Infrared chemical imaging and Multivariate Curve Resolution for beginners, undergraduate and graduate students. In this tutorial, using MATLAB environment, the instrumental signals will be separated into two matrices, of spectral profiles and relative concentrations in the samples. Then, chemical images were generated to identify and verify the distribution of the components in the pellet samples. Chemical imaging was obtained from the mapping of predetermined area and the results for relative concentrations obtained in the ALS optimization for the analyzed area. This approach was successfully tested in chemistry undergraduate course at the Chemistry Institute of Campinas State University.

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“…24 The approach in the treatment of HSI data involves the use of chemometric techniques combined or developed to meet specific needs. 25 Among the various multivariate analysis strategies, pattern recognition techniques have implementation advantages in highly complex matrices employing HSI-NIR. 26,27 Classification applications using HSI and PLS-DA have received considerable attention because they are easy to implement and provide better results.…”

Section: 18mentioning

confidence: 99%

Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis

Aires

Gambarra-Neto

Coutinho

et al. 2018

J. Spectral Imaging

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Hyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. Five strains of CG and 46 strains of CGC were used. CG and CGC strains were grown on Czapek-agar medium at 25 °C under a 12-hour photoperiod for 15 days. Molecular identification was performed as a reference for the CG and CGC classes by polymerase chain reaction of the intergenic spacer region of rDNA. The scattering coefficient µs and the absorption coefficient µa were obtained, which resulted in a µs value for CG of 1.37 × 1019 and for CGC of 5.83 × 10–11. These results showed that the use of the standard normal variate was no longer essential and reduced the spectral range from 1000–2500 nm to 1000–1381 nm. The results evidenced two type II errors for the CG 457-2 and CGC 39 samples in the soft independent modelling model of the analogy model. There were no classification errors using the algorithm of the successive projections for variable selection in linear discriminant analysis (SPA-LDA). A parallel validation of the results obtained with SPA-LDA was performed using a box plot analysis with the 11 variables selected by SPA, in which there were no outliers for the HSI-NIR models. The new HSI-NIR and SPA-LDA procedures for the classification of CG and CGC etiological agents are noted for their greater analytical speed, accuracy, simplicity, lower cost and non-destructive nature.

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Hyperspectral image analysis. A tutorial

Cited by 266 publications

References 79 publications

Multivariate analysis of energy dispersive X-ray diffraction data for the detection of illicit drugs in border control

Multivariate analysis of energy dispersive X-ray diffraction data for the detection of illicit drugs in border control

Experimento Didático De Quimiometria Para O Mapeamento De Pellets Farmacêuticos Utilizando Espectroscopia De Imagem Na Região Do Infravermelho Próximo E Resolução Multivariada De Curvas Com Mínimos Quadrados Alternantes: Um Tutorial, Parte Iv

Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis

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