Support vector machines in hyperspectral imaging spectroscopy with application to material identification

A processing methodology based on Support Vector Machines is presented in this paper for the classification of hyperspectral spectroscopic images. The accurate classification of the images is used to perform on-line material identification in industrial environments. Each hyperspectral image consists of the diffuse reflectance of the material under study along all the points of a line of vision. These images are measured through the employment of two imaging spectrographs operating at Vis-NIR, from 400 to 1000 nm, and NIR, from 1000 to 2400 nm, ranges of the spectrum, respectively. The aim of this work is to demonstrate the robustness of Support Vector Machines to recognise certain spectral features of the target. Furthermore, research has been made to find the adequate SVM configuration for this hyperspectral application. In this way, anomaly detection and material identification can be efficiently performed. A classifier with a combination of a Gaussian Kernel and a non linear Principal Component Analysis, namely k-PCA is concluded as the best option in this particular case. Finally, experimental tests have been carried out with materials typical of the tobacco industry (tobacco leaves mixed with unwanted spurious materials, such as leathers, plastics, etc.) to demonstrate the suitability of the proposed technique

Automatic classification of steel plates based on laser induced breakdown spectroscopy and support vector machines

Welding processes are one of the most widely spread industrial activities, and their quality control is an important area of research. The presence of residual traces from the protective antioxidant coating, is a problematic issue since it causes a significant reduction in the welding seam strength. In this work, a solution based on a Laser Induced Breakdown Spectroscopy (LIBS) setup and a Support Vector Machines (SVMs) classifier to detect and discriminate antioxidant coating residues in the welding area without destroying the sample before the welding procedure is proposed. This system could be an interesting and fast tool to detect aluminium impurities

Rapid authentication and composition determination of cellulose films by UV-VIS-NIR spectroscopy

In recent years, efforts to develop new materials for the food industry have focused mainly on polysaccharides- and proteins-based films or coatings. Fast and inexpensive analytical tools are needed to guarantee their compositions. This work evaluates the feasibility of a rapid and accurate method based on UV-VIS-NIR spectroscopy combined with chemometric techniques to analyze polysaccharide-based films for authentication and composition determination. As case study, cellulose-based films (vegetable and bacterial) combined with chitosan and polyvinyl alcohol were used as biocomposite models. Applying chemometric techniques, it was obtained models to predict the content of chitosan, polyvinyl alcohol and cellulose. Linear discriminant analysis was used to authenticate cellulose films, showing an accuracy of 100% to classify cellulose films as function on the cellulose source (vegetable or bacterial). It was concluded that UV-VIS-NIR spectroscopy combined with chemometrics can be used to authenticate the origin and determine the composition of polysaccharide-based filmsS

Spectral Textile Detection in the VNIR/SWIR Band

Dismount detection, the detection of persons on the ground and outside of a vehicle, has applications in search and rescue, security, and surveillance. Spatial dismount detection methods lose e effectiveness at long ranges, and spectral dismount detection currently relies on detecting skin pixels. In scenarios where skin is not exposed, spectral textile detection is a more effective means of detecting dismounts. This thesis demonstrates the effectiveness of spectral textile detectors on both real and simulated hyperspectral remotely sensed data. Feature selection methods determine sets of wavebands relevant to spectral textile detection. Classifiers are trained on hyperspectral contact data with the selected wavebands, and classifier parameters are optimized to improve performance on a training set. Classifiers with optimized parameters are used to classify contact data with artificially added noise and remotely-sensed hyperspectral data. The performance of optimized classifiers on hyperspectral data is measured with Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The best performances on the contact data are 0.892 and 0.872 for Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs), respectively. The best performances on the remotely-sensed data are AUC = 0.947 and AUC = 0.970 for MLPs and SVMs, respectively. The difference in classifier performance between the contact and remotely-sensed data is due to the greater variety of textiles represented in the contact data. Spectral textile detection is more reliable in scenarios with a small variety of textiles

Multivariate analysis for quality control of agrifood materials using near infrared spectroscopy

Seguridad y calidad alimentaria son uno de los conceptos más demandados actualmente en la industria agroalimentaria. La mayoría de análisis de control de los productos alimentarios se lleva a cabo mediante métodos tradicionales (vía húmeda). Los principales problemas relacionados con este tipo de análisis son el consumo de tiempo para la obtención de los resultados de una sola muestra, el coste del análisis, así como la limitación en cuanto a su implantación en la línea de producción o en el campo, entre otros. Paralelamente al desarrollo e innovación tecnológica, numerosos métodos han sido implementados para la determinación, evaluación y control de la calidad de los productos agroalimentarios en las últimas décadas. Estos métodos están basados en la detección de varias propiedades tanto físicas como químicas correlacionadas con ciertos factores cualitativos de los productos. Uno de los métodos más difundido y aún en desarrollo debido a su gran aplicabilidad, es la espectroscopía de infrarrojo cercano (tecnología NIRS, Near Infrared Spectroscopy). Han pasado más de 20 años desde su primera introducción como potente herramienta hecha por Karl Norris en el análisis de la composición de los cereales. El planteamiento de esta tesis nace de la necesidad, cada vez mayor, del control de los parámetros de calidad de los productos agroalimentarios de manera rápida y precisa. La categorización del trigo en función de su calidad o el valor añadido que adquiere la soja según el porcentaje de proteína o grasa presente en una determinada variedad ha llevado al estudio de la aplicación de la espectroscopía de infrarrojo cercano en dichos productos. El objetivo general de la investigación ha consistido en la aplicación de la tecnología NIRS para la determinación de parámetros de calidad en muestras de...Food safety and quality are currently the most popular concepts in the food industry. Usually, most control analyses of food products are carried out by conventional methods (wet chemistry). However, some of the main negative issues of these methods are: they are time consuming in order to obtain the results of a single sample, the raising price and the limitation on its implementation in the production line or in the field, among others. At the same time to the technological innovation and development, during the last decades many methods have been implemented for the identification, assessment and quality control of food products. These methods are based on the detection of various physical and chemical properties correlated with certain product quality factors. One of the most widespread due to its wide applicability is the near-infrared spectroscopy (NIRS technology, Near Infrared Spectroscopy). It has been over 20 years since its first introduction as a powerful tool made by Karl Norris in the analysis of the composition of the grains. The approach of this thesis arises from the increasing need of fast and accurate analyses of quality parameters control on food products. The categorization of wheat in terms of quality and the added value acquired by the percentage of soy protein or fat in a particular variety has led to the study of the application of near infrared spectroscopy in these products. The general objective of the research has been the application of NIRS technology for the determination of quality parameters in wheat and soybean samples. As a result, this study has led to the development of four chapters: - "Development of robust soybean NIR Calibration Models with temperature compensation and high variability in the data basis." This chapter was focused on the development of robust calibrations by adding in the group of samples instrumental and environmental variability..

Critical review of real-time methods for solid waste characterisation: Informing material recovery and fuel production

Waste management processes generally represent a significant loss of material, energy and economic resources, so legislation and financial incentives are being implemented to improve the recovery of these valuable resources whilst reducing contamination levels. Material recovery and waste derived fuels are potentially valuable options being pursued by industry, using mechanical and biological processes incorporating sensor and sorting technologies developed and optimised for recycling plants. In its current state, waste management presents similarities to other industries that could improve their efficiencies using process analytical technology tools. Existing sensor technologies could be used to measure critical waste characteristics, providing data required by existing legislation, potentially aiding waste treatment processes and assisting stakeholders in decision making. Optical technologies offer the most flexible solution to gather real-time information applicable to each of the waste mechanical and biological treatment processes used by industry. In particular, combinations of optical sensors in the visible and the near-infrared range from 800 nm to 2500 nm of the spectrum, and different mathematical techniques, are able to provide material information and fuel properties with typical performance levels between 80% and 90%. These sensors not only could be used to aid waste processes, but to provide most waste quality indicators required by existing legislation, whilst offering better tools to the stakeholders

NIR spectroscopy for personal screening

This work presents investigations into the use of the near-infrared (NIR) signals to interrogate, detect and image specific chemical compounds of interest in a security screening application, including when such compounds are hidden behind single layers of clothing fabric. In an initial set of experiments, the mechanisms governing the interaction of NIR signals with clothing fabrics and similar materials has been studied, in order to account for the influence of fabric layers when detecting hidden chemicals. Throughout the rest of the work, NIR spectroscopy has been used as a means to perform qualitative and quantitative analysis, in order to detect the presence of chemicals, and quantify the concentration in aqueous solution of liquids. It has been shown that, while the compounds can be identified on the basis of the characteristic features that appear in the relevant NIR spectra, the origin and nature of these spectra necessitate that such identification be performed with a chemometricsbased approach. Accordingly, multivariate calibration models based on neural networks and partial least squares regression (PLSR) have been developed to perform the requisite analyses. Results of calibration and testing with a range of data are reported. In order to facilitate operation in practical security screening, the development and testing of a software-based lock-in amplifier is reported, as a mean to enhance the signal-to-noise ratio (SNR) of the spectral data. It is shown that the amplifier can process up to 40 wavelength channels in parallel, to extract the spectral data buried in noise in each channel. Hence, with the SNR of the input signal set as low as -60 dB (by introducing software-generated additive white noise in the spectra), adequate noise suppression has been obtained, allowing the resulting spectral data to be used for requisite chemical detection. Finally, an integrated spectroscopic imaging application is developed to perform twodimensional cross-sectional scans of chemical samples, carry out lock-in amplification of the recorded intensity spectra, and plot the results of neural network-based chemical detection in the form of intensity images colour-coded to depict the presence of the pertinent chemicals at the scanned coordinates