High resolution spectroscopy of ammonia in a hollow-core fiber

We have demonstrated frequency modulation saturation spectroscopy of the ν1+ν3 band of ammonia in hollow-core photonic bandgap fibers (HC-PBFs). Previously blended lines have been resolved and the corresponding molecular transitions assigned. Cross-over resonances are observed between transitions that do not share a common level. We have measured the pressure dependence of the line shape and determined the collisional self-broadening coefficients for ammonia. The many absorption lines of ammonia in the 1.5 µm wavelength region are potential frequency references lines for optical communication as well as candidates for spectroscopic trace gas monitoring

In-process automatic wavelength calibration for CCD-spectrometers

In CCD-spectrometers, the relation between the CCD-pixel number and the associated wavelength is established by means of a calibration polynomial, whose coefficients are typically obtained using a calibration lamp with known emission line wavelengths and a regression procedure. A recalculation of this polynomial has to be performed periodically, as the pixel number versus wavelength relation can change with ambient temperature variations or modifications in the optics attached to the spectrometer connector. Given that this calibration procedure has to be performed off-line, it implies a disturbance for industrial scenarios, where the monitoring setup must be altered. In this paper an automatic wavelength calibration procedure for CCD-spectrometers is proposed. It is based on a processing scheme designed for the in-process estimation of the plasma electronic temperature, where several plasma emission lines are identified for each spectral capture. This identification stage involves the determination, by means of a sub-pixel algorithm, of the central wavelength of those lines, thus allowing an on-line wavelength calibration for each single acquired spectrum. The proposed technique will be demonstrated by means of several experimental arc-welding tests

Efficient processing technique based on plasma optical spectroscopy for on-line welding quality monitoring

In this paper a new spectroscopic analysis technique is proposed for on-line welding quality monitoring. This approach is based on the estimation of the wavelength associated with the maximum intensity of the background signal (continuum) of the welding plasma spectra. It will be demonstrated that this parameter exhibits a clear correlation with the welding quality of the seams, as it also happens with the traditional spectroscopic approach based on the determination of the plasma electronic temperature, thus allowing an identification of the appearance of weld defects. This technique offers a relevant improvement in terms of computational performance, what enables to detect smaller defects within the seam

Unsupervised grouping of industrial textile dyes using K-means algorithm and optical fibre spectroscopy

A method for the unsupervised clustering of optically thick textile dyes based on their spectral properties is demonstrated in this paper. The system utilizes optical fibre sensor techniques in the Ultraviolet-Visible-Near Infrared (UV-Vis-NIR) to evaluate the absorption spectrum and thus the colour of textile dyes. A multivariate method is first applied to calculate the optimum dilution factor needed to reduce the high absorbance of the dye samples. Then, the grouping algorithm used combines Principal Component Analysis (PCA), for data compression, and K-means for unsupervised clustering of the different dyes. The feasibility of the proposed method for textile applications is also discussed in the paper

Data processing method applying Principal Component Analysis and Spectral Angle Mapper for imaging spectroscopic sensors

A data processing method for hyperspectral images is presented. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. This data processing method is composed of two blocks: data compression and classification unit. Data compression is performed by means of Principal Component Analysis (PCA) and the spectral interpretation algorithm for classification is the Spectral Angle Mapper (SAM). This strategy of classification applying PCA and SAM has been successfully tested on the raw material on-line characterization in the tobacco industry. In this application case the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, candy paper, etc. Hyperspectral images are recorded by a spectroscopic sensor consisting of a monochromatic camera and a passive Prism- Grating-Prism device. Performance results are compared with a spectral interpretation algorithm based on Artificial Neural Networks (ANN)

Optical fibre spectroscopy sensor for the quantitative determination of industrial textile dyes

In this paper, an extrinsic optical fibre sensor (OFS) for the quantitative determination of dyes used in the textile industry is presented. The system proposed is based on absorption spectroscopy and multivariate calibration methods to infer the concentration of different textile dyes. The performance of the sensor has been successfully assessed using calibrated dyes, with a very good correlation between the multivariate calibration models and the predicted values. The sensor system here demonstrated could be used to predict the colour of dye mixtures during the dyebath and, therefore, reduce the manufacturing costs

Industrial defect discrimination applying infrared imaging spectroscopy and artificial neural networks

A non-intrusive infrared sensor for the detection of spurious elements in an industrial raw material chain has been developed. The system is an extension to the whole near infrared range of the spectrum of a previously designed system based on the Vis-NIR range (400 - 1000 nm). It incorporates a hyperspectral imaging spectrograph able to register simultaneously the NIR reflected spectrum of the material under study along all the points of an image line. The working material has been different tobacco leaf blends mixed with typical spurious elements of this field such as plastics, cardboards, etc. Spurious elements are discriminated automatically by an artificial neural network able to perform the classification with a high degree of accuracy. Due to the high amount of information involved in the process, Principal Component Analysis is first applied to perform data redundancy removal. By means of the extension to the whole NIR range of the spectrum, from 1000 to 2400 nm, the characterization of the material under test is highly improved. The developed technique could be applied to the classification and discrimination of other materials, and, as a consequence of its non-contact operation it is particularly suitable for food quality control

Spectral marks for qualitative discriminant analysis

In this paper, a method for the automatic qualitative discrimination of liquid samples based on their absorption spectrum in the ultraviolet, visible and near-infrared regions is presented. An alternative implementation of conventional spectrum matching methodologies is proposed working towards the improvement of the response time of the discrimination system. The method takes advantage of not making assumptions on the probability density function of the data and it is also capable of automatic outlier removal. Preliminary discrimination results have been evaluated on the classification of different oil samples from seeds and olives. The system here proposed could be easily and efficiently implemented in hardware platforms, improving in this way the system performance

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

Adaptive illumination source for multispectral vision system applied to material discrimination

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry