Hollow-core photonic crystal fibre (HC-PCF) offers strong light confinement and long interaction lengths in an optofluidic channel. These unique advantages have motivated its recent use as a highly efficient and versatile microreactor for liquid-phase photochemistry and catalysis. In this work, we use a soft-glass HC-PCF to carry out photochemical experiments in a high-index solvent such as toluene. The high-intensity and strong confinement in the fibre is demonstrated to enhance the performance of a proof-of-principle photolysis reaction.
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.
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.
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.
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.
Gas detection and gas sensing based on hollow core photonic bandgap fiber (HC-PBF) is a very promising technique due
to the long interaction light-gas lengths that are achievable. However, long path-lengths also imply higher gas filling
times of the hollow fiber and higher response times of the detection systems what can constitute a serious practical
inconvenience. In this paper, the high sensitivity is maintained but the sensor response time is reduced by using multiple-coupling
fiber gaps. The results and conclusions extracted from a systematic experimental study (comparing the spectra
and filling time of different HC-PBF lengths and different number of coupling gaps) are presented and discussed.
Finally, the maximum number of gaps allowed in the system is modelled.
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.
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.
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.
In recent years, hollow-core photonic bandgap fibers (HC-PBFs) have been demonstrated to be a promising technology
for gas sensing. In particular, the long interaction path lengths available with these fibers are especially advantageous for
the detection of weakly absorbing gases such as methane. In the near-infrared region, methane has the strongest
absorption band, 2ν3, at 1670 nm. However, HC-PBFs were not available until recently in this wavelength range and gas
sensing devices based on HC-PBFs were previously made in the weaker band of 1300 nm. In this paper, we report the
demonstration of a methane sensor based on a 1670-nm-band HC-PBF. A strong spectral feature, the R(6) manifold
(near 1645 nm), was selected for sensing purposes as it shows a good signal-to-noise ratio. This absorption line is
comprised of six energy transitions, strongly overlapped at our experimental conditions. For that reason, we applied a
multiline algorithm that used information from the six transitions to fit the manifold. The goodness of the fitting was
assessed measuring the concentration of different methane samples. With this method, a minimum detectivity of 10
ppmv for the system configuration was estimated.
KEYWORDS: Hyperspectral imaging, Near infrared, Raw materials, Principal component analysis, Imaging spectroscopy, Cameras, Spectrographs, Artificial neural networks, Data compression, Calibration
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.
KEYWORDS: Principal component analysis, Hyperspectral imaging, Raw materials, Data processing, Imaging spectroscopy, Data compression, Sensors, Spectroscopes, Optical sensors, Image 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).
In this paper the application of the Inverse Least Squares algorithm (ILS) to the detection of methane using its behaviour in the near-infrared band is presented. In order to test the effectiveness of this method, different methane concentrations were measured. Wavelength Modulation Spectroscopy (WMS) was employed to obtain the first and second harmonics of the modulation signal. The use of both harmonics in spectroscopy eliminates the dependence of the measured absorbance on parameters such as: fiber misalignments, optical power fluctuations, etc. This property greatly increases the accuracy of the concentration readings. The benefits of analysing multiple lines in gas detection are discussed together with the capabilities of the ILS algorithm. The ILS algorithm is based on the Beer-Lambert law. This law is extended to include multiple wavelengths and rearranged in such a way that the concentration of the chemical species depends on the measured absorbances. In order to apply the previous algorithm, three absorption lines centered at 1665.961 nm, 1666.201 nm and 1666.483 nm were used. The obtained results are compared with the most usual single-line calibration method based on linear regression. This comparison shows that ILS gives a superior performance. Specifically, results indicate that the ILS multiline algorithm is less noise dependent and has a higher reliability than single-line calibration methods.
Experimental results on the polarization fluctuations of the optical signal in a erbium doped fiber amplifier (EDFA) are reported in this paper. Its influence on the visibility of interferometric optical fiber sensors is also analyzed.
Polarization state of the optical signal in erbium doped fiber is experimentally checked. Dependencies with the pump power, the input signal power and with the spectrum are reported.
A device for the measure of index of refraction of liquids in situ based on polymer optical fibers (POFs) was demonstrated. It consists in a sensor head of three passive POFs where two are coupled to two detectors and an electronic unit for the differential measure of signals. A differential operating principle is utilized to reduce noise such as light intensity fluctuation. The device was successfully checked measuring refraction index changes of the water with different concentrations of sugar, salt and alcohol.
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