| 1. |
- Alander, Jarmo, et al.
(författare)
-
Near infrared wavelength relevance detection of ultraviolet radiation-induced erythema
- 2008
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 16:3, s. 233-241
-
Tidskriftsartikel (refereegranskat)abstract
- The acute effects of sun-bathing on the near-infrared absorption spectra of human skin were studied by exposing the shoulders of a male test subject to bright Finnish high summer mid-day sun. The spectra were measured before, immediately after and for several days after exposure. Four different spectral. processing and classification methods were applied to the data set to identify differences caused by exposure to the sun. The spectrophotometer and measuring procedure were found to cause some systematic errors, calling for further development, even though they could, to a large extent, be compensated for computationally. Spectral regions indicating ultraviolet radiation-induced erythema were Located and the degree of erythema could be predicted correctly but the signal is weak. This paper discusses promising wavelength selection methods to study the dermal effects of exposure to the sun, as well as difficulties and remedies of near infrared spectroscopic measurements of the skin.
|
|
| 2. |
- Bargigia, Ilaria, et al.
(författare)
-
Diffuse optical techniques applied to wood characterisation
- 2013
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 21:4, s. 259-268
-
Tidskriftsartikel (refereegranskat)abstract
- We propose an optical method for non-invasive characterisation of wood samples based on two optical techniques: time-resolved diffuse optical spectroscopy and gas in scattering media absorption spectroscopy. While the latter is sensitive to gases present inside wood pores, the former extracts information on the bulk material regarding light scattering and absorption. Measurements on spruce samples, cut along different wood fibre directions, are presented to show an example of the advantages of this combined approach: by applying these two non-destructive techniques together, in fact, relevant information on wood such as porosity, permeability and moisture content can be assessed. Furthermore, the chemical composition, internal structure and the anisotropy due to the wood fibres can be investigated.
|
|
| 3. |
- Geladi, Paul, et al.
(författare)
-
A multivariate NIR study of skin alterations in diabetic patients as compared to control subjects
- 2000
-
Ingår i: Journal of Near Infrared Spectroscopy. - 0967-0335 .- 1751-6552. ; 8:4, s. 217-227
-
Tidskriftsartikel (refereegranskat)abstract
- A group of 15 diabetic persons with different degrees of diabetes complications, including skin changes, was studied by Fourier Transform Near Infrared (FT-NIR) spectroscopy. Skin reflectance spectra were measured with a fibre-optic probe in four locations (sites): hand, arm, leg and foot. For reference, a group of 28 healthy controls was also measured. Multivariate analysis of the NIR spectra obtained shows a high potential for classification and discrimination of the skin conditions. Valuable indications for future experiments can be observed.
|
|
| 4. |
- Geladi, Paul
(författare)
-
Chemometric monitoring of designed composting processes using laboratory measurements and near infrared spectroscopy
- 2009
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 17, s. 275-287
-
Tidskriftsartikel (refereegranskat)abstract
- Fox manure, food waste and yard waste were used as pure composts to construct a laboratory-scale designed (simplex mixture) experiment with repeated centre points that was monitored over 31 calendar days by near infrared (NIR) spectroscopy (900–1700 nm) and additionally by sampling for wet chemical and physical laboratory measurements: pH, energy, moisture content, NH3/NH4+ (simply called ammonium) concentration, and temperature. Three methods of data analysis were tested on the resulting data matrices: (1) modelling of the mixture design by Scheffé models, (2) principal component analysis (PCA) with interpretation of score and loading plots for the laboratory parameters and the NIR spectra separately and (3) partial least squares (PLS) regression modelling between NIR spectra and laboratory parameters. Significant Scheffé models were obtained and these could be used to make response surfaces of the parameters as a function of time. The PCA scores for the laboratory data reproduced the mixture triangles at the start of the experiment and showed that all the aerobic composts evolved to a common endpoint region. The PCA analysis of the NIR data indicated that score plots are a useful tool for monitoring the decomposition process of composts. Each of the composts could be followed over time by observing directions in the score space and changes in the step distances in score space. PLS models were built for each of the laboratory parameters and, additionally, composting time against NIR spectra, where spectra from the three centre points were used as an independent test set. The parameters pH, temperature, ammonium concentration and composting time all gave RER values above 10 and RPD values above 3 were obtained for temperature, pH and composting time.
|
|
| 5. |
- Geladi, Paul
(författare)
-
Hyperspectral near infrared image analysis of a phenol formaldehyde resin curing reaction
- 2012
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 20, s. 559-572
-
Tidskriftsartikel (refereegranskat)abstract
- In the production of sandpaper, a phenol formaldehyde polymer bonds the abrasive material to a backing material made of paper or a textile. The reaction takes place at elevated temperatures (90-120 degrees C) for a long time (at least eight hours) and is thereby a very energy-demanding process step. A possible future application would be to use near infrared (NIR) hyperspectral imaging (1000-2498 nm) for (1) monitoring the curing reaction and determining an endpoint for it and (21 for identifying inhomogeneous regions with low adhesion in the final product. A feasibility study was carried out on four series of resin-coated backing materials without abrasives. These were imaged at half hour intervals for eight hours of curing using a NIR line scan imager. In order to analyse the influence of the backing material on the net NIR signal from resin-coated samples, spectra of the backing material (paper, textile) were also collected using a moving grating NIR spectrometer (1100-2498 nm). Results from principal components analysis of the hyperspectral images indicated that the reaction was stabilised after five to six hours, although it continued slowly for at least 16 more hours. A relevant question was when to finish the heating (curing) and still obtain a final product of high quality. Partial least squares regression models for predicting the curing time were thus also evaluated. A calibration made on image mean spectra was used for predicting the curing time of each pixel in the full set of hyperspectral images. The predicted images showed the curing progress, inhomogeneous regions where the reaction had progressed to a slower extent and other physical abnormalities (for example air bubbles). Pixel prediction distribution analysis of the images was found useful for determining the significant number of components of the proposed regression models.
|
|
| 6. |
- Geladi, Paul
(författare)
-
Indirect detection of Fusarium verticillioides in maize (Zea mays L.) kernels by near infrared hyperspectral imaging
- 2010
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 18, s. 49-58
-
Tidskriftsartikel (refereegranskat)abstract
- Near infrared (NIR) hyperspectral imaging and hyperspectral image analysis were evaluated for their potential to distinguish between Fusarium verticillioides infected and sound whole maize (Zea mays L.) kernels. Hyperspectral images of infected and sound kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Background, bad pixels and shading were removed using exploratory principal component analysis (PCAI on absorbance images. PCA could be used effectively on the cleaned images to identify classes including infected and non-infected regions on individual kernels. A distinct difference between infected and sound kernels along principal component (PC) one with two distinguishable clusters was found. The loading line plot of the first PC of the sisuChema hypercube showed important absorbance peaks for the two classes, i.e. 1960 nm and 2100nm for the infected class and 1450 nm, 2300nm and 2350nm for the non-infected class. Partial least squares discriminant analysis (PLS-DA) was applied. The coefficient of determination was 0.73 for the MatrixNIR image and 0.86 for the sisuChema image, both after three PLS components. These PLS-DA models could be used to calculate predictions on a test set image. The predictions could be shown as prediction images and an acceptable root mean square error of prediction was obtained (0.23). NIR hyperspectral imaging has the potential to be used as a rapid, objective means of indentifying fungal infected maize kernels and infected regions.
|
|
| 7. |
- Geladi, Paul
(författare)
-
Near infrared and mid-infrared spectroscopy for the quantification of adulterants in ground black pepper
- 2012
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 20, s. 521-528
-
Tidskriftsartikel (refereegranskat)abstract
- Adulteration of food products remains a food security risk. Spices are food components with a high value per unit mass due to their desired flavour attributes and are therefore economically worthwhile targets for adulteration. Vibrational spectroscopy techniques could be ideal to detect adulterants due to benefits such as speed of analysis. Near infrared (NIR) and mid-infrared (mid-IR) spectra were used to quantify the amount of adulterant (buckwheat or millet) in ground black pepper. NIR spectra used for calibration were the average spectra of individual hyperspectral images recorded with a spatial resolution of 300 mu m x 300 mu m from 1000 nm to 2500 nm at 6.3 nm intervals; hyperspectral images were collected to cope with the heterogeneity of the samples. The calibrations calculated, using the averaged NIR spectra, were more accurate than those calculated from mid-IR spectra. This was a direct result of sample heterogeneity and the insufficient sampling area in the mid-IR measurements. NIR-based calibrations were suitable for process control when the appropriate spectral data pre-treatment was used: standard normal variate followed by first derivative pre-processing of the 1100-2500 nm spectral range resulted in a root mean square error of prediction equal to 2.7% w/w and a ratio of standard error of prediction to standard deviation (validation set) of 11.1.
|
|
| 8. |
- Karlsson, Therese, 1987, et al.
(författare)
-
Hyperspectral imaging and data analysis for detecting and determining plastic contamination in seawater filtrates
- 2016
-
Ingår i: Journal of Near Infrared Spectroscopy. - Chichester, England : SAGE Publications. - 0967-0335 .- 1751-6552. ; 24:2, s. 141-149
-
Tidskriftsartikel (refereegranskat)abstract
- One possible way of monitoring plastic particles in sea water is by imaging spectroscopic measurements on filtrates. The idea is that filters from seawater sampling can be imaged in many wavelengths and that a multivariate data analysis can give information on (1) spatial location of plastic material on the filter and (2) composition of the plastic materials. This paper reports on simulated samples with spiked reference plastic particles, and real seawater filtrates containing microplastic pollutants. These real samples were previously identified through visual examination in a microscope. The samples were imaged using three different imaging systems. The different wavelength ranges were 375–970nm, 960–1662nm and 1000–2500nm. Data files from all three imaging systems were analysed by hyperspectral image analysis. The method using the wavelength span 1000–2500nm was shown to be the most applicable to this specific type of samples and gave a 100% particle recognition on reference plastic, above 300 µm and an 84% pixel recognition on household polyethylene plastic. When applied to environmental samples the technique showed an increase in identified particles compared with visual investigations. These initial tests indicate a potential underestimation of microplastics in environmental samples. This is the first study to demonstrate that hyperspectral imaging techniques can be used to study microplastics down to 300µm, which is a common size limit used in microplastic surveys.
|
|
| 9. |
- Koljonen, Janne, et al.
(författare)
-
A review of genetic algorithms in near infrared spectroscopy and chemometrics : past and future
- 2008
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 16
-
Tidskriftsartikel (refereegranskat)abstract
- Global optimisation and search problems are abundant in science and engineering, including spectroscopy and its applications. Therefore, it is hardly surprising that general optimisation and search methods such as genetic algorithms (GAs) have also found applications in the area of near infrared INIRI spectroscopy. A brief introduction to genetic algorithms, their objectives and applications in NIR spectroscopy, as well as in chemometrics, is given. The most popular application for GAs in NIR spectroscopy is wavelength, or more generally speaking, variable selection. GAs are both frequently used and convenient in multi-criteria optimisation; for example, selection of pre-processing methods, wavelength inclusion, and selection of Latent variables can be optimised simultaneously. Wavelet transform has recently been applied to pre-processing of NIR data. In particular, hybrid methods of wavelets and genetic algorithms have in a number of research papers been applied to pre-processing, wavelength selection and regression with good success. In all calibrations and, in particular, when optimising, it is essential to validate the model and to avoid over-fitting. GAs have a Large potential when addressing these two major problems and we believe that many future applications will emerge. To conclude, optimisation gives good opportunities to simultaneously develop an accurate calibration model and to regulate model complexity and prediction ability within a considered validation framework.
|
|
| 10. |
- Lestander, Torbjörn, et al.
(författare)
-
Near infrared image analysis for online identification and separation of wood chips with elevated levels of extractives
- 2012
-
Ingår i: Journal of Near Infrared Spectroscopy. - : SAGE Publications. - 0967-0335 .- 1751-6552. ; 20, s. 591-599
-
Tidskriftsartikel (refereegranskat)abstract
- Forest-based biorefinery feedstocks are usually broken up into wood chips prior to any form of processing. These wood chips have a complex and highly variable composition, although they may look identical to an inexperienced observer. Some chips have high contents of valuable extractives. Therefore, it would be desirable to separate such chips that are rich in extractives. Various fractions of pine and spruce wood were used to acquire near infrared 11000-2498 nm) hyperspectral images in order to explore the usefulness of multivariate image analysis for detection and separation purposes. Multivariate modelling by image principal component analysis detected large variations in extractive content among wood chips of different biomass types, for example, sapwood, heartwood and knotwood. The extractive parts could be classified in the images and their content could be reasonably well predicted. Partial least squares (PLS) regression models could be made between collected spectra and measured extractive contents. These worked better for milled and homogenised bulk samples than for average image spectra. Regression coefficients showed that the C-H bonds in the spectra were responsible for the validity of the models. The average image PLS models could be used to make prediction images showing the location of the regions with high extractive content in knotwood. The results indicate that extremely rapid spectral-based fractionation could be used to separate tailored biomass streams of wood chips.
|
|
