| 1. |
- Dåbakk, Eigil, et al.
(författare)
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Inferring lake water chemistry from filtered seston using NIR spectrometry
- 2000
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Ingår i: Water Research. ; 34:5, s. 1666-72
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Tidskriftsartikel (refereegranskat)abstract
- Near-infrared spectrometry (NIR) is a rapid, inexpensive and reagent-free technique, widely used in industry in areas such as quality control and process management. The technique has great potential for environmental monitoring of aqueous systems. This study assesses relationships, using PLS regression, between NIR spectra of seston collected on glass fibre filters and the following measured lake water parameters: total organic carbon (TOC), total phosphorus (TP), Abs420 and pH. Water samples were collected from 271 oligotrophic lakes during autumn 1995. The predictive model for TOC explained 68% of the variance (SEP=2.1 mg L-1, range 14.9 mg L-1), and that for colour 71% (SEP=0.04 A, range 0.36 A), while the explained variances for pH and TP were 72% (SEP=0.36 μg L-1, range 3.13 μg L-1) and 45% (SEP=4 μg L-1, range 41 μg L-1), respectively. A model correlating NIR spectra and the actual amount of phosphorus in the seston captured on filters explained 86% of the variance (SEP=0.044 μg/filter, range 0.47). Several pretreatments and regression techniques were used in an attempt to enhance modeling performance. However, straightforward PLS on raw data performed best in all cases.
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| 2. |
- Geladi, Paul, et al.
(författare)
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A multivariate NIR study of skin alterations in diabetic patients as compared to control subjects
- 2000
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Ingår i: Journal of Near Infrared Spectroscopy. - 0967-0335 .- 1751-6552. ; 8:4, s. 217-227
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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.
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| 3. |
- Geladi, Paul, et al.
(författare)
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From experimental design to images to particle size histograms to multiway analysis. An example of peat dewatering
- 2000
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Ingår i: Journal of Chemometrics. ; 14:3, s. 197-211
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Tidskriftsartikel (refereegranskat)abstract
- The efficiency of peat dewatering by filtering slurries is dependent on the sizes of fine and colloidal particles that clog the filter. A designed experiment was carried out to check the use of different treatments on particle coagulation. The resulting particle sizes were studied under the microscope by automated digital image analysis, leading to area histograms for 21 size classes. Seven treatments on five peat types give a two-way ANOVA in all-qualitative variables, but the 21 response variables are a bit too much for an ANOVA or MANOVA analysis. The data can also be arranged in a 5 (peat types) x 7 (treatments) x 21 (size classes) three-way array. This array is analyzed by PARAFAC and gives an effective three-way rank of 4. The three-way data have no obvious underlying trilinear structure, and curve resolution results are not expected. The three-way analysis gives a very parsimonious model that is easily interpreted as a function of the problem definition. The emphasis is on visualization of the results.
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| 4. |
- Lied, Thorbjørn T, et al.
(författare)
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Multivariate image regression (MIR): implementation of image PLSR - first forays
- 2000
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Ingår i: Journal of Chemometrics. ; 14:5-6, s. 585-98
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Tidskriftsartikel (refereegranskat)abstract
- In the effort of analysing multivariate images, image PLS has been considered interesting. In this paper, image PLS (MIR) is compared with image PCA (MIA) by studying a comparison data set. While MIA has been commercially available for some time, image PLS has not. The kernel PLS algorithm of Lindgren has been implemented in a development environment which is a combination of G (LabVIEW) and MATLAB. In this presentation the power of this environment, as well as an early example in image regression, will be demonstrated. With kernel PLS, all PLS vectors (eigenvectors and eigenvalues) can be calculated from the joint variance-covariance (XY and YX) and association (YY and XX) matrices. The dimensions of the kernel matrices XYYX and YXXY are K × K (K is the number of X-variables) and M × M (M is the number of Y-variables) respectively. Hence their size is dependent only on the number of X and Y-variables and not on the number of observations (pixels), which is crucial in image analysis. The choice of LabVIEW as development platform has been based on our experience of a very short implementation time combined with user-friendly interface possibilities. Integrating LabVIEW with MATLAB has speeded up the decomposition calculations, which otherwise are slow. Also, algorithms for matrix calculations are easier to formulate in MATLAB than in LabVIEW. Applying this algorithm on a representative test image which shows many of the typical features found in technical imagery, we have shown that image PLS (MIR) decomposes the data differently than image PCA (MIA), in accordance with chemometric experience from ordinary two-way matrices. In the present example the Y-reference texture-related image used turned out to be able to force a rather significant tilting compared with an ordinary MIA of the primary structures in the original, spectral R/G image.
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