| 1. |
- Blomquist, G., et al.
(författare)
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Reproducibility of pyrolysis-gas chromatographic analyses of the mold Penicillium brevi-compactum
- 1979
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Ingår i: Journal of Chromatography A. - 0021-9673. ; 173:1, s. 7-17
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Tidskriftsartikel (refereegranskat)abstract
- The reproducibility of multivariate measurements is discussed. Reproducibility of a second kind is defined, in which part of the variability between samples is described by a principal components model. The use of this generalized reproducibiity is shown to give an improved precision in the pyrolysis-gas chromatography of a Penicillium species.
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| 2. |
- Persson, Bertil R.R., et al.
(författare)
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Gel chromatography column scanning for the analysis of 99mTc-labelled compounds
- 1974
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Ingår i: Journal of Chromatography A. - 0021-9673. ; 101:2, s. 315-326
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Tidskriftsartikel (refereegranskat)abstract
- The gel chromatography column scanning (GCS) method has been studied, special attention being paid to its suitability for the analysis of 99mTc-labelled compounds and radio-pharmaceuticals. The sample to be analyzed was applied at the top of a column filled with Sephadex G gel. Elution was carried out with 0.9% sodium chloride solution with such a volume that all the radioactivity was retained in the column. The column was then sealed and scanned with a 1 mm slit-collimated NaI(Tl) crystal. The GCS method is discussed and different factors influencing the scanning profile are considered. The relationship between the position of the activity peak in the scanning profile and the molecular weight of the sample is given for Sephadex G-25 Medium gel.
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| 3. |
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| 4. |
- Blomquist, G., et al.
(författare)
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Classification of fungi by means of pyrolysis-gas chromatography pattern recognition
- 1979
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Ingår i: Journal of Chromatography A. - 0021-9673. ; 173:1, s. 19-32
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Tidskriftsartikel (refereegranskat)abstract
- SUMMARY Repetitive samples of three strains of the moujd Penicillium were subjected to pyrolysis-gas chromatography (Py-GC). From the chromatograms, 26 peak heights were used in a subsequent SIMCA pattern recognition analysis. This data analysis gives a marked improvement in the classification of the samples (100% correct, 85% unique) in comparison with the traditional analysis based on the average chromatogram of each class (92% correct, 45% unique)_ The data analytical method is described in detail using the Py-GC data as an illustration.
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