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Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

Lestander, Torbjörn (2003). Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.). Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae. Silvestria, 1401-6230 ; 282
ISBN 91-576-6516-8
[Doctoral thesis]

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This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra.

Authors/Creators:Lestander, Torbjörn
Title:Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)
Series Name/Journal:Acta Universitatis Agriculturae Sueciae. Silvestria
Year of publishing :October 2003
Number of Pages:63
ALLI. Lestander, T.A. and Odén, P.C. 2002. Separation of viable and non-viable filled Scots pine seeds by differentiating between drying rates using single seed near infrared transmittance spectroscopy. Seed Science and Technology, 30(2): 383-392. II. Lestander, T.A. and Geladi, P. 2003. NIR spectroscopic measurement of moisture content in Scots pine seeds. Analyst, 128 (4): 389-396. III. Lestander, T.A., Leardi, R. and Geladi, P. 2003. Selection of NIR wavelengths by genetic algorithms for the determination of seed moisture content. (submitted). IV. Lestander, T.A., Geladi, P. 2003. How does multivariate regression predict moisture content from NIR spectra of seeds? (submitted).
Place of Publication:Umeå
ISBN for printed version:91-576-6516-8
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:F Plant production > F03 Seed production and processing
Subjects:Not in use, please see Agris categories
Agrovoc terms:seeds, infrared spectrophotometry, reflectance, statistical methods, seed, viability, seed moisture content, pinus sylvestris
Keywords:Single seed, near infrared spectroscopy, reflectance, transmittance, multivariate analysis, wavelength selection, PCA, OLS, PLS, bi-orthogonal PLS, interval PLS, genetic algorithms, seed viability, seed moisture content.
Permanent URL:
ID Code:371
Department:(S) > Institutionen för skogsskötsel
Deposited By: Torbjörn Lestander
Deposited On:16 Oct 2003 00:00
Metadata Last Modified:02 Dec 2014 10:04

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