A New Method for Determining the Area of Cell Walls in Rye Doughs Based on Fluorescence Microscopy and Computer-assisted Image Analysis

Parkkonen, Teija; Heinonen, Ritva; Autio, Karin

doi:10.1006/fstl.1997.0262

Cited by 15 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total area calculated was 40 mm2. The surface area of insoluble cell walls was studied using computerised image analysis (Cue-2) and Ñuorescence microscopy (Olympus, Vanox T, Japan) (Parkkonen et al 1997).…”

Section: Analysesmentioning

confidence: 99%

Effects of enzymes in fibre-enriched baking

Laurikainen

Härkönen

Autio

et al. 1998

J. Sci. Food Agric.

173

View full text Add to dashboard Cite

Section: Analysesmentioning

confidence: 99%

Effects of enzymes in fibre-enriched baking

Laurikainen

Härkönen

Autio

et al. 1998

J. Sci. Food Agric.

173

View full text Add to dashboard Cite

“…Grains were humidified for 16 h at 4 °C and fixed in 20 mL L −1 gluteraldehyde in 0.1 mol L −1 phosphate buffer (pH 7.0) for 4 h at 20 °C, washed in water five times for 10 min. Cross-sections, 60 μm thick, were cut with a vibratome (Microcut H1200) and stained with Nile Red (Nile Blue A Oxazone) and Calcofluor White M2R (Fluorescent Brightener 28), counterstained by Fast Green as described by Martelli et al . Stains were purchased from Sigma-Aldrich Co. (USA).…”

Section: Methodsmentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy and Chemometrics: A Novel Potential Method to Analyze Wheat Grains

Martelli

Brygo²,

Sadoudi

et al. 2010

J. Agric. Food Chem.

View full text Add to dashboard Cite

Laser-induced breakdown spectroscopy (LIBS) has been widely used to evaluate the elemental composition (e.g., minerals or metal accumulation) on vegetal tissues. The main objective of this work was to differentiate wheat outer tissues during the grain ablation using LIBS and univariate/multivariate analysis. A high resolution spectrometer and a Nd:YAG laser (532 nm, 5 ns) was first used in order to easily identify atomic wheat emission lines. Then a pulsed excimer laser ArF (193 nm, 15 ns) and a compact fiber optic spectrometer was used to acquire LIBS spectral data from each pulse. Univariate and multivariate analyses (MW2D, PLS-DA) were carried out to provide more in depth information from the LIBS experiment. The number of pulses needed to ablate wheat tissues was successfully predicted by the supervised pattern recognition procedure. LIBS used in conjunction with multivariate analysis could be an interesting technique for rapid structural analysis of vegetal material.

show abstract

“…Sections were cut 4 pm thick in a Leica rotary microtome HM 355 (Heidelberg, Germany) using a steel knife. The sections were transferred onto glass slides and stained with Light Green and Lugol's iodine solution (Fulcher and Wong 1980;Wood et al 1983;Parkkonen et al 1994). Protein was stained with aqueous 0.1 % (w/v) Light Green for 1 min (Gun, BDH Ltd, Poole, UK) and starch with 1:lO diluted Lugol's iodine solution (I, 0.33 $4, w/v and KI 0.67%, wh).…”

Section: Microscopy and Image Analysismentioning

confidence: 99%

Applying Machine Learning Methods in Studying Relationships Between Mouthfeel and Microstructure of Oat Bread

Salmenkallio-Marttila

Roininen

Lindgren

et al. 2004

Journal of Texture Studies

Self Cite

View full text Add to dashboard Cite

The aim was to study whether machine learning can be applied in analysing microstructure of food from microscopy images and whether the state of macromolecules, protein and starch, can be related to sensory mouthfeel. Addition of gluten and transglutaminase modified the microstructure and mouthfeel of five oat bread samples. Light microscopy, instrumental texture profile analysis and descriptive sensory analysis were used to analyse the test breads. Digital image analysis was used to obtain numerical data on starch and protein phases in the breads. Degree of starch gelatinization and protein network properties were extracted from microscopy images by expert ratings. Many of the bread sensory properties could be predicted using instrumental texture and image analysis features. Machine learning of degree of starch gelatinization and protein network structure properties from expert classified microscope images was studied. Color histograms and co‐occurrence matrix statistics were suitable preprocessing techniques for the images. In both starch gelatinization and protein network structure classification, the prediction error of an induced model tree was lower than the standard deviation between independent predictions given by experts.

show abstract

A New Method for Determining the Area of Cell Walls in Rye Doughs Based on Fluorescence Microscopy and Computer-assisted Image Analysis

Cited by 15 publications

References 9 publications

Effects of enzymes in fibre-enriched baking

Effects of enzymes in fibre-enriched baking

Laser-Induced Breakdown Spectroscopy and Chemometrics: A Novel Potential Method to Analyze Wheat Grains

Applying Machine Learning Methods in Studying Relationships Between Mouthfeel and Microstructure of Oat Bread

Contact Info

Product

Resources

About