scholarly journals Computer Image Analysis as a Method of Evaluating the Quality of Selected Fine-Grained Food Mixtures

2021 ◽  
Vol 13 (6) ◽  
pp. 3018
Author(s):  
Dominika Barbara Matuszek ◽  
Łukasz Andrzej Biłos
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
High Reliability ◽  
Digital Camera ◽  
Simple Linear Regression ◽  
Computer Image Analysis ◽  
Fine Grained ◽  
Stable Conditions ◽  
Mixing Vessels

This work presents the possibility of using computer image analysis to assess the quality of fine-grained food mixtures. The research was carried out using a mixture of wheat flour and algae. These types of ingredients are used, among others, to produce pasta, which is a functional food due to its enrichment with algae. The tests were carried out for mixtures with different shares of algae: 2%, 3% and 4% w/w. Mixing was carried out in a 3D mixer (Turbula® mixer), in which 20, 40 and 60 mL mixing vessels were placed. At the end of the process, samples were taken from four parts (sectors) of the mixing vessels, and then photos were taken with a digital camera. For this purpose, a specially prepared chamber was used, ensuring stable conditions for taking photos. The obtained images were analyzed in the Patan® program, determining the color on the RGB-256 scale. The obtained values were compared with the previously prepared reference specimen (simple linear regression formula). Based on this, it was possible to determine the share of algae in the samples taken and thus to estimate the homogeneity of the tested mixtures. The obtained results indicate the high reliability of the proposed solution.

Lightness of the color measured by computer image analysis as a factor for assessing the quality of pork meat

Meat Science ◽  
2011 ◽  
Vol 88 (3) ◽  
pp. 566-570 ◽  
Author(s):  
Marta Chmiel ◽  
Mirosław Słowiński ◽  
Krzysztof Dasiewicz
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Pork Meat ◽  
Computer Image Analysis

scholarly journals Artificial neural networks and computer image analysis in the evaluation of selected quality parameters of pea seeds

2019 ◽  
Vol 132 ◽  
pp. 01027 ◽  
Author(s):  
Katarzyna Szwedziak
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Good Method ◽  
Quality Parameters ◽  
Computer Image Analysis ◽  
Pea Seeds ◽  
Artificial Neural

The aim of the study was to develop an innovative method of modelling the process of evaluating the quality of agricultural crops on the basis of computer image analysis and artificial neural networks (ANN). It was therefore assumed that on the basis of the prepared application for processing and analysing the acquired digital images, based on the RGB colour recognition model, a quick and good method of assessing the quality of products would be obtained. An experiment was conducted on the evaluation of selected parameters of pea seeds quality using computer image analysis and the obtained results were verified by artificial neural networks using the geostatic function.

scholarly journals Use of computer image analysis for in vivo estimates of the carcass quality of bulls

2008 ◽  
Vol 52 (No. 12) ◽  
pp. 430-436
Author(s):  
P. Polák ◽  
T. Sakowski ◽  
E.N. Blanco Roa ◽  
J. Huba ◽  
E. Krupa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Stepwise Regression ◽  
Carcass Quality ◽  
Regression Equations ◽  
Computer Image Analysis ◽  
Photometric Measurements ◽  
Top View

The aims of the paper were to construct models for the estimation of carcass quality by means of computer image analysis and to verify computer photometry as an <i>in vivo</i> method of carcass quality prediction. Results of photometric measurements and carcass quality of 118 Slovak Pied bulls slaughtered at the age of 15 to 18 months were analysed. Nine length dimensions and four area dimensions were measured on the images of the top, left and rear view of each animal. Hot carcass weight (HCW), weight of meat in carcass (WMC) and weight of meat in valuable cuts (WMVC) were obtained after slaughter treatment and carcass dissection. HCW, WMC and WMVC revealed a maximum correlation with the top-view body area (<i>r</i> = 0.54–0.60) and thurl width (<i>r</i> = 0.58–0.60). Stepwise regression was applied to construct linear regression equations for HCW, WMC and WMVC in two alternatives using photometrical dimensions with and without weight before slaughter (WBS). <i>R</i><sup>2</sup> in an alternative without WBS were lower (<i>R</i><sup>2</sup> = 0.47–0.55); however <i>R</i><sup>2</sup> in an alternative with weight before slaughter were higher and highly significant (<i>R</i><sup>2</sup> = 0.83–0.92). In both alternatives, the equation for HCW had the highest <i>R</i><sup>2</sup> and the equation for WMVC had the lowest <i>R</i><sup>2</sup>. Equations using photometric dimensions and WBS are suitable to estimate HCW, WMC and WMVC without detailed dissection.

Computer image analysis in evaluating the quality of dried meat, case study: poultry meat

2019 ◽  
Author(s):  
Krzysztof Koszela ◽  
Jolanta Gawałek ◽  
Piotr Boniecki ◽  
Sebastian Kujawa ◽  
Wojciech Mueller ◽  
...  
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Poultry Meat ◽  
Computer Image Analysis

Recent developments in automated Transmission Electron MI

1989 ◽  
Vol 47 ◽  
pp. 46-47
Author(s):  
W.J. de Ruijter ◽  
P. Rez ◽  
David J. Smith
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Lens Design ◽  
Computer Image Analysis ◽  
Transmission Electron ◽  
Resolution Electron ◽  
Recent Developments ◽  
On Line ◽  
Routine Procedures ◽  
Near Future

There is growing interest in the on-line use of computers in high-resolution electron n which should reduce the demands on highly skilled operators and thereby extend the r of the technique. An on-line computer could obviously perform routine procedures hand, or else facilitate automation of various restoration, reconstruction and enhan These techniques are slow and cumbersome at present because of the need for cai micrographs and off-line processing. In low resolution microscopy (most biologic; primary incentive for automation and computer image analysis is to create a instrument, with standard programmed procedures. In HREM (materials researc computer image analysis should lead to better utilization of the microscope. Instru (improved lens design and higher accelerating voltages) have improved the interpretab the level of atomic dimensions (approximately 1.6 Å) and instrumental resolutior should become feasible in the near future.

Computer image analysis of intramuscular adipocytes and marbling in the longissimus muscle of cattle1

2006 ◽  
Vol 84 (12) ◽  
pp. 3251-3258 ◽  
Author(s):  
X. J. Yang ◽  
E. Albrecht ◽  
K. Ender ◽  
R. Q. Zhao ◽  
J. Wegner
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Longissimus Muscle ◽  
Computer Image Analysis

scholarly journals Computer Image Analysis for Marbling Change within Longissimus Muscle with Cut Section of Japanese Black Steers

2007 ◽  
Vol 78 (4) ◽  
pp. 441-446 ◽  
Author(s):  
Yoshinobu NAKAHASHI ◽  
Shin MARUYAMA ◽  
Shinji SEKI ◽  
Satoshi HIDAKA ◽  
Keigo KUCHIDA
Keyword(s):  
Image Analysis ◽  
Computer Image ◽  
Longissimus Muscle ◽  
Computer Image Analysis
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