M
Mahdi Ghasemi-Varnamkhasti
Researcher at Shahrekord University
Publications - 84
Citations - 2952
Mahdi Ghasemi-Varnamkhasti is an academic researcher from Shahrekord University. The author has contributed to research in topics: Electronic nose & Linear discriminant analysis. The author has an hindex of 27, co-authored 79 publications receiving 2317 citations. Previous affiliations of Mahdi Ghasemi-Varnamkhasti include University of Tehran & Metz.
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An assessment of wind energy potential as a power generation source in the capital of iran, tehran
TL;DR: In this paper, the statistical data of eleven years' wind speed measurements of the capital of Iran, Tehran, are used to find out the wind energy potential, and other wind characteristics with the help of two methods of meteorological and Weibull are assessed to evaluate of which at a height of 10 meters above ground level and in open area.
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Biomimetic-based odor and taste sensing systems to food quality and safety characterization: An overview on basic principles and recent achievements
TL;DR: In this article, the authors have discussed some aspects of such systems (odor sensor and taste sensor) and some of more recent applications and advances for food control in food research and technology are discussed as well.
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Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods
TL;DR: The Romanian National Authority for Scientific Research and Innovation, CNCS as mentioned in this paper, UEFISCDI, 2014 project number PN II-RU-TE-2014-4-1093
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Application of electronic nose systems for assessing quality of medicinal and aromatic plant products: A review
TL;DR: A review of the applications of e-nose technologies for assessing the quality and safety of commercially-available medicinal plant products and advantages and limitations of these systems are covered.
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Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)
TL;DR: The applications of newly developed chemical sensor systems for meat quality assessment, where fast detection methods are essential for appropriate product management, are described.