Porovnat metody

	Elektronický nos	HPLC	Analýza texturového profilu
Obor	Věda o potravinách	Věda o potravinách	Věda o potravinách
Rodina	Process / pipeline	Process / pipeline	Process / pipeline
Rok vzniku	1982	1970	1968
Tvůrce	Krishna Persaud	Csaba Horváth	Malcolm Bourne
Typ	Chemical Sensing Device	Separation and Quantification Technique	Mechanical Texture Method
Původní zdroj	Persaud, K., & Dodd, G. (1982). Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299(5881), 352-355.	Snyder, L. R., Kirkland, J. J., & Dolan, J. W. (2010). Introduction to modern liquid chromatography (3rd ed.). Wiley.	Bourne, M. C. (1968). Texture profile of foods. Journal of Food Science, 33(3), 280-283.
Další názvy	e-Nose, artificial olfaction	HPLC, high-pressure liquid chromatography	TPA
Příbuzné	3	3	3
Shrnutí	An electronic nose (e-nose) is an instrumental analytical device that mimics the mammalian olfactory system to detect and identify volatile organic compounds (odors) in food products. Developed by Persaud and Dodd in 1982, e-noses use arrays of non-selective chemical sensors combined with pattern recognition algorithms to create electronic signatures of food aromas, enabling objective, rapid quality assessment and shelf-life prediction.	High-Performance Liquid Chromatography (HPLC) is an analytical technique that separates, identifies, and quantifies components in a complex food sample by passing the sample through a pressurized column packed with a stationary phase. Developed by Horváth in the early 1970s, HPLC enables rapid, sensitive measurement of nutrients, contaminants, additives, and bioactive compounds in food products with high precision and accuracy.	Texture Profile Analysis (TPA) is an objective, mechanical method that simulates mastication (chewing) to measure the textural properties of food products. Developed by Bourne in 1968, TPA uses a texture analyzer (a machine that applies defined forces and movements to a sample) to generate a force-time curve from which multiple texture attributes (hardness, springiness, chewiness, cohesiveness, adhesiveness) are extracted and quantified.