Methoden vergelijken

	Elektronisch Neusje	Gaschromatografie-Olfactometrie	Texture Profile Analysis
Vakgebied	Levensmiddelenwetenschap	Levensmiddelenwetenschap	Levensmiddelenwetenschap
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Jaar van ontstaan	1982	1997	1968
Grondlegger	Krishna Persaud	Terry Acree	Malcolm Bourne
Type	Chemical Sensing Device	Sensory-Instrumental Coupling	Mechanical Texture Method
Oorspronkelijke bron	Persaud, K., & Dodd, G. (1982). Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature, 299(5881), 352-355.	Acree, T. E. (1997). GC/Olfactometry. Analytical Chemistry, 69(5), 170A-175A.	Bourne, M. C. (1968). Texture profile of foods. Journal of Food Science, 33(3), 280-283.
Aliassen	e-Nose, artificial olfaction	GC-O	TPA
Verwant	3	3	3
Samenvatting	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.	Gas Chromatography-Olfactometry (GC-O) combines the separation power of gas chromatography with human olfactory perception to identify which volatile compounds in a food sample contribute to its aroma. Developed by Acree and colleagues in the 1990s, GC-O allows researchers to bypass the human nose's inability to consciously identify which of many simultaneous odors they are perceiving, replacing the 'olfactory bulb' with a trained human panelist.	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.