Matches in SemOpenAlex for { <https://semopenalex.org/work/W2091873268> ?p ?o ?g. }
- W2091873268 endingPage "769" @default.
- W2091873268 startingPage "751" @default.
- W2091873268 abstract "Abstract The paper presents a method for selection of polymers for SAW vapor sensing arrays. The method applies fuzzy c-means (FCM) clustering algorithm on the solvation data for vapor-polymer sorptive interactions. The data set consists of equilibrium partition coefficients for target vapors in a large set of prospective polymers. The FCM algorithm sorts out polymers of similar characteristics into c fuzzy clusters, and subset of polymers representing the centers of these clusters are selected for making the sensor array. The partition coefficient data are calculated by using Abraham's linear-solvation-energy relationship (LSER), and the clustering algorithm considers the polymers as objects and the vapors as observables. The value of c ≥ 2 is determined by repeated clustering with c incremented by 1 until a common subset of polymers emerges that appears in every successive FCM implementation. The method is validated by simulation of SAW sensor arrays for detection of freshness and spoilage marker volatiles in headspace of milk and fish. The identification and concentration estimation are done by applying the radial basis function neural network on simulation data. It is suggested that the present FCM method of polymer selection prior to sensor fabrication could be helpful in optimizing performance and reducing cost of SAW electronic noses for various applications." @default.
- W2091873268 created "2016-06-24" @default.
- W2091873268 creator A5082499961 @default.
- W2091873268 creator A5084293972 @default.
- W2091873268 date "2015-03-01" @default.
- W2091873268 modified "2023-10-04" @default.
- W2091873268 title "Polymer selection for SAW sensor array based electronic noses by fuzzy c-means clustering of partition coefficients: Model studies on detection of freshness and spoilage of milk and fish" @default.
- W2091873268 cites W1570834090 @default.
- W2091873268 cites W1967220402 @default.
- W2091873268 cites W1968748899 @default.
- W2091873268 cites W1970922405 @default.
- W2091873268 cites W1972125708 @default.
- W2091873268 cites W1972727440 @default.
- W2091873268 cites W1973667828 @default.
- W2091873268 cites W1979253116 @default.
- W2091873268 cites W1984692084 @default.
- W2091873268 cites W1984981454 @default.
- W2091873268 cites W1985669629 @default.
- W2091873268 cites W1987363251 @default.
- W2091873268 cites W1992824238 @default.
- W2091873268 cites W1994615618 @default.
- W2091873268 cites W1995450389 @default.
- W2091873268 cites W1997115500 @default.
- W2091873268 cites W1997452178 @default.
- W2091873268 cites W2002409169 @default.
- W2091873268 cites W2005020926 @default.
- W2091873268 cites W2010502140 @default.
- W2091873268 cites W2013477944 @default.
- W2091873268 cites W2017958554 @default.
- W2091873268 cites W2020984028 @default.
- W2091873268 cites W2023726552 @default.
- W2091873268 cites W2024026781 @default.
- W2091873268 cites W2026209437 @default.
- W2091873268 cites W2026285305 @default.
- W2091873268 cites W2026478439 @default.
- W2091873268 cites W2034329339 @default.
- W2091873268 cites W2035403293 @default.
- W2091873268 cites W2038191600 @default.
- W2091873268 cites W2038494636 @default.
- W2091873268 cites W2044128726 @default.
- W2091873268 cites W2045041863 @default.
- W2091873268 cites W2050125642 @default.
- W2091873268 cites W2050648073 @default.
- W2091873268 cites W2052114723 @default.
- W2091873268 cites W2052279591 @default.
- W2091873268 cites W2053188435 @default.
- W2091873268 cites W2054341846 @default.
- W2091873268 cites W2061543408 @default.
- W2091873268 cites W2062340430 @default.
- W2091873268 cites W2066294495 @default.
- W2091873268 cites W2070800952 @default.
- W2091873268 cites W2072974222 @default.
- W2091873268 cites W2079296123 @default.
- W2091873268 cites W2080309051 @default.
- W2091873268 cites W2081279549 @default.
- W2091873268 cites W2082308533 @default.
- W2091873268 cites W2083745755 @default.
- W2091873268 cites W2085653635 @default.
- W2091873268 cites W2087397362 @default.
- W2091873268 cites W2087431419 @default.
- W2091873268 cites W2087725059 @default.
- W2091873268 cites W2089740062 @default.
- W2091873268 cites W2090849035 @default.
- W2091873268 cites W2094507754 @default.
- W2091873268 cites W2098139755 @default.
- W2091873268 cites W2101026422 @default.
- W2091873268 cites W2104604169 @default.
- W2091873268 cites W2107213898 @default.
- W2091873268 cites W2127021801 @default.
- W2091873268 cites W2133654042 @default.
- W2091873268 cites W2134520731 @default.
- W2091873268 cites W2142146537 @default.
- W2091873268 cites W2142411985 @default.
- W2091873268 cites W2143457129 @default.
- W2091873268 cites W2148648899 @default.
- W2091873268 cites W2148989159 @default.
- W2091873268 cites W2159570360 @default.
- W2091873268 cites W2160808377 @default.
- W2091873268 cites W2163763334 @default.
- W2091873268 cites W2166682796 @default.
- W2091873268 cites W2168269899 @default.
- W2091873268 cites W2170276030 @default.
- W2091873268 cites W2172043263 @default.
- W2091873268 cites W2316146675 @default.
- W2091873268 cites W2321854267 @default.
- W2091873268 cites W31791639 @default.
- W2091873268 cites W4233726717 @default.
- W2091873268 cites W71591729 @default.
- W2091873268 cites W2006203655 @default.
- W2091873268 doi "https://doi.org/10.1016/j.snb.2014.11.149" @default.
- W2091873268 hasPublicationYear "2015" @default.
- W2091873268 type Work @default.
- W2091873268 sameAs 2091873268 @default.
- W2091873268 citedByCount "52" @default.
- W2091873268 countsByYear W20918732682015 @default.
- W2091873268 countsByYear W20918732682016 @default.
- W2091873268 countsByYear W20918732682017 @default.
- W2091873268 countsByYear W20918732682018 @default.