03763nam a22003854u 450000100220000000300060002200500170002800700150004500800410006002000300010102400310013104100080016224501490017025000060031926000160032526400160034130000120035733600260036933700260039533800360042134700210045734700080047852021260048665000220261265000330263465000430266765302340271070000270294470000350297170000270300677600360303385600640306985601020313385601420323510.1002/9781119137252Wiley20251030120312.0cr nn 008mam|a231113t2023||||xxu    fs|   |0|| 0|eng||  a9781119137252qelectronic7 a10.1002/97811191372522doi  aeng00aSource Separation in Physical‐Chemical Sensingh[electronic resource]cEdited by Christian Jutten, Leonardo Tomazeli Duarte, Saïd Moussaoui.  a1  bWileyc2023 1bWileyc2023  a352pp.;  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext file2rdaft  bPDF  aSource Separation in Physical-Chemical Sensing Master advanced signal processing for enhanced physical and chemical sensors with this essential guide In many domains (medicine, satellite imaging and remote sensing, food industry, materials science), data is obtained from large sets of physical/chemical sensors or sensor arrays. Such sophisticated measurement techniques require advanced and smart processing for extracting useful information from raw sensing data. Usually, sensors are not very selective and record a mixture of the useful latent variables. An innovative technique called Blind Source Separation (BSS) can isolate and retrieve the individual latent variables from a mixed-source data array, allowing for refined analysis that fully exploits these cutting-edged imaging and signal-sensing technologies. Source Separation in Physical-Chemical Sensing, supplies a thorough introduction to the principles of BSS, main methods and algorithms and its potential applications in various domains where data are obtained through physical or chemical sensors. Designed to bridge the gap between chemical/physical analysis and signal processing, it promises to be invaluable in many fields. Its alertness to the latest technologies and the full range of potential BSS applications make it an indispensable introduction to this cutting-edge method. Source Separation in Physical-Chemical Sensing readers will also find: BSS examples on chemical and physical sensors and devices to enhance processing and analysis. Detailed treatment of source separation in potentiometric sensors, ion-sensitive sensors, hyperspectral imaging, Raman and fluorescence spectroscopy, chromatography, and others. Thorough discussion of Bayesian source separation, nonnegative matrix factorization, tensorial methods, geometrical methods, constrained optimization, and more. Source Separation in Physical-Chemical Sensing is a must-have for researchers and engineers working in signal processing and statistical analysis, as well as for chemists, physicists or engineers looking to apply source separation in various application domains. 4aSignal Processing 4aChemometrics & Data Handling 4aSensors, Instrumentation & Measurement00aBSS; signal processing; mass spectrography; chromatography; spectral unmixing; hyperspectral imaging; Bayesian source separation; non-negative matrix factorization; tensorial methods; geometrical methods; constrained optimization1 aJutten, Christian4edt1 aDuarte, Leonardo Tomazeli4edt1 aMoussaoui, Saïd4edt08iPrinted edition:z978111913722140uhttps://doi.org/10.1002/9781119137252aWiley Online Library40uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781119137252aWiley Online Library423Cover Imageuhttps://onlinelibrary.wiley.com/cms/asset/52b856df-d974-4267-bb4f-8c7a003cf29b/9781119137252.cover.gifaWiley Online Library