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A 21st century technique for food control: Electronic noses
Authors:Miguel Peris  Laura Escuder-Gilabert
Institution:a Departamento de Química, Universidad Politécnica de Valencia, 46071 Valencia, Spain
b Departamento de Química Analítica, Universitat de Valencia, C/Vicente Andrés Estellés s/n, E-46100 Burjasot, Valencia, Spain
Abstract:This work examines the main features of modern electronic noses (e-noses) and their most important applications in food control in this new century. The three components of an electronic nose (sample handling system, detection system, and data processing system) are described. Special attention is devoted to the promising mass spectrometry based e-noses, due to their advantages over the more classical gas sensors. Applications described include process monitoring, shelf-life investigation, freshness evaluation, authenticity assessment, as well as other general aspects of the utilization of electronic noses in food control. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in food control are also mentioned.
Keywords:4EP  4-ethylphenol  4EG  4-ethylguaiacol  ANN  artificial neural network  APC  aerobic plate count  API  atomic pressure ionization  BAW  bulk acoustic wave  BP-ANN  back-propagation artificial neural network  BP  backpropagation  CA  cluster analysis  CART  classification and regression tree  CP  conducting polymer  CP-ANN  counterpropagation artificial neural network  DFA  discriminant factor analysis  DHS  dynamic headspace  DOE  design of experiments  e-nose  electronic nose  EC  electrochemical sensor  FCM  fuzzy C means  FDA  factorial discriminant analysis  FSGDA  forward step-wise general discriminant analysis  GA  genetic algorithm  GC  gas chromatography  GC-MS  gas chromatography-mass spectrometry  HS  headspace  HS-MS  head-space mass spectrometry  IMS  ion mobility spectrometry  INDEX  inside-needle dynamic extraction  k-NN  k-nearest neighbors  KOSM  Kohonen self-organizing map  LDA  linear discriminant analysis  LVQ-NN  learning vector quantisation neural network  MDA  multiple discriminant analysis  MIMS  membrane introduction mass spectrometry  MLP  multilayer percepton  MLR  multiple linear regression  MOS  metal oxide semiconductors  MOSFET  metal oxide semiconductor field effect transistor  MS  mass spectrometry  MSE-nose  mass spectrometry based electronic nose  p-AV  anisidine value  P&  T  purge and trap  PARAFAC  parallel factor analysis  PCA  principal component analysis  PCR  principal component regression  PLS  partial least squares  PLS-DA  partial least square-discriminant analysis  PNN  probabilistic neural network  PR  pattern recognition  PTR  proton transfer reaction  PV  peroxide value  QDA  quadratic discriminant analysis  QLSR  quadratic least squares regression  QMB or QCM  quartz microbalances  REP-PCR  repetitive extragenic palindromic polymerase chain reaction  SAW  surface acoustic wave  SBSE  stir bar sorptive extraction  SHS  static headspace  SIMCA  soft independent modelling of class analogy  SLDA  stepwise linear discriminant analysis  SOM  self-organizing map  SPME  solid-phase microextraction  SPR  surface plasmon resonance  SVM  support vector machine  TBARS  thiobarbituric acid reactive substances  TDNN  time-delay neural networks  TSM  thickness shear mode  VOCs  volatile organic compounds  WPTER  wavelet packet transform for efficient pattern recognition
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