Performance of an e-nose in hops classification

Lamagna, A.; Reich, S.L.; Rodríguez, Dario; Scoccola, N. N.

doi:10.1016/j.snb.2004.04.032

Cited by 18 publications

(10 citation statements)

References 14 publications

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“…The second type is based on curve fitting [ 27 ], which fits the response curves based on a specific model and extracts a set of fitting parameters as the features. The third one is based on some transforms, and very often the fast Fourier transform (FFT) [ 28 , 29 , 30 , 31 ] and the discrete wavelet transform (DWT) have been used as feature extraction tools for this purpose [ 6 , 28 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Feature Extraction Methods: A Review

Jia

Guo

Duan

et al. 2015

Sensors

247

132

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Many research groups in academia and industry are focusing on the performance improvement of electronic nose (E-nose) systems mainly involving three optimizations, which are sensitive material selection and sensor array optimization, enhanced feature extraction methods and pattern recognition method selection. For a specific application, the feature extraction method is a basic part of these three optimizations and a key point in E-nose system performance improvement. The aim of a feature extraction method is to extract robust information from the sensor response with less redundancy to ensure the effectiveness of the subsequent pattern recognition algorithm. Many kinds of feature extraction methods have been used in E-nose applications, such as extraction from the original response curves, curve fitting parameters, transform domains, phase space (PS) and dynamic moments (DM), parallel factor analysis (PARAFAC), energy vector (EV), power density spectrum (PSD), window time slicing (WTS) and moving window time slicing (MWTS), moving window function capture (MWFC), etc. The object of this review is to provide a summary of the various feature extraction methods used in E-noses in recent years, as well as to give some suggestions and new inspiration to propose more effective feature extraction methods for the development of E-nose technology.

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Section: Introductionmentioning

confidence: 99%

Electronic Nose Feature Extraction Methods: A Review

Jia

Guo

Duan

et al. 2015

Sensors

247

132

View full text Add to dashboard Cite

show abstract

“…Electronic nose systems have also been tested to evaluate aroma fingerprint changes in beer during the ageing process. M. GhasemiVarnamkhasti et al reviewed the last applications of e-nose in brewery applied to quality assessment [20], reporting applications for hop's quality assessment [21], on-line process control [22], monitoring the odor and taste of active compounds [23] and as a brand authenticity checking tool [24,25]. All works reported in this review take advantage Chemometrics and Intelligent Laboratory Systems 142 (2015) [131][132][133][134][135][136][137][138][139][140][141][142] of the use of multivariate techniques for data analysis, being reported a plethora of techniques used in exploratory data analysis, calibration and pattern recognition tasks.…”

Section: Introductionmentioning

confidence: 99%

Chemometric analysis of the volatile fraction evolution of Portuguese beer under shelf storage conditions

Rendall

Reis

Pereira

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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“…Hops classification was performed in Ref. [37]. Using a commercial e-nose (FOX 2000), discrimination of different malt types could be done [38].…”

Section: E-nose Applications In Beermentioning

confidence: 99%

Electronic Noses Applications in Beer Technology

Santos¹,

Lozano²,

Aleixandre³

2017

Brewing Technology

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This chapter describes and explains in detail the electronic noses (e-noses) as devices composed of an array of sensors that measure chemical volatile compounds and apply classification or regression algorithms. Then, it reviews the most significant applications of such devices in beer technology, with examples about defect detection, hop classification, or beer classification, among others. After the review, the chapter illustrates two applications from the authors, one about beer classification and another about beer defect detection. Finally, after a comparison with other analytical techniques, the chapter ends with a summary, conclusions, and the compelling future of the e-noses applied to beer technology.

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Performance of an e-nose in hops classification

Cited by 18 publications

References 14 publications

Electronic Nose Feature Extraction Methods: A Review

Electronic Nose Feature Extraction Methods: A Review

Chemometric analysis of the volatile fraction evolution of Portuguese beer under shelf storage conditions

Electronic Noses Applications in Beer Technology

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