&lt;title&gt;Neural networks and PCA for determining region of interest in sensory data preprocessing&lt;/title&gt;

Waldemark, Joakim T. A.; Roppel, T.; Wilson, Denise; Dunman, K.; Padgett, Mary Lou; Lindblad, Thomas

doi:10.1117/12.343057

Cited by 9 publications

(4 citation statements)

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“…But it is not optimized for classification tasks since it ignores the identity (class label) of the odor examples in the database. LDA, on the contrary, tries to find projections that maximize the distance between examples from different odorants and minimize the distance between examples of the same class [25,26].…”

Section: Electronic Nosesmentioning

confidence: 99%

“…An ANN is a mathematical algorithm that has the same function as that of the human brain in the biological sense of smell. The typical structure of an ANN is a network with two or more layers of neurons that are connected with synaptic weights-real number multipliers that connect the output of neurons to the inputs of neurons in the next layer [25][26][27]. During training, the ANN tries to learn the patterns of the different odorants by adapting the weights in order to obtain the desired output.…”

Section: Electronic Nosesmentioning

confidence: 99%

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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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Section: Electronic Nosesmentioning

confidence: 99%

Section: Electronic Nosesmentioning

confidence: 99%

Electronic Noses Applications in Beer Technology

Santos¹,

Lozano²,

Aleixandre³

2017

Brewing Technology

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“…6 and are late saturation, saturation slope, early saturation, transient slope, and time to threshold [16][17].…”

Section: Discussionmentioning

confidence: 99%

Sensor characterization for multisensor odor-discrimination system

Belchi

Rothpfeffer

Pelegrí-Sebastiá

et al. 2013

Sensors and Actuators A: Physical

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“…Figure 4 depicts the features of the first method: transient slope, saturation slope, and late saturation [19][20]. From experimental results, we conclude that more information can be obtained about the substance itself than just the level response by calculating the sensor responses to different substances.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

E-nose application to food industry production

Chilo

Pelegrí-Sebastiá

Cupane³

et al. 2016

IEEE Instrum. Meas. Mag.

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Food companies worldwide must constantly engage in product development to stay competitive, cover existing markets, explore new markets, and meet key consumer requirements. This ongoing development places high demands on achieving quality at all levels, particularly in terms of food safety, integrity, quality, nutrition, and other health effects. Food product research is required to convert the initial product idea into a formulation for upscaling production with ensured significant results. Sensory evaluation is an effective component of the whole process. It is especially important in the last step in the development of new products to ensure product acceptance. In that stage, measurements of product aroma play an important role in ensuring that consumer expectations are satisfied. To this end, the electronic noses (enose) can be useful tools to achieve this purpose. The e-nose is a combination of various sensors used to detect gases by generating signals for an analysis system. Our research group has investigated the scent factor in some foodstuff and attempted to develop e-noses based on low-cost technology and compact size. In this paper, we present a summary of our research to date on applications of e-noses in the food industry.

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<title>Neural networks and PCA for determining region of interest in sensory data preprocessing</title>

Cited by 9 publications

References 0 publications

Electronic Noses Applications in Beer Technology

Electronic Noses Applications in Beer Technology

Sensor characterization for multisensor odor-discrimination system

E-nose application to food industry production

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