Neural-network-integrated electronic nose system for identification of spoiled beef

Panigrahi, Suranjan; Balasubramanian, Sundaravadivel; Gu, Hanming; Logue, Catherine M.; Marchello, M. J.

doi:10.1016/j.lwt.2005.01.002

Cited by 103 publications

(56 citation statements)

References 25 publications

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“…An additional advantage of sensor arrays is the possibility to make analysis of poor defined analytes, such as aromas or taste of food products. Conductometric electronic noses based on CP were applied for detection of fire [132], aromatic hydrocarbons [133], bacteria and fungi [134][135][136][137][138][139][140], pollutants in water [134,141], for monitoring of emissions from sewage plants [142] or for analysis of wines [143], olive oils [144,145], different soils [146] and grain quality [147] and other poor defined analytes. Surprisingly, instead of using of different classes of CP, derivatives of one CP were typically used to get different selectivity of single sensors of the electronic noses.…”

Section: Electronic Noses and Tonguesmentioning

confidence: 99%

Conducting polymers in chemical sensors and arrays

Lange¹,

Roznyatovskaya²,

Mirsky³

2008

Analytica Chimica Acta

850

434

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Section: Electronic Noses and Tonguesmentioning

confidence: 99%

Conducting polymers in chemical sensors and arrays

Lange¹,

Roznyatovskaya²,

Mirsky³

2008

Analytica Chimica Acta

850

434

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“…ANNs are computational modeling tools that have been extensively used in many disciplines to model complex problems [21]. They have been applied to E-nose data for the purpose of classification [22][23][24][25][26][27][28][29]. The trained ANN can be employed for classification of fish freshness and the identification of the day after catching.…”

Section: Neural Network Classifiersmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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“…The data obtained from 32 sensors gave information that the two principal components (PC1 85% and PC2 15%) accounted for our desired 100% of total variation. Henderson et al (2010) and Panigrahi et al (2006) suggested switching off a few sensors that were sensitive to water vapour. In our research, we performed two kinds of tests with 32 and 28 sensors (except for 4 that were sensitive to water vapour), but clearer and more stable results were obtained with 32 sensors (Fig.…”

Section: Resultsmentioning

confidence: 99%

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

Gancarz

Wawrzyniak

Gawrysiak‐Witulska

et al. 2017

International Agrophysics

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A b s t r a c t. Investigations were performed to examine the possibility of using an electronic nose to monitor development of fungal microflora during the first eighteen days of rapeseed storage. The Cyranose 320 device manufactured by Sensigent was used to analyse volatile organic compounds. Each sample of infected material was divided into three parts and the degree of spoilage was measured in three ways: analysis of colony forming units, determination of ergosterol content, and measurement of volatile organic compounds with the e-nose. Principal component analysis was performed on the generated patterns of signals and six groups of different spoilage levels were isolated. An analysis of sensorgrams for a few sensors with a strong signal for each group of rapeseed spoilage was performed. The ratio of the association time to the steady state was calculated. This ratio was different for the low level and the highest level of ergosterol and colony forming units. The results have shown that the e-nose can be a useful tool for quick estimation of the degree of rapeseed spoilage.

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Neural-network-integrated electronic nose system for identification of spoiled beef

Cited by 103 publications

References 25 publications

Conducting polymers in chemical sensors and arrays

Conducting polymers in chemical sensors and arrays

Intelligent Fish Freshness Assessment

Electronic nose with polymer-composite sensors for monitoring fungal deterioration of stored rapeseed

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