Wine Quality Assessment using Electronic Nose

Pulluri, Kranthi Kumar; Kumar, Vaegae Naveen

doi:10.1109/asiancon51346.2021.9544828

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…However, sensations and age of humans have a significant effect on odor recognition and classification [19]. E-nose is additionally used in a variety of food sector applications, including shelf life determination [28], [29], [30], identity [31], counterfeit detection [32], evaluation [33], [34], freshness assessment [35], and authenticity [36]. Furthermore, E-Nose is used in the industrial world to identify contaminants [37], [38], allergies [39], [40], and dangerous compounds in food [41].Furthermore, toxic agents in the sample and testing time are critical obstacles in the capacity to determine smells through the human olfactory system [20].…”

Section: Introductionmentioning

confidence: 99%

A Literature Review on the Quality Evaluation of Soybeans Using Electronic Nose

2024

IRJMETS

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Electronic nose (e-nose) is an innovative device that mimics the human olfactory system, allowing for the detection and identification of numerous scents. This literature review primarily focuses on studies about the application of electronic nose on evaluating the quality of soybeans in different forms. The researchers gathered, compiled, and summarized the results of the related studies and assessed the accuracy and precision of the electronic nose. It also covers the problems and potential future applications of electronic nose technology that could affect its reliability in assessing the quality of products. The literature review highlights the importance of this technology in improving our capacity to evaluate complex scents in several domains.

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Section: Introductionmentioning

confidence: 99%

A Literature Review on the Quality Evaluation of Soybeans Using Electronic Nose

2024

IRJMETS

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“…Electronic Nose are generally built with a set of metal oxide conducting chemical sensors (MOS), responsible for identifying the Volatile Organic Compounds released by the odor-emitting components. Its functioning is based on the olfactory function of mammals and has been studied since the 1980s 11 . Just like a real nose, the E-nose aims to identify patterns from the VOCs identified by the sensors, whose reading values are analyzed and classified by an Artificial Intelligence (AI) model.…”

Section: Introductionmentioning

confidence: 99%

Breaking Barriers in Candida spp. Detection: Harnessing Electronic Noses and AI for Swift Diagnosis

Bastos

Lima

Benevides³

et al. 2023

Preprint

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The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly from blood, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis e C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose’s low production costs. Our experimental results demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.

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“…The development at all stages of E-nose design made it a good alternative to traditional methods. E-nose is employed in different food industries such as the meat industry [ 37 , 38 ], oil industry [ 39 ], alcohol industry [ 40 ], tea industry, [ 41 ] etc. Furthermore, the E-nose is used in various food industry applications such as adulteration detection [ 42 ], origin identification [ 43 ], authentication [ 44 , 45 ], grading [ 46 , 47 ], freshness assessment [ 40 ], and shelf-life determination [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

“…E-nose is employed in different food industries such as the meat industry [ 37 , 38 ], oil industry [ 39 ], alcohol industry [ 40 ], tea industry, [ 41 ] etc. Furthermore, the E-nose is used in various food industry applications such as adulteration detection [ 42 ], origin identification [ 43 ], authentication [ 44 , 45 ], grading [ 46 , 47 ], freshness assessment [ 40 ], and shelf-life determination [ 48 ]. In this study, we develop a smart E-Nose (SE-Nose) for the qualitative and quantitative analysis of pork adulteration in beef.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Pulluri

Kumar

2022

Sensors

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Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.

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Wine Quality Assessment using Electronic Nose

Cited by 10 publications

References 27 publications

A Literature Review on the Quality Evaluation of Soybeans Using Electronic Nose

A Literature Review on the Quality Evaluation of Soybeans Using Electronic Nose

Breaking Barriers in Candida spp. Detection: Harnessing Electronic Noses and AI for Swift Diagnosis

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

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