AIMC Topic: Electronic Nose

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Application of an electronic tongue and hyperspectral imaging with a CNN-transformer fusion model for rapid detection of botanical origins of honey.

Analytical methods : advancing methods and applications
The botanical origin of honey significantly impacts its nutritional composition, quality, and price. Traditional identification methods are often complex, require expensive equipment, and are time-consuming. This article proposes a rapid detection me...

Electronic-Nose Technology for Lung Cancer Detection: A Non-Invasive Diagnostic Revolution.

Lung
BACKGROUND: Lung cancer (LC) remains a leading cause of cancer-related mortality worldwide, primarily due to late-stage diagnosis and the absence of effective early detection methods.

Discrimination of Respiratory Tract Infections by a Reduced Graphene Oxide Array Modified with Metal-Organic Frameworks and Metal Phthalocyanines.

ACS nano
As a prevalent clinical condition, it is critical to distinguish between bacterial and viral respiratory tract infections given their pivotal role in guiding appropriate pharmaceutical interventions and preventing antibiotic misuse. Exhaled breath (E...

Gas Sensor Drift Compensation Using Semi-Supervised Ensemble Classifiers with Multi-Level Features and Center Loss.

ACS sensors
The drift compensation of gas sensors is a significant and challenging issue in the field of electronic noses (E-nose). Compensating sensor drift has a great benefit in improving the performance of E-nose systems. However, conventional methods often ...

Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies.

Food chemistry
Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were e...

Progress in machine learning-supported electronic nose and hyperspectral imaging technologies for food safety assessment: A review.

Food research international (Ottawa, Ont.)
The growing concern over food safety, driven by threats such as food contaminations and adulterations has prompted the adoption of advanced technologies like electronic nose (e-nose) and hyperspectral imaging (HSI), which are increasingly enhanced by...

Leaf-Face Classifier Based on an Integrated Electrochemical Tongue and Machine Learning.

ACS sensors
Botanical sourcing seriously impacts the safety and potency of herbal medicines, restricting the development of the traditional Chinese medicinal industry. Rapid and convenient identification of plant resources is important to address this problem. H...

Machine learning-based classification and prediction of typical Chinese green tea taste profiles.

Food research international (Ottawa, Ont.)
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods we...

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Toxins
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety ...

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

PloS one
Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We d...