An Optimal Faster Region-Based Convolutional Neural Network for Oil Adulteration Detection

Natural oils such as avocado oil (AO), corn oil (CO), chamomile oil (CMO), and rapeseed oil (RSO) are used for improving health, skin, and hair care for centuries in different parts of the world. The AO, CO, and RSO oil are known for their antibacterial and moisturizing properties. The growth of the...

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Vydané v:Arabian journal for science and engineering (2011) Ročník 48; číslo 2; s. 2231 - 2245
Hlavní autori: Surya, V., Senthilselvi, A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Predmet:
Artificial neural networks
Engineering
Humanities and Social Sciences
multidisciplinary
Neural networks
Rapeseed
Research Article-Computer Engineering and Computer Science
Science
Vegetable oils
Oil adulteration
War strategy optimization algorithm
Chemometric methods
Excitation-emission matrix fluorescence
Faster region-based convolutional neural network
ISSN:2193-567X, 1319-8025, 2191-4281
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Shrnutí:Natural oils such as avocado oil (AO), corn oil (CO), chamomile oil (CMO), and rapeseed oil (RSO) are used for improving health, skin, and hair care for centuries in different parts of the world. The AO, CO, and RSO oil are known for their antibacterial and moisturizing properties. The growth of the modern health and wellness industries has deceived people to find affordable, additive-free, and effective products. Consumers are always willing to pay a higher price for the unadulterated oils due to the major benefits it offers in terms of complexion and health. However, the AO, CMO, and RSO oils are rarely available in the market and the ones available are subject to adulteration risks which affect the consumer’s health and also violate their rights. This paper presents a war strategy optimized faster Region-based Convolutional Neural Network (RCNN) architecture named Modified Faster RCNN for oil adulteration detection. The pure natural AO, CO, CMO, and RSO oils are adultered with different oils such as vegetable oils with different ratios and these oils themselves. Spectra analysis was conducted to identify the adultered vegetable oil samples and the Modified Faster RCNN was employed to classify the pure oil from these unadultered samples. The results show that the proposed model is effective in rapidly identifying the adulterated components presented in high-quality oils.
Bibliografia:ObjectType-Article-1
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-022-07115-7