Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration

Iymen, Gokce; Tanriver, Gizem; Hayirlioglu, Yusuf Ziya; Ergen, Onur

doi:10.1016/j.ifset.2020.102527

Cited by 35 publications

(12 citation statements)

References 14 publications

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“…The entire setup must be enclosed within a plastic amplifier so that the sound waves produced from the speaker will not be reflected and scattered. The sound produced must be recorded by capturing the sound using a condenser microphone which is sensitive to sounds (8,9). The spectrum can be recorded from the microphone using an apparatus called DSO.…”

Section: Methods Using Acousticsmentioning

confidence: 99%

Exploring Structural and Chemical Changes in Honey Caused by Photonic and Acoustic Effects Towards Detection of Adulterants

2022

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Section: Methods Using Acousticsmentioning

confidence: 99%

Exploring Structural and Chemical Changes in Honey Caused by Photonic and Acoustic Effects Towards Detection of Adulterants

2022

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“…In the paper of Linko [16], that advantages that an expert system could have in the food industry is explained. In recent years, with the growth of artificial intelligence, expert systems are considered an approach to artificial intelligence and are used to improve their solutions in [17][18][19]. While [17] is focused on a review of the chemical industry, [18] is focused on detection of food adulteration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, with the growth of artificial intelligence, expert systems are considered an approach to artificial intelligence and are used to improve their solutions in [17][18][19]. While [17] is focused on a review of the chemical industry, [18] is focused on detection of food adulteration. In the case of [19], Jha et al present also a review, but in this case focused on automation in agriculture.…”

Section: Literature Reviewmentioning

confidence: 99%

Conceptual and Preliminary Design of a Shoe Manufacturing Plant

Méndez

Cremades²,

Fernando³

et al. 2021

Applied Sciences

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This article presents a procedure for designing footwear production plants with a Decision Support System combined with an expert system and a simulation approach. The footwear industry has many operations and is labour intensive. Optimisation of plant layout, machinery, and human resources is very important to design the footwear manufacturing system, making adequate investment in space and equipment. In the industry it is essential to reduce the process time, so the research is based on a Decision Support System combined with an expert system and simulation to improve the design of the manufacturing plan. This work contains two case studies, direct injection manufacturing and assembly and carburising methods, which are compared to analyse all the necessary resources to have the best cost–benefit ratio. In each case, a precise knowledge of the type and quantity of machinery and human resources is needed to estimate the production. This comparison has been done through simulations and using a knowledge base of an expert system. The conclusions are presented in which an improvement in production time is obtained by applying the methodology developed in the study.

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“…For example, Wojcik and Jakubowska (2021) used a deep neural network to predict adulteration in apple juice with an R2 prediction that reached 0.98. Moreover, the AI has successfully detected butter counterfeit with 82.66% accuracy (Iymen et al, 2020). Likewise, Support Vector Machine (SVM) (kind of AI) can detect cassava starch adulteration (86.9% accuracy) (Cardoso & Poppi, 2021) and white rice counterfeiting (90% accuracy) (Lim et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration

Cited by 35 publications

References 14 publications

Exploring Structural and Chemical Changes in Honey Caused by Photonic and Acoustic Effects Towards Detection of Adulterants

Exploring Structural and Chemical Changes in Honey Caused by Photonic and Acoustic Effects Towards Detection of Adulterants

Conceptual and Preliminary Design of a Shoe Manufacturing Plant

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