Review on food quality assessment using machine learning and electronic nose system

Anwar, Hassan; Anwar, Talha; Murtaza, Shamas

doi:10.1016/j.biosx.2023.100365

Cited by 26 publications

(10 citation statements)

References 40 publications

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“…Computer science's sub-field of machine learning is categorized as an artificial intelligence technique (Chawla et al, 2023). Machine learning is the ability of a computer to learn without being taught for a particular job (El Naqa and Murphy, 2015;Anwar et al, 2023). Machine learning could either be supervised or unsupervised (Anwar et al, 2023).…”

Section: Machine Learningmentioning

confidence: 99%

“…Machine learning is the ability of a computer to learn without being taught for a particular job (El Naqa and Murphy, 2015;Anwar et al, 2023). Machine learning could either be supervised or unsupervised (Anwar et al, 2023). Samuel (1959) initially proposed the concept of machine learning, which is the study of how to enable computers to learn without being explicitly programmed.…”

Section: Machine Learningmentioning

confidence: 99%

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Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Ayısı Larbı,

Osei

2024

Journal of AI

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This paper reviews current knowledge on the role of smart tools and biosensors based on artificial intelligence in reducing seafood loss and wastage. This study shows that a variety of biosensors, categorised according to how they function, can be used to measure the quality of seafood. These include optical biosensors, enzyme-based biosensors, immunosensors, microbial biosensors, DNA-based biosensors, electrochemical biosensors, optical biosensors, tissue-based biosensors, and piezoelectric biosensors. Among these biosensors, optical biosensors, electrochemical biosensors, and mechanical biosensors are the most significant. Again, this study report that, for seafood traceability and management, a variety of smart solutions including blockchain technology, quick response (QR) codes, data analytics, digital twins, and radio frequency identification (RFID) tags can be utilised. Catch data, vessel tracking data, and data from the processing plant are some of the different data sources that can be utilised to trace seafood products. Artificial intelligence tools like neural networks, deep learning, machine learning, and others can be used to forecast and improve seafood quality. It is crucial to study the development of biosensors that can properly identify the earliest signs of seafood contamination or rotting.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Ayısı Larbı,

Osei

2024

Journal of AI

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“…Digital leaders have the ability to establish a culture of innovation by promoting ethical conduct and fostering collaboration across different departments (Zhai et al, 2022). Efficient digital leadership enables an organisation to successfully navigate the intricate process of digital transformation and ultimately achieve a consistent level of performance (Anwar, Anwar, & Murtaza, 2023b).…”

Section: Mediating Role Of Digital Leadershipmentioning

confidence: 99%

Impact of Digital Transformation and AI through Fostering Digital Leadership Excellence: A Focus on Sustainable Organizational Performance

Mahmood,

Khakwani,

Zafar

et al. 2024

JAFEE

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Purpose: The aim of this paper is to examine how artificial intelligence and digital transformation affect sustainable organisational performance with a particular emphasis on mediating role of digital leadership. Design/Methodology/Approach: The survey data from 245 employees employed in different industries and enterprises is considered. The data is collected through structured questionnaire and analysed with PLS-SEM. The study investigates the interrelationship between digital transformation, AI and sustainable organizational performance with mediating role that digital leadership. Findings: The conclusions of the study bring about the need for digital leadership in integrating sustainability and artificial intelligence. From this, it could be distinguished that combining digital leadership with AI is going to increase the performance and productivity of an organization. The integration of AI and digital leadership increased the capacity to innovate which in turn made an affirmative effect on sustainable organizational performance. Implications/Originality/Value: This study provides new perspectives towards the importance of AI and creative digital behaviors for the achievement of long-term sustainable organizational performance. It also follows that digital leadership can be viewed as a way to promote an innovative and sustainable culture, hence highlighting the mediation role of digital leadership.

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“…In addition, machine learning is capable of assessing the quality of products through large-scale reviews with the assistance of demographic data or food product data. Two studies mention the use of ML algorithms, one focused on the association of demographic data and food choice motives [11] and the other focused on food quality assessment [12].…”

Section: Introductionmentioning

confidence: 99%

AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis

Mpouziotas,

Besharat,

Tsoulos

et al. 2024

Information

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AliAmvra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf (GP) to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action to form a business identity with high added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this study is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmvra project, with a target goal of improving customer experience and product quality. An extensive analysis was conducted for this study by collecting data through surveys that took place in the gastronomes of the AliAmvra project. Our objective was to conduct two types of reviews, one focused in data analysis and the other on evaluating model-driven algorithms. Each review utilized a survey with an individual structure, with each one serving a different purpose. In addition, our model review focused its attention on developing a robust recommendation system with said data. The algorithms we evaluated were MLP (multi-layered perceptron), RBF (radial basis function), GenClass, NNC (neural network construction), and FC (feature construction), which were used for the implementation of the recommendation system. As our final verdict, we determined that FC (feature construction) performed best, presenting the lowest classification rate of 24.87%, whilst the algorithm that performed the worst on average was RBF (radial basis function). Our final objective was to showcase and expand the work put into the AliAmvra project through this analysis.

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Review on food quality assessment using machine learning and electronic nose system

Cited by 26 publications

References 40 publications

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Impact of Digital Transformation and AI through Fostering Digital Leadership Excellence: A Focus on Sustainable Organizational Performance

AliAmvra—Enhancing Customer Experience through the Application of Machine Learning Techniques for Survey Data Assessment and Analysis

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