Hamed Sardari | Food Safety | Best Researcher Award

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Mr. Hamed Sardari | Food Safety | Best Researcher Award

Research Assistant at University of Tehran, Iran

Hamed Sardari is a dedicated researcher with expertise in food quality assessment, agricultural engineering, and food processing. He has made significant contributions to the fields of ultrasound-assisted freezing, machine vision in agriculture, and deep learning for food quality detection. His research has been published in reputable journals and presented at international congresses. With a strong background in mechanical engineering of biosystems, Hamed combines advanced computational techniques with engineering principles to enhance food safety and processing methods.

Publications Profile

Scopus

Orcid

Google Scholar

🎓 Education Details

  • M.Sc. in Mechanical Engineering of Biosystems, University of Tehran (Sep 2020 – Feb 2023)
    • GPA: 4.0/4.0 (19.58/20)
    • Thesis: Development and Optimization of an Ultrasound-Assisted Freezing System for Meat
    • Supervisors: Dr. Mahmoud Soltani Firouz, Dr. Soleiman Hosseinpour
  • B.Sc. in Mechanical Engineering of Biosystems, Shahid Chamran University of Ahvaz (Sep 2016 – Jul 2020)
    • GPA: 3.41/4.0 (16.48/20)
    • Supervisor: Dr. Abbas Asakereh

👩‍🔬 Professional Experience

  • Research Assistant, University of Tehran (March 2023 – Present)
    • Specialized in laboratory techniques for food safety and agricultural product analysis.
  • Teaching Assistant, University of Tehran (Sep 2021 – April 2022)
    • Courses: Engineering Principles of Processing of Food and Agricultural Materials, Non-Destructive Tests in Agriculture
  • Teaching Assistant, Shahid Chamran University of Ahvaz (Sep 2017 – April 2019)
    • Courses: Computer Programming, Technical Drawings II

🌱 Research Interests

  • Food Quality Assessment
  • Agricultural Engineering
  • Food Processing

🏆 Awards and Honors

  • Best Oral-Presentation Award, 15th National & 1st International Congress on Mechanical Engineering of Biosystems (Sep 2023)
  • Full-Tuition Waiver for M.Sc., University of Tehran (Sep 2020)
  • Full-Tuition Waiver for B.Sc., Shahid Chamran University of Ahvaz (Sep 2016)

🔍 Conclusion

Hamed Sardari is an accomplished researcher and engineer with a passion for advancing food processing and agricultural engineering. His expertise in machine learning, deep learning, and mechatronics enhances his ability to develop innovative solutions for food quality assessment and safety. His academic achievements, research contributions, and professional experience reflect his commitment to excellence in biosystems engineering.

Publications 📚

📄 Ultrasound Assisted Processing of Milk: Advances and Challenges
📚 Journal of Food Process Engineering (June 2023)
🔗 DOI: 10.1111/jfpe.14173
👥 Authors: Mahmoud Soltani, Hamed Sardari, Mahsa Soofiabadi, Soleiman Hosseinpour
🆔 ISSN: 0145-8876, 1745-4530

📄 Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing
📚 Food Engineering Reviews (September 2022)
🔗 DOI: 10.1007/s12393-022-09307-1
👥 Authors: Mahmoud Soltani, Hamed Sardari
🆔 ISSN: 1866-7910, 1866-7929

📄 Power Ultrasound in the Meat Industry (Freezing, Cooking, and Fermentation): Mechanisms, Advances, and Challenges
📚 Ultrasonics Sonochemistry (May 2022)
🔗 DOI: 10.1016/j.ultsonch.2022.106027
👥 Authors: Mahmoud Soltani Firouz, Hamed Sardari, Peyman Alikhani Chamgordani, Maryam Behjati
🆔 ISSN: 1350-4177

 

 

 

Qing Liang | Food Safety and Quality Control | Best Researcher Award

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Ms. Qing Liang | Food Safety and Quality Control | Best Researcher Award

College of Mechanical and Electrical Engineering at College of Mechanical and Electrical Engineering, China

Qing Liang is a graduate student in Mechanical Engineering with a strong focus on food safety and quality control. His research combines non-destructive testing methods, such as dielectric spectroscopy, with machine learning to address challenges in food quality, particularly within the dairy industry. Liang has contributed to several publications in well-regarded journals, demonstrating his expertise and commitment to advancing food science. With a passion for innovation, he is working towards bridging the gap between mechanical engineering and food safety. Liang’s ongoing research positions him as a promising young researcher in the fields of engineering and food technology.

Professional Profile

Education

Qing Liang is currently pursuing a postgraduate degree in Mechanical Engineering, graduating in the class of 2023. His academic journey has been marked by a strong foundation in engineering principles, alongside specialized focus areas such as non-destructive testing and machine learning applications in food safety and quality control. Liang’s academic training has equipped him with advanced problem-solving skills, research methodologies, and a deep understanding of both mechanical engineering and food science. His rigorous coursework and research have allowed him to contribute to innovative solutions in food safety, further solidifying his expertise in the intersection of engineering and food technology.

Professional Experience

Qing Liang currently works as a graduate student researcher at the Xinjiang Production and Construction Corps Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, China. In this role, he focuses on developing and testing innovative solutions for food safety and quality control, particularly within the dairy industry. His professional experience spans non-destructive testing, machine learning, and food safety methodologies. Qing has contributed to various projects that explore advanced techniques like dielectric spectroscopy to improve the quality and safety of agricultural products. Through his research, he actively bridges mechanical engineering and food science to address industry challenges.

Research Interests

Qing Liang’s research interests lie at the intersection of mechanical engineering, food safety, and machine learning. He focuses on applying non-destructive testing methods, such as dielectric spectroscopy, to enhance food quality control and safety, particularly in dairy products. His work explores innovative approaches to detecting adulteration, protein content, and other key factors influencing food safety. Liang is also passionate about integrating machine learning techniques with traditional testing methods to improve accuracy and efficiency in food quality assessment. By combining engineering principles with food science, his research aims to develop sustainable solutions that address current challenges in food safety and quality control.

Awards and Honors

Although Qing Liang is still early in his career, his research has already earned recognition in academic circles. He has authored multiple research papers in high-impact journals, such as the Journal of Food Science and Foods, focusing on innovative methods for food safety and quality control. Liang’s work has garnered citations, reflecting its growing influence in the field. His contributions to advancing non-destructive testing and machine learning applications in food science showcase his potential for future awards and honors. As a promising researcher, Liang’s continuous commitment to innovation positions him for further accolades in his field.

Conclusion

While LiangQing has demonstrated promise as an emerging researcher with a focus on innovative topics, they may currently lack the breadth and depth of achievements typically expected for the Best Researcher Award. Strengthening their profile with completed high-impact research, industry collaborations, and leadership roles in the scientific community would make them a stronger contender for future awards.

Publications Top Noted

  • Non-destructive detection of water adulteration level in fresh milk based on combination of dielectric spectrum technology and machine learning method
    • Authors: Liang, Q., Liu, Y., Zhang, H., Xia, Y., Li, S.
    • Journal: Journal of Food Composition and Analysis
    • Year: 2024
    • Volume: 136
    • Article Number: 106807
    • Citations: 0
  • The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning
    • Authors: Che, J., Liang, Q., Xia, Y., Zhang, H., Lan, H.
    • Journal: Foods
    • Year: 2024
    • Volume: 13(21)
    • Article Number: 3522
    • Citations: 3
  • Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk
    • Authors: Liang, Q., Liu, Y., Zhang, H., Che, J., Guo, J.
    • Journal: Journal of Food Science
    • Year: 2024
    • Volume: 89(11)
    • Pages: 7791–7802
    • Citations: 0
  • Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties
    • Authors: Tang, Y., Zhang, H., Liang, Q., Che, J., Liu, Y.
    • Journal: Horticulturae
    • Year: 2024
    • Volume: 10(6)
    • Article Number: 572
    • Citations: 1