The landscape of food analysis is being reshaped by the transformative power of data handling tools, including chemometrics, machine learning, and artificial intelligence (AI) [...]
Amir Pourmoradian, Mohsen Barzegar, Ángel A. Carbonell-Barrachina, Luis Noguera-Artiaga
This study develops a comprehensive workflow integrating Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS) with advanced supervised machine learning to authenticate the botanical origin of honeys from five distinct floral sources—coriander…
Ozlem Emir Coban, Ilhan Firat Kilincer, Aniseh Jamshidi, Mehmet Zulfu Coban
This study aimed to develop shrimp gravlax (Penaeus japonicus) as a ready-to-eat seafood product and to determine its shelf life. The product was prepared using a curing method and stored at 4 °C for 30 days. Quality changes were monitored at five-day intervals through analyses o…
Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao
Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. A new dataset of high moisture c…
Bo Han, Jingjing Zhang, Rolla Almodfer, Yingchao Wang, Wei Sun, Tao Bai, et al.
In the domain of food science, apple grading holds significant research value and application potential. Currently, apple grading predominantly relies on manual methods, which present challenges such as low production efficiency and high subjectivity. This study marks the first i…
Valentyn A. Maidannyk, Yuriy Simonov, Noel A. McCarthy, Quang Tri Ho
Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders’ rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be…
Ibrar Ahmad, Aftab Khaliq, Bushra Siddique, Mostafa Gouda, Ting Huang, Jinxian Tao, et al.
Manual mango variety classification is time-consuming, error-prone, and contributes significantly to post-harvest losses in developing economies. This study aims to develop a computationally efficient and highly accurate artificial intelligence framework for automated mango varie…