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Analytical Technologies and Intelligent Applications in Future Food
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Analytical Technologies and Intelligent Applications in Future Food

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Food Chemistry".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 632

Special Issue Editors

Dr. Chang-Wei Hsieh

E-Mail Website
Guest Editor
Department of Food Science and Biotechnology, National Chung Hsing University, Taichung, Taiwan
Interests: innovative food processing technologies; extraction of natural products; isolation and identification of bioactive compounds; bioavailability
Special Issues, Collections and Topics in MDPI journals
Dr. Chunhuan Liu

E-Mail Website
Guest Editor
School of Food Science and Engineering, Ocean University of China, Qingdao 266404, China
Interests: nanolipid science and technology; droplet microfluidics; encapsulation and delivery of food active ingredients
Special Issues, Collections and Topics in MDPI journals
Dr. Shipeng Yin

E-Mail Website
Guest Editor Assistant
School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
Interests: seafood fermentation; foodomics; flavor formation mechanism; protein–flavor interactions; functional substances
Dr. Xiuhang Chai

E-Mail Website
Guest Editor Assistant
School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
Interests: fat crystallization; food colloids; structured oils; oleogel; oil–protein–starch interactions

Special Issue Information

Dear Colleagues,

The intersection of advanced analytical technologies and artificial intelligence (AI) is revolutionizing modern food chemistry. As food matrices become increasingly complex and consumer demands for nutrition, safety, traceability, and sensory quality soar, the need for analytical solutions that are precise, sensitive, rapid, intelligent, and adaptable has never been greater.

This Special Issue delves into cutting-edge analytical methodologies—including chromatography, spectroscopy, mass spectrometry, and sensor technologies—augmented by AI, machine learning, and chemometric modeling. These intelligent approaches are fundamentally transforming food analysis and data interpretation, enabling unprecedented accuracy in detecting nutrients, bioactive compounds, contaminants, flavor volatiles, and processing indicators across diverse food systems. We seek original research and review articles that showcase the development of innovative applications of intelligent analytical strategies within various food categories, such as meat, dairy, grains, plant-based alternatives, fermented products, and functional beverages.

Key topics of interest include, but are not limited to, the following: AI-driven flavoromics and sensory profiling, predictive modeling for shelf-life estimation, sophisticated algorithms for food fraud detection and authenticity verification, and the development of real-time monitoring platforms for ensuring food quality and safety. By forging stronger links between food chemistry and intelligent technologies, this Special Issue aims to catalyze data-driven innovations, empower smart food processing, and provide novel insights into the future of precision food science.

Dr. Changwei Hsieh
Dr. Chunhuan Liu
Guest Editors

Dr. Shipeng Yin
Dr. Xiuhang Chai
Guest Editors Assistants

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI) in food chemistry
  • machine learning for food analysis
  • intelligent sensing systems for food
  • food quality, safety, and authenticity
  • non-targeted screening in food
  • detection of food adulteration and contaminants
  • big data analytics in food science
  • smart food processing and quality control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

18 pages, 1777 KB  
Article
Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers
by Krzysztof Przybył, Daria Cicha-Wojciechowicz, Natalia Drabińska and Małgorzata Anna Majcher
Molecules 2025, 30(15), 3199; https://doi.org/10.3390/molecules30153199 - 30 Jul 2025
Viewed by 464
Abstract
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is [...] Read more.
The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. Full article
(This article belongs to the Special Issue Analytical Technologies and Intelligent Applications in Future Food)
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