AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2037

Special Issue Editor


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Guest Editor
Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
Interests: spectroscopy; foods; statistics; chemometrics; material sciences; climate change; catalytic reactions; mixture analysis
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Special Issue Information

Dear Colleagues,

Data handling tools, encompassing chemometric methods, machine learning algorithms, and artificial intelligence (AI), play a crucial role in the field of food analysis. These powerful tools enable efficient data processing, feature extraction, pattern recognition, and predictive modelling, revolutionizing food quality assessment, safety evaluation, and authenticity verification.

In this Special Issue on "AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning" we delve into the latest developments and applications of these techniques, with a focus on the integration of AI, in the realm of food analysis. By harnessing the potential of chemometrics, machine learning, and AI approaches, researchers can extract valuable insights from complex food datasets and enhance decision-making processes.

Chemometric methods, such as principal component analysis (PCA), partial least squares regression (PLSR), and discriminant analysis (DA), have long been pivotal in extracting pertinent information, building prediction models, and conducting multivariate data analysis in food analysis. By integrating AI techniques, these methods provide deeper insights into the relationships between variables, uncover relevant features, and enable accurate classification and quantification of food components.

In recent years, machine learning algorithms and AI approaches have gained remarkable popularity in food analysis due to their ability to handle large and high-dimensional datasets, as well as their potential to unearth intricate patterns and relationships. Powerful algorithms such as support vector machines (SVM), random forests (RF), deep learning, and artificial neural networks (ANN), among others, have showcased remarkable potential in various food analysis applications, including classification, regression, clustering, and anomaly detection.

This Special Issue welcomes original research contributions, reviews, and perspectives on the cutting-edge advances in data handling tools, with a special emphasis on AI, for food analysis. Topics of interest include, but are not limited to:

  • Novel developments and comparisons of chemometric, machine learning, and AI methods for food analysis.
  • Integration of diverse data handling tools, leveraging AI, to improve the accuracy and robustness of food analysis models.
  • Application of advanced data handling techniques, empowered by AI, in food safety assessment, quality control, and authenticity verification.
  • Innovative approaches for feature selection, dimensionality reduction, and data visualization in food analysis, harnessing the power of AI.
  • Showcasing case studies highlighting the practical implementation and benefits of AI-powered data handling tools in real-world food analysis scenarios.

Through the presentation of the latest advancements in AI-powered data handling, from chemometrics to machine learning, within the realm of food analysis, this Special Issue seeks to foster collaboration, idea exchange, and knowledge dissemination among researchers and practitioners. Together, we can unlock the full potential of AI to advance the field of food analysis, ensuring the safety, quality, and authenticity of food products.

Dr. Mourad Kharbach
Guest Editor

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. Foods 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 2900 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

  • data handling tools
  • chemometrics
  • machine learning
  • artificial intelligence (AI)
  • food analysis
  • food quality assessment
  • safety evaluation
  • authenticity verification
  • feature extraction
  • multivariate data analysis

Published Papers (1 paper)

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16 pages, 2426 KiB  
Article
A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction
by Yu Song, Sihao Chang, Jing Tian, Weihua Pan, Lu Feng and Hongchao Ji
Foods 2023, 12(18), 3386; https://doi.org/10.3390/foods12183386 - 9 Sep 2023
Viewed by 1754
Abstract
Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the [...] Read more.
Taste determination in small molecules is critical in food chemistry but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as valuable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2601 molecules. The results reveal that GNN-based models outperform other approaches in taste prediction. Moreover, consensus models that combine diverse molecular representations demonstrate improved performance. Among these, the molecular fingerprints + GNN consensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds. Full article
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