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AI-Powered Advances in Data Handling for Enhanced Food Analysis: From...
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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: 15 July 2025 | Viewed by 6235

Special Issue Editor

Dr. Mourad Kharbach

E-Mail Website
Guest Editor
Department of Food and Nutrition, University of Helsinki, 00014 Helsinki, Finland
Interests: spectroscopy; foods; statistics; chemometrics; material sciences; climate change; catalytic reactions; mixture analysis
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (4 papers)

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Research

22 pages, 961 KiB  
Article
Enhancing Food Image Recognition by Multi-Level Fusion and the Attention Mechanism
by Zengzheng Chen, Jianxin Wang and Yeru Wang
Foods 2025, 14(3), 461; https://doi.org/10.3390/foods14030461 - 31 Jan 2025
Viewed by 305
Abstract
As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food [...] Read more.
As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food images falls within the domain of Fine-Grained Visual Classification (FGVC), which presents challenges such as inter-class similarity, intra-class variability, and the complexity of capturing intricate local features. Researchers have primarily focused on deep information in deep convolutional neural networks for fine-grained visual classification, often neglecting shallow and detailed information. Taking these factors into account, we propose a Multi-level Attention Feature Fusion Network (MAF-Net). Specifically, we use feature maps generated by the Convolutional Neural Networks (CNNs) backbone network at different stages as inputs. We apply a self-attention mechanism to identify local features on these feature maps and then stack them together. The feature vectors obtained through the attention mechanism are then integrated with the original input to enhance data augmentation. Simultaneously, to capture as many local features as possible, we encourage multi-scale features to concentrate on distinct local regions at each stage by maximizing the Kullback-Leibler Divergence (KL-divergence) between the different stages. Additionally, we present a novel approach called subclass center loss (SCloss) to implement label smoothing, minimize intra-class feature distribution differences, and enhance the model’s generalization capability. Experiments conducted on three food image datasets—CETH Food-101, Vireo Food-172, and UEC Food-100—demonstrated the superiority of the proposed model. The model achieved Top-1 accuracies of 90.22%, 89.86%, and 90.61% on CETH Food-101, Vireo Food-172, and UEC Food-100, respectively. Notably, our method not only outperformed other methods in terms of the Top-5 accuracy of Vireo Food-172 but also achieved the highest performance in the Top-1 accuracies of UEC Food-100. Full article
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
16 pages, 2175 KiB  
Article
Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy
by Wenwen Zhang, Mingxuan Pan, Peng Wang, Jiao Xue, Xinghu Zhou, Wenke Sun, Yadong Hu and Zhaopeng Shen
Foods 2024, 13(19), 3023; https://doi.org/10.3390/foods13193023 - 24 Sep 2024
Viewed by 1220
Abstract
This study explored the performance and reliability of three predictive models—extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)—for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to [...] Read more.
This study explored the performance and reliability of three predictive models—extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)—for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model’s high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety. Full article
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
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13 pages, 7132 KiB  
Article
Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy
by Haijun Du, Yaru Zhang, Yanhua Ma, Wei Jiao, Ting Lei and He Su
Foods 2024, 13(14), 2187; https://doi.org/10.3390/foods13142187 - 11 Jul 2024
Cited by 1 | Viewed by 1128
Abstract
The crude protein (CP) content is an important determining factor for the quality of alfalfa, and its accurate and rapid evaluation is a challenge for the industry. A model was developed by combining Fourier transform infrared spectroscopy (FTIS) and chemometric analysis. Fourier spectra [...] Read more.
The crude protein (CP) content is an important determining factor for the quality of alfalfa, and its accurate and rapid evaluation is a challenge for the industry. A model was developed by combining Fourier transform infrared spectroscopy (FTIS) and chemometric analysis. Fourier spectra were collected in the range of 4000~400 cm−1. Adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky–Golay (SG) were used for preprocessing the spectral data; competitive adaptive reweighted sampling (CARS) and the characteristic peaks of CP functional groups and moieties were used for feature selection; partial least squares regression (PLSR) and random forest regression (RFR) were used for quantitative prediction modelling. By comparing the combined prediction results of CP content, the predictive performance of airPLST-cars-PLSR-CV was the best, with an RP2 of 0.99 and an RMSEP of 0.053, which is suitable for establishing a small-sample prediction model. The research results show that the combination of the PLSR model can achieve an accurate prediction of the crude protein content of alfalfa forage, which can provide a reliable and effective new detection method for the crude protein content of alfalfa forage. Full article
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
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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
Cited by 1 | Viewed by 2844
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
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
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