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Foods
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Application of Rapid Detection Technology of Lipids in Food
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Application of Rapid Detection Technology of Lipids in Food

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

Deadline for manuscript submissions: 10 August 2025 | Viewed by 395

Special Issue Editors

Dr. Qi Zhao

E-Mail Website
Guest Editor
National Engineering Research Center of Seafood, School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
Interests: nanomaterials; solid phase extraction
Special Issues, Collections and Topics in MDPI journals
Dr. Fang Wei

E-Mail Website
Guest Editor
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China
Interests: lipidomic profiling methods and applications; lipid quality evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lipids are essential nutrients in food, performing critical functions in the human body. Research on their type, content, and degree of peroxidation has gained increasing attention. While food testing technologies are crucial for ensuring lipid biosafety, traditional methods struggle with rapid and non-destructive detection. Thus, there is a pressing need to develop novel, efficient, and sensitive lipid detection techniques to address these challenges.

This Special Issue collectively explores the latest advancements in rapid lipid detection technologies for food, focusing on key areas such as lipid rapid detection pre-treatment methods and equipment, spectral data pre-processing, and comprehensive monitoring techniques (including fluorescence, infrared, and visible spectroscopy), machine learning-based chemometric modeling, as well as nano-sensors and composite materials. These studies aim to enhance the dynamic monitoring of lipid changes in food, improve food safety, strengthen quality control, promote healthy food production and consumption, and facilitate the rapid and sustainable development of the food industry.

Dr. Qi Zhao
Dr. Fang Wei
Guest Editors

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

  • lipid detection
  • fluorescence spectroscopy
  • infrared spectrum
  • raman spectroscopy
  • chemometrics
  • machine learning
  • nano-sensors

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Published Papers (1 paper)

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Research

17 pages, 4366 KiB  
Article
Quantitative Analysis of 3-Monochloropropane-1,2-diol in Fried Oil Using Convolutional Neural Networks Optimizing with a Stepwise Hybrid Preprocessing Strategy Based on Fourier Transform Infrared Spectroscopy
by Xi Wang, Siyi Wang, Shibing Zhang, Jiping Yin and Qi Zhao
Foods 2025, 14(10), 1670; https://doi.org/10.3390/foods14101670 - 9 May 2025
Viewed by 252
Abstract
As one kind of ‘probable human carcinogen’ (Group 2B) compound classified by the International Agency for Research on Cancer, 3-MCPD is mainly formed during the thermal processing of food. Tedious pretreatment techniques are needed for the existing analytical methods to quantify 3-MCPD. Hence, [...] Read more.
As one kind of ‘probable human carcinogen’ (Group 2B) compound classified by the International Agency for Research on Cancer, 3-MCPD is mainly formed during the thermal processing of food. Tedious pretreatment techniques are needed for the existing analytical methods to quantify 3-MCPD. Hence, a nondestructive sensing technique that offers low noise interference and high quantitative precision must be developed to address this problem. Following this, Fourier transform infrared spectroscopy association with an convolutional neural network (CNN) model was employed in this investigation for the nondestructive quantitative measurement of 3-MCPD in oil samples. Before building the CNN model, NL-SGS-D2 was utilized to enhance the feature extraction capability of model by eliminating the background noise. Under the optimal hyperparameter settings, calibration model achieved a determination coefficient (R2C) of 0.9982 and root mean square error (RMSEC) of 0.0181 during validation, along with a 16% performance enhancement enabled by the stepwise hybrid preprocessing strategy. The LODs (0.36 μg/g) and LOQs (1.10 μg/g) of the proposed method met the requirements for 3-MCPD detection in oil samples by the Commission Regulation issued of EU. The method proposed by CNN model with hybrid preprocessing was superior to the traditional model, and contributed to the quality monitoring of edible oil processing industry. Full article
(This article belongs to the Special Issue Application of Rapid Detection Technology of Lipids in Food)
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