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Innovative Analytical Techniques in Food Chemistry
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Innovative Analytical Techniques in Food Chemistry

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2961

Special Issue Editors

Prof. Dr. Yukun Yang

E-Mail Website
Guest Editor
School of Life Science, Xinghuacun College (Shanxi Institute of Brewing Technology and Industry), Shanxi University, Taiyuan 030006, China
Interests: intelligent visualization photoelectric chemical sensing; rapid detection; target recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Huilin Liu

E-Mail Website
Guest Editor
Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing, China
Interests: food analysis; food biosensing; food nanotechnology; whole-cell biosensing; visualization technology; quality control; synthetic biology
Special Issues, Collections and Topics in MDPI journals
Dr. Ying Gu

E-Mail Website
Guest Editor
Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming, China
Interests: food safety; sensing analysis; signal amplification; advanced functional materials; AI in food safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

While food safety has always been a focus, rapid social and economic development has gradually improved people's living standards, generating higher food safety requirements. To ensure food quality and protect people's health, there is a need to actively develop more efficient and faster analytic and testing techniques. Traditional instrumental analysis methods have shortcomings such as expensive instrumentation and time-consuming, labor-intensive sample pre-treatment, requiring specialized personnel; as such, they are not conducive to rapid on-site food safety testing. In recent years, the intersection of different disciplines and various new technologies, including sensing analysis, has introduced more reliable and accurate detection methods. Due to its fast detection speed, high sensitivity, low cost, and easy equipment miniaturization, sensing analysis provides new ideas and methods for food safety testing, adapting to new food safety problems. We are pleased to invite you to contribute an article to this Special Issue on the application of sensing analysis innovations in food safety testing, including topics such as novel sample pre-treatment techniques, recognition and signal amplification strategies, advanced functional materials, dual-mode sensors, microfluidics, microarray technology, and visualization, portable and multi-residue analyses.

Dr. Yukun Yang
Prof. Dr. Huilin Liu
Dr. Ying Gu
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. 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

  • sensor
  • biosensor
  • food safety
  • advanced functional material
  • dual-mode
  • visualization
  • array sensor
  • paper-based sensor

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Published Papers (5 papers)

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Research

12 pages, 2547 KiB  
Article
Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables
by Feng Gao, Yage Xing, Jialong Li, Lin Guo, Yiye Sun, Wen Shi and Leiming Yuan
Molecules 2025, 30(7), 1543; https://doi.org/10.3390/molecules30071543 - 30 Mar 2025
Viewed by 264
Abstract
Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination [...] Read more.
Total soluble solids (TSSs) serve as a crucial maturity indicator and quality determinant in apricots, influencing harvest timing and postharvest management decisions. This study develops an advanced framework integrating adaptive boosting (Adaboost) ensemble learning with high-frequency spectral variables selected by uninformative variable elimination (UVE) for the rapid non-destructive detection of fruit quality. Near-infrared (NIR) spectra (1000~2500 nm) were acquired and then preprocessed through robust principal component analysis (ROBPCA) for outlier detection combined with z-score normalization for spectral pretreatment. Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. The experimental findings revealed the following: (1) The model established based on high-frequency wavelengths outperformed both full-spectrum model and full-characteristic wavelength model. (2) The optimized UVE-PLS-Adaboost model achieved the peak performance (R = 0.889, RMSEP = 1.267, MAE = 0.994). This research shows that the UVE-Adaboost fusion method enhances model prediction accuracy and generalization ability through multi-dimensional feature optimization and model weight allocation. The proposed framework enables the rapid, non-destructive detection of apricot TSSs and provides a reference for the quality evaluation of other fruits in agricultural applications. Full article
(This article belongs to the Special Issue Innovative Analytical Techniques in Food Chemistry)
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15 pages, 4283 KiB  
Article
Non-Destructive Detection of Soybean Storage Quality Using Hyperspectral Imaging Technology
by Yurong Zhang, Wenliang Wu, Xianqing Zhou and Jun-Hu Cheng
Molecules 2025, 30(6), 1357; https://doi.org/10.3390/molecules30061357 - 18 Mar 2025
Viewed by 307
Abstract
(1) Background: Soybean storage quality is crucial for subsequent processing and consumption, making it essential to explore an objective, rapid, and non-destructive technology for assessing its quality. (2) Methods: crude fatty acid value is an important indicator for evaluating the storage quality of [...] Read more.
(1) Background: Soybean storage quality is crucial for subsequent processing and consumption, making it essential to explore an objective, rapid, and non-destructive technology for assessing its quality. (2) Methods: crude fatty acid value is an important indicator for evaluating the storage quality of soybeans. In this study, three types of soybeans were subjected to accelerated aging to analyze trends in crude fatty acid values. The study focused on acquiring raw spectral information using hyperspectral imaging technology, preprocessing by the derivative method (1ST, 2ND), multiplicative scatter correction (MSC), and standard normal variate (SNV). The feature variables were extracted by a variable iterative space shrinkage approach (VISSA), competitive adaptive reweighted sampling (CARS), and a successive projections algorithm (SPA). Partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models were developed to predict crude fatty acid values of soybeans. The optimal model was used to visualize the dynamic distribution of these values. (3) Results: the crude fatty acid values exhibited a positive correlation with storage time, functioning as a direct indicator of soybean quality. The 1ST-VISSA-SVM model was the optimal predictive model for crude fatty acid values, achieving a coefficient of determination (R2) of 0.9888 and a root mean square error (RMSE) of 0.1857 and enabling the visualization of related chemical information. (4) Conclusions: it has been confirmed that hyperspectral imaging technology possesses the capability for the non-destructive and rapid detection of soybean storage quality. Full article
(This article belongs to the Special Issue Innovative Analytical Techniques in Food Chemistry)
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13 pages, 1512 KiB  
Article
Simultaneous Determination of Six Acidic Herbicides and Metabolites in Plant Origin Matrices by QuEChERS-UPLC-MS/MS
by Qiqi Jin, Qianwen Xu, Zhiyong Zhao, Wenshuai Si, Bing Bai, Lei Chen and Changyan Zhou
Molecules 2025, 30(4), 852; https://doi.org/10.3390/molecules30040852 - 12 Feb 2025
Viewed by 728
Abstract
This study presents a method for the simultaneous determination of six acidic herbicides and their metabolites in various matrices, including fruits, vegetables, grains, and edible oils. The method employs acidified acetonitrile extraction combined with dispersive solid-phase extraction cleanup (dSPE) using MgSO4, [...] Read more.
This study presents a method for the simultaneous determination of six acidic herbicides and their metabolites in various matrices, including fruits, vegetables, grains, and edible oils. The method employs acidified acetonitrile extraction combined with dispersive solid-phase extraction cleanup (dSPE) using MgSO4, Florisil, and Graphitized carbon black (GCB). The analysis was performed by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) with electrospray ionization (ESI) in both positive and negative modes using multiple reaction monitoring (MRM). The mass concentrations of six herbicide pesticides and their metabolites were predominantly within the range of 0.0005~0.050 mg/L and exhibited strong linear relationships with the corresponding peak area, with the coefficient of determination (R2) exceeding 0.993. The limits of detection (LOD) for the method ranged from 0.0001 to 0.008 mg/kg. The recovery rates of adding recovery experiments to cabbage, chives, pear, wheat flour, and soybean oil were 69.8~120%, and the relative standard deviation (RSD) was 0.6~19.5%. The results indicate that this method is efficient and fast, and can be used for the detection of compounds in various actual matrices. Full article
(This article belongs to the Special Issue Innovative Analytical Techniques in Food Chemistry)
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19 pages, 927 KiB  
Article
Rapid Profiling of Volatile Organic Compounds Associated with Plant-Based Milks Versus Bovine Milk Using an Integrated PTR-ToF-MS and GC-MS Approach
by Antonia Corvino, Iuliia Khomenko, Emanuela Betta, Federico Ivan Brigante, Luana Bontempo, Franco Biasioli and Vittorio Capozzi
Molecules 2025, 30(4), 761; https://doi.org/10.3390/molecules30040761 - 7 Feb 2025
Viewed by 750
Abstract
The growing demand for plant-based beverages has underscored the importance of investigating their volatile profiles, which play a crucial role in sensory perception and consumer acceptance. This is especially true for plant-based milks (PBMs) that have a clear reference model in bovine milk. [...] Read more.
The growing demand for plant-based beverages has underscored the importance of investigating their volatile profiles, which play a crucial role in sensory perception and consumer acceptance. This is especially true for plant-based milks (PBMs) that have a clear reference model in bovine milk. This study characterises the volatile organic compounds (VOCs) in soy, almond and oat beverages compared to bovine milk using proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) as a rapid and noninvasive screening tool, complemented by gas chromatography-mass spectrometry (GC-MS) for compound identification. A total of 188 mass peaks were detected by PTR-ToF-MS, all showing significant differences from the blank, while GC-MS allowed the identification of 50 compounds, supporting the tentative identifications performed with PTR-MS analysis. In order to facilitate a comparison of different milks, after statistical analysis, these 188 mass peaks were further categorised into two groups: one consisting of VOCs with minimal variability across all samples and another comprising VOCs with significantly different abundances, distinctly characterising each beverage. Principal component analysis revealed a clear separation between bovine milk and PBMs, with almond beverages exhibiting the richest volatilome, while oat beverages displayed a more homogeneous volatile profile. PTR-ToF-MS demonstrated its ability to analyse volatile profiles rapidly, with excellent complementarity to GC-MS in terms of analytical versatility. The results provided a valuable basis for testing new experimental designs aimed to characterise and enhance flavour profiles in plant-based beverages, also after processing, in case of new product development that considers using these milks as raw materials. Full article
(This article belongs to the Special Issue Innovative Analytical Techniques in Food Chemistry)
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12 pages, 2298 KiB  
Article
PTR-ToF-MS VOC Profiling of Raw and Cooked Gilthead Sea Bream Fillet (Sparus aurata): Effect of Rearing System, Season, and Geographical Origin
by Iuliia Khomenko, Valentina Ting, Fabio Brambilla, Mirco Perbellini, Luca Cappellin and Franco Biasioli
Molecules 2025, 30(2), 402; https://doi.org/10.3390/molecules30020402 - 18 Jan 2025
Viewed by 634
Abstract
This study explores the impact of geographical origin, harvest time, and cooking on the volatile organic compound (VOC) profiles of wild and reared seabream from the Adriatic and Tyrrhenian Seas. A Proton Transfer Reaction–Time of Flight–Mass Spectrometry (PTR-ToF-MS) allowed for VOC profiling with [...] Read more.
This study explores the impact of geographical origin, harvest time, and cooking on the volatile organic compound (VOC) profiles of wild and reared seabream from the Adriatic and Tyrrhenian Seas. A Proton Transfer Reaction–Time of Flight–Mass Spectrometry (PTR-ToF-MS) allowed for VOC profiling with high sensitivity and high throughput. A total of 227 mass peaks were identified. Principal component analysis (PCA) showed a clear separation between cooked and raw samples, with cooking causing a significant increase in 64% of VOCs, especially hydrogen sulphide, methanethiol, and butanal. A two-way ANOVA revealed significant effects of origin, time, and their interaction on VOC concentration, with 102 mass peaks varying significantly based on all three factors. Seasonal effects were also notable, particularly in reared fish from the Adriatic Sea, where compounds like monoterpenes and aromatics were higher during non-breeding months, likely due to environmental factors unique to that area. Differences between wild and reared fish were influenced by lipid content and seasonal changes, impacting the VOC profile of seabream. These findings provide valuable insights into how cooking, geographical origin, and seasonality interact to define the flavour profile of seabream, with potential applications in improving quality control and product differentiation in seafood production. Full article
(This article belongs to the Special Issue Innovative Analytical Techniques in Food Chemistry)
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