Functional Nanomaterial-Based Sensors for Food Analysis

A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Nanostructures for Chemical Sensing".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1187

Special Issue Editor


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Guest Editor
Department of School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: functional nanomaterials; biosensors and chemical sensors; biomolecules; food safety; material characterization
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Special Issue Information

Dear Colleagues,

Functional nanomaterial-based sensors are increasingly vital in food analysis due to their high sensitivity, selectivity, and real-time detection of contaminants and quality changes. These sensors leverage the unique properties of nanomaterials, which arise from their nanoscale dimensions. As nanotechnology proliferates across fields such as bioanalytics, medical diagnostics, and environmental applications, there is a growing need for novel nanoscale materials to enhance physicochemical, catalytic, and electronic properties in order to improve sensor performance. The integration of nanomaterials, nanocomposites, and hybrid materials—including metal and metal oxide nanoparticles, quantum dots, graphene, and metal–organic frameworks (MOFs)—has spurred significant advancements in biosensors/chemical sensors. Current research focuses on tuning the properties of these materials to boost sensitivity, stability, and selectivity.

This Special Issue aims to showcase cutting-edge research on nanoscale materials and their application in biosensors/chemical sensors. We invite review articles and original research papers on topics such as the following:

  • Nanomaterials, nanocomposites, and hybrid materials for biosensors/chemical sensors;
  • Engineering, functionalization, and characterization of novel nanomaterials;
  • Nanomaterial-based environmental sensors, food packaging sensors, and bioanalytical sensors;
  • Smart nanomaterials for wearable devices;
  • Optical, electrochemical, colorimetric, and magnetic sensors;
  • Emerging applications of nanoscale-based materials in biosensors/chemical sensors. 

We look forward to your contributions!

Best regards,

Dr. Huanhuan Li
Guest Editor

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Keywords

  • nanomaterials
  • nanoparticles, nanocomposites, and hybrid materials
  • biosensors/chemical sensors
  • material preparation and characterization
  • metal nanoparticles (NPs)
  • carbon-based nanomaterials
  • quantum dots (QDs)
  • metal–organic frameworks (MOFs)
  • polymeric nanoparticles
  • food safety
  • food freshness monitoring
  • pathogen detection
  • detection of chemical contaminants
  • allergen detection

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Published Papers (1 paper)

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Research

16 pages, 3550 KiB  
Article
Investigation of Microbial Fermentation Degree of Pu-Erh Tea Using Deep Learning Coupled Colorimetric Sensor Array via Prediction of Total Polyphenols
by Min Liu, Cui Jiang, Md Mehedi Hassan, Xinru Zhang, Runxian Wang, Renyong Cao, Wei Sheng and Huanhuan Li
Chemosensors 2024, 12(12), 265; https://doi.org/10.3390/chemosensors12120265 - 16 Dec 2024
Viewed by 903
Abstract
The degree of tea fermentation is crucial as it ultimately indicates the quality of the tea. Hence, this study developed a total polyphenol prediction system for Pu-erh tea liquid using eight porphyrin dyes and one pH dye in a printed colorimetric sensor array [...] Read more.
The degree of tea fermentation is crucial as it ultimately indicates the quality of the tea. Hence, this study developed a total polyphenol prediction system for Pu-erh tea liquid using eight porphyrin dyes and one pH dye in a printed colorimetric sensor array (CSA) coupled with a convolutional neural network (CNN) during microbial fermentation. Firstly, the Box–Behnken sampling method was applied to optimize the degree of microbial fermentation of Pu-erh tea liquid using the response surface methodology. Under optimized conditions, the polyphenol degradation rate reached up to 66.146%. CSA images were then collected from the volatile compounds of Pu-erh tea-reacted CSA sensors. Subsequently, six chemometric approaches were comparatively investigated, and CNN achieved the best results for predicting total polyphenol content. Therefore, the results suggest that the proposed approach can be used to predict the degree of fermentation by measuring total polyphenols in microbial-fermented Pu-erh tea liquid. Full article
(This article belongs to the Special Issue Functional Nanomaterial-Based Sensors for Food Analysis)
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