ArticleFoods (Basel, Switzerland)2022
Preparation of Mesoporous Silica Nanosphere-Doped Color-Sensitive Materials and Application in Monitoring the TVB-N of Oysters.
Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 13 citations in OpenAlex.
- Intelligent Responsive Nanostructures Toward Food Preservation: Synergizing Antibacterial Activity and Environmental Regulation.Foods (Basel, Switzerland) · 2026Review
- Integration of Colorimetric Sensing Dyes and Multi-Substrate Arrays for Food Quality and Safety Monitoring: Design, Optimization, and Analysis.Comprehensive reviews in food science and food safety · 2026Review
- Advances in pH-Responsive Release Technologies in Food System: Mechanisms, Strategies, Application Forms and Future Directions.Foods (Basel, Switzerland) · 2025Review
- Innovative Technologies Reshaping Meat Industrialization: Challenges and Opportunities in the Intelligent Era.Foods (Basel, Switzerland) · 2025Review
- Effect of Neutral Protease on Freshness Quality of Shucked Pacific Oysters at Different Storage Conditions.Foods (Basel, Switzerland) · 2024Article
- Marine Bioactive Peptides: Anti-Photoaging Mechanisms and Potential Skin Protective Effects.Current issues in molecular biology · 2024Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
In this work, a new colorimetric sensor based on mesoporous silica nanosphere-modified color-sensitive materials was established for application in monitoring the total volatile basic nitrogen (TVB-N) of oysters. Firstly, mesoporous silica nanospheres (MSNs) were synthesized based on the improved Stober method, then the color-sensitive materials were doped with MSNs. The "before image" and the "after image" of the colorimetric senor array, which was composed of nanocolorimetric-sensitive materials by a charge-coupled device (CCD) camera were then collected. The different values of the before and after image were analyzed by principal component analysis (PCA). Moreover, the error back-propagation artificial neural network (BP-ANN) was used to quantitatively predict the TVB-N values of the oysters. The correlation coefficient of the colorimetric sensor array after being doped with MSNs was greatly improved; the Rc and Rp of BP-ANN were 0.9971 and 0.9628, respectively when the principal components (PCs) were 10. Finally, a paired sample
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