Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food

Mostajabodavati, Saba; Mousavizadegan, Maryam; Hosseini, Morteza; Mohammadimasoudi, Mohammad; Mohammadi, Javad

doi:10.1016/j.foodchem.2024.139113

Food Chemistry

2024

DOI: 10.1016/j.foodchem.2024.139113

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Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food

Saba Mostajabodavati,

Maryam Mousavizadegan,

Morteza Hosseini

et al.

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Cited by 5 publications

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The use of artificial intelligence in liquid crystal applications: A review

Chattha,

Chan,

Upreti

2024

Can J Chem Eng

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Recent advancements in artificial intelligence (AI) have significantly influenced scientific discovery and analysis, including liquid crystals. This paper reviews the use of AI in predicting the properties of liquid crystals and improving their sensing applications. Typically, liquid crystals are utilized as sensors in biomedical detection and diagnostics, and in the detection of heavy metal ions and gases. Traditional methods of analysis used in these applications are often subjective, expensive, and time‐consuming. To surmount these challenges, AI methods such as convolutional neural networks (CNN) and support vector machines (SVM) have been recently utilized to predict liquid crystal properties and improve the resulting performance of the sensing applications. Large amounts of data are, however, required to fully realize the potential of AI methods, which would also need adequate ethical oversight. In addition to experiments, modelling approaches utilizing first principles as well as AI may be employed to supplement and furnish the data. In summary, the review indicates that AI methods hold great promise in the further development of the liquid crystal technology.

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The use of artificial intelligence in liquid crystal applications: A review

Chattha,

Chan,

Upreti

2024

Can J Chem Eng

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Progress of machine learning-based biosensors for the monitoring of food safety: A review

Hassan,

Xu,

Sayada

et al. 2025

Biosensors and Bioelectronics

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An emerging assay for rapid diagnosis of live Salmonella Typhimurium by exploiting aqueous/liquid crystal interface

Mehrzad,

Verdian,

Sarabi-Jamab

et al. 2024

Journal of Hazardous Materials

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Machine learning-assisted liquid crystal-based aptasensor for the specific detection of whole-cell Escherichia coli in water and food

Cited by 5 publications

References 36 publications

The use of artificial intelligence in liquid crystal applications: A review

The use of artificial intelligence in liquid crystal applications: A review

Progress of machine learning-based biosensors for the monitoring of food safety: A review

An emerging assay for rapid diagnosis of live Salmonella Typhimurium by exploiting aqueous/liquid crystal interface

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