Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods

Xu, Min; Wang, Xingbin; Xu, Zedong; Wang, Yao; Jia, Pengfei; ding, Wenwu; Dong, Shirong; Liu, Ping

doi:10.1007/s11694-024-02410-3

Food Measure

2024

DOI: 10.1007/s11694-024-02410-3

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Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods

Min Xu,

Xingbin Wang,

Zedong Xu

et al.

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Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Liu,

Sun,

Bao

et al. 2024

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Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom‐thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom‐thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom‐thin materials and ML in fragrance sensing, providing an in‐depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

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Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Liu,

Sun,

Bao

et al. 2024

Small

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show abstract

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Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods

Cited by 1 publication

References 40 publications

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

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