Machine Learning Approaches for GC–MS Data Interpretation in Flavour and Fragrance Analysis
The review explores machine learning integration in GC‐MS data analysis for the fragrance and flavour industry, highlighting recent advances and techniques in a context constrained by data scarcity and intellectual property concerns. ABSTRACT This review explores the integration of machine learning (ML) in the analysis of mass spectrometry data ...
Jean‐Baptiste Coffin +3 more
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ENet-CAEM: a field strawberry disease identification model based on improved EfficientNetB0 and multiscale attention mechanism. [PDF]
Chang J, Li H, Fu X, Jiao Y.
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Implementing nature‐based climate solutions is important for mitigating climate change, which is a global issue, but requires local adjustments in management practices. Using the association between soil carbon and minerals as a proxy for carbon persistence, we evaluated the effect of different management regimes on soil carbon sequestration and loss ...
Adam Pellegrini +3 more
wiley +1 more source
Abstract Earth System Models provide spatiotemporally continuous environmental exposure data but remain underused in environmental epidemiology because of uncertainty from measurement errors. We developed a novel latent‐variable approach to correct for measurement error characterized by spatiotemporal error covariance, which was derived from ...
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wiley +1 more source
Robust zero-watermarking for color images using hybrid deep learning models and encryption. [PDF]
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Compressed sensing acceleration of radial 3-D alternating Look-Locker T 1 mapping. [PDF]
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Optimized Reinforcement Learning-Driven Model for Remote Sensing Change Detection. [PDF]
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Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI. [PDF]
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From Upconversion Nanoparticles to Proteins: Probing Hydration-Water Density Fluctuations by Luminescence Thermometry. [PDF]
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Efficient and Accurate Epilepsy Seizure Prediction and Detection Based on Multi-Teacher Knowledge Distillation RGF-Model. [PDF]
Cao W, Li Q, Zhang A, Wang T.
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