AIMC Topic: Odorants

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Differential modulation of feedforward inhibition reflects topographic organization in the olfactory system.

Nature communications
The nervous system flexibly processes information under different conditions. To do this, neural networks frequently rely on uniform expression of modulatory receptors by distinct classes of neurons to fine tune the computations supported by each neu...

AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing system.

Scientific reports
Despite the high nutritional value of fish, it is often under-consumed due to its characteristic odor and laborious cleaning process. This sensory barrier significantly diminishes the appeal of fish, particularly in regions or cultures where individu...

Advancing olfactory perception research with EEG analysis: a dynamic approach of understanding brain responses to almond deterioration.

Food chemistry
Electroencephalography (EEG) enables the investigation of olfactory perception through neuronal electrical activity. Decoding dynamic oscillatory changes in sensory-cognitive processing is critical to understanding odor-induced brain responses. First...

Machine learning-assisted aroma profile prediction in tomato puree based on flavoromics.

Food chemistry
Flavor serves as a key quality indicator in tomato puree (TP) processing; however, conventional methods often fall short in providing rapid and accurate assessments. To address this limitation, this study integrated flavoromics with machine learning ...

Joule Heating-Driven Modulation of Analyte Partitioning in Chemically Diverse, Conducting Composite Sensor Arrays: A New Approach for Active Sensing in Machine Olfaction.

ACS sensors
Animals adapt to dynamic olfactory environments through active sensing behaviors like sniffing, which create a dynamic baseline to detect new odors. In this work, we explore the use of Joule heating of individual sensors as an efficient thermal modul...

Photoresponsive HOF-Based Platforms for Large Language Model-Assisted Multimodal Diagnosis of Metabolic Diseases.

Analytical chemistry
Artificial intelligence (AI), particularly large language models (LLMs) such as chat generative pretrained transformer (ChatGPT), is revolutionizing various fields. Here, we present an AI-enhanced olfactory diagnosis platform that integrates a photor...

Intelligent sensory technologies, NIR spectroscopy and chemometrics combined with machine learning based on multi-source data fusion for comprehensive evaluation of Sinapis Semen in different processing degrees.

Journal of pharmaceutical and biomedical analysis
Sinapis Semen, as a traditional Chinese medicine, has an unclear relationship between its stir-frying degrees and sensory characteristics. Therefore, it is essential to develop a multi-index evaluation method to classify the processing degree of Sina...

Understanding the relationship between rosemary odor and mental workload through deep learning.

Neuroscience
This research explores the novel application of aromatic odors, specifically rosemary, in reducing mental workload, employing deep learning methods to analyze electroencephalogram (EEG) signals without feature extraction. Thirty volunteers participat...

Recent advances in analytical approaches for aroma interaction of fermented foods: A review.

Food chemistry
The research of aroma interaction in fermented food is of great significance for in-depth analysis of aroma formation mechanism. The analytical approach for aroma interaction is the bridge between aroma perception and interaction mechanism. This revi...

Wine discrimination based on multi-sensor fusion of GASF and Mel spectrogram features using an enhanced EfficientNet-B0 model.

Food chemistry
This study presents a novel multi-sensor fusion strategy for discriminating wines made from eight different raw materials using identical brewing processes. Aroma and taste signals were collected using a broad-spectrum electronic nose and noble metal...