Literatura académica sobre el tema "Odor quality prediction"
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Artículos de revistas sobre el tema "Odor quality prediction"
Lee, Brian K., Emily J. Mayhew, Benjamin Sanchez-Lengeling, Jennifer N. Wei, Wesley W. Qian, Kelsie A. Little, Matthew Andres et al. "A principal odor map unifies diverse tasks in olfactory perception". Science 381, n.º 6661 (septiembre de 2023): 999–1006. http://dx.doi.org/10.1126/science.ade4401.
Texto completoKang, Jeong-Hee, JiHyeon Song, Sung Soo Yoo, Bong-Jae Lee y Hyon Wook Ji. "Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)". Atmosphere 11, n.º 8 (24 de julio de 2020): 784. http://dx.doi.org/10.3390/atmos11080784.
Texto completoQiu, Shanshan, Pingzhi Hou, Jingang Huang, Wei Han y Zhiwei Kang. "The Monitoring of Black-Odor River by Electronic Nose with Chemometrics for pH, COD, TN, and TP". Chemosensors 9, n.º 7 (5 de julio de 2021): 168. http://dx.doi.org/10.3390/chemosensors9070168.
Texto completoTolba, Ahmed, Nihal N. Mostafa, Ali Wagdy Mohamed y Karam M. Sallam. "Hybrid Deep Learning Approach for Milk Quality Prediction". Precision Livestock 1 (9 de enero de 2024): 1–13. http://dx.doi.org/10.61356/j.pl.2024.1199.
Texto completoCLIFF, MARGARET, KAREEN STANICH, JUDITH MORAN TRUJILLO, PETER TOIVONEN y CHARLES F. FORNEY. "DETERMINATION AND PREDICTION OF ODOR THRESHOLDS FOR ODOR ACTIVE VOLATILES IN A NEUTRAL APPLE JUICE MATRIX". Journal of Food Quality 34, n.º 3 (junio de 2011): 177–86. http://dx.doi.org/10.1111/j.1745-4557.2011.00383.x.
Texto completoDong, Bo, Shihu Shu y Dengxin Li. "Optimization of Secondary Chlorination in Water Distribution Systems for Enhanced Disinfection and Reduced Chlorine Odor Using Deep Belief Network and NSGA-II". Water 16, n.º 18 (19 de septiembre de 2024): 2666. http://dx.doi.org/10.3390/w16182666.
Texto completoWang, Yu, Qilong Zhao, Mingyuan Ma y Jin Xu. "Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine". Applied Sciences 12, n.º 17 (31 de agosto de 2022): 8777. http://dx.doi.org/10.3390/app12178777.
Texto completoIra Mae Gallo Caray, King Paulo Ramos Ditchon y Edwin Remeroso Arboleda. "Smart coffee aromas: A literature review on electronic nose technologies for quality assessment". World Journal of Advanced Research and Reviews 21, n.º 2 (28 de febrero de 2023): 506–14. http://dx.doi.org/10.30574/wjarr.2024.21.2.0407.
Texto completoPerrot, N. Mejean, Alice Roche, Alberto Tonda, Evelyne Lutton y Thierry Thomas-Danguin. "Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise". Mathematical Biosciences and Engineering 20, n.º 12 (2023): 20528–52. http://dx.doi.org/10.3934/mbe.2023908.
Texto completoWang, Yangfeng, Xinyi Jin, Lin Yang, Xiang He y Xiang Wang. "Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments". Foods 12, n.º 18 (8 de septiembre de 2023): 3372. http://dx.doi.org/10.3390/foods12183372.
Texto completoTesis sobre el tema "Odor quality prediction"
Hladiš, Matej. "Réseaux de neurones en graphes et modèle de langage des protéines pour révéler le code combinatoire de l'olfaction". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5024.
Texto completoMammals identify and interpret a myriad of olfactory stimuli using a complex coding mechanism involving interactions between odorant molecules and hundreds of olfactory receptors (ORs). These interactions generate unique combinations of activated receptors, called the combinatorial code, which the human brain interprets as the sensation we call smell. Until now, the vast number of possible receptor-molecule combinations have prevented a large-scale experimental study of this code and its link to odor perception. Therefore, revealing this code is crucial to answering the long-term question of how we perceive our intricate chemical environment. ORs belong to the class A of G protein-coupled receptors (GPCRs) and constitute the largest known multigene family. To systematically study olfactory coding, we develop M2OR, a comprehensive database compiling the last 25 years of OR bioassays. Using this dataset, a tailored deep learning model is designed and trained. It combines the [CLS] token embedding from a protein language model with graph neural networks and multi-head attention. This model predicts the activation of ORs by odorants and reveals the resulting combinatorial code for any odorous molecule. This approach is refined by developing a novel model capable of predicting the activity of an odorant at a specific concentration, subsequently allowing the estimation of the EC50 value for any OR-odorant pair. Finally, the combinatorial codes derived from both models are used to predict the odor perception of molecules. By incorporating inductive biases inspired by olfactory coding theory, a machine learning model based on these codes outperforms the current state-of-the-art in smell prediction. To the best of our knowledge, this is the most comprehensive and successful application of combinatorial coding to odor quality prediction. Overall, this work provides a link between the complex molecule-receptor interactions and human perception
Capítulos de libros sobre el tema "Odor quality prediction"
Rani K. P., Asha y Gowrishankar S. "Integration of Advanced Design Patterns in Deep Learning for Agriculture Along With Waste Processing". En Revolutionizing Automated Waste Treatment Systems, 320–54. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6016-3.ch021.
Texto completoActas de conferencias sobre el tema "Odor quality prediction"
Hyung, Jinseok, Jaeyoung Kwon, Taehyeon Kim, Haekuem Park y Jayong Koo. "Development of prediction model of ozone dosage and residual ozone concentration using machine learning methods in ozone process of drinking water treatment process". En 2nd WDSA/CCWI Joint Conference. València: Editorial Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/wdsa-ccwi2022.2022.14777.
Texto completoChris G Henry, Steve J Hoff, Larry D Jacobsen, Dennis D Schulte, Peter C D'Abreton, Robin J Ormerod, Geordie G Galvin y David P Billesbach. "Downwind Odor Predictions from Four Swine Finishing Barns Using CALPUFF". En International Symposium on Air Quality and Waste Management for Agriculture, 16-19 September 2007, Broomfield, Colorado. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2007. http://dx.doi.org/10.13031/2013.23857.
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