摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据《中国人民代表大会长沙消息》报道，NewsRx记者称，“在解决水臭问题时，了解气味的感官属性是气味跟踪的核心。然而，由于气味识别和气味评价的复杂性，对真实水中数万种气味进行实验测定并不实用。”我们的新闻记者引用湖南大学的一篇研究，“本研究提出了第一个针对水中气味物质的机器学习(ML)模型，该模型可以使用分子结构或质谱作为输入特征，并证明了使用质谱的模型性能与使用明确结构的模型几乎相同。”通过对真实水样的非目标分析获得的MS光谱，证明了该模型在预测未知化学品气味感官属性方面的鲁棒性，在此基础上，确定了官能团之间复杂的相互作用是气味感官属性的主要影响因素，并强调了碳链长度、分子量等对气味感官属性的重要作用。内在嗅觉机制。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Changsha, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Knowing odor sensory attributes of odorants lies at the core of odor tracking when addressing waterb orne odor issues. However, experimental determination covering tens of thousands of odorants in authentic water is not pragmatic due to the complexity of odoran t identification and odor evaluation.” Our news journalists obtained a quote from the research from Hunan University, “ In this study, we propose the first machine learning (ML) model to predict odor perception/threshold aiming at odorants in water, which can use either molecular structure or MS spectra as input features. We demonstrate that model performanc e using MS spectra is nearly as good as that using unequivocal structures, both with outstanding accuracy. We particularly show the model’s robustness in predic ting odor sensory attributes of unidentified chemicals by using the experimental ly obtained MS spectra from nontarget analysis on authentic water samples. Inter preting the developed models, we identify the intricate interaction of functiona l groups as the predominant influence factor on odor sensory attributes. We also highlight the important roles of carbon chain length, molecular weight, etc., i n the inherent olfactory mechanisms.”
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