Coffee known for its diverse aromas shaped by postharvest treatments, particularly the roasting process, plays a pivotal role in determining the quality of the brewed beverage. This study focuses on classifying the aroma of Arabica coffee beans based on roasting temperature, employing an electronic nose equipped with a TGS gas array sensor. The classification methodology integrates deep learning through an artificial neural network (ANN), along with a calculation analysis utilizing the Pearson correlation coefficient. Raw Robusta coffee beans were subjected to five distinct roasting treatments (185 °C, 195 °C, 205 °C, 215 °C, and 225 °C), resulting in light roasts, light to medium roasts, medium to dark roasts, medium to dark roasts, and dark roasts. The repeatability test affirms the TGS sensor's reliability, exhibiting a standard deviation (STD) below 20%. Notably, the TGS 2612 and TGS 2611 sensors, dedicated to odor detection, demonstrated excellent validity with an STD below 10% across various roasting temperatures. Classification results from deep learning cross-validation showcase impressive accuracy: 98.2% for Light Roasts, 98.4% for Light to Medium Roasts, 98.8% for Medium Roasts, 97.8% for Medium Roasts, and 95.9% for Dark Roasts. In conclusion, this study reveals that the -nose, utilizing the TGS gas sensor array with deep learning analysis, effectively detects and classifies coffee types based on roasting time with high accuracy.

中文翻译：

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电子鼻结合人工神经网络对咖啡烘焙曲线进行分类

咖啡以其采后处理（尤其是烘焙过程）形成的多样化香气而闻名，在决定冲泡饮料的质量方面发挥着关键作用。本研究的重点是使用配备 TGS 气体阵列传感器的电子鼻，根据烘焙温度对阿拉比卡咖啡豆的香气进行分类。该分类方法集成了通过人工神经网络 (ANN) 进行的深度学习以及利用皮尔逊相关系数的计算分析。罗布斯塔咖啡豆经过五种不同的烘焙处理（185 °C、195 °C、205 °C、215 °C 和 225 °C），产生浅度烘焙、浅度至中度烘焙、中度至深度烘焙、中度烘焙。到深度烘焙和深度烘焙。重复性测试证实了 TGS 传感器的可靠性，标准偏差 (STD) 低于 20%。值得注意的是，专用于气味检测的 TGS 2612 和 TGS 2611 传感器在各种烘焙温度下表现出出色的有效性，STD 低于 10%。深度学习交叉验证的分类结果显示出令人印象深刻的准确度：轻度烘焙为 98.2%，轻度至中度烘焙为 98.4%，中度烘焙为 98.8%，中度烘焙为 97.8%，深度烘焙为 95.9%。总之，这项研究表明，鼻子利用具有深度学习分析功能的 TGS 气体传感器阵列，可以根据烘焙时间有效地高精度检测和分类咖啡类型。