Chinese Brewed Vinegar Dataset from Handheld Electronic Nose

This dataset was collected using a custom-designed handheld electronic nose (e-nose) device equipped with eight MOS gas sensors. It includes six types of brewed vinegar, each bearing the title of "China Time-Honored Brand," specifically: Jiangsu Hengshun, Sichuan Baoning, Tianjin Tianli, Shanxi Laifu, Liaoning Gaoqiao, and Shanxi Donghu, which are labeled as JSHS, SCBN, TJTL, SXLF, LNGQ, and SXDH, respectively. The data were recorded at a sampling rate of 20 samples per second via the 12-bit ADC on the ESP32-S3 microcontroller, capturing vinegar odor measurements consisting of 4500 data points per sensor across the eight-sensor array (MQ136, MQ9B, MQ7B, MQ2, MQ8, MQ138, MQ137, and MQ5). Each sensor is coupled with a 4.7 kΩ resistor to form a half-bridge circuit, with a reference voltage maintaining the circuit at 2.5 V. The dataset is organized into six folders—JSHS, SCBN, TJTL, SXLF, LNGQ, and SXDH—each containing 25 samples in Excel format, which reflect the characteristic response of the sensor array to the corresponding vinegar odor. For further information, kindly refer to our research paper: Xin Weng, Jun Fu, Jiayu Ye, Ruifen Hu, Jieyu Yin, Bowen Zhao, Ruo He. OdorNet: A lightweight odor recognition method for TinyML in handheld electronic noses using spatiotemporal pseudo-images. Sensors and Actuators B: Chemical, 2025, 444(1): 138393. (https://doi.org/10.1016/j.snb.2025.138393).本数据集采用定制设计的手持式电子鼻（electronic nose，简称e-nose）设备采集所得，该设备搭载8个金属氧化物半导体（MOS）气体传感器。数据集涵盖6款获评"中华老字号"的酿造食醋，具体包括江苏恒顺、四川保宁、天津天立、山西来福、辽宁高桥及山西东湖，分别标注为JSHS、SCBN、TJTL、SXLF、LNGQ与SXDH。 数据以20个采样点每秒的采样率，通过搭载于ESP32-S3微控制器的12位模数转换器（ADC）进行记录。该八传感器阵列包含MQ136、MQ9B、MQ7B、MQ2、MQ8、MQ138、MQ137与MQ5共8款气体传感器，针对每种食醋气味的测量中，每个传感器均可采集到4500个数据点。每个传感器搭配4.7 kΩ电阻构成半桥电路，以2.5 V的参考电压维持电路稳定。本数据集以6个文件夹进行分类存储，分别对应JSHS、SCBN、TJTL、SXLF、LNGQ与SXDH六个标注代号，每个文件夹内包含25份Excel格式的样本数据，可表征传感器阵列对对应食醋气味的特征响应。 如需获取更多相关信息，请参阅本团队发表的研究论文：翁鑫、付俊、叶佳钰、胡瑞芬、尹杰宇、赵博文、贺若。《OdorNet：面向手持式电子鼻TinyML应用的轻量级气味识别方法——基于时空伪图像》。《传感器与执行器B辑：化学》，2025，444(1): 138393。（https://doi.org/10.1016/j.snb.2025.138393）。