A Cyranose 320 (eNose) and a Fast Gas Chromatograph (CG) analyser (zNose™) were used to measure the headspace odour of solid samples from dairy operations. The measurements of both sensors were trained by Levenberg–Marquardt Back-propagation Neural Network (LMBNN) to match human assessments. A trained human panel was used to assess the odours based on hedonic tone method and provide the model targets. A multi-sensor data fusion approach was developed and applied to integrate the eNose and zNose readings for higher predictive accuracy compared to each sensor alone. Principle Component Analysis, Forward Selection, and Gamma Test were applied to reduce the model input dimensions. Measurement fusion models and information fusion model approaches were applied. The information fusion prediction models were shown to be more accurate than all other models, including single instrument models. The information fusion model based on eNose with Gamma Test data reduction + zNose showed the best results of all cases in validation mean square error (0.34 odour units), R value (0.92), probability of the prediction falling within 10% of the target (96%), and probability of the prediction falling within 5% of the target (63%).

中文翻译：

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使用仪器测量和多传感器数据融合集成神经网络基于享乐色调法预测人体气味评估

使用 Cyranose 320 (eNose) 和快速气相色谱 (CG) 分析仪 (zNose™) 测量来自乳制品操作的固体样品的顶空气味。两个传感器的测量均由 Levenberg-Marquardt 反向传播神经网络 (LMBNN) 进行训练，以匹配人类评估。训练有素的人类小组被用来评估基于享乐基调方法的气味并提供模型目标。开发并应用了一种多传感器数据融合方法来集成 eNose 和 zNose 读数，与单独使用每个传感器相比，具有更高的预测精度。应用主成分分析、前向选择和 Gamma 检验来降低模型输入维度。应用了测量融合模型和信息融合模型方法。信息融合预测模型被证明比所有其他模型更准确，包括单一仪器型号。基于eNose with Gamma Test data reduction+zNose的信息融合模型在验证均方误差（0.34气味单位）、R值（0.92）、预测落在目标10%以内的概率（ 96%)，预测落在目标 5% 以内的概率 (63%)。