使用输入变量选择优化人类乳品气味评估的预测
fangle changa, paul h. heinemannb,?
a department of agricultural and biological engineering, the pennsylvania state university, 105 agricultural engineering building, university park, pa 16802, usa
b department of agricultural and biological engineering, pennsylvania state university, 105 agricultural engineering building, university park, pa 16802, usa
a b s t r a c t
use of instruments instead of human panels to assess odors can make the collection and measurement process more efficient and reliable. odor-emitting samples from dairy farms, including manure, feed, and bedding materials, were collected and assessed by an electronic nose and a human panel. artificial neural networks based on the levenberg-marquardt back-propagation algorithm were used to build prediction models to predict human response to odor pleasantness. feature selection methods, including forward selection (fs), gamma test (gt), and principal component analysis (pca), were applied to reduce the dimensionality of the measurements, potentially eliminating noise. out of the 28 variable candidates (enose sensors), 10 variables were selected when pca was applied, and 16 variables were selected when either fs or gt approaches were applied. the model developed using gt provided the lowest mean square error of 0.56 (2.5%) hedonic scale units for separate validation. the gt-based model was able to predict the human assessments within 10% of the target for 81% of the independent validation samples and within 5% of the target for 63% of the independent validation samples.
使用仪器而不是人体感官来评估气味，可以使收集和测量过程更加有效和可靠。从奶牛场采集臭味样本，包括粪便、饲料和床上用品，并通过电子鼻和人体感官进行评估。采用基于levenberg-marquardt反向传播算法的人工神经网络建立预测模型，预测人类对气味愉悦的反应。采用前向选择(fs)、伽玛检验(gt)和主成分分析(pca)等特征选择方法来降低测量的维数，有可能消除噪声。在28个变量候选(enose传感器)中，当应用pca时选择10个变量，当应用fs或gt方法时选择16个变量。使用gt开发的模型为单独验证提供了0.56(2.5%)标度单位的小均方误差。基于gt的模型能够预测81%独立验证样本的目标值的10%范围内的人类评估，63%独立验证样本的目标值的5%范围内的人类评估。