欧亚国际

刚刚，科学家利用Videometer多光谱成像系统，在知名期刊Sensors上发表了题为“Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging ”文章。 

结合特征选择技术和神经模糊系统-使用多光谱成像预测牛肉片中的总活菌数

摘要

在食品工业中，质量和安全问题与消费者的健康状况有关。人们越来越关注应用各种非侵入性感官技术来快速获得质量属性。其中高光谱/多光谱成像技术已广泛用于各种食品的检测。本文利用多光谱成像信息，开发了一种基于堆叠的集成预测系统，用于预测牛肉片样品中微生物的总活菌数，它是导致肉变质的重要原因。由于从多光谱成像系统中选择重要波长被认为是预测方案的重要阶段，因此还探索了一种特征融合方法，即结合从各种特征选择技术中提取的波长。集成子组件包括两个基于聚类的高级神经模糊网络预测模型，一个利用来自平均反射率值的信息，另一个利用每个波长像素强度的标准偏差。将神经模糊模型的性能与已建立的回归算法（如多层感知器、支持向量机和偏最小二乘法）进行了比较。所获得的结果证实了所提出的假设的有效性，即利用特征选择方法与神经模糊模型相结合来评估肉制品的微生物质量。

关键字：

神经网络;集成系统;模糊逻辑;牛肉;总活菌数;回归;多光谱成像;机器学习

Combining Feature Selection Techniques and Neurofuzzy Systems for the Prediction of Total Viable Counts in Beef Fillets Using Multispectral Imaging

Abstract

In the food industry, quality and safety issues are associated with consumers’ health condition. There is a growing interest in applying various noninvasive sensorial techniques to obtain quickly quality attributes. One of them, hyperspectral/multispectral imaging technique has been extensively used for inspection of various food products. In this paper, a stacking-based ensemble prediction system has been developed for the prediction of total viable counts of microorganisms in beef fillet samples, an essential cause to meat spoilage, utilizing multispectral imaging information. As the selection of important wavelengths from the multispectral imaging system is considered as an essential stage to the prediction scheme, a features fusion approach has been also explored, by combining wavelengths extracted from various feature selection techniques. Ensemble sub-components include two advanced clustering-based neuro-fuzzy network prediction models, one utilizing information from average reflectance values, while the other one from the standard deviation of the pixels’ intensity per wavelength. The performances of neurofuzzy models were compared against established regression algorithms such as multilayer perceptron, support vector machines and partial least squares. Obtained results confirmed the validity of the proposed hypothesis to utilize a combination of feature selection methods with neurofuzzy models in order to assess the microbiological quality of meat products.

Keywords:

neural networks; ensemble systems; fuzzy logic; beef; total viable counts; regression; multispectral imaging; machine learning