The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination () is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

中文翻译：

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利用电子鼻，计算机视觉和人工触觉技术评估肉类新鲜度的综合方法

传统方法不能满足肉类新鲜度快速，客观检测的要求。电子鼻（E-Nose），计算机视觉（CV）和人工触觉（AT）感官技术可用于在判断肉质（新鲜度）时模仿人的嗅觉，外观和触觉的压缩感官功能。尽管已使用单独的E-Nose，CV和AT传感技术来检测肉的新鲜度，但检测结果却有所不同且不可靠。本文提出了一种通过结合E-Nose，CV和AT传感技术来捕获综合的肉类新鲜度参数的新方法，以及用于分析具有不同维度和单位的六个气味参数单位的复杂数据的数据融合方法。电子鼻，CV的9种颜色参数，和4个AT的橡胶状参数，可有效检测肉类新鲜度。已选择猪肉和鸡肉进行验证测试。总挥发性碱氮（TVB-N）测定法被用于定义肉类新鲜度，作为验证所提出方法有效性的标准标准。主成分分析（PCA）和支持向量机（SVM）分别用作无监督和监督模式识别方法，以分别分析这三种仪器的源数据和融合数据。实验和数据分析结果表明，与单一技术相比，E-Nose，CV和AT技术的融合显着提高了各种新鲜肉制品的检测性能。此外，偏最小二乘（PLS）用于构建TVB-N值预测模型，输入融合数据。猪肉和鸡肉的均方根误差预测（RMSEP）分别为1.21和0.98，其中确定系数（）是0.91和0.94。这意味着所提出的方法可用于有效检测肉的新鲜度和储存时间（天）。