Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, School of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, China.
School of Computing, Ulster University, Belfast, BT37 0QB, UK.
Sensors (Basel). 2018 May 23;18(6):1667. doi: 10.3390/s18061667.
As the expectation for higher quality of life increases, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food is what it says it is. A popular approach to food authentication is based on spectroscopy, which has been widely used for identifying and quantifying the chemical components of an object. This approach is non-destructive and effective but expensive. This paper presents a computer vision-based sensor system for food authentication, i.e., differentiating organic from non-organic apples. This sensor system consists of low-cost hardware and pattern recognition software. We use a flashlight to illuminate apples and capture their images through a diffraction grating. These diffraction images are then converted into a data matrix for classification by pattern recognition algorithms, including -nearest neighbors (-NN), support vector machine (SVM) and three partial least squares discriminant analysis (PLS-DA)- based methods. We carry out experiments on a reasonable collection of apple samples and employ a proper pre-processing, resulting in a highest classification accuracy of 94%. Our studies conclude that this sensor system has the potential to provide a viable solution to empower consumers in food authentication.
随着人们对生活质量期望的提高，消费者对高质量食品的需求也越来越高。食品认证是确保食品名副其实的技术手段。一种流行的食品认证方法基于光谱学，它已被广泛用于识别和定量物体的化学成分。这种方法是非破坏性的，也是有效的，但价格昂贵。本文提出了一种基于计算机视觉的传感器系统用于食品认证，即区分有机苹果和非有机苹果。该传感器系统由低成本硬件和模式识别软件组成。我们使用手电筒照亮苹果，并通过衍射光栅捕获它们的图像。这些衍射图像随后被转换为数据矩阵，通过模式识别算法进行分类，包括最近邻 (-NN)、支持向量机 (SVM) 和三种偏最小二乘判别分析 (PLS-DA) 方法。我们在合理收集的苹果样本上进行了实验，并采用了适当的预处理，得到了最高 94%的分类准确率。我们的研究表明，该传感器系统有可能为消费者提供可行的食品认证解决方案。
