Turmeric (Curcuma longa) is a popular food ingredient which is widely used in powdered form. Despite different food and medicinal advantages it is often adulterated. Metanil yellow (MET) is one such synthetic chemical which can be easily mixed with turmeric powder and such mixing is difficult to detect. This paper presents a computer vision framework using the potential of deep neural network towards detection of MET adulteration in turmeric powder and random forests regressor to predict the possible amount of adulterant. An in-house database consisting of features from turmeric images of five variants of pure and adulterated turmeric powder has been used for experimentations. A new frequency domain annular-mean filter-based feature extraction has been used. The results show the potential of the presented method that can perform with more than 98% accuracy in both identification and prediction tasks. The reported technique can be considered as a motivating step towards development of a non-invasive and low-cost mobile device towards food adulteration detection in future.

中文翻译：

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一种深度神经网络和随机森林驱动的计算机视觉框架，用于识别和预测姜黄粉中的甲基黄掺假

姜黄（Curcuma longa）是一种流行的食品成分，广泛以粉末形式使用。尽管有不同的食物和药用优势，但它经常被掺假。Metanil Yellow (MET) 是一种这样的合成化学品，它很容易与姜黄粉混合，而且这种混合很难检测。本文提出了一种利用深度神经网络的潜力检测MET的计算机视觉框架姜黄粉中的掺假和随机森林回归器来预测可能的掺假量。一个内部数据库由五种不同的纯姜黄粉和掺假姜黄粉的姜黄图像特征组成，已用于实验。使用了一种新的基于频域环形均值滤波器的特征提取。结果显示了所提出方法的潜力，该方法可以在识别和预测任务中以超过 98% 的准确率执行。所报告的技术可被视为未来开发非侵入性和低成本移动设备以检测食品掺假的激励步骤。