Adulterants can cause different health hazards upon prolonged consumption, but it is difficult to detect with human eyes. Non-destructive turmeric adulteration detection is a challenging research area. The existing adulteration detection processes are largely instrumental and analytical with high accuracy but include limitations like long testing time, expensiveness, and lack of mobility. This work reports a new computer vision framework, which can simultaneously detect the presence of adulteration and predict the possible percentage of adulteration addressing the stated limitations. The scope has been remained to screening of Sudan dye-I adulteration in turmeric powder. An in-house database prepared with images of pure and adulterated turmeric powder samples has been used for experimentation. Random Forest algorithm has been employed for both classification and prediction. The model has been validated with standard internal and external validation methods to assess the stability and generalization potential of the model to avoid over- and under-fitting problems. The results of classification show that the presented framework can provide more than 99% accuracy in detection while high correlation coefficient (R²) value in the tune of .99 for prediction. The novelty of the work is its simple histogram-based color feature extraction, development of ensemble Random Forest prediction model that resulted in high accuracy and development of a faster, non-invasive, less-expensive, and validated screening method for adulterated turmeric powder that can be considered as a potential immediate screening method in the supply chain of powdered spices prior to confirmatory testing methods following two-tiered food fraud testing approach.

中文翻译：

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基于多重随机森林的颜色通道结合计算机视觉技术检测和预测姜黄粉中苏丹红-I掺假

掺假物长期食用会造成不同的健康危害，但人眼难以察觉。无损姜黄掺假检测是一个具有挑战性的研究领域。现有的掺假检测过程主要是仪器化和高精度分析，但存在测试时间长、成本高和缺乏流动性等限制。这项工作报告了一个新的计算机视觉框架，它可以同时检测掺假的存在并预测可能的掺假百分比，以解决所述限制。范围仍停留在姜黄粉中苏丹红-I掺假的筛查。使用纯姜黄粉样品和掺假姜黄粉样品的图像准备的内部数据库已用于实验。随机森林算法已被用于分类和预测。该模型已经通过标准的内部和外部验证方法进行了验证，以评估模型的稳定性和泛化潜力，以避免过度和欠拟合问题。分类结果表明，所提出的框架可以提供超过 99% 的检测准确率，同时具有较高的相关系数 (R ² ) 0.99 范围内的预测值。这项工作的新颖之处在于其简单的基于直方图的颜色特征提取、集成随机森林预测模型的开发，该模型的准确性高，并开发了一种更快、非侵入性、成本更低且经过验证的掺假姜黄粉筛选方法，可以被认为是粉状香料供应链中一种潜在的直接筛选方法，在两级食品欺诈检测方法之后的确认检测方法之前。