China Military Institute of Chinese Medicine, 302 Military Hospital, Beijing, China.
PLoS One. 2012;7(11):e48887. doi: 10.1371/journal.pone.0048887. Epub 2012 Nov 7.
Experiential and sensory evaluation is an ancient method that remains important in the current quality control system of Traditional Chinese Medicines (TCMs). The process is rapid and convenient when evaluating the quality of crude materials in TCM markets. However, sensory evaluation has been met with skepticism because it is mainly based on experience and lacks a scientific basis. In this study, rhubarb was selected to demonstrate how color-based sensory evaluation could differentiate the quality of herbal medicines objectively. The colors of the rhubarb samples, expressed as RGB values, were obtained from different parts and forms of the plant, including the plant's surface, fracture surface color, and a powdered form with or without treatment with a color-developing reagent. We first divided the rhubarb samples into three grades based on the total content of five hydroxyanthraquinone derivatives, the major pharmacological components in rhubarb. Then, a three-layer back-propagation artificial neural network (BP-ANN), calibrated with selected training samples, was used to correlate the quality of the rhubarb with its color. The color of the rhubarb powder after coloration attained the highest accuracy (92.3%) in predicting the quality grade of the test samples with the established artificial neural networks. Finally, a standardized colorimetric grading scale was created based on the spatial distribution of the rhubarb samples in a two-dimensional chromaticity diagram according to the colors of the powdered rhubarb after color enhancement. By comparing the color between the scale and the tested samples, similar to performing a pH test with indicator paper, subjects without sensory evaluation experience could quickly determine the quality grade of rhubarb. This work illustrates the technical feasibility of the color-based grading of rhubarb quality and offers references for quantifying and standardizing the sensory evaluation of TCMs, foods and other products.
体验式和感官评价是一种古老的方法，在当前的中药（TCM）质量控制系统中仍然很重要。在评估中药市场原材料质量时，该过程快速便捷。然而，由于感官评价主要基于经验，缺乏科学依据，因此受到了质疑。在本研究中，选择大黄来演示如何通过基于颜色的感官评价来客观地区分草药的质量。大黄样品的颜色表示为 RGB 值，取自植物的不同部位和形式，包括植物表面、断裂面颜色以及未经或经显色试剂处理的粉末形式。我们首先根据大黄中主要的药理成分五种羟基蒽醌衍生物的总含量将大黄样品分为三个等级。然后，使用经过选择的训练样本校准的三层反向传播人工神经网络（BP-ANN），将大黄的质量与其颜色相关联。经过显色的大黄粉末的颜色在预测测试样本的质量等级方面具有最高的准确性（92.3%），这是使用建立的人工神经网络实现的。最后，根据显色后大黄粉末的颜色，在二维比色图上创建了标准化的比色分级量表。通过比较量表和测试样本之间的颜色，类似于使用指示剂纸进行 pH 值测试，没有感官评价经验的主体可以快速确定大黄的质量等级。这项工作说明了基于颜色的大黄质量分级的技术可行性，并为量化和标准化中药、食品和其他产品的感官评价提供了参考。
