本文探讨了运用python处理食品生物化学实验中还原糖浓度测定的新方法。分别将稀释成不同浓度的还原糖检测样品放在96孔酶标板上，利用扫描仪对其扫描，得到tiff格式的颜色位图，以利用本课题组自行开发的在线分析平台上的RGB分析模块读取样品溶液的RGB颜色数据。通过考察样品取样量、最适图片分辨率等条件，建立一种基于RGB色彩体系的食品中还原糖快速检测方法。检测葡萄糖的结果，在葡萄糖浓度相同，样品取样量为100 μL，图片分辨率为1200 dpi的条件下，拟合系数大于0.98；RGB颜色数据与葡萄糖浓度之间的关系准确度更高，该结果与分光光度法结果相近，但本方法更方便、快速、可多通道处理。与此同时，通过测定实际样品中还原糖的含量，并与分光光度法测定结果比对，验证了利用python/RGB色彩数据分析平台快速测定食品中还原糖浓度的可靠性和可行性。

A new method for determining reducing sugar concentration through food biochemical experiments using python was investigated in this study. Reducing sugar-containing samples were diluted to different concentrations and placed on the 96-well plates, before being scanned by the scanner to obtain the color images in tiff. The RGB color data of the samples were collected by the RGB analysis module of the online analysis platform developed by our research group. By investigating the conditions such as the sampling size and optimal image resolution, a rapid detection method for reducing sugar in food based on RGB color system was established. For glucose detection, the fitting coefficient was higher than 0.98 when the concentration of glucose remained the same with the sample size as 100 μL and the image resolution as 1200 dpi. The accuracy of the relationship between RGB color data and the concentration of reducing sugar was higher, and the result was similar to the that obtained by spectrophotometry, but this method is more convenient and faster and allowed multi-channel processing. At the same time, through measuring the content of reducing sugar in the real sample and comparing with the results obtained by spectrophotometry, the reliability and feasibility of using Python/RGB color data analysis platform to determine quickly the concentration of reducing sugar in food were verified.

广东省基础与应用基础研究基金佛山市联合基金（粤佛联合基金）青年基金项目（2019A1515110621）；广东普通高校青年创新人才项目（自然科学类）（2017KQNCX217）；佛山科学技术学院高层次人才启动项目（GG07016）；大学生创新创业训练计划项目（201911847023；S201911847097；S201911847091；XJ2019213；S202011847068；S202011847086；XJ2020219；XJ2020220）；广东省科技创新战略专项资金项目（pdjh2020b0627）；广州无远生物科技有限公司-企业科研基金(012319705010)