Abstract The use of fast and low-cost methods to optimize the total phenolic compounds (TPC) extraction has been gaining attention in ethnopharmacological research. Extraction conditions of the bioactive compounds from Calycophyllum spruceanum barks were established through multivariate regression models. In this sense, fractional factorial design (FFD) and central rotational composite design (CCRD) were developed using partial least squares regression (PLSR) combined with the information from the color images and spectrophotometry tools to evaluate the antioxidant activity from C. spruceanum barks. In fact, was possible to optimize the extraction of TPC with AA (ethanol 10% v/v, 1 h extraction time at 75 °C temperature). Besides, the precision and performance of generated models were established for the three response variables (TPC, AA by ABTS and FRAP methods) with R2 above 0.98 in the PLSR and residual predictive value (RPD) above 3. Thus, the approaches suggested in this study, with emphasis on the use of image analysis, proved to be potential and promising as simple, fast, non-destructive methods for quantifying TPC and antioxidant activity in C. spruceanum barks.

中文翻译：

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RGB 图像模式可以快速有效地预测 Calycophyllum spruceanum 树皮的抗氧化潜力

摘要 使用快速、低成本的方法来优化总酚类化合物 (TPC) 提取已在民族药理学研究中受到关注。通过多元回归模型建立了云杉树皮中生物活性化合物的提取条件。在这个意义上，部分因子设计 (FFD) 和中心旋转复合设计 (CCRD) 是使用偏最小二乘回归 (PLSR) 结合彩色图像和分光光度计工具的信息开发的，以评估云杉树皮的抗氧化活性。事实上，可以优化用 AA 提取 TPC（乙醇 10% v/v，在 75 °C 温度下提取时间为 1 小时）。此外，为三个响应变量（TPC，