Abstract Rose (Rosa spp.) petals have been used for the preparation of new cooking recipes, due to their sensory properties. In this paper were evaluated the micro (Co, Cu, Fe, Mn, Mo, Ni and Zn), macro (Ca, Mg, Na and P) and toxic elements (As, Cd and Cr) of organic cultivation rose by ICP OES and ICP-MS. The bioactive compounds (total phenolics, total flavonoids, total anthocyanins and antioxidant capacity in DPPH) were determined by UV-VIS spectrophotometry. Chemometric tools were used in method optimization, such as full factorial design for acid digestion and Box-Behnken design for total phenolics, in addition to the application of Artificial Neural Networks with Kohonen Neural Network for bioactive compounds. The application of PCA and HCA showed that the mineral composition of rose petal samples had differences between summer and winter. The accuracy of the method was confirmed by the analysis of three certified reference materials: apple leaves (1515), spinach leaves (1570a) and peach leaves (1547), from the National Institute of Standards and Technology (NIST). Digestion efficiency was evaluated by residual carbon content. The developed methods were promising to evaluate the minerals, toxic elements and bioactive compounds in rose petals, and the results indicate that they can be added as a potential nutritional supplement in the human diet.

中文翻译：

---

使用化学计量工具和人工神经网络评估玫瑰花瓣 (Rosa spp.) 中的矿物质、有毒元素和生物活性化合物

摘要 玫瑰 (Rosa spp.) 花瓣因其感官特性而被用于制备新的烹饪食谱。本文通过ICP OES对有机栽培玫瑰的微量（Co、Cu、Fe、Mn、Mo、Ni和Zn）、宏观（Ca、Mg、Na和P）和有毒元素（As、Cd和Cr）进行了评价。和 ICP-MS。生物活性化合物（总酚类、总黄酮类、总花青素和 DPPH 中的抗氧化能力）通过 UV-VIS 分光光度法测定。除了将人工神经网络和 Kohonen 神经网络应用于生物活性化合物之外，还使用化学计量学工具进行方法优化，例如酸消化的全因子设计和总酚类的 Box-Behnken 设计。PCA和HCA的应用表明，玫瑰花瓣样品的矿物成分在夏季和冬季之间存在差异。该方法的准确性通过对美国国家标准与技术研究院 (NIST) 的三种有证标准物质的分析得到证实：苹果叶 (1515)、菠菜叶 (1570a) 和桃叶 (1547)。通过残余碳含量评估消化效率。开发的方法有望评估玫瑰花瓣中的矿物质、有毒元素和生物活性化合物，结果表明它们可以作为潜在的营养补充剂添加到人类饮食中。