The adulteration of natural products with multiple azo dyes has become a serious public health concern. Thus, on-site trace additive detection is demanded. Herein, we developed a gold-nanorod-based surface-enhanced Raman scattering (SERS) sensor to detect trace amounts of azo dyes, including lemon yellow, sunset yellow, golden orange II, acid red 73, coccine, and azorubine. After optimizing pre-processing steps, the additives were separated and identified through visual observation. The stable and sensitive SERS sensor developed enabled accurate detection of the added colorants. Density Functional Theory confirmed that the characteristic SERS peaks of the six dyes were accurate and credible. The optimized SERS sensor achieved a detection limit of 50 mg of dye per kilogram of raw material. A SERS spectral dataset comprising 960 replicates from all 64 potential dye combinations was generated, forming robust training sets. The K-Nearest Neighbor model exhibited best performance, identifying dye additives in real samples with a 91% success rate. This model was further validated by screening nine randomly collected safflower batches, identifying three with illegal dye additives, which were subsequently confirmed by HPLC. Summarily, the developed SERS sensor and classification model offer an ultrasensitive, and reliable approach for on-site detection of hazardous dyes in natural products.

中文翻译：

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机器学习辅助的 SERS 传感器，用于对天然产物中的多重有害染料进行快速、超灵敏分析


天然产物中掺杂多种偶氮染料已成为一个严重的公共卫生问题。因此，需要现场进行痕量添加剂检测。在此，我们开发了一种基于金纳米棒的表面增强拉曼散射 （SERS） 传感器，用于检测痕量的偶氮染料，包括柠檬黄、日落黄、金橙 II、酸性红 73、球红和偶氮红素。在优化了前处理步骤后，通过目视观察分离和鉴定添加剂。开发的稳定灵敏的 SERS 传感器能够准确检测添加的着色剂。密度泛函理论证实，六种染料的特征 SERS 峰准确可信。优化的 SERS 传感器实现了每公斤原材料 50 毫克染料的检测限。生成了一个 SERS 光谱数据集，其中包含来自所有 64 种潜在染料组合的 960 个重复，形成了强大的训练集。K-Nearest Neighbor 模型表现出最佳性能，以 91% 的成功率识别真实样品中的染料添加剂。通过筛选 9 个随机收集的红花批次，确定了 3 个含有非法染料添加剂的批次，随后通过 HPLC 证实了该模型。总之，开发的 SERS 传感器和分类模型为天然产物中有害染料的现场检测提供了一种超灵敏且可靠的方法。