Wang Xiangxin, You Hong, Zhou Yucheng, Abraham Evelyn, Chastain Jonathan, Kneedler Scott, Xie Qinggang
Heilongjiang Feihe Dairy Co., Ltd., C-16, 10A Jiuxianqiao Rd., Chaoyang, Beijing 100015, China.
Eurofins US Food, 2951 Saturn St, Brea, California 92821, United States.
Anal Chem. 2025 Jul 1;97(25):12971-12980. doi: 10.1021/acs.analchem.4c06037. Epub 2025 Jun 16.
The rising popularity of berry dietary supplements for their antioxidant and anti-inflammatory benefits has raised concerns about mislabeling and adulteration. Traditional authentication methods often compare anthocyanin profiles to a certified reference material, often overlooking variances from different cultivars, environments, and cultivation practices. Thus, a more comprehensive approach is imperative. This study developed a chemometric approach using anthocyanin profiles to distinguish bilberry ( L.), blueberry ( L.), and cranberry ( Aiton) from one another and from potential adulterants. Anthocyanin fingerprints from 48 and non- samples were generated via LC-MS/MS due to its ability to rapidly quantify low-level analytes across diverse sample matrices. Principal component analysis (PCA) was applied to the relative abundance ratios of 18 selected anthocyanins, followed by a Mahalanobis Distance Classification model for classifying unknown samples with a decision boundary approach. By using voucher information and high-performance-thin layer chromatography (HPTLC) test results, the model successfully classified 25 authentic ingredients, non- ingredients, and -containing supplements with 100% accuracy in a verification study. Among the four dietary supplements tested, three were correctly labeled, while one product was determined to be adulterated, as confirmed by HPTLC analysis. While the number of reference samples was constrained by marketplace availability, the model demonstrates strong initial potential for authentication, providing a foundation for future validation using larger data sets. To the best of our knowledge, this is the first study to authenticate three high-value species in dietary ingredients and supplements using anthocyanin fingerprints and chemometric methods. This approach complies with FDA cGMP 21 CFR Part 111 and targets to meet AOAC (Association of Analytical Collaboration International) 2014.007 Standard Method Performance Requirements, showing promising potential to be adopted as a consensus method for authentication.
由于其抗氧化和抗炎功效，浆果膳食补充剂越来越受欢迎，这引发了人们对标签错误和掺假的担忧。传统的鉴定方法通常将花青素谱与认证参考物质进行比较，往往忽略了不同品种、环境和种植方式造成的差异。因此，需要一种更全面的方法。本研究开发了一种化学计量学方法，利用花青素谱来区分欧洲越橘（Vaccinium myrtillus L.）、蓝莓（Vaccinium corymbosum L.）和蔓越莓（Vaccinium macrocarpon Aiton），并将它们与潜在的掺假物区分开来。由于液相色谱-串联质谱（LC-MS/MS）能够快速定量分析不同样品基质中的低水平分析物，因此通过LC-MS/MS生成了48个真实样品和非真实样品的花青素指纹图谱。主成分分析（PCA）应用于18种选定花青素的相对丰度比，随后采用马氏距离分类模型，通过决策边界方法对未知样品进行分类。在一项验证研究中，通过使用凭证信息和高效薄层色谱（HPTLC）测试结果，该模型成功地对25种真实的浆果成分、非浆果成分和含浆果的补充剂进行了分类，准确率达到100%。在测试的四种膳食补充剂中，三种标签正确，而一种浆果产品被确定为掺假，HPTLC分析证实了这一点。虽然参考样品的数量受到市场可得性的限制，但该模型显示出很强的初步鉴定潜力，为未来使用更大数据集进行验证奠定了基础。据我们所知，这是第一项使用花青素指纹图谱和化学计量学方法对膳食成分和补充剂中的三种高价值浆果品种进行鉴定的研究。这种方法符合美国食品药品监督管理局（FDA）的《21 CFR Part 111现行药品生产质量管理规范》，目标是满足美国官方分析化学师协会（AOAC）2014.007标准方法性能要求，并显示出有望被采纳为浆果鉴定的共识方法的潜力。
