Le Hieu M, Li Tianqi, Villareal Jimena G, Gao Jie, Hu Yaxi
Department of Chemistry, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, K1S 5B6 Canada.
Biotechnology Engineering, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, 64700 Monterrey, N.L, Mexico.
J AOAC Int. 2025 Mar 12. doi: 10.1093/jaoacint/qsaf022.
Plant-based milk alternatives (PBMA) are increasingly popular due to rising lactose intolerance and environmental concerns over traditional dairy products. However, limited efforts have been made to develop rapid authentication methods to verify their biological origin.
In this study, we developed a rapid, on-site analytical method for the authentication and identification of PBMA made by six different plant species utilizing a portable Raman spectrometer coupled with machine learning.
Unprocessed PBMA (i.e., blended raw nut/grain) and processed PBMA that mimic the industrial processing procedures (i.e., filtration and pasteurization) were prepared in lab and subjected to Raman spectral collection without any sample preparation. Three machine learning algorithms [i.e., k-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF)] were tested and compared.
RF achieved the best performance in recognizing the plant sources for the unprocessed PBMA, with accuracies of 96.88% and 95.83% in the cross-validation and test set prediction, respectively. Due to small sample size and risk of overfitting, classification models for the biological origin of processed PBMA were constructed by combining Raman spectra of the unprocessed and processed samples. Again, RF models achieved the highest accuracy in identifying the species, i.e., 94.27% in cross-validation and 94.44% in prediction.
These results indicated that the portable Raman spectrometer captured the chemical fingerprints that can effectively identify the plant species of different PBMA. Using this non-destructive Raman spectroscopic based method, the overall analysis from sample to answer was completed within 5 min, providing inspection laboratories a rapid and reliable screening tool to ensure the authenticity of the biological origin of PBMA.
This study presents a novel method for rapid and non-destructive identification of the plant sources of PBMA (both unprocessed and processed) based on the Raman spectroscopic technique and machine learning algorithms.
由于乳糖不耐受情况增多以及对传统乳制品环境问题的关注，植物基牛奶替代品（PBMA）越来越受欢迎。然而，在开发快速鉴定方法以验证其生物来源方面所做的努力有限。
在本研究中，我们开发了一种快速的现场分析方法，利用便携式拉曼光谱仪结合机器学习来鉴定和识别由六种不同植物制成的PBMA。
在实验室中制备未加工的PBMA（即混合的生坚果/谷物）和模拟工业加工过程（即过滤和巴氏杀菌）的加工后的PBMA，无需任何样品制备即可进行拉曼光谱采集。测试并比较了三种机器学习算法[即k近邻（KNN）、支持向量机（SVM）和随机森林（RF）]。
随机森林在识别未加工PBMA的植物来源方面表现最佳，在交叉验证和测试集预测中的准确率分别为96.88%和95.83%。由于样本量小和过拟合风险，通过结合未加工和加工样品的拉曼光谱构建了加工后PBMA生物来源的分类模型。随机森林模型在识别物种方面再次达到最高准确率，即交叉验证中为94.27%，预测中为94.44%。
这些结果表明，便携式拉曼光谱仪捕捉到的化学指纹可以有效识别不同PBMA的植物种类。使用这种基于拉曼光谱的非破坏性方法，从样品到得出结果的整个分析在5分钟内完成，为检测实验室提供了一种快速可靠的筛选工具，以确保PBMA生物来源的真实性。
本研究提出了一种基于拉曼光谱技术和机器学习算法的快速无损鉴定PBMA（未加工和加工）植物来源的新方法。
