Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China.
Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou 324000, China.
Food Chem. 2024 Dec 1;460(Pt 1):140532. doi: 10.1016/j.foodchem.2024.140532. Epub 2024 Jul 20.
Larimichthys crocea (LYC) holds significant economic value as a marine fish species. However, inaccuracies in labeling its origin can adversely affect consumer interests. Herein, a laser assisted rapid evaporative ionization mass spectrometry (LA-REIMS) and machine learning (ML) was developed for geographical authentication. When compared to iKnife, the LA demonstrated to be superior owing to reduced thermal damage to sample tissue, enhanced automation, and ease of use. Analysis of LYC from six distinct geographical origins across China revealed a total of 798 ions, which were then subjected to six classifiers to establish ML models. Following hyperparameter optimization and feature engineering, the Chi2(15%)-KNN model exhibited the highest training and testing accuracy, achieving 98.4 ± 0.9% and 98.5 ± 1.4%, respectively. This LA-REIMS/ML methodology offers a rapid, accurate, and intelligent solution for tracing the origin of LYC, thereby providing valuable technical support for the establishment of traceability systems in the aquatic product industry.
黄唇鱼（LYC）作为一种海洋鱼类，具有重要的经济价值。然而，其产地标签不准确可能会损害消费者利益。在此，我们开发了一种基于激光辅助快速蒸发解吸电离质谱（LA-REIMS）和机器学习（ML）的地理认证方法。与 iKnife 相比，LA 具有更低的样品组织热损伤、更高的自动化程度和易用性等优点。对来自中国六个不同地理产地的 LYC 进行分析，共检测到 798 个离子，然后使用 6 种分类器建立 ML 模型。经过超参数优化和特征工程处理后，Chi2(15%)-KNN 模型在训练和测试中的准确率最高，分别达到 98.4±0.9%和 98.5±1.4%。该 LA-REIMS/ML 方法为追踪 LYC 的产地提供了一种快速、准确和智能的解决方案，为水产养殖业溯源系统的建立提供了有价值的技术支持。
