Abstract Oilfish is banned in some countries because of the unpleasant side-effects of eating oilfish. However, oilfish was easily mislabeled and sold as cod due to their closed appearance. In this study, an in situ and real-time method was developed to distinguish cod from oilfish by coupling intelligent surgical knife (iKnife) and rapid evaporative ionization mass spectrometry (REIMS). The fillets collected from the same position of adult fishes were analyzed directly without sample preparation. The iKnife-REIMS conditions were optimized as cutting speed ca. 5 mm·s−1 and stripe length 2 cm, power output 30 W, and flow rate of auxiliary solvent (propan-2-ol) 100 μL·min–1. 200 spectral data from oilfish and cod was acquired in negative ionization mode and the data was processed by background subtraction, peak detection, and absolute counts calculation, which were further statistically analyzed by multivariate statistical analyses, including principal component analysis (PCA) and orthogonal partial least-square analysis (OPLS-DA). The major contributors of cluster separation were verified by permutation test and receiver operating characteristic (ROC) analysis. The identities of oilfish and cod (Greenland cod, Pacific cod, and Atlantic cod) were successfully recognized with accuracy 96–100%. This study filled the application niche of REIMS in cod authenticity and paved the way for the administration of food security and quality for related functional departments and bureaus.

中文翻译：

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基于智能手术刀快速蒸发电离质谱法实时鉴别油鱼鳕鱼的方法

摘要 油鱼在一些国家被禁止食用，因为食用油鱼会产生令人不快的副作用。然而，油鱼由于外观封闭，很容易被贴错标签并作为鳕鱼出售。在这项研究中，通过耦合智能手术刀 (iKnife) 和快速蒸发电离质谱 (REIMS)，开发了一种原位实时方法来区分鳕鱼和油鱼。从成年鱼的同一位置收集的鱼片直接分析，无需样品制备。iKnife-REIMS 条件被优化为切割速度约。5 mm·s-1，条纹长度2 cm，输出功率30 W，辅助溶剂（propan-2-ol）流速100 μL·min-1。在负电离模式下采集了油鱼和鳕鱼的 200 个光谱数据，并通过背景扣除、峰值检测、和绝对计数计算，通过多变量统计分析进一步进行统计分析，包括主成分分析（PCA）和正交偏最小二乘分析（OPLS-DA）。通过置换测试和接收器操作特征 (ROC) 分析验证了集群分离的主要因素。油鱼和鳕鱼（格陵兰鳕鱼、太平洋鳕鱼和大西洋鳕鱼）的身份被成功识别，准确率为 96-100%。本研究填补了REIMS在鳕鱼真伪的应用领域，为相关职能部门和部门的食品安全和质量管理工作铺平了道路。通过置换测试和接收器操作特征 (ROC) 分析验证了集群分离的主要因素。油鱼和鳕鱼（格陵兰鳕鱼、太平洋鳕鱼和大西洋鳕鱼）的身份被成功识别，准确率为 96-100%。本研究填补了REIMS在鳕鱼真伪领域的应用空白，为相关职能部门和部门的食品安全和质量管理工作铺平了道路。通过置换测试和接收器操作特征 (ROC) 分析验证了集群分离的主要因素。油鱼和鳕鱼（格陵兰鳕鱼、太平洋鳕鱼和大西洋鳕鱼）的身份被成功识别，准确率为 96-100%。本研究填补了REIMS在鳕鱼真伪领域的应用空白，为相关职能部门和部门的食品安全和质量管理工作铺平了道路。