Abstract Substitution of high-priced fish species with inexpensive alternatives and mislabeling frozen-thawed fish fillets as fresh are two important fraudulent practices of concern in the seafood industry. This study aimed to develop multimode hyperspectral imaging techniques to detect substitution and mislabeling of fish fillets. Line-scan hyperspectral images were acquired from fish fillets in four modes, including reflectance in visible and near-infrared (VNIR) region, fluorescence by 365 nm UV excitation, reflectance in short-wave infrared (SWIR) region, and Raman by 785 nm laser excitation. Fish fillets of six species (i.e., red snapper, vermilion snapper, Malabar snapper, summer flounder, white bass, and tilapia) were used for species differentiation and frozen-thawed red snapper fillets were used for freshness evaluation. All fillet samples were DNA tested to authenticate the species. A total of 24 machine learning classifiers in six categories (i.e., decision trees, discriminant analysis, Naive Bayes classifiers, support vector machines, k-nearest neighbor classifiers, and ensemble classifiers) were used for fish species and freshness classifications using four types of spectral data in three different datasets (i.e., full spectra, first ten components of principal component analysis, and bands selected by sequential feature selection method). The highest accuracies were achieved at 100% using full VNIR reflectance spectra for the species classification and 99.9% using full SWIR reflectance spectra for the freshness classification. The VNIR reflectance mode gave the overall best performance for both species and freshness inspection, and it will be further investigated as a rapid technique for detection of fish fillet substitution and mislabeling.

中文翻译：

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使用多模式高光谱成像技术检测鱼片替代和错误标记

摘要 用廉价替代品替代高价鱼种和将冻融鱼片误贴为新鲜鱼片是海产品行业关注的两种重要欺诈行为。本研究旨在开发多模式高光谱成像技术来检测鱼片的替代和错误标记。以四种模式从鱼片中获取线扫描高光谱图像，包括可见光和近红外 (VNIR) 区域的反射、365 nm 紫外激发的荧光、短波红外 (SWIR) 区域的反射和 785 nm 的拉曼激光激发。使用六种鱼片（即红鲷鱼、朱红色鲷鱼、马拉巴鲷鱼、夏比目鱼、白鲈和罗非鱼）进行物种区分，并使用冻融红鲷鱼片进行新鲜度评价。所有鱼片样品都经过 DNA 测试以验证物种。使用四种类型的光谱对鱼类种类和新鲜度进行分类，共使用六类（即决策树、判别分析、朴素贝叶斯分类器、支持向量机、k-最近邻分类器和集成分类器）的 24 个机器学习分类器。三个不同数据集中的数据（即全光谱、主成分分析的前十个成分以及通过顺序特征选择方法选择的波段）。使用全 VNIR 反射光谱进行物种分类时达到了 100% 的最高准确度，使用全短波红外反射光谱进行新鲜度分类时达到了 99.9%。VNIR 反射模式为物种和新鲜度检查提供了整体最佳性能，