Applications of Fourier transform near infrared (FT-NIR) spectroscopy in fisheries science are currently limited. This current analysis of otolith spectral data demonstrate the potential applicability of FT-NIR spectroscopy to otolith chemistry and spatial variability in fisheries science. The objective of this study was to examine the use of NIR spectroscopy as a tool to differentiate among marine fishes in four large marine ecosystems. We examined otoliths from 13 different species, with three of these species coming from different regions. Principal component analysis described the main directions along which the specimens were separated. The separation of species and their ecosystems may suggest interactions between fish phylogeny, ontogeny, and environmental conditions that can be evaluated using NIR spectroscopy. In order to discriminate spectra across ecosystems and species, four supervised classification model techniques were utilized: soft independent modelling of class analogies, support vector machine discriminant analysis, partial least squares discriminant analysis, and k-nearest neighbor analysis (KNN). This study showed that the best performing model to classify combined ecosystems, all four ecosystems, and species was the KNN model, which had an overall accuracy rate of 99.9%, 97.6%, and 91.5%, respectively. Results from this study suggest that further investigations are needed to determine applications of NIR spectroscopy to otolith chemistry and spatial variability.

中文翻译：

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使用耳石的近红外漫反射光谱对来自不同生态系统的鱼类进行分类

傅里叶变换近红外 (FT-NIR) 光谱在渔业科学中的应用目前有限。目前对耳石光谱数据的分析证明了 FT-NIR 光谱对渔业科学中的耳石化学和空间变异性的潜在适用性。本研究的目的是检查使用 NIR 光谱作为区分四个大型海洋生态系统中海洋鱼类的工具。我们检查了 13 种不同物种的耳石，其中三种来自不同地区。主成分分析描述了样本分离的主要方向。物种及其生态系统的分离可能表明鱼类系统发育、个体发育和环境条件之间的相互作用，可以使用 NIR 光谱进行评估。为了区分生态系统和物种的光谱，使用了四种监督分类模型技术：类类比的软独立建模、支持向量机判别分析、偏最小二乘判别分析和 k-最近邻分析 (KNN)。该研究表明，对组合生态系统、所有四个生态系统和物种进行分类的最佳模型是 KNN 模型，其总体准确率分别为 99.9%、97.6% 和 91.5%。这项研究的结果表明，需要进一步研究以确定 NIR 光谱在耳石化学和空间变异性中的应用。支持向量机判别分析、偏最小二乘判别分析和 k-近邻分析 (KNN)。该研究表明，对组合生态系统、所有四个生态系统和物种进行分类的最佳模型是 KNN 模型，其总体准确率分别为 99.9%、97.6% 和 91.5%。这项研究的结果表明，需要进一步研究以确定 NIR 光谱在耳石化学和空间变异性中的应用。支持向量机判别分析、偏最小二乘判别分析和 k-近邻分析 (KNN)。该研究表明，对组合生态系统、所有四个生态系统和物种进行分类的最佳模型是 KNN 模型，其总体准确率分别为 99.9%、97.6% 和 91.5%。这项研究的结果表明，需要进一步研究以确定 NIR 光谱在耳石化学和空间变异性中的应用。