Department of Chemistry, Lomonosov Moscow State University, Moscow 119234, Russia.
Department of Inorganic and Analytical Chemistry, Faculty of Science and Informatics, University of Szeged, Szeged 6720, Hungary.
Analyst. 2022 Jul 12;147(14):3248-3257. doi: 10.1039/d2an00143h.
Modern analytical techniques, including laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy, yield multidimensional data, which are most efficiently used in conjunction with chemometric techniques, including multi-block algorithms. In this study, we use several algorithms for the processing of laser-induced breakdown and Raman spectra of zooplankton organisms, which are found to accumulate lithium for an unknown reason. Correlations between elemental and molecular composition of zooplankton have been found. We studied 29 samples: crustaceans, arrow worms, and sea snails. The obtained spectra were examined by principal component analysis (PCA), non-negative matrix factorization (NMF), consensus PCA (CPCA), and analysis of common components and specific weights (CCSWA, or ComDim). LIBS spectra are more sensitive towards taxonometric differences than Raman spectra. All the algorithms gave similar results, although still differing in details. Data fusion revealed a number of relationships, including the correlation of Li with potassium ( = 0.83, = 14), with Raman bands of carotenoids ( = 0.89, = 11) and tryptophan ( = 0.94, = 9). The correlations were most pronounced in light-coloured parts of the inhomogeneous biological material. Ratios of fatty acids are associated with Li concentration if above 200 mg kg. Valine is also related to the Li accumulation. Thus, it is shown that the combination of LIBS and Raman spectroscopy, followed by appropriate mathematical treatment, is a convenient tool for comprehensive studies of environmental objects.
现代分析技术，包括激光诱导击穿光谱（LIBS）和拉曼光谱，可产生多维数据，这些数据与化学计量技术（包括多块算法）结合使用最为高效。在这项研究中，我们使用了几种算法来处理浮游生物生物体的激光诱导击穿和拉曼光谱，这些生物体由于未知原因积累了锂。我们发现了浮游生物的元素和分子组成之间的相关性。我们研究了 29 个样本：甲壳类动物、箭虫和海蜗牛。通过主成分分析（PCA）、非负矩阵分解（NMF）、共识 PCA（CPCA）以及共同成分和特定权重分析（CCSWA 或 ComDim）来检查获得的光谱。LIBS 光谱比拉曼光谱对分类差异更为敏感。所有算法都给出了相似的结果，尽管细节上仍存在差异。数据融合揭示了一些关系，包括锂与钾（ = 0.83， = 14）、类胡萝卜素的拉曼带（ = 0.89， = 11）和色氨酸（ = 0.94， = 9）的相关性。在非均匀生物材料的浅色部分，相关性最为显著。如果脂肪酸的比例高于 200 毫克/千克，则与 Li 浓度相关。缬氨酸也与 Li 的积累有关。因此，结果表明，LIBS 和拉曼光谱的组合，以及适当的数学处理，是对环境对象进行综合研究的便捷工具。
