Microplastic (MP) research faces challenges due to costly, time-consuming, and error-prone analysis techniques. Additionally, the variability in data quality across studies limits their comparability. This study addresses the critical need for reliable and cost-effective MP analysis methods through validation of a semi-automated workflow, where environmentally relevant MP were spiked into and recovered from marine fish gastrointestinal tracts (GITs) and blue mussel tissue, using Nile red staining and machine learning automated analysis of different polymers. Parameters validated include trueness, precision, uncertainty, limit of quantification, specificity, sensitivity, selectivity, and method robustness. For fish GITs a 95 ± 9 % recovery rate was achieved, and 87 ± 11 % for mussels. Polymer identification accuracies were 76 ± 8 % for fish GITs and 80 ± 13 % for mussels. Polyethylene terephthalate fragments showed more variability with lower accuracies. The proposed validation parameters offer a step towards quality management guidelines, as such aiding future researchers and fostering cross-study comparability.

中文翻译：

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获得可靠的数据：使用尼罗红染色验证基于机器学习的海洋生物微塑料分析方法


由于分析技术成本高昂、耗时且容易出错，微塑料 (MP) 研究面临着挑战。此外，不同研究之间数据质量的差异限制了它们的可比性。本研究通过验证半自动化工作流程解决了对可靠且经济高效的 MP 分析方法的迫切需求，其中使用尼罗红染色将环境相关的 MP 加入到海鱼胃肠道 (GIT) 和蓝贻贝组织中并从中回收以及不同聚合物的机器学习自动分析。验证的参数包括真实性、精密度、不确定性、定量限、特异性、灵敏度、选择性和方法稳健性。鱼类胃肠道的回收率达到了 95 ± 9%，贻贝的回收率为 87 ± 11%。鱼类 GIT 的聚合物识别准确度为 76 ± 8 %，贻贝的聚合物识别准确度为 80 ± 13 %。聚对苯二甲酸乙二醇酯片段显示出更多的变异性和较低的准确度。拟议的验证参数为质量管理指南迈出了一步，从而帮助未来的研究人员并促进交叉研究的可比性。