Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling – more specifically, Latent Dirichlet Allocation (LDA) – in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO₂/O₂/N₂): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N₂ (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.

概率主题建模通常用于机器学习和统计分析中，以从复杂数据集中提取潜在信息。尽管与自然语言处理和文本挖掘紧密相关，但这些方法仍具有一些属性，这些属性使其在代谢组学应用中特别有吸引力，而传统代谢组统计的适用性往往受到限制。因此，本研究的目的是在新颖的实验环境下引入概率主题建模-更具体地讲，是潜在的狄利克雷分配（LDA）-基于挥发物的（海洋）食物腐败特征。这是作为一个案例研究而实现的，其重点是在不同的气体气氛（％CO _2）下模拟大西洋鲑（Salmo salar）在4°C下的腐败/ O ₂ / N ₂）：0/0/100（A），空气（B），60 / ₀ / 40（C）或60/40/0（D）。首先，进行了探索性分析，以优化模型调整并因此对100％N ₂下的鲑鱼腐败建模（一种）。基于获得的结果，建立了系统的腐败特性描述协议，并用于在所有测试存储条件下识别潜在的挥发性腐败指标。总之，LDA可用于提取潜在的VOC轮廓集并识别表示鲑鱼变质的特征，引起了与模型调整和/或变质分析相关的关键点的广泛讨论。根据先前建立的基于偏最小二乘回归分析（PLS）的方法，鉴定出的化合物很好。总体而言，研究结果不仅反映了LDA在变质表征方面的潜在潜力，而且还为开发以数据为导向的食品质量分析方法提供了一些新见解。