Species determination of the various life stages of flies (Order: Diptera) is challenging, particularly for the immature forms, because analogous life stages of different species are difficult to differentiate based on morphological features alone. It is demonstrated here that direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) combined with supervised Kohonen Self-Organizing Maps (SOM) enables accomplishment of species-level identification of larva, pupa, and adult life stages of carrion flies. DART-HRMS data for each life stage were acquired from analysis of ethanol suspensions representing Calliphoridae, Phoridae, and Sarcophagidae families, without additional sample preparation. After preprocessing, the data were subjected to a combination of minimum Redundancy Maximal Relevance (mRMR) and Sparse Discriminant Analysis (SDA) methods to select the most significant variables for creating accurate SOM models. The resulting data were divided into training and validation sets and then analyzed by the SOM method to define the proper discrimination models. The 5-fold venetian blind cross-validation misclassification error was below 7% for all life stages, and the validation samples were correctly identified in all cases. The multiclass SOM model also revealed which chemical components were the most significant markers for each species, with several of these being amino acids. The results show that processing of DART-HRMS data using artificial neural networks (ANNs) based on the Kohonen SOM approach enables rapid discrimination and identification of fly species even for the immature life stages. The ANNs can be continuously expanded to include a larger number of species and can be used to screen DART-HRMS data from unknowns to rapidly determine species identity.

中文翻译：

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实时质谱直接分析和Kohonen人工神经网络用于识别腐肉昆虫的幼虫，Pu和成虫生命阶段

确定果蝇各个生命阶段的物种（顺序：双翅目）具有挑战性，特别是对于未成熟形态而言，因为很难仅根据形态特征来区分不同物种的相似生命阶段。此处证明了实时高分辨率质谱法（DART-HRMS）的直接分析与有监督的Kohonen自组织图（SOM）的结合可实现对幼虫，和成年腐肉的生命阶段进行物种鉴定苍蝇。每个生命阶段的DART-HRMS数据均通过分析代表Calliphoridae，Phoridae和Sarcophagidae的乙醇悬浮液获得家庭，无需额外的样品制备。预处理之后，对数据进行最小冗余最大相关性（mRMR）和稀疏判别分析（SDA）方法的组合，以选择最重要的变量以创建准确的SOM模型。将所得数据分为训练集和验证集，然后通过SOM方法进行分析以定义适当的判别模型。所有生命阶段的5倍百叶帘式盲法交叉验证错误分类错误均低于7％，并且在所有情况下均正确识别了验证样本。多类SOM模型还揭示了哪种化学成分是每个物种最重要的标记，其中一些是氨基酸。结果表明，使用基于Kohonen SOM方法的人工神经网络（ANN）处理DART-HRMS数据，即使在生命周期不成熟的情况下，也能快速区分和鉴定蝇类。人工神经网络可以不断扩展以包括更多种类的物种，并且可以用于从未知物中筛选DART-HRMS数据以快速确定物种身份。