Abstract：
In the present contribution, a novel approach based on multivariate curve resolution and deep learning (DL) is proposed for quantitative mass spectrometry imaging (MSI) as a potent technique for identifying different compounds and creating their distribution maps in biological tissues without need for sample preparation. As a case study, chlordecone as a carcinogenic pesticide was quantitatively determined in mouse liver using matrix-assisted laser desorption ionization-MSI (MALDI-MSI). For this purpose, data from seven standard spots containing 0 to 20 picomoles of chlordecone and four unknown tissues from the mouse livers infected with chlordecone for 1, 5, and 10 days were analyzed using a convolutional neural network (CNN). To solve the lack of sufficient data for CNN model training, each pixel was considered as a sample, the designed CNN models were trained by pixels in training sets, and their corresponding amounts of chlordecone were obtained by multivariate curve resolution-alternating least-squares (MCR-ALS). The trained models were then externally evaluated using calibration pixels in test sets for 1, 5, and 10 days of exposure, respectively. Prediction R2 for all three data sets ranged from 0.93 to 0.96, which was superior to support vector machine (SVM) and partial least-squares (PLS). The trained CNN models were finally used to predict the amount of chlordecone in mouse liver tissues, and their results were compared with MALDI-MSI and GC-MS methods, which were comparable. Inspection of the results confirmed the validity of the proposed method.
摘要：
在目前的贡献中，提出了一种基于多变量曲线分辨率和深度学习（DL）的新方法，用于定量质谱成像（MSI），作为识别不同化合物并在生物组织中创建其分布图而无需样品制备的有效技术。作为一个案例研究，使用基质辅助激光解吸电离MSI（MALDI-MSI）在小鼠肝脏中定量测定了十氯酮作为致癌农药。为此,使用卷积神经网络（CNN）分析了7个含有0至20皮摩尔十氯酮的标准点和4个未知组织的数据，这些组织来自感染十氯酮1、5和10天的小鼠肝脏。为了解决CNN模型训练缺乏足够数据的问题，每个像素被视为一个样本，设计的CNN模型是通过训练集中的像素来训练的，通过多元曲线分辨率-交替最小二乘法(MCR-ALS)获得相应的十氯酮含量。然后使用测试集中的校准像素在1、5和10天的暴露中对训练的模型进行外部评估。分别。所有三个数据集的预测R2范围为0.93至0.96，优于支持向量机（SVM）和偏最小二乘（PLS）。经过训练的CNN模型最终用于预测小鼠肝脏组织中的十氯酮含量，并将其结果与MALDI-MSI和GC-MS方法进行了比较，这是可比的。结果检验证实了所提出方法的有效性。
