Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter “Gaia” hyperspectral sorter was used to extract 254 bands of plasma images, ranging from 900 nm to 1700 nm. Four different machine learning algorithms were used, including decision tree, Gaussian Naive Bayes (GaussianNB), perceptron, and stochastic gradient descent models. First, the preliminary classification accuracies were compared with the original data, which showed that the effects of the decision tree and GaussianNB models were better; their average accuracies could reach over 90%. Then, the feature dimension reduction was performed on the original data. The results showed that the effects of the decision tree were better with a classification accuracy of 93.33%. the classification of chylous plasma using different chylous indices suggested that the accuracies of the decision trees model both before and after the feature dimension reductions were the best with over 80% accuracy. The results of feature dimension reduction showed that the characteristic bands corresponded to all kinds of plasma, thereby showing their classification and identification potential. By applying the spectral characteristics of plasma to medical technology, this study suggested a rapid and effective method for the identification of chylous plasma and provided a reference for the blood detection technology to achieve the goal of reducing wasting blood resources and improving the work efficiency of the medical staff.

中文翻译：

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基于机器学习和高光谱技术的乳糜血浆检测

乳糜血是造成无偿献血者血液不合格、报废的主要原因。因此，需要一种能够在献血前快速、准确地识别乳糜血的诊断方法。在这项研究中，GaiaSorter“Gaia”高光谱分选机用于提取 254 个波段的等离子体图像，范围从 900 nm 到 1700 nm。使用了四种不同的机器学习算法，包括决策树、高斯朴素贝叶斯 (GaussianNB)、感知器和随机梯度下降模型。首先将初步分类精度与原始数据进行对比，结果表明决策树和GaussianNB模型的效果更好；他们的平均准确率可以达到90%以上。然后对原始数据进行特征降维。结果表明，决策树的分类效果较好，分类准确率为93.33%。使用不同乳糜指数对乳糜血浆进行分类表明，特征降维前后决策树模型的准确率最好，准确率超过80%。特征降维结果表明，特征谱带对应于各种等离子体，从而显示出它们的分类和识别潜力。本研究将血浆的光谱特征应用于医疗技术，提出了一种快速有效的乳糜血浆鉴别方法，为血液检测技术提供参考，以达到减少血液资源浪费、提高工作效率的目的。医护人员。