Objective: To investigate the DRS of ovine joint tissue to determine the optimal optical wavelengths for tissue differentiation and relate these wavelengths to the biomolecular composition of tissues. In this study, we combine machine learning with DRS for tissue classification and then look further at the weighting matrix of the classifier to further understand the key differentiating features. Methods: Supervised machine learning was used to analyse DRS data. After normalising the data, dimension reduction was achieved through multiclass Fisher’s linear discriminant analysis (Multiclass FLDA) and classified with linear discriminant analysis (LDA). The classifier was first run with all the tissue types and the wavelength range 190 nm – 1081 nm. We analysed the weighting matrix of the classifier and then ran the classifier again, the first time using the ten highest weighted wavelengths and the second using only the single highest. Our method was applied to a dataset containing ovine joint tissue including cartilage, cortical and subchondral bone, fat, ligament, meniscus, and muscle. Results: It achieved a classification accuracy of 100% using the wavelength 190 nm – 1081 nm (2048 attributes) with an accuracy of 90% being present for 10 attributes with the exception of those with comparable compositions such as ligament and meniscus. An accuracy greater than 70% was achieved using a single wavelength, with the same exceptions. Conclusion: Multiclass FLDA combined with LDA is a viable technique for tissue identification from DRS data. The majority of differentiating features existed within the wavelength ranges 370-470 and 800-1010 nm. Focusing on key spectral regions means that a spectrometer with a narrower range can potentially be used, with less computational power needed for subsequent analysis.

中文翻译：

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机器学习中使用漫反射光谱（DRS）进行绵羊关节组织分化的波长加权

目的：研究绵羊关节组织的DRS，以确定组织分化的最佳光波长，并将这些波长与组织的生物分子组成相关联。在这项研究中，我们将机器学习与DRS相结合以进行组织分类，然后进一步查看分类器的加权矩阵以进一步了解关键的区别特征。方法：监督机器学习用于分析DRS数据。将数据标准化后，通过多类Fisher线性判别分析（Multiclass FLDA）进行降维，并通过线性判别分析（LDA）进行分类。首先使用所有组织类型和波长范围190 nm – 1081 nm进行分类器。我们分析了分类器的加权矩阵，然后再次运行分类器，第一次使用十个最高加权波长，第二次仅使用单个最高波长。我们的方法应用于包含绵羊关节组织的数据集，包括软骨，皮质和软骨下骨，脂肪，韧带，半月板和肌肉。结果：使用190 nm – 1081 nm波长（2048个属性）可以达到100％的分类精度，除韧带和半月板等可比成分外，它对10个属性的分类精度为90％。使用单个波长可以达到大于70％的精度，但有相同的例外。结论：多类FLDA与LDA结合是一种从DRS数据进行组织识别的可行技术。大多数区别特征存在于370-470和800-1010 nm的波长范围内。专注于关键光谱区域意味着可以使用范围更窄的光谱仪，而后续分析所需的计算能力更低。