MIR spectroscopy is becoming an increasingly important tool potentially useful for diagnosis purposes especially by studying body fluids. Indeed, diseases induce changes in the composition of fluids modifying the MIR spectra. However, such changes can be difficult to capture if the structure of the data is not considered. Our objective was to improve MIR spectra analysis by using approximation of the spectra by B-splines at different specific resolutions and to combine these spectra representations with a machine learning model to predict hepatic steatosis from serum study. The different resolutions make it possible to identify changes in shape over bands of various widths. The multiresolution model helps to improve the hepatic steatosis prediction compared to conventional approaches where the absorbances are considered as unstructured variables. In addition, B-splines provide both localized and compressed information that can be translated into biochemical terms more easily than with other classical approximation methods (wavelets, Fourier transforms).

中红外光谱正成为一种日益重要的工具，可能用于诊断目的，尤其是通过研究体液。事实上，疾病会引起改变 MIR 光谱的流体成分的变化。但是，如果不考虑数据的结构，就很难捕捉到这种变化。我们的目标是通过使用 B 样条在不同特定分辨率下的光谱近似来改进 MIR 光谱分析，并将这些光谱表示与机器学习模型相结合，以从血清研究中预测肝脏脂肪变性。不同的分辨率可以识别不同宽度带上的形状变化。与将吸光度视为非结构化变量的传统方法相比，多分辨率模型有助于改善肝脏脂肪变性的预测。