Optimized Preprocessing and Machine Learning for Quantitative Raman Spectroscopy in Biology
Raman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a pre-processing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics using Raman requires a robust model which can withstand routine spectra discrepancies due to unavoidable variations such as age, diet, and medical background. A wealth of pre-processing methods are available, and it is often up to trial-and-error or user experience to select the method which gives the best results. This process can be incredibly time consuming and inconsistent for multiple operators. In this study we detail a method to analyze the statistical variability within a set of training spectra and determine suitability to form a robust model. This allows us to selectively qualify or exclude a pre-processing method, predetermine robustness, and simultaneously identify the number of components which will form the best predictive model. We demonstrate the ability of this technique to improve predictive models of two artificial biological fluids. Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine health monitoring which maximizes comfort is increasingly crucial, particularly in epidemic-level diabetes diagnoses. High variability in spectra of biological samples can hinder Raman's adoption for these methods. Our technique allows the decision of optimal pre-treatment method to be determined for the operator; model performance is no longer a function of user experience. We foresee this statistical technique being an instrumental element to widening the adoption of Raman as a monitoring tool in a field of biofluid analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
A review of artificial intelligence methods combined with Raman spectroscopy to identify the composition of substances
In general, most of the substances in nature exist in mixtures, and the noninvasive identification of mixture composition with high speed and accuracy remains a difficult task. However, the development of Raman spectrosc…
Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation
Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoper…
Transfer LearningDeep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline …
BIG-bench Machine LearningGeneral ClassificationRaman Spectrum Matching with Contrastive Representation Learning
Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference database, which requires careful preproce…
BIG-bench Machine LearningConformal PredictionRepresentation LearningNon-Destructive Carotenoid Quantification in Leaves via Raman Spectroscopy: Optimizing Treatment for Linear Discriminant Analysis
This study introduces a novel method for quantifying challenging carotenoids in leaf tissues, which typically produce less stable signals than fruits, grains, and roots, by applying Linear Discriminant Analysis (LDA) mod…