Adaptive band target entropy minimization: Optimization for the decomposition of spatially offset Raman spectra of bone

Abstract

We report a novel variant of band target entropy minimization (BTEM), adaptive band target entropy minimization (A-BTEM), which offers an improved ability to accurately decompose mixed spectra obtained from complex multicomponent systems. Several key challenges have existed in the application of the basic BTEM approach to decompose spatially offset Raman spectra of bone underneath soft tissues and other multilayer systems demonstrating high collinearity between the spectra of individual components; these have included instabilities, signal mixing, and spectral artefacts that have precluded the reliable use of BTEM in such situations. By using automatic factor selection, an adaptive penalty function in addition to metaheuristic optimization strategies, we demonstrate that high-quality spectral reconstructions of underlying pure component spectra can be obtained with typical correlation coefficients >0.996. Furthermore, we ascertain the behaviour of A-BTEM with different input datasets, both synthetic and real, displaying varying signal-to-noise ratio, signal composition, and numbers of spectra. We thereby identify the multifarious parameter space in the application of A-BTEM and the quality of data required for the most accurate spectral estimates of pure component spectra. KEYWORDSband target entropy minimization, bone disease, multivariate curve resolution, self-modelling curve resolution, spatially offset Raman spectroscopy

Spatially offset Raman spectroscopy

“…One method for extracting pure Raman spectra of individual layers is band target entropy minimization, which uses an algorithm that relies on the fact that, in general, a higher order exists in pure spectra than those resulting from the summation of spectra from multiple layers 78 . A more advanced version applicable to noisier data is adaptive band target entropy minimization, which dynamically adapts convergence parameters during the iterative search for real spectra 79 . An alternative effective approach for SORS spectral decomposition is based on regression analysis, using the Raman spectra of known sample components with the inclusion of regression residuals 80 .…”

Detection of Age-Related Changes in Tendon Molecular Composition by Raman Spectroscopy—Potential for Rapid, Non-Invasive Assessment of Susceptibility to Injury

“…Fluorescence has been used as an indirect measure of AGEs in human skin in vivo, and a positive correlation with age demonstrated, interestingly, a negative correlation with physical training [38][39][40], suggesting that physical activity can modify the accumulation of age-related chemical modifications. Spatially offset Raman spectroscopy provides the opportunity to measure tendon fluorescence level and Raman spectra simultaneously in a non-invasive manner [18,19,41], and could potentially detect age-related changes, as observed here, in tendon and other tissues in vivo.…”

Spatially offset Raman spectroscopy for biomedical applications

“…Recently a more advanced variant has been developed for specifically retrieving Raman signals from biological samples (bone in soft tissue) and is termed “adaptive BTEM” (ABTEM). 23 Alternatively, the spectral unmixing can be performed using an overconstrained extraction algorithm based on fitting with spectral libraries. 24 General applications of SORS in biological areas include the identification of particular biomarkers, or the quantification of some biological subcomponents relative to some other measurable Raman entity in the sample.…”

“…Recently, the use of adaptive-band targeted entropy mineralization has been applied to spectral un-mixing of SORS spectra from bone samples buried beneath tissue. 23 The algorithm was successfully applied to transcutaneous SORS spectra and represents a step towards development of an optimized clinical SORS system, specific for diagnosis of bone disease in patients.…”