Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Abstract

Significance: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met.Aim: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH 2 ∕CH 3 deformation, and the amide bands.Approach: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy. Results:The method can separate high-and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively. Conclusions:The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.

Machine Learning-Assisted Sampling of Surfance-Enhanced Raman Scattering (SERS) Substrates Improve Data Collection Efficiency

“…Work by Dallaire et al discusses the importance of spectral quality for further downstream analyses. 46 In particular, they also note the caveat in current literature reports; the spectral quality assessment is largely made offline based on qualitative visual inspection instead of using unbiased and systematic quantitative criteria. The authors elaborately demonstrate the effectiveness of excluding ''bad'' spectra in cancer detection application.…”

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

“…The background was finally removed using a rolling ball algorithm [38]. In addition, a quality factor QF(n), was computed which consists in summing the Raman signal‐to‐noise ratio SNR R over N spectral bands: where r j and a j are the Raman signal and the background signal (mostly instrument response and fluorescence from the sample) within spectral band j , respectively [37]. Due to its ubiquitous presence in biological tissues [28, 39–41], the Raman peak at 1441 cm −1 was used as the unique band for the quality factor measurement.…”

“…The raw spectroscopic data were first averaged over the number of spectra (n) acquired for each point. A cosmic ray removal algorithm was applied and the dark count signal was subtracted [37]. The signal was subsequently normalized with the NIST standard measurement and an x-axis calibration performed using the acetaminophen measurement.…”

“…where r j and a j are the Raman signal and the background signal (mostly instrument response and fluorescence from the sample) within spectral band j, respectively [37]. Due to its ubiquitous presence in biological tissues [28,[39][40][41], the Raman peak at 1441 cm −1 was used as the unique band for the quality factor measurement.…”

Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM

“…The single-component quantitative detection technology of substances is mature, such as in the direct measurement of electrochemical sensors and the use of spectroscopy combined with chemometrics to establish quantitative models [1][2][3][4]. Currently, multicomponent quantitative and qualitative analysis of complex systems has become the focus of research.…”