Abstract

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Impact of preprocessing methods on the Raman spectra of brain tissue

Joel Wahl, Elisabeth Klint, Martin Hallbeck, Jan Hillman, Karin Wårdell, and Kerstin Ramser
Biomed. Opt. Express 13(12) 6763-6777 (2022)

Automated decomposition algorithm for Raman spectra based on a Voigt line profile model

Yunliang Chen and Liankui Dai
Appl. Opt. 55(15) 4085-4094 (2016)

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method

Yixin Guo, Weiqi Jin, Weilin Wang, Yuqing He, and Su Qiu
Appl. Opt. 62(18) 4766-4776 (2023)

Automated interferogram analysis based on an integrated expert system

Wonjong Joo and Soyoung S. Cha
Appl. Opt. 34(32) 7486-7496 (1995)

Blood species identification based on deep learning analysis of Raman spectra

Shan Huang, Peng Wang, Yubing Tian, Pengli Bai, DaQing Chen, Ce Wang, JianSheng Chen, ZhaoBang Liu, Jian Zheng, WenMing Yao, JianXin Li, and Jing Gao
Biomed. Opt. Express 10(12) 6129-6144 (2019)