Signal Processing in Electrochemistry with Python: Applications to the US Opioids Crisis
summary
Cyclic voltammetry data are processed with scipy.signal Savitzky‑Golay smoothing, FFT decomposition, and db4 wavelet denoising to recover peaks obscured by added noise levels (0–100%, seed 30). Savitzky‑Golay window/order combinations produce similar MSLE/MSSE and no statistically significant differences; hyperopt (20 trials) tuning yields no clear improvement over handpicked parameters. FFT shows a distinguishing component at harmonic five for a two‑substance mixture, and wavelet reconstruction succeeds up to moderate noise but fails near 100% corruption.
AI-written summary · about
chapters (13)
- 0:00Introduction
- 1:08Opioids crisis overview
- 3:13Fentanyl detection challenges
- 7:57Electrochemical biosensors
- 9:01Cyclic voltammetry basics
- 11:07Savitzky-Golay filter
- 13:02Optimizing signals with Python
- 16:42Hyperopt optimization
- 19:39Optimization approaches
- 20:01Discrete Fourier transform
- 22:30Discrete wavelet transform
- 26:10Key takeaways
- 29:22Q&A
related talks
27mDecoding Binary Files - a Primer (specifically optical spectra for analysis)
32mEDA Techniques for Data Analysis
37mUsing Python to visualize and analyze ion beam experiment data at a particle accelerator facility
01h 53mAnalyze your data in Python
33mApplication of Multi-class Supervised Algorithms to Identify COVID-19 Viral Strains
37mTracking the tracker: Time Series Analysis in Python From First Principles
55mDerivatives Analytics with Python & Numpy
18mData science and machine learning in wastewater intelligence