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Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used.
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

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Updated: May 16, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Published on: August 19, 2021

PlasticAnalytics: A Deep Learning-Powered Spectral Library and Analytical Suite.

Dr Joseph M Levermore1, Professor Frank J Kelly1, Dr Stephanie L Wright1

  • 1Environmental Research Group, MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W12 0BZ, United Kingdom.

Environmental Science & Technology
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

PlasticAnalytics automates microplastic analysis using Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). This workflow significantly speeds up data processing and improves the accuracy of identifying microplastics in environmental samples.

Keywords:
deep residual networkfourier transform infrared spectroscopymicroplasticsout-of-distribution detectionraman spectroscopyspectral library

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Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
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Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
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Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Materials Science

Background:

  • Microplastic pollution is a global environmental concern.
  • Vibrational spectroscopy (Raman, FTIR) is crucial for microplastic identification.
  • Current spectroscopic analysis workflows are often slow and labor-intensive.

Purpose of the Study:

  • To develop an automated workflow, PlasticAnalytics, for efficient microplastic analysis.
  • To address bottlenecks in vibrational spectroscopic data preprocessing and analysis.
  • To enhance the speed and accuracy of microplastic identification from complex environmental samples.

Main Methods:

  • Implemented an iterative asymmetric penalized least-squares (i-arPLS) baseline correction algorithm.
  • Developed a hybrid rule-based and machine learning framework for spectral data preprocessing (peak removal, resampling, normalization, smoothing).
  • Utilized a deep residual network with an uncertainty-aware classifier for accurate microplastic identification and quality control.

Main Results:

  • Achieved high classification accuracies: 96.9% for Raman and 97.9% for FTIR.
  • Reduced spectral imaging processing time by over 90% (from hours to under 7 minutes).
  • Successfully removed substrate spectra and spurious peaks, ensuring analysis of particulate signals.

Conclusions:

  • PlasticAnalytics offers a scalable, reproducible, and automated solution for microplastic analysis.
  • The workflow significantly accelerates processing times for both Raman and FTIR spectroscopy.
  • The system demonstrates high accuracy and reliability for identifying microplastics in diverse environmental matrices.