4.5 Article

Quantitative Analysis of Industrial Solid Waste Based on Terahertz Spectroscopy

Journal

PHOTONICS
Volume 9, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/photonics9030184

Keywords

terahertz spectroscopy; industrial solid waste; quantitative analysis of mixtures

Categories

Funding

  1. National Natural Science Foundation of China [81961138014, 61922059]
  2. Key projects of domestic scientific and technological cooperation in Shanghai [21015800200]

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Industrial solid waste is the solid waste produced in industrial production activities, which may cause environmental pollution if not treated and released correctly. This study proposes a method for the quantitative analysis of harmful components in industrial solid waste using terahertz spectroscopy combined with chemometrics. Compared with the conventional Raman method, the terahertz method shows higher accuracy in concentration analysis. In addition, the method is also effective for the quantitative analysis of mixtures.
Industrial solid waste refers to the solid waste that is produced in industrial production activities. Without correct treatment and let-off, industrial solid waste may cause environmental pollution due to a variety of pollutants and toxic substances that are contained in it. Conventional detection methods for identifying harmful substances are high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), which are complicated, time-consuming, and highly demanding for the testing environment. Here, we propose a method for the quantitative analysis of harmful components in industrial solid waste by using terahertz (THz) spectroscopy combined with chemometrics. Pyrazinamide, benazepril, cefprozil, and bisphenol A are four usual hazardous components in industrial solid waste. By comparing with the Raman method, the THz method shows a much higher accuracy for their concentration analysis (90.3-99.8% vs. 11.7-86.9%). In addition, the quantitative analysis of mixtures was conducted, and the resulting prediction accuracy rate was above 95%. This work has high application value for the rapid, accurate, and low-cost detection of industrial solid waste.

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