Qualitative Identification of Terahertz Time-domain Spectra Based on Analytic Hierarchy Process

Terahertz time-domain spectroscopy (THz-TDS) is a type of far-infrared spectroscopy that reflects the internal characteristics of substances and provides rich physical and chemical information. Therefore, terahertz waves can be used to qualitatively identify food additives containing nitrogen. Hierarchical analysis, originally developed for solving evaluation-type problems, is introduced in this study to the field of qualitative analysis of terahertz spectra. This paper proposes and evaluates a qualitative identification method that combines THz-TDS with hierarchical analysis. In this study, six nitrogen-containing food additives were selected as experimental samples. First, the acquired terahertz time-domain spectral data were preprocessed and transformed into four datasets: peak values, peak positions, peak numbers, and overall spectral trends. Next, the data were divided into comparison and test sets to construct a qualitative identification model incorporating hierarchical analysis, followed by parameter optimization. The results indicated that the qualitative identification accuracy of additives based on single factors: overall trend, peak value, peak position, and peak number were 80.23%, 70.93%, 67.44%, and 40.70%, respectively. The multi-factor hierarchical analysis-based method improved the identification accuracy to 92.44%. In addition, by binarizing the fuzzy characterization of the absorption spectrum data during preprocessing and using it as the basis for assessing overall trends, the recognition accuracy increased to 94.19% when combined with the hierarchical analysis model. These results demonstrate the effectiveness of the proposed qualitative identification algorithm. The method is straightforward, does not require training, and is well-suited for rapid qualitative detection of small sample sets.