基于太赫兹时域光谱的食用油过氧化值定量分析研究

doi:10.3964/j.issn.1000-0593(2021)05-1387-06

Abstract
References
Related Citation (15)

Download: PDF (3237 KB)
Export: BibTeX | EndNote (RIS)

Abstract At present, Terahertz (THz) spectroscopy techniques are mainly used for qualitative analysis, but the application of THz technology can hardly be found in quantitative analysis in the detection of edible oil’s quality. This paper presents an approach to analyze edible oil quality based on Attenuated Total Reflection (ATR) and Terahertz Time Domain Spectroscopy(THz-TDS). Firstly, the THz-TDS of edible oil samples with different types and degrees of oxidation were collected, the effective signal band was filtered and the optical constants were extracted, the preprocessing algorithm corrected the optical constants, a variety of chemometrics methods were used to establish quantitative analysis models, in order to quickly and accurately predicted the peroxide value of edible oils. 70 experimental samples were used, including soybean oils, rapeseed oils and corn oils, the peroxide value ranged from 0.41 to 10.23 mmol·kg^-1, and the peroxide value distribution of the samples was evenly distributed. A TeraPulse 4000 terahertz pulse spectroscopy system equipped with an ATR detection module belonging to TeraView was used to collect samples’ THz-TDS signals. According to THz-TDS characteristics, the effective band 10 to 86.78 cm^-1 was selected for modeling analysis. The frequency domain signals were obtained by fast Fourier transform, and the optical constants were extracted: refractive index and absorption coefficient. Refractive index and absorption coefficient were preprocessed separately through Savitzky-Golay 7-points convolution smoothing, which had achieved the purpose of removing interference signals. The SPXY algorithm was used to divide the calibration set, and prediction set samples in a 3∶1 ratio. The peroxide value analysis models based on refractive index and absorption coefficient were established by the principal component regression algorithm and partial least square algorithm. The root mean square error and correlation coefficient of the model evaluation indexes were analyzed, the peroxide value analysis model based on the refractive index was modeled by partial least squares algorithm had ideal prediction accuracy. When the optimal principal component number was selected to be 6, RMSEC is 0.168%，R² is 0.981，RMSEP is 0.231%，r² is 0.977. The principal component regression algorithm modeled the peroxide value analysis model based on the absorption coefficlent, and the prediction model had the best robustness. When the optimal principal component number was selected to be 10, RMSEC is 0.192%，R² is 0.979，RMSEP is 0.262%，r² is 0.97. This study verifies it is feasible to detect the peroxide value of edible oil by THz technology, and the more important innovation is a high-precision, stable performance, fast and non-destructive detection method for the evaluation of edible oil quality has been found. Furthermore, this research has important guiding significance for improving the safety of edible oil quality and building edible risk assessment systems.

Key words：Edible oil; Terahertz time domain spectroscopy technology; Principal component regression; Partial least squares; Quantitative analysis

Received: 2020-05-08 Accepted: 2020-08-06

ZTFLH:

O657.3

Corresponding Authors: YANG Yu-fei E-mail: yangyf0919@163.com

Cite this article:

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)05-1387-06 OR http://www.gpxygpfx.com/EN/Y2021/V41/I05/1387