Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-05, Vol.21 (9), p.3238
• Main Authors: Hu, Jun, Chen, Rui, Xu, Zhen, Li, Maopeng, Ma, Yungui, He, Yong, Liu, Yande
• Format: Article
• Language: English
• Subjects: Adaptive sampling; Additives; Antibiotics; Aqueous solutions; Benzoic acid; Bioassays; Chromatography; Composite materials; Correlation coefficients; Design; Electromagnetism; Food additives; Food cans; Food safety; LS-SVM; Metamaterials; Methods; Pesticides; Pretreatment; resonance enhancement; Resonators; Simulation; Smoothing; Spectra; Support vector machines; Terahertz frequencies; THz detection technology; trace additives
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21093238

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10−6, and the limit of detection (LOD) is 2.3610 × 10−5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.