西北农林科技大学学报（自然科学版）2026，Vol.54Issue(9)：78-88,99,12.DOI:10.13207/j.jnwafu.2026.09.008
结合太赫兹光谱与密度泛函理论的黄曲霉毒素B1指纹特征验证
Verification of the fingerprint characteristics of aflatoxin B1 using terahertz spectroscopy combined with density functional theory
摘要
Abstract
[Objective]This study integrates Density Functional Theory(DFT)and Terahertz Time-Do-main Spectroscopy(THz-TDS),to provide a reference for high-precision quantitative analysis and fingerprint verification of Aflatoxin B1(AFB1).[Method]Based on DFT,the AFB1 molecule was structurally optimized and its vibrational frequencies were calculated to obtain the theoretical absorption spectrum in the 0.1-5.0 THz range,with absorption peaks interpreted from the perspective of molecular vibrational modes.Seven spec-tral preprocessing methods,including S-G smoothing,movement smoothing,normalization,standard normal variate(SNV),multiplicative scatter correction(MSC),first derivative,and second derivative,were systemati-cally compared to determine their effects on the PLSR modeling of AFB1 terahertz spectra.To address the inade-quacy of traditional feature extraction methods(SPA,MCUVE,CARS,SFLA)in characterizing low-concen-tration signals,quantitative prediction models for AFB1 based on one-dimensional convolutional neural network(1D-CNN)and residual metwork(ResNet)were constructed.Through convolution and pooling operations,the models adaptively performed local perception on raw spectra,and leveraged deep learning to capture implicit fea-tures from adjacent spectral bands,thereby enhancing the extraction efficiency of fingerprint peaks.[Result]In the 0.5-3.0 THz range,the experimental absorption peaks were highly consistent with theoretical predictions,validating the reliability of the C=O/C―O vibrational modes to obtain characteristic fingerprint peaks for AFB1.In the concentration range of 0.25-10.00 μg/mL,the prediction set of 1D-CNN model achieved a de-termination coefficient(R)of 0.92,with a root mean square error of 0.77.Compared to traditional methods,the characteristic frequencies extracted by the 1D-CNN model corresponded well with the theoretical absorption peaks:the extracted frequencies almost fully covered the theoretical peak at 2.114 THz,while only a 0.05 THz deviation was observed at 1.108 THz.[Conclusion]The vibrational modes at 1.108,1.767,and 2.114 THz show a high degree of agreement with the experimental spectrum.The 1D-CNN model demonstrates optimal predictive capability for aflatoxin B1.The established method for verifying the characteristic terahertz fingerprint peaks of AFB1 provides a new paradigm for detecting trace toxins in complex matrices and serves as a valuable reference for developing on-site rapid detection technologies in the field of food safety.关键词
黄曲霉毒素B1/太赫兹时域光谱/密度泛函理论/指纹峰分析/机器学习Key words
aflatoxin B1/terahertz time-domain spectroscopy/density functional theory/fingerprint peak analysis/machine learning分类
轻工纺织引用本文复制引用
张思怡,杨硕,梁晓颖,姚志凤,陈煦,宋怀波..结合太赫兹光谱与密度泛函理论的黄曲霉毒素B1指纹特征验证[J].西北农林科技大学学报（自然科学版）,2026,54(9):78-88,99,12.基金项目
国家自然科学基金项目(32201662) （32201662）
陕西省重点研发计划项目(2024NC-YBXM-218) （2024NC-YBXM-218）
