Application of Terahertz Spectroscopy in the Detection of Bioactive Peptides
WANG Pu1, HE Ming-xia1*, LI Meng2, QU Qiu-hong2, LIU Rui3, CHEN Yong-de4
1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
2. LET Terahertz (Tianjin) Technology Co., Ltd., Tianjin 300019, China
3. School of Food Engineering and Biotechnology, Tianjin University of Science and Technology, Tianjin 300222, China
4. Bai Defu Biological Technology Co., Ltd., Tangshan 063000, China
Abstract：Bioactive peptides, as the new darling of human health in the 21st century, have been proved that they have a good effect on human life activities, and their detection methods are also of great concern. Terahertz time-domain spectroscopy technology has incomparable advantage in detecting bioactive peptides because of its unique properties. In this paper, three bioactive peptides, bovine bone peptide, sea cucumber peptide and fish peptide, were used to obtain the absorption coefficient curve of 0.5~2 THz by the transmission terahertz time domain spectroscopy system. From the terahertz absorption coefficient curve, the absorption coefficient of the fish peptide is higher than that of sea cucumber peptide and fish bone peptide. Because of the interaction between the amino acid species of bioactive peptides and peptide bonds, there is no obvious absorption peak in the terahertz frequency band. In order to better detect and distinguish them, a classification discriminant model is established to find the most suitable for such substances. After the S-G smoothing and normalization preprocess performed on the terahertz original absorption coefficient data, two-thirds of the pre-processed data are randomly selected into training sets, and the rest are prediction set. The classification discriminant model is introduced. The model includes two parts: the classifier and the optimal parameter selection. The classifier selects the supervised classification method such as support vector machine, random forest and extreme learning machine, and uses the intelligent optimization algorithm such as genetic algorithm, particle swarm optimization and grid search to select the support vector machine optimal parameters. In order to reduce the original spectral data dimension and improve the computational speed of the model, Principal Component Analysis is used for preprocessing, and the results after dimensionality reduction are imported into the classification model. Considering the factors such as accuracy and running time, although the support vector machine based on particle swarm optimization has the highest accuracy rate of 98.3%, the running time is longer than 180 seconds; the ultimate learning machine can have the shortest running time of 0.2 seconds. However, the accuracy rate is 73.3%. The support vector machine based on grid search has an accuracy rate of 96% and a running time of 11 seconds. It can use a shorter time in the case of higher accuracy, and proves that the support vector machine based on grid search is better for detecting bioactive peptide. The results show that the use of terahertz time-domain spectroscopy combined with machine learning algorithms can achieve rapid and non-destructive detection of bioactive peptides, providing a new idea for the detection of bioactive peptides. It also demonstrates that THz-TDS combined with machine learning is a way better way for the identification of inconspicuous peptides.
王 璞，何明霞，李 萌，曲秋红，刘 锐，陈永德. 太赫兹光谱技术在生物活性肽检测中应用研究[J]. 光谱学与光谱分析, 2020, 40(09): 2696-2701.
WANG Pu, HE Ming-xia, LI Meng, QU Qiu-hong, LIU Rui, CHEN Yong-de. Application of Terahertz Spectroscopy in the Detection of Bioactive Peptides. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2696-2701.
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