Application Of Terahertz Spectrum In The Identification Of Urinary Tract Stones And Coffee Recognition

Terahertz(THz)wave bridges the far-infrared wave and the microwave in the electromagnetic spectrum.Many substances have characteristic spectra in the THz band,Therefore,the terahertz time-domain spectrum technology has been one of the important methods for detecting the composition of substances,and widely used in various fields such as biology,food and industry.In this paper,quantum chemistry theory and machine learning methods are used to inspect and analyze the terahertz time-domain spectrum of urinary tract stones and various coffees.First,the main components of urinary tract stones were detected with a terahertz time-domain spectrometer,and the absorption spectrum in the range of 0.1-2.5 THz of hypoxanthine,xanthine,uric acid,L-cystine and calcium oxalate were obtained after data processing.Material composition has rich absorption spectrum information in the THz band because this band is wider.Hence,the terahertz time-domain spectrum technique can be used as a method for detecting urinary tract stone.In order to study the cause of the absorption peak,the theoretical spectrum of the single molecule structure was obtained through the theoretical analysis of quantum chemistry and calculation in Gausian09 software by using three different calculation methods.By comparing the theoretical spectrum and the experimental spectrum,it is found that the B3 LYP calculation method in the density functional theory has achieved a result that is in good agreement with the experimental ones.The absorption peak of L-cystine in the terahertz band(0.1-2.5 THz)is mainly caused by the twisting and vibration in the molecule and the vibration of the molecular skeleton,which is explained from the perspective of molecular structure.Second,the absorption spectra of six coffee was measured with a terahertz time-domain spectrometer,and the characteristic absorption peaks of each coffee in the terahertz band were compared.Then,machine learning methods were used to identify coffee based on the terahertz frequency domain spectral data of the coffee.Firstly,BP neural network,decision tree and support vector machine are used to establish a classification model based on the coffee samples in the training set.Then use the coffee samples in the training set to train the classification model.Finally,the trained classification model is used to identify the coffee samples in the prediction set.The results show that the support vector machine classification model established after principal component analysis can correctly identify the type of coffee samples in the prediction set up to 100%.