Qualitative And Quantitative Detection Of Materials Based On Terahertz Time-domain Spectroscopy

In recent years, terahertz (THz) wave has become an extremely attractive researchfield. Since THz wave exhibits the properties of spectroscopy, good penetration andsafety, THz technology has promising applications for the detection and identification ofmaterials. Terahertz time-domain spectroscopy (THz-TDS) is a new technique thatapplied to spectroscopic measurement based on ultrafast femtosecond laser, and it iscurrently a key technique for the study of materials using THz radiation. Thisdissertation is focusd on developing new methods for qualitative and quantitativedetection of materials in the fields of security inspection, nondestructive testing (NDT)and quality control (QC) based on THz-TDS. The author’s major contributions areoutlined as follows:1. Methods for detection and identification of explosives in the field of securityinspection based on THz-TDS have been studied. An approach for automaticidentification of THz spectra of explosives is proposed based on fuzzy patternrecognition with fuzzy cluster analysis. On the basis of studying the absorption spectraof several pure explosives, mixed explosives and potential confusion materials，fourrelatively strong characteristic absorption peaks in the frequency range from0.2to2.2THz are extracted as the characteristic parameters of classification and recognition, andfuzzy pattern recognition is used to carry out the classification and recognition of theirTHz characteristic absorption spectra. Firstly, fuzzy cluster analysis method based onfuzzy equivalent matrix is used to cluster the THz absorption spectra of these materials,thus the sample clustering is obtained and the standard model library of THz absorptionspectra is formed. Secondly, two materials, which have the same main ingredient as thatof the training samples, are used as the objects to be identified; and the fuzzy patternrecognition method based on the principle of fuzzy closeness optimization is adopted toidentify the objects. Study results indicate that the fuzzy pattern recognition methodbased on fuzzy cluster analysis has strong similar character clustering function and highrecognition rate for THz absorption spectra, which provides an effective method in thedetection and identification of explosives using THz spectroscopy.2. Methods for qualitative detection of materials in the fields of nondestructivetesting and quality control based on THz-TDS have been studied. We report the first useof principal component analysis (PCA) combined with fuzzy pattern recognition toidentify the THz spectra of biomolecules in this dissertation, and THz transmittancespectra of some typical amino acid and saccharide biomolecular samples are investigated to prove its feasibility. Firstly, PCA is applied to reduce the dimensionalityof the original spectrum data and extract features of the data. Secondly, instead of theoriginal spectrum variables, the selected principal component scores matrix is fed intothe model of fuzzy pattern recognition, where a principle of fuzzy closeness basedoptimization is employed to identify those samples. Study results demonstrate that THzspectroscopy combined with PCA and fuzzy pattern recognition can be efficientlyutilized for automatic identification of biomolecules. The proposed approach provides anew effective method in the detection and identification of biomolecules using THzspectroscopy.3. Methods for quantitative determination of materials in the fields ofnondestructive testing and quality control based on THz-TDS have been studied. Anapproach for simultaneous quantitative determination of both active pharmaceuticalingredient (API) and excipient concentrations of multicomponent pharmaceuticalmixtures is proposed using THz-TDS with chemometrics, and75ternary mixturesformulated with anhydrous theophylline, lactose monohydrate, magnesium stearate and100quaternary mixtures composed of acetaminophen, lactose monohydrate,microcrystalline cellulose and soluble starch are investigated to prove its feasibility andefficiency. The THz spectra for ternary mixtures of anhydrous theophylline, lactosemonohydrate, magnesium stearate, and quaternary mixtures of acetaminophen, lactosemonohydrate, microcrystalline cellulose and soluble starch are measured usingTHz-TDS. Two multivariate calibration methods, principal component regression (PCR)and partial least squares (PLS) regression, are employed to correlate THz absorbancespectra with the pharmaceutical tablet concentrations. Both API and excipientconcentrations of mixtures are predicted simultaneously, and the PLS method providesbetter result than PCR method. The correlation coefficients of calibration (Rcal) andvalidation (Rval) for ternary mixtures’ components, anhydrous theophylline and lactosemonohydrate, are all more than0.98. The Rcaland Rvalfor quaternary mixtures’components, acetaminophen, lactose monohydrate, microcrystalline cellulose andsoluble starch, are all more than0.93,0.98,0.63and0.86, respectively. Experimentalresults show that THz-TDS combined with chemometrics is feasible in nondestructivequantitative analysis of multicomponent mixtures, and it can be widely applied in thefields of pharmaceutical analysis and others.4. Interval selection algorithms are investigated systematically in this dissertation,and THz-TDS coupled with these algorithms is used to perform quantitative analysis of component concentrations in multicomponent mixtures, which can effectively improvethe precision and reduce the complexity for the quantitative analysis models. The THzspectra of100quaternary pharmaceutical mixtures composed of lactose monohydrate,acetaminophen, microcrystalline cellulose and soluble starch are measured usingTHz-TDS system. Four spectral interval selection methods, including interval partialleast-squares regression (iPLS), backward interval PLS (biPLS), synergy interval PLS(siPLS) and moving window PLS (mwPLS), are employed to select spectral intervals ofTHz absorbance spectra of multicomponent mixtures and correlate THz absorbancespectra with the concentrations of lactose monohydrate. Compared to the other threeinterval selection methods and full-spectrum PLS, the mwPLS method yields the mostaccurate result. The optimal mwPLS model is obtained with higher correlationcoefficient for calibration (RC) of0.9960, higher correlation coefficient for prediction(RP) of0.9951, lower root mean square error of cross-validation (RMSECV) of0.9803,and lower root mean square error of prediction (RMSEP) of1.1141. Study resultsindicate that spectral interval selection combined with THz-TDS could be successfullyapplied as an accurate and rapid method to determine component concentrations inmulticomponent mixtures, and it has potential application in the fields of biology, food,pharmaceutical analysis and so on.