Article

Terahertz Spectroscopy

Department of Chemical and Biological Engineering, Drexel University, Philadelphia, Pennsylvania 19104, USA.
Analytical Chemistry (Impact Factor: 5.64). 06/2011; 83(12):4342-68. DOI: 10.1021/ac200907z
Source: PubMed
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    • "Hence, terahertz spectroscopy and imaging is regarded as a promise technique for chemicals analysis and mapping, food safety [18] [19] [20]. Terahertz time-domain spectroscopy (THz-TDS) has been the most commonly used mode in the field of THz spectroscopic application [11]. By coupling with chemometric methods, such as partial least squares (PLS) and artificial neural networks (ANN), THz-TDS enables to perform qualitative and quantitative analyses with speed and accuracy [21] [22] [23] [24] [25] and, thus, offers a possibility to apply THz-TDS into practical food process monitoring and quality control, such as antibiotic detection [26] [27] [28] [29], pesticides detection [21] [22] [30]. "
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    ABSTRACT: Terahertz time-domain spectroscopy (THz-TDS) has been utilized as an effective tool for quantitative analysis of imidacloprid in rice powder samples. Unlike previous studies, our method for sample preparation was mixing imidacloprid with rice powder instead of polyethylene. Then, terahertz time domain transmission spectra of these mixed samples were measured and the absorption coefficient spectra of the samples with frequency range extending from 0.3 to 1.7 THz were obtained. Asymmetric least square (AsLS) method was utilized to correct the slope baselines that are presented in THz absorption coefficient spectra and improve signal-to-noise ratio of THz spectra. Chemometrics methods, including partial least squares (PLS), support vector regression (SVR), interval partial least squares (iPLS), and backward interval partial least squares (biPLS), were used for quantitative model building and prediction. To achieve a reliable and unbiased estimation, bootstrapped Latin partition was chosen as an approach for statistical cross-validation. Results showed that the mean value of root mean square error of prediction (RMSEP) for PLS (0.5%) is smaller than SVR (0.7%), these two methods were based on the whole absorption coefficient spectra. In addition, PLS performed a better performance with a lower RMSEP (0.3%) based on the THz absorption coefficient spectra after AsLS baseline correction. Alternatively, two methods for variable selection, namely iPLS and biPLS, yielded models with improved predictions. Comparing with conventional PLS and SVR, the mean values of RMSEP were 0.4% (iPLS) and 0.3% (biPLS) by selecting the informative frequency ranges. The results demonstrated that an accurate quantitative analysis of imidacloprid in rice powder samples could be achieved by terahertz time-domain transmission spectroscopy combined with chemometrics. Furthermore, these results demonstrate that THz time-domain spectroscopy can be used for quantitative determinations of other pesticides in other agricultural products.
    Full-text · Article · Aug 2015 · Journal of Quantitative Spectroscopy and Radiative Transfer
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    • "Hence, terahertz spectroscopy and imaging is regarded as a promise technique for chemicals analysis and mapping, food safety [18] [19] [20]. Terahertz time-domain spectroscopy (THz-TDS) has been the most commonly used mode in the field of THz spectroscopic application [11]. By coupling with chemometric methods, such as partial least squares (PLS) and artificial neural networks (ANN), THz-TDS enables to perform qualitative and quantitative analyses with speed and accuracy [21] [22] [23] [24] [25] and, thus, offers a possibility to apply THz-TDS into practical food process monitoring and quality control, such as antibiotic detection [26] [27] [28] [29], pesticides detection [21] [22] [30]. "
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    ABSTRACT: Endometrial carcinoma is one of the most common gynecologic cancers. Near infrared spectroscopy (NIRS) combined with chemometric methods was applied for diagnosis of this kind of cancer. This presentation was focused on variable selections of near infrared spectra because feature selection is important for useful information extraction and for good model establishment. Locally linear embedding (LLE) is introduced as a dimensionality reduction method for feature extraction of near infrared spectra from reflectance measurements of endometrial tissue sections. LLE was evaluated and compared with principal component compression (PCC) by using support vector machine (SVM) classifiers (as shown in following figure). The projected difference resolution (PDR) method was also used to evaluate the LLE method. Results indicate that LLE can be a promising tool for dimensionality reduction of NIR spectra, capable of extracting characteristic features from spectra, and LLE can also be used as a good alternative of PCA for dimensionality reduction. Furthermore, a novel method based on discrete particle swarm optimization (DPSO) and support vector machine (SVM) was proposed for the variable interval selection of tissue sections of endometrial carcinoma by near infrared spectroscopy. The DPSO-SVM algorithm includes a multi-stage screening. The variable intervals with high probabilities were selected and further used in the next screening. The subset of variable intervals with the highest classification rate was considered as the optimal variable intervals. The results showed that the informative variables from the NIR spectra could be selected and high classification accuracy was achieved by the proposed approach.
    Full-text · Conference Paper · Jun 2015
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    • "The situation has radically changed since the introduction of THz Time-Domain-Spectroscopy (TDS) systems based on femtosecond laser pulses and ultrafast photoconductors manufactured from semiconductors with subpicosecond carrier lifetimes [1]. Half-cycle electrical pulses with the wavelengths in far-infrared (THz pulses) used in these optoelectronic systems provided unprecedented insights into the nature of electron dynamics in semiconductors, vibrations of organic molecules, protein kinetics, etc., [2] [3]. "
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    ABSTRACT: Several applications of terahertz radiation pulses for characterizing semiconductor bulk materials and structures are described. Terahertz pulses emitted at the surfaces illuminated by femtosecond laser of a tunable wavelength are demonstrated to provide information on the electron energy spectrum in the conduction band as well as on the subsurface band bending. On the other hand, by sampling the conductivity of various structures with short electrical field transient photoexcited electron dynamics can be directly studied at its initial, subpicosecond time scale. Narrow gap semiconductors InSb and InAs as well as novel materials such as GaAsBi or self-assembled InAs quantum dots were characterized by using terahertz radiation pulses.
    Full-text · Article · Oct 2012 · Proceedings of SPIE - The International Society for Optical Engineering
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