Water and its distribution and transport dynamics in green plant leaves are vital to the growth of plants. Owing to the high sensitivity of terahertz (THz) wave to water, THz spectroscopy has great advantages in analyzing the water status of plant leaves. This paper presents a new approach to estimate the water status of plant leaves by the THz time-domain spectroscopy (THz-TDS) technique. Spatial distribution of THz transmission amplitudes located in vein xylem and mesophyll of all three kinds of leaves including wintersweet, ginkgo and bamboo is detected by THz-TDS measurements. Based on the transmission amplitude, the reconstructed THz images show that the water loss in the basal leaf region is more than that in the distal region during the natural drying process for all three plants. A good agreement is reached between the THz imaging method and the direct water weight measurement. To illustrate the accuracy and the sensitivity of the THz technique, the temporal and spatial variations of the water content in the damaged ginkgo leaf with a wound by cutting are also investigated for comparison. The water flow from the basal region to the distal region of the leaf is inferred according to the variation of THz transmission amplitude with the leaf region in different dehydration periods, which is consistent with the string-of-lakes model prediction. This work shows the feasibility of using THz technology to monitor the temporal and spatial variability of the water status in plant leaves.

This paper proposes a structured model for the identification of green tea, as well as tracing its geographical origins. Considering that the features of different types of green tea are similar under THz time-domain spectroscopy, we designed a program to perform principal component analysis (PCA) of the spectroscopic data of various green tea samples and to determine the data sequences of principal components. We then established a training set for the principal components to train a support vector machine (SVM) model via a genetic algorithm (GA). We used this model to optimize the parameters and develop a GA-based SVM model with an identification rate of 96.25% for the tested samples. Taken together, our results confirm that THz time-domain spectroscopy combined with GA-SVM can be effectively applied to rapidly identify types of green tea with different geographical origins.

An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623~1783 cm⁻¹ were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination model, a linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were attempted comparatively in the work. The results showed that the QDA model is better than the LDA model. The optimal QDA model was generated with 12 principal components. The classification rates are 100% in the calibration and prediction set, respectively. From the experimental results, it is concluded that Raman spectroscopy combined with appropriate discriminant analysis methods has significant potential in human cell detection.

To convert the polarization of terahertz waves pumped by a femtosecond laser in a terahertz time domain system, a broadband anisotropic metamaterial is proposed. The metamaterial is constructed with a single-layer gapped metallic ring array, which supports different resonant modes in orthogonal directions. With the aid of simulations and measurements, the anisotropy of the terahertz transmission is demonstrated and discussed. The experimental results of THz transmission in the metamaterial indicate that the anisotropic band is as wide as 0.56 THz, which accords well with our theoretical prediction.

A terahertz modulation structure based on hybrid metamaterial and graphene is proposed and demonstrated in this work. The metamaterial with a square slit ring array excites terahertz resonance in the slits and enhances the interaction between the terahertz wave and graphene. The graphene layer acting as the active material is tuned by the applied electrical field. With the separation by a dielectric layer between the graphene and the metallic structure, the resonant frequency and transmitted energy are both modulated by the graphene. Experimental result indicates that the modulation depth of the terahertz transmitted amplitude is 65.1% when the applied modulation voltage is tuned 5 V.