In order to meet the requirements of sustainable growth and development for human beings, biomedical research has been the focus of our attention. Terahertz (THz) is recognized as one of the most promising technologies and it has been widely used in biomedicine. A depiction of the basic principle of THz spectroscopy and THz imaging at the beginning of this article, some emerging techniques like THz microfluidic chips and scattering-type scanning near-field THz spectroscopy (S-SNTS) are investigated as well and it is indicated that these techniques have the potential to tackle some shortcomings of THz technology. This article reviews the applications of THz characterization techniques and THz biological effects in biomedicine between 2010 and 2018. The applications of THz characterization techniques are overviewed by five parts: amino acid and polypeptide, DNA, protein, the detection of cancer, and other applications. The research of THz biological effects mainly concentrates on the effect of THz on the tissue and cells of organisms. Besides, this article briefly introduces the basics of chemometric methods which are mainly applied in data preprocessing and multivariate analysis. Furthermore, we address the drawbacks of THz technology in biomedicine at the end of this article, these need our further study.

This study investigated the potential of using hyperspectral imaging for detecting different diseases on tomato leaves. One hundred and twenty healthy, one hundred and twenty early blight and seventy late blight diseased leaves were selected to obtain hyperspectral images covering spectral wavelengths from 380 to 1023 nm. An extreme learning machine (ELM) classifier model was established based on full wavelengths. Successive projections algorithm (SPA) was used to identify the most important wavelengths. Based on the five selected wavelengths (442, 508, 573, 696 and 715 nm), an ELM model was re-established. Then, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) at the five effective wavelengths were extracted to establish detection models. Among the models which were established based on spectral information, all performed excellently with the overall classification accuracy ranging from 97.1% to 100% in testing sets. Among the eight texture features, dissimilarity, second moment and entropy carried most of the effective information with the classification accuracy of 71.8%, 70.9% and 69.9% in the ELM models. The results demonstrated that hyperspectral imaging has the potential as a non-invasive method to identify early blight and late blight diseases on tomato leaves.

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3–2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.

This study investigated the feasibility of using hyperspectral imaging technique for nondestructive measurement of color components (ΔL*, Δa* and Δb*) and classify tea leaves during different drying periods. Hyperspectral images of tea leaves at five drying periods were acquired in the spectral region of 380–1030 nm. The three color features were measured by the colorimeter. Different preprocessing algorithms were applied to select the best one in accordance with the prediction results of partial least squares regression (PLSR) models. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the effective wavelengths, respectively. Different models (least squares-support vector machine [LS-SVM], PLSR, principal components regression [PCR] and multiple linear regression [MLR]) were established to predict the three color components, respectively. SPA-LS-SVM model performed excellently with the correlation coefficient (r_p) of 0.929 for ΔL*, 0.849 for Δa*and 0.917 for Δb*, respectively. LS-SVM model was built for the classification of different tea leaves. The correct classification rates (CCRs) ranged from 89.29% to 100% in the calibration set and from 71.43% to 100% in the prediction set, respectively. The total classification results were 96.43% in the calibration set and 85.71% in the prediction set. The result showed that hyperspectral imaging technique could be used as an objective and nondestructive method to determine color features and classify tea leaves at different drying periods.

In this study, confocal Raman microspectroscopy was used to detect lipids in microalgae rapidly and non-destructively. Microalgae cells were cultured under nitrogen deficiency. The accumulation of lipids in Scenedesmus obliquus was observed by Nile red staining, and the total amount of lipids accumulated in the cells was measured by gravimetric method. The signals from different microalgae cells were collected by confocal Raman microspectroscopy to establish a prediction model of intracellular lipid content, and surface scanning signals for drawing pseudo color images of lipids distribution. The images can show the location of pyrenoid and lipid accumulation in cells. Analyze Raman spectrum data and build PCA-LDA model using four different bands (full bands, pigments, lipids, and mixed features). Models of full bands or pigment characteristic bands were capable of identifying S. obliquus cells under different nitrogen stress culture time. The prediction accuracy of model of lipid characteristic bands is relatively low. The correlation between the fatty acid content measured by the gravimetric method and the integral Raman intensity of the oil characteristic peak (1445 cm−1) measured by Raman spectroscopy was analyzed. There was significant correlation (R 2 = 0.83), which means that Raman spectroscopy is applicable to semi-quantitative detection of microalgal lipid content.