This study aims to investigate the potential of honey discrimination by visible and near-infrared (vis-NIR) spectroscopy with wavelength reduction. A total of 80 samples from four brands of honey produces were measured by a mobile fiber-type USB4000 spectrophotometer with recorded wavelength range of 380.17~939.98 nm for model calibration. Firstly, principal components analysis (PCA) was used for extracting principal components (PCs). Next, the first seven PCs, which accounted for 97% of variance of the spectra, were combined separately with support vector machine (SVM) and linear discriminate analysis (LDA) to develop PC-SVM and PC-LDA models, both of which achieved 100% discrimination accuracy. In addition, the spectra were subjected to successive wavelength reduction rates (WRRs) of 2^x, x = 1–9, for wavelength reduction. The PC-LDA and PC-SVM models developed for these reduced wavelengths produced almost the same performance as compared with those developed for original full wavelengths. This experiment suggests that vis-NIR spectral wavelengths can be reduced at large spacing interval, which allows easing data analysis as well as developing a simpler and cheaper sensor for honey discrimination in practice.

Rapid characterization of biochar for energy and ecological purpose utilization is fundamental. In this work, visible and near-infrared (vis-NIR) spectroscopy was used to measure ash, volatile matter, fixed carbon contents, and calorific value of three types of biochar produced from pine wood, cedar wood, and cotton stalk, respectively. The vis-NIR spectroscopy was also used to discriminate biochar feedstock types and pyrolysis temperature. Prediction result shows that partial least squares (PLS) regression calibrating the spectra to the values of biochar properties achieved very good or excellent performance with coefficient of determination (R2) of 0.86~0.91 and residual prediction deviation (RPD) of 2.58~3.32 for ash, volatile matter, and fixed carbon, and good prediction with R2 of 0.81 and RPD of 2.30 for calorific value. Linear discrimination analysis (LDA) of the principal components (PCs) produced from PCA of wavelength matrix shows that three types of biochar can be successfully discriminated with 95.2% accuracy. The classification of biochar with different pyrolysis temperatures can be conducted with 69% accuracy for all three types and 100% accuracy for single type of cotton stalk. This experiment suggests that the vis-NIR spectroscopy is promising as an alternative of traditionally quantitative and qualitative analysis of biochar properties.

Nondestructive in situ measurement of tomato fruits is essential to determine growing stages and to assist in automatic picking of fruits. This study evaluates the applicability of visible and near-infrared (Vis-NIR) spectroscopy for in situ determination of growing stages and harvest time of three cultivars of tomato fruits. A mobile fiber-type AgroSpec Vis-NIR spectrophotometer (Tec5 Co., Germany) with a spectral range of 350–2200 nm was used to measure tomato spectra in reflection mode. A new growing stage (GS) index defined as the ratio of the current growing age in days to the on-vine duration before harvest in days was proposed. After dividing spectra into a calibration set (70%) and an independent prediction set (30%), spectra in the calibration set were subjected to a partial least squares regression (PLSR) with leave-one-out cross-validation to establish calibration models relating GS to the spectra of tomato fruits. Separate models were developed for each tomato cultivar and compared with a general model that used combined spectra of all three cultivars. The results show that PLSR based on the new GS is successful and robust in predicting the growing stages and harvest time of tomato fruits. Validation of calibration models on the independent prediction set indicates that successful prediction of GS can be achieved using the three models developed separately for each cultivar with a coefficient of determination ( R ² ) of 0.91–0.92, root mean square error of prediction (RMSEP) of 0.081–0.097, and residual prediction deviation (RPD) of 3.29–3.70. General calibration using the combined spectra produces good prediction performance, although less accurate than that for the three individual cultivar models. The analysis of regression coefficient plots resulting from PLSR analysis indicates consistent assignment of important wavelengths for individual cultivar spectra and combined spectra. It is concluded that the Vis-NIR PLSR based on GS index can be adopted successfully for in situ determination of growing stages and harvest time of on-vine tomato fruits, which allows for automatic picking of fruits by a horticultural robot.

Based on the 0.5 μm GaAs enhancement/depletion (E/D) Pseudomorphic High Electron Mobility Transistor (pHEMT) process, a 7.5–9 GHz two-channel amplitude phase control multi-function chip (MFC) was developed successfully. The chip was integrated with a 6-bit digital phase shifter, a 6-bit digital attenuator, and a single pole single throw (SPST) switch in each channel. A design for the absorptive SPST switch is deployed to optimize the return loss and control channel array calibration. In the 8 dB and 16 dB attenuation bit, a switched-path-type topology is employed in order to obtain a good flatness of attenuation characteristic and achieve low additive phase shift. A 27-bit serial-to-parallel converter (SPC) was introduced to decrease the control lines and pads of the chip, and the power consumption was less than 70 mW. The measurement result shows that the insertion loss is less than −13 dB and the return loss is better than −19 dB. In both channels, the 64-state root mean square (RMS) errors of the phase shifter is less than 2° and the RMS parasitic amplitude error is less than 0.2 dB. The RMS attenuation error is less than 0.45 dB and the RMS parasitic phase error is less than 2.4°. The size of the chip is 3.5 mm × 4.5 mm.

Three-dimension (3D) digitization of human face is of great value for medical plastic surgery, video making, virtual reality, and so on. Subject to the restriction of 3D scanning equipment, a single angle of scanning measurement only obtains partial information of a face. In this paper, a multi-Kinect measurement system was proposed for the scanning of human face from multiple angles simultaneously. Firstly, different sides of human face point clouds scanned by three Kinect sensors were registered by an ICP algorithm to form a full human face point cloud. Then, a bilateral filtering algorithm was refined to remove noise in the point cloud. Finally, a point feature histogram and a refined shared nearest neighbor cluster algorithm were applied to improve the iterative speed and efficiency of the ICP algorithm. Experiment shows that the refined point cloud registration algorithm is efficient for 3D digitization of full human face.