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Supplemental material, sj-docx-1-pih-10.1177_09544119211058914 for Enhancing the differentiation of walking and standing via the ratio of plantar pressures by Jing Jin Shen, Xiao Xiao Jin, Shu Xing Bao, Zhen Yu Zhou, Feng Yu Xu and Rong Qing Xu in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine

When using near infrared spectroscopy to predict the moisture content of potato leaves, a large amount of spectral data needs to be processed, resulting in a time-consuming and labor-intensive calculation process. This paper proposes to use a variety of feature wavelength extraction methods to reduce the amount of calculation of near-infrared spectral data, and according to the comparison of prediction results, the feature wavelength extraction method with the best extraction effect is obtained. First, the spectral reflectance information of 110 fresh potato leaves in the 900~2100nm band is collected, and then the Regression Coefficient (RC), Principal Component Analysis (PCA), first-order derivative correlation extraction are used respectively Method, extract the characteristic wavelengths from the full-band spectral data, and finally establish a BP neural network prediction model according to the characteristic wavelengths extracted in three different ways, and compare the prediction results to obtain the optimal characteristic wavelength extraction method. The results show that the BP neural network model established by the characteristic wavelength extracted by the Regression Coefficient (RC) has the best prediction effect, the prediction set decision coefficient (R2) is 0.9698, and the root mean square error (RMSE) is 0.3177. In this experiment, on the basis of reducing the amount of near infrared spectroscopy data by more than 90%, a good prediction effect was achieved, and the purpose of quickly and concisely predicting the moisture content of potato leaves was achieved.

Two non-destructive detection methods for potato blight based on hyperspectral imaging were used: convolutional neural network (CNN) and support vector machine (SVM) to classify potato leaves. By comparing the classification results, the advantages and disadvantages of different methods are analyzed. In the experiment, normal potato leaves and early blight leaves were selected as research objects. Hyperspectral images of samples were obtained by hyperspectral imaging system, and then principal component images were extracted by principal component analysis method. It was found that the principal component images of normal leaves and blight leaves were significantly different, and finally two models of blight detection were established for convolutional neural network and support vector machine. The experimental results showed that the convolutional neural network was better than the support vector function in the detection of potato blight.

Surface enhanced Raman scattering (SERS) has been proved to be a highly sensitive method to detect organic molecules at very low concentrations. In recent years, many researchers have reported that 1-dimension semiconductor nanomaterials assembled noble metal nanoparticles can get a strong SERS effect. In this paper, we succeeded to synthesize TiO2 nanorod thin films on fluorine-doped tin oxide (FTO) glass with hydrothermal synthesis which were able to be used as SERS substrates. Gold nanoparticles were assembled to TiO2 nanorod thin films using the physical sputtering method and the citrate reduction method, respectively. The field emission scanning electron microscope (FESEM) images show that the later method could achieve the more desirable and uniform distribution of gold nanoparticles. Rhodamine 6G (R6G) was chosen as the probe molecule to study the SERS performance of our novel SERS substrates. Raman scattering measurement proved that the substrates were able to enhance Raman signals by several orders of magnitude and could be applied to biochemical detection. The whole fabrication process was facile and cost-effective, and the SERS activity and reproducibility of the substrates were pretty good.