Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology.

Microplastics (MPs) in farming soils can have a substantial impact on soil ecology and agricultural productivity, as well as affecting human health and the food chain cycle. As a result, it is vital to study MPs detection technologies that are rapid, efficient, and accurate in agriculture soils. This study investigated the classification and detection of MPs using hyperspectral imaging (HSI) technology and a machine learning methodology. To begin, the hyperspectral data was preprocessed using SG convolution smoothing and Z-score normalization. Second, the feature variables were extracted from the preprocessed spectral data using bootstrapping soft shrinkage, model adaptive space shrinkage, principal component analysis, isometric mapping (Isomap), genetic algorithm, successive projections algorithm (SPA), and uninformative variable elimination. Finally, three support vector machine (SVM), back propagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN) models were developed to classify and detect three microplastic polymers: polyethylene, polypropylene, and polyvinyl chloride, as well as their combinations. According to the experimental results, the best approaches based on three models were Isomap-SVM, Isomap-BPNN, and SPA-1D-CNN. Among them, the accuracy, precision, recall and F1_score of Isomap-SVM were 0.9385, 0.9433, 0.9385 and 0.9388, respectively. The accuracy, precision, recall and F1_score of Isomap-BPNN were 0.9414, 0.9427, 0.9414 and 0.9414, respectively, while the accuracy, precision, recall and F1_score of SPA-1D-CNN were 0.9500, 0.9515, 0.9500 and 0.9500, respectively. When their classification accuracy was compared, SPA-1D-CNN had the best classification performance, with a classification accuracy of 0.9500. The findings of this study shown that the SPA-1D-CNN based on HSI technology can efficiently and accurately identify MPs in farmland soils, providing theoretical backing as well as technical means for real-time detection of MPs in farmland soils.

Facile Fabrication of High-Performance Pen Ink-Decorated Textile Strain Sensors for Human Motion Detection.

Recently, wearable strain sensors have increasingly attracted much attention due to their potential applications in human motion detection and personal health monitoring. To date, it is still a challenge to fabricate a flexible strain sensor with both comfort and high performance. In this study, we dip the commercially available spandex/polyamide fabric into carbonic pen ink to prepare a textile strain sensor with good skin affinity. The textile strain sensor exhibits a high gauge factor (∼62.9) and an excellent linearity (R2 ∼ 0.99) in the strain range of 0-30%. Both before and after washing, the sensor exhibits high stability in more than 5000 cycles. Owing to the facile integration of the ink-decorated fabric on clothes, the sensor can be conveniently attached to the human body to monitor human motions, thus showing great potential in practical applications.