The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

Dried tangerine peel is a Chinese medicine with high medicinal value. The storage age is an important indicator of its medicinal value, so it is very significant to accurately identify the storage age of dried tangerine peel. Traditional physical and chemical analysis methods can be used to achieve this goal, but these methods are limited by their operability and convenience. Near infrared (NIR) spectroscopy and machine learning have excellent performance in the rapid detection of food and pharmaceutical samples. This study investigated the novel application of integrating a hand-held NIR spectrometer combined with machine learning to rapidly and accurately identify the storage age of Xinhui dried tangerine peel. Savitzky–Golay convolution smoothing, standard normal variate (SNV), first derivative, and second derivative pretreatments were employed to preprocess spectral data. Principal component analysis (PCA) was used to reduce the spectral data dimensions and obtain the characteristic spectral variables of each sample. Support vector machine (SVM) and k-nearest neighbor were applied to establish the qualitative discriminant models. The SNV-PCA-SVM model discriminant accuracy was 99.60% in the validation set and was 96.50% in the test set, showing excellent generalization performance. The results indicated that the method of using a hand-held NIR spectrometer combined with machine learning could be applied to rapidly identify the storage age of Xinhui dried tangerine peel. This is a promising and economical hand-held NIR spectroscopic method for assuring the dried tangerine peel age on-site.

Stereoscopic microscopy is a promising technology to obtain three-dimensional microscopic images. Such microscopes are based on the parallax effect, and as such require two lenses to focus at two different points. Geometrical constraints, however, restrict their numerical apertures to about 0.2, thus limiting the system’s resolution. Higher numerical apertures ( ∼ 0.35 ) can be achieved with designs using only one bulk lens, but such systems are ∼ 10 times more costly than the conventional ones. Thus, there is a pressing need for alternative solutions to improve the resolution of stereoscopic systems. Here, we show that high-resolution and low-cost stereoscopic systems can be obtained using birefringent single-layer metalenses. We design and fabricate a birefringent metalens operating at 532 nm with a numerical aperture as high as 0.4. The metalens is then used to demonstrate high-resolution stereoscopic imaging of biological samples. The microscopic images are further displayed and perceived vividly in an autostereoscopic display. Our demonstration paves the way to a new strategy to achieve high-resolution and low-cost stereoscopic microscopes.