Spectroscopy and its imaging techniques are now popular methods for quantitative and qualitative analysis in fields such as agricultural products and foods, and combined with various chemometric methods. In fact, this is the application basis for spectroscopy and spectral imaging techniques in other fields such as genetics and transgenic monitoring. To date, there has been considerable research using spectroscopy and its imaging techniques (especially NIR spectroscopy, hyperspectral imaging) for the effective identification of agricultural products and foods. There have been few comprehensive reviews that cover the use of spectroscopic and imaging methods in the identification of genetically modified organisms. Therefore, this paper focuses on the application of NIR spectroscopy and its imaging techniques (including NIR spectroscopy and hyperspectral imaging techniques) in transgenic agricultural product and food detection and compares them with traditional detection methods. A large number of studies have shown that the application of NIR spectroscopy and imaging techniques in the detection of genetically modified foods is effective when compared to conventional approaches such as polymerase chain reaction and enzyme-linked immunosorbent assay.

Pattern encoding and decoding are two challenging problems in a three-dimensional (3D) reconstruction system using coded structured light (CSL). In this paper, a one-shot pattern is designed as an M-array with eight embedded geometric shapes, in which each 2 × 2 subwindow appears only once. A robust pattern decoding method for reconstructing objects from a one-shot pattern is then proposed. The decoding approach relies on the robust pattern element tracking algorithm (PETA) and generic features of pattern elements to segment and cluster the projected structured light pattern from a single captured image. A deep convolution neural network (DCNN) and chain sequence features are used to accurately classify pattern elements and key points (KPs), respectively. Meanwhile, a training dataset is established, which contains many pattern elements with various blur levels and distortions. Experimental results show that the proposed approach can be used to reconstruct 3D objects.