In this paper, laser-induced fluorescence (LIF) is used to characterize and distinguish between different vegetable oils, including soybean, olive, grapeseed, rapeseed, corn, peanut, sunflower, canola, and walnut oils. A 532 nm laser, rather than an ultraviolet (UV) light source, is proposed and used as an excitation light source for the fluorescence analysis of edible oils. It was found that this laser is superior to UV lasers, the fluorescent characteristics become more distinct under 532 nm laser excitation. Edible oils were differentiated by LIF combined with principal component analysis which was used to reduce the dimensionality of data by finding key attributes, and support vector machine. This paper demonstrates, that for ten popular edible oils, the recognition rate can reach up to 100% when a 532 nm laser serves as an excitation light source.

The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85), weakest root-mean-square error (203 m), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 d experiments using other algorithms, reasonable parameter selection can help in distinguishing between layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically address the uncertainty with multiple structures and obtain a stable NBLH with a high temporal resolution, which is expected to contribute to air pollution monitoring and climatology, as well as model verification.

To analyze and detect edible oils, a spectral recognition method is introduced based on time-resolved fluorescence (TRF). Contour diagrams of TRF intensities superior to steady-state fluorescence are constructed to serve as unique fingerprints for edible oils, which can be used to identify vegetable oils. The proposed approach provides a rapid and reliable means for analyzing and classifying edible oils in food security research fields. Reliability and stability are verified by TRF measurements of peanut oils of three brands in a supplementation trial.