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ObjectiveTo establish an ultra-high performance liquid chromatography-tandem mass spectrometry method for the detection of two typical advanced glycation end products （AGEs） in bread.MethodsProtein-bound carboxymethyl lysine （CML） and carboxyethyl lysine （CEL） were used as the detection objects. Bread sample was subjected to reduction incubation， protein precipitation， addition of isotope internal standard， acid hydrolysis， nitrogen blowing， reconstitution with nonafluorovaleric acid aqueous solution， and then underwent multi-reaction monitoring to quantitative analysis.ResultsThe detection limit and quantification limit of the method were 4.5 ng/g and 20 ng/g for CML， and 0.5 ng/g and 2 ng/g for CEL. The spike recovery rates of CML and CEL were 89.62%-95.65% and 86.38%-97.17%. The linear range were 2.5-800 ng/mL and 0.25-80 ng/mL， and the coefficients of determination were both above 0.999.ConclusionThe method was accurate and sensitive， and could meet the detection requirements of AGEs in bread.

Due to the presence of stacking angles during the transportation of coal flow by belt conveyors, the shape of the coal flow is approximately triangular. It can easily lead to blind spots in detection. In order to solve this problem, a coal flow detection system for belt conveyors based on dual lidar is proposed. The method places two single-line lidars on the left and right sides above the belt conveyor, and measures the outer contour feature points of the coal flow in each half of the area. The method uses the fusion algorithm to fuse the outer contour feature points of the coal flow in the left and right areas. Then the method uses the least squares polynomial fitting algorithm to calculate the outer contour of the coal flow in the entire area, thus achieving blind spot-free measurement of the coal flow contour. The method uses the photoelectric encoder to achieve the real-time detection of the conveyor belt running speed. The method uses the trapezoidal area accumulation method to calculate the coal flow cross-sectional area. The method uses the panel integration method to calculate of the coal flow rate of the belt conveyor. The on-site test results show that when there is no coal bias, the scanning results of single/dual lidar are basically consistent, and the system measurement error is 2%~3%. The results meet the requirements of coal flow detection. When there is coal bias, the system error based on a single lidar is relatively large. The result cannot meet the requirements of coal flow detection. The measurement error based on a dual lidar system can still be maintained at 2% to 3%. The paper proposes a selection criterion for single/dual lidar. It is concluded that the coal flow detection system based on dual lidar is more suitable for belt conveyors in the presence of coal bias or large blocks of coal.