Inspection of maleic anhydride in starch powder using line-scan hyperspectral Raman chemical imaging technique

Excessive use of maleic anhydride(MAN)in starch production is potentially harmful for consumers’health.This study presents a macro-scale Raman chemical imaging method for detection and quantification of MAN particles mixed in starch powder.MAN was mixed into corn starch at eight concentration levels from 50 ppm to 6400 ppm(w/w).Each mixture was put in a sample holder with a 150 mm×100 mm area and a 2 mm depth to create a large surface and a thin layer of the powdery sample for inspection.A 30 W 785 nm line laser was projected on the sample surface,from which hyperspectral images were obtained by a line-scan Raman imaging system with a spatial resolution of 0.2 mm.Fluorescence signals generated by laser-sample interactions were eliminated by a mathematical baseline correction method.A unique Raman peak was selected at 1839 cm-1 for the MAN detection,at which single-band fluorescence-corrected images were extracted from the mixture of each concentration and used to generate chemical images for MAN detection and mapping.The MAN detection limit was estimated at 100 ppm based on the Raman imaging measurement results.Pixel concentrations of the MAN in the chemical images were found linearly correlated with mass concentrations of the MAN particles in the starch powder,suggesting the Raman chemical imaging method has the potential for quantitative detection of the MAN in the starch-MAN mixtures.

Automatic sweet pepper detection based on point cloud images using subtractive clustering

Automatic identification and detection of fruit on trees by machine vision is the basis of developing automatic harvesting robots in agriculture.The occlusion of branches,leaves and other fruits in canopy images will affect the accuracy of fruit detection.To provide a scientific and reliable technical guidance for fruit harvesting robots,a method using point cloud images was proposed in this study to detect red fruits to overcome the impact of occlusion on detection.Firstly,the fruit regions were segmented from a tree’s point cloud by applying the color threshold of red and green.Then,the noise in fruit point clouds was removed with sparse outlier removal.Finally,the point cloud of each fruit was detected and counted based on the subtractive clustering algorithm.For the sweet pepper dataset,the true positive rate(TPR)is 90.69%and the false positive rate(FPR)is 6.97%for all fruits that are at least partially visible in the scene.