Nondestructive detection of lead content in oilseed rape leaves based on MRF‐HHO‐SVR and hyperspectral technology

Abstract

AbstractLead (Pb) pollution poses a huge threat to environmental quality and food safety. Eating products made by excessive Pb contaminated oilseed rape can seriously harm human health. To detect Pb content in rape leaves more accurately, a method based on visible‐near infrared (400.68–1000.61 nm) hyperspectral technology was studied. First, the first derivative was used to pretreat the original spectral data. To reduce the number of iterations and the randomness of variable selection, the random frog (RF) algorithm was improved and named modified random frog (MRF). Then, the characteristic wavelengths were selected by MRF and competitive adaptive reweighted sampling (CARS), respectively, and support vector regression (SVR) model was established to predict the Pb content in rape. MRF was determined as the best feature selection algorithm. Finally, Harris Hawks Optimizer (HHO) was used to optimize SVR and the coefficient of determination (R2) and root mean square error (RMSE) of the prediction set were 0.9431 and 0.1645 mg/kg, respectively. Therefore, the combination of hyperspectral technology and the optimal model (MRF‐HHO‐SVR) is feasible for nondestructive detection of Pb content in rape leaves.Practical applicationsIn recent years, the development of mineral resources has caused more and more serious pollution of heavy metals in the soil. The problem of lead pollution is particularly prominent, and various crops have been polluted to varying degrees. The physiological indicators and nutrient content of Pb contaminated oilseed rape have obvious changes. Excessive consumption of lead contaminated rape products can be hazardous to human health. A method for detecting lead content in rape leaves based on hyperspectral technology was studied in this article. The results show that the hyperspectral technology could be used for the accurate and nondestructive determination of lead content in rape leaves, which provides a new method and idea for the detection of lead content in crops.