摘要
The quality of laver is significantly affected by adulteration with sea lettuce(Ulva lactuca),a green seaweed that adheres to Pyropia nets during cultivation and adversely impacts productivity and quality.Acid treatment agents are commonly utilized;however,residual sea lettuce may persist in the final product if treatment is insufficient.Traditional detection methods,such as sensory evaluation,are susceptible to human error,time-consuming,and inefficient,while DNA sequencing is ineffective for processed laver due to DNA degradation.Given these limitations,non-destructive technologies are garnering interest in seafood quality assessment.This study evaluates the potential of hyperspectral imaging for detecting sea lettuce in laver.Hypercubes collected in two spectral ranges(visible/near-infrared(VIS/NIR)and short-wave infrared(SWIR))were utilized to establish a partial least squares regression(PLSR)model for quantification.Characteristic wavelengths were selected using competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and their hybrid methods(CARS-UVE,UVE-CARS).Model efficiency and robustness improved with spectral feature selection.For raw laver,UVE-CARS achieved the highest Rp2(0.86)with 14.3%of full wavelengths in VIS/NIR,while for dried laver,SWIR with CARS-UVE yielded Rp2(0.90)using 18.3%of full wavelengths.This study addresses a critical gap in seafood quality control by demonstrating that hyperspectral imaging enables non-destructive,efficient quantification of sea lettuce contamination in laver,contributing to improved industry standards.
基金
supported by the Korea Institute of Marine Science and Technology Promotion(KIMST),funded by the Ministry of Oceans and Fisheries(No.20210695).
