You-fei Hou , Yan-bing Li , Nan Chen , Shang-tao Ou-yang , Qi Wang , Yang Wang , Bin Li , Yan-de Liu
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引用次数: 0
Abstract
Pea storage stability and germination rely on moisture content and morphology, but traditional destructive methods cause sample damage, low efficiency, and subjective errors, limiting practical use. To overcome the destructive and inefficient limitations of traditional methods for pea quality assessment, this study develops an integrated, non-destructive framework for the simultaneous and rapid measurement of pea moisture content and size using hyperspectral imaging combined with deep learning. We innovatively converted one-dimensional spectral data into two-dimensional texture images via Gramian Angular Field (GAF) encoding and input them into a residual 2D Convolutional Neural Network (2D-CNN) for moisture prediction. For dimensional analysis, a novel algorithm based on irregular polygon geometry was proposed to accurately measure pea length and width. The GAF-2D-CNN model achieved superior performance for moisture prediction (prediction set R²=0.9818, RMSEP=0.0318 %, RPD=7.4780), significantly outperforming 1D-CNN, Least Squares Support Vector Machine (LSSVM), and Partial Least Squares Regression (PLSR) models. The dimensional algorithm also demonstrated high accuracy, especially for length measurement (R²=0.9946, RPD=13.94). This framework provides a robust, accurate, and high-throughput solution for automated pea quality grading, offering significant potential for applications in precision agriculture and storage management.
期刊介绍:
The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects.
The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.