This invention relates to a rapid, non-
destructive testing method and
system for
lipid content and deterioration degree in
red pine nuts based on
hyperspectral imaging and
deep learning. The invention belongs to the field of non-
destructive testing technology for agricultural and
forestry product quality and safety. It addresses the problems of existing methods for detecting
lipid content and oxidation degree in
red pine nut kernels, which generally suffer from complex operation procedures, high costs for large-scale testing, long
processing times, and difficulty in achieving full-process testing during storage and transportation. Key technical points: The original near-
infrared spectral data of pine nut samples are obtained through hyperspectral acquisition; the true values of lipid reference content and reference degree of oxidation deterioration of pine nut samples at different sampling times are determined, and a
database is established based on these values. The original near-
infrared spectral data of
red pine nuts are preprocessed, and the data from different batches of red pine nut samples are divided into training and validation sets. An improved one-dimensional dilated convolutional network based on a dynamic weight allocation module is designed to construct a
deep learning model suitable for the spectral feature analysis of red pine nut kernels. The constructed model is trained using a
backpropagation algorithm, and the network weight parameters are updated in reverse by minimizing the
loss function. When the constructed model meets the requirements for
rapid detection, it can be used for efficient and non-destructive simultaneous detection of
lipid content and oxidative rancidity in red pine nuts.