食品科学2025，Vol.46Issue(6)：245-253,9.DOI:10.7506/spkx1002-6630-20240830-232
基于改进一维卷积神经网络模型的蛋清粉近红外光谱真实性检测
Authenticity Detection of Egg White Powder Using Near-Infrared Spectroscopy Based on Improved One-Dimensional Convolutional Neural Network Model
摘要
Abstract
An improved one-dimensional convolutional neural network(1D-CNN)model for the authenticity detection of egg white powder was constructed based on near-infrared spectroscopy(NIRS).This model required no spectral preprocessing.To enhance its ability to extract spectral features,an efficient channel attention module(ECA)and a one-dimensional global average pooling(1D-GAP)layer were singly or together incorporated into the model,consequently reducing noise interference.The experimental results indicated that the improved model integrating ECA and 1D-GAP,EG-1D-CNN,could distinguish between authentic and adulterated egg white powder samples,with a detection rate of 97.80%for adulterated samples and an overall accuracy rate(AAR)of 98.93%.The lowest recognition concentrations(LLRC)for single adulterants such as starch,soy protein isolate,melamine,urea,and glycine were 1%,5%,0.1%,1%,and 5%,respectively,and those for multiple adulterants ranged from 0.1%to 1%.The average time spent(AATS)for the detection was 0.004 4 seconds.Compared with traditional 1D-CNN network structure and other improved algorithms,the EG-1D-CNN model exhibited higher accuracy,faster detection speed,and smaller model footprint,thus making it more suitable for deployment on embedded devices.This research provides a theoretical foundation for the development of portable near-infrared spectroscopy-based detectors for egg powder quality testing.关键词
蛋清粉/近红外光谱/真实性检测/一维卷积神经网络/深度学习Key words
egg white powder/near-infrared spectroscopy/authenticity detection/one-dimensional convolutional neural networks/deep learning分类
轻工纺织引用本文复制引用
祝志慧,李沃霖,韩雨彤,金永涛,叶文杰,王巧华,马美湖..基于改进一维卷积神经网络模型的蛋清粉近红外光谱真实性检测[J].食品科学,2025,46(6):245-253,9.基金项目
国家自然科学基金面上项目(32372426) （32372426）
蛋品加工技术国家地方联合研究中心-蛋品肉品加工分析平台项目(109/11090010147) （109/11090010147）
