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

作　　者：祝志慧[1,2] 李沃霖 韩雨彤 金永涛 叶文杰 王巧华 马美湖[3] ZHU Zhihui;LI Wolin;HAN Yutong;JIN Yongtao;YE Wenjie;WANG Qiaohua;MA Meihu(College of Engineering,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River,Ministry of Agriculture and Rural Affairs,Wuhan 430070,China;College of Food Science and Technology,Huazhong Agricultural University,Wuhan 430070,China)

机构地区：[1]华中农业大学工学院,湖北武汉430070 [2]农业农村部长江中下游农业装备重点实验室,湖北武汉430070 [3]华中农业大学食品科学技术学院,湖北武汉430070

出　　处：《食品科学》2025年第6期245-253,共9页Food Science

基　　金：国家自然科学基金面上项目(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)。

摘　　要：引入近红外光谱检测技术,构建改进一维卷积神经网络(one-dimensional convolutional neural network,1D-CNN)蛋清粉真实性检测模型。该模型基于1D-CNN模型,无需对光谱数据进行预处理;同时在网络中加入有效通道注意力模块和一维全局平均池化层,提高模型提取光谱特征的能力,减少噪声干扰。结果表明,改进后的EG-1D-CNN模型可判别蛋清粉样本的真伪,对于掺假蛋清粉的检测率可达到97.80%,总准确率(AAR)为98.93%,最低检测限(LLRC)在淀粉、大豆分离蛋白、三聚氰胺、尿素和甘氨酸5种单掺杂物质上分别可达到1%、5%、0.1%、1%、5%,在多掺杂中可达到0.1%~1%,平均检测时间(AATS)可达到0.004 4 s。与传统1D-CNN网络结构及其他改进算法相比,改进后的EG-1D-CNN模型在蛋清粉真实性检测上具有更高精度,检测速度快,且模型占用空间小,更适合部署在嵌入式设备中。该研究可为后续开发针对蛋粉质量检测的便携式近红外光谱检测仪提供一定的理论基础。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 0.0044 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 nearinfrared 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 

分 类 号：TS253.7[轻工技术与工程—农产品加工及贮藏工程]