高光谱成像结合ELM的鸡蛋品种鉴别  

Hyperspectral Imaging Combined With ELM for Eggs Variety Identification

作　　者：张伏 王梦瑶 颜宝苹 张方圆 袁叶 张亚坤 付三玲 ZHANG Fu;WANG Meng-yao;YAN Bao-ping;ZHANG Fang-yuan;YUAN Ye;ZHANG Ya-kun;FU San-ling(College of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang 471003,China;School of Physical Engineering,Henan University of Science and Technology,Luoyang 471023,China)

机构地区：[1]河南科技大学农业装备工程学院,河南洛阳471003 [2]河南科技大学物理工程学院,河南洛阳471023

出　　处：《光谱学与光谱分析》2025年第3期836-841,共6页Spectroscopy and Spectral Analysis

基　　金：国家重点研发计划项目(2017YFD0301106);龙门实验室前沿探索课题(LMQYTSKT032);河南省高等教育教学改革研究与实践项目(研究生教育类)成果(2023SJGLX180Y);河南省高等学校青年骨干教师培养计划项目(2017GGJS062)资助。

摘　　要：鸡蛋是营养丰富的农产品,不同品种鸡蛋所含营养物质成分不同。市场上出现品种以次充好、掺假等问题对食品安全造成严重威胁,急需解决鸡蛋品种鉴别难题。以4种鸡蛋为试验样本,按2∶1划分训练集和测试集,分别为160枚和80枚。高光谱成像采集系统获取935.61~1720.23 nm范围内鸡蛋光谱图像,对其黑白校正后框选鸡蛋样本中心大小为30×30 pixel的感兴趣区域(ROI),将该区域内各像素点反射率均值作为样本原始光谱数据。为减少原始光谱数据首尾端随机噪声的影响,截取949.43~1709.49 nm范围内光谱信息用于后续研究,采用SG平滑(SG)和多元散射校正(MSC)对其预处理,连续投影算法(SPA)、竞争性自适应重加权算法(CARS)、CARS-SPA、CARS+SPA四种方式对预处理后的光谱数据提取特征波长,基于全波段(FB)和特征波段建立支持向量机(SVM)、粒子群算法(PSO)优化的SVM(PSO-SVM)、极限学习机(ELM)等模型,对比鉴别准确率以寻找最佳鸡蛋品种鉴别模型。试验结果表明,SG-SPA-ELM模型鉴别效果最佳,鉴别准确率为85.00%,高光谱成像技术结合ELM可有效实现鸡蛋品种无损高效准确检测,为鸡蛋和其他农产品品种鉴别提供参考。Different varieties of eggs contain different nutrients and ingredients as a nutritious agricultural product.The phenomenon of inferior quality and adulteration poses a serious threat to food safety,which makes an urgent need to solve the problem of egg variety detection.Four egg varieties as research objects were divided into the training and test sets according to 2∶1 with 160 and 80 eggs respectively.A hyperspectral imaging system was utilized to capture the egg spectral image in the 935.61~1720.23 nm range.Region of Interest(ROI)with a center size of 30×30 pixels of egg sample was selected after black and white correction,and the average reflectivity of each pixel in the region was extracted as the original spectral data of the sample.The average spectral information in the 949.43~1709.49 nm range was intercepted for the subsequent study to reduce the influence of random noise at both ends.Savitzky-Golay(SG)smoothing algorithm and multiple scattering correction(MSC)were used to pretreat the effective bands after denoising.The feature wavelengths of the preprocessed spectral data were extracted using a successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)single screening,and combinations of CARS-SPA and CARS+SPA,respectively.Support vector machine(SVM),particle swarm optimization(PSO)optimized SVM model(PSO-SVM),and extreme learning machine(ELM)model were established based on full bands(FB)and feature band,which were compared to find the best variety classification model.The experimental results showed that the SG-SPA-ELM model has the best identification effect with the best classification accuracy of 85.00%.Hyperspectral imaging technology combined with ELM can effectively realize non-destructive,efficient,and accurate identification of egg varieties and provide references for egg adulteration detection and identification of other agricultural products.

关 键 词：高光谱成像技术 鸡蛋 品种鉴别 极限学习机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]