摘要
以凡纳滨对虾为研究对象,探索一种高效快速无损的新鲜度检测方法。挥发性盐基氮(TVB-N)是判断虾新鲜度的重要化学指标,然而传统方法耗时耗力,限制了大批量的实时检测。高光谱技术是一种集成图像和光谱信息的分析技术,高光谱图像上的每个像素包含整个波段的光谱信息,近年来,该技术已经被应用于肉类新鲜度检测。连续8 d采集了样品的860~1700 nm高光谱数据,在去除异常样本后确定150组试验样本,每组采集254维光谱数据,对原始的高光谱图像进行黑白校正,并从高光谱图像中提取光谱数据。为确保所提取的光谱数据和TVB-N指数之间有对应关系,所选择的感兴趣区域的位置保持固定在虾样本的第二和第四肢。计算了感兴趣区域的平均光谱以获得光谱数据矩阵,该矩阵被转换成ASCII码并保存。同时,通过凯氏定氮法获得TVB-N真实值含量。为减少环境和虾表面的高含水量的干扰,有效地消除不相关的信息和噪声,预处理方法是多元散射校正(MSC)算法,并选择出7个敏感波段,分别为875,894,919,953,983,1024和1094 nm。最后,以120组训练集样本,建立了凡纳滨对虾TVB-N总量的定量预测模型,以30组验证集样本,对比BP神经网络、径向基神经网络、主成分分析三种预测模型算法。BPNN算法预测模型的相关系数(r)和归一化均方根误差(NRMSE)分别为0.9021和0.2140,RBFNN算法的预测模型为0.8683和0.2230,PCR算法预测模型为0.7576和0.3900。结果表明,MSC-BPNN模型的预测效果最佳,凡纳滨对虾的高光谱反射率与新鲜度间存在较密切的相关性,为基于光谱的虾类新鲜度检测提供了支持。
In this study,Penaeus vannamei was taken as the research object to explore an efficient,rapid and non-destructive freshness detection method.Total volatile basic nitrogen(TVB-N)is an important chemical index to judge the freshness of shrimp.However,the traditional method is time-consuming and labor-consuming,which limits the real-time detection of large quantities.In recent years,hyperspectral technology has been an analysis technology integrating image and spectral information.Each pixel in the hyperspectral image contains the spectral information of the whole band.This technology has become a technology of meat freshness detection.This study collected 860~1700 nm hyperspectral data of Penaeus vannamei samples for 8 consecutive days.After removing the abnormal samples,150 groups of test samples were determined.We collected 254-dimensional spectral data in each group.The original hyperspectral image was corrected in black and white,and ENVI software extracted the spectral data from the hyperspectral image.We ensured the corresponding relationship between the extracted spectral data and the TVB-N index.The average spectrum of the ROI is calculated to obtain the spectral data matrix,which is converted into ASCII code and saved.At the same time,the true value of TVB-N was obtained by the Kjeldahl method.In order to reduce the interference of water content of environment and shrimp surface and effectively eliminate the irrelevant information and noise,this study used a multiple scattering correction algorithms to preprocess the shrimp hyperspectral and selected seven sensitive bands.Finally,a quantitative prediction model of TVB-N of Penaeus vannamei was established based on 120 training set samples and 30 validation set samples.We compared the model of BPNN,RBFNN and PCA.The r and NRMSE of the BPNN model were 0.9021 and 0.2140,the RBFNN model were 0.8683 and 0.2230,PCR model were 0.7576 and 0.3900,respectively.The results showed that the MSC-BPNN model had the best prediction effect,and there is a close correlation between hyperspectral reflectance and freshness of Penaeus vannamei.This paper supports the freshness detection of shrimp based on spectral technology.
作者
朱晨光
刘亚军
李鑫星
宫薇薇
郭渭
ZHU Chen-guang;LIU Ya-jun;LI Xin-xing;GONG Wei-wei;GUO Wei(Beijing Laboratory of Food Quality and Safety,College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Chengde No.8 Middle School,Chengde 067000,China;Energy and Environment Engineering Institute,Nanchang Institute of Technology,Nanchang 330044,China;Transportation and Economic Research Institute of China Academy of Railway Sciences Group Corporation Limited,Beijing 100081,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第1期107-110,共4页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划项目(2018YFD0701003)
国家自然科学基金项目(61802411)资助。
关键词
高光谱
新鲜度
多元校正散射
BP神经网络
径向基神经网络
Hyperspectral
Freshness
Multivariate correction scattering
BP neural network
Radial basis function neural network
