摘要
挥发性盐基氮(Total volatile basic nitrogen,TVB-N)是动物性食品的新鲜度指标。传统的TVB-N检测技术工序繁杂,对鱼肉具有不可逆的破坏性。本研究拟用近红外光谱技术进行金鲳鱼肉质新鲜度的检测,采用一阶微分(1st Der)、二阶微分(2nd Der)、多元散射校正(Multiplicative scatter correction,MSC)、标准正态变换(Standard normal variate transform,SNV)对金鲳鱼鱼肉的近红外光谱数据进行预处理,通过比较预测结果,确定多元散射校正为最优预处理方法。分别采用偏最小二乘法(PLS)和主成分回归法(PCR)建立金鲳鱼鱼肉TVB-N的预测模型,最终确立了基于MSC和PLS的最佳模型,其中预测集均方根误差(RMSEP)为1.8454,决定系数(R^(2))为0.8841。由研究结果看出,基于近红外光谱建立的金鲳鱼肉质预测模型具有较高的精度,可为快速检测金鲳鱼的肉质新鲜度提供理论依据。
The total volatile basic nitrogen(TVB-N)is the freshness index of animal food.The traditional TVB-N detection technology is complicated and has irreversible damage to fish.In this study,near-infrared spectroscopy was used to detect the meat freshness of pomfret.The first order differential(1st Der),second order differential(2nd Der),standard normal variate transform(SNV),multiplicative scatter correction(MSC)were used to preprocess the near infrared spectrum data.The MSC was determined as the optimal pretreatment method by comparing the predicted results.Partial least squares(PLS)and principal component regression(PCR)were used to establish the TVB-N prediction model.The best model was the prediction model based on MSC and PLS.RMSEP of the model was 1.8454,and R^(2) was 0.8841.The results show that the prediction model of pomfret meat freshness based on near-infrared spectroscopy has high accuracy,which provides theoretical basis for rapid detection of pomfret meat freshness.
作者
方瑶
谢天铧
郭渭
白雪冰
李振波
李鑫星
FANG Yao;XIE Tian-hua;GUO Wei;BAI Xue-bing;LI Zhen-bo;LI Xin-xing(College of Information and Electrical Engineering, China Agricutural University, Beijing 100083, China;College of Engineering, China Agricultural University, Beijing 100083, China)
出处
《江苏农业学报》
CSCD
北大核心
2021年第1期213-218,共6页
Jiangsu Journal of Agricultural Sciences
基金
“十三五”国家重点研发计划项目(2018YFD0701003)
北京市创新创业项目。
关键词
近红外光谱
新鲜度
金鲳鱼
挥发性盐基氮
偏最小二乘法
多元散射校正
near infrared spectroscopy
freshness
pomfret
total volatile basic nitrogen
partial least squares
multiplicative scatter correction
