基于近红外光谱技术的猪肉新鲜度等级研究  

Research on Freshness Level of Meat Based on Near-Infrared Spectroscopic Technique

作　　者：郭培源[1] 林岩[1] 付妍[1] 王昕琨[1] 袁芳[1] 许冠男[1] 

机构地区：[1]北京工商大学计算机与信息工程学院,北京100048

出　　处：《激光与光电子学进展》2013年第3期180-186,共7页Laser & Optoelectronics Progress

基　　金：北京市自然科学基金(4122020)资助课题

摘　　要：利用近红外光谱技术检测猪肉在腐败过程中不同时刻的光谱,研究了猪肉新鲜度等级划分的可行性,并运用近红外OPUS软件建立了分析模型。为了更好地反映猪肉新鲜度等级,用SOM神经网络聚类方法重新划分了总挥发性盐基氮(TVBN)国家标准等级,由原来的3个等级划分成5个等级标准。为了提高模型预测准确度,在选用一阶导数+矢量归一化(平滑点数为13)预处理方法基础上,在聚类分析前用主成分分析方法进行降维,使预测偏差减小,使样品预测正确率得到进一步提高,预测级别偏差减少,提高了模型预测能力。We study the feasibility of pork freshness grading using the spectra detected at different time with near infrared spectroscopy and establish the analysis model by using near infrared OPUS software. In order to reflect the pork freshness level better, we use SOM neural network clustering method to differentiate afresh the volatile base nitrogen national standard （Total Volatile Basic Nitrogen, TVBN） level, from 3 levels to 5 levels. In order to improve the prediction accuracy, based on the pretreatment by selecting a derivativeS-vector normalization method （13 smooth points）, principal component analysis is used for dimension reduction before clustering analysis. It can reduce the prediction deviation, further improve the prediction accuracy of sample, reduce the prediction level deviation, and improve the prediction ability.

关 键 词：光谱学 近红外光谱技术 主成分分析 聚类分析 猪肉新鲜度 

分 类 号：TS207.3[轻工技术与工程—食品科学] TP29[轻工技术与工程—食品科学与工程]