基于近红外光谱法快速测定许氏平鲉脂肪和水分含量的方法研究  

Research on rapid determination of fat and moisture content of Sebastes schlegeli by near infrared spectroscopy

作　　者：孙永[1] 刘申申[1] 李智慧[1] 刘楠[1] 周德庆[1] 

机构地区：[1]中国水产科学研究院黄海水产研究所,青岛266071

出　　处：《食品安全质量检测学报》2016年第12期4826-4833,共8页Journal of Food Safety and Quality

基　　金：国家科技支撑计划课题(2015BAD17B01);烟台市高端人才引进‘双百计划’(XY-04-18-01)~~

摘　　要：目的建立近红外光谱法结合偏最小二乘法测定许氏平鲉鱼肉中的脂肪和水分含量,以期简便、快速地对许氏平鲉进行品质分析与评价。方法采用常规分析手段测定70个样品的脂肪和水分含量,同时采集其近红外光谱数据,结合偏最小二乘法(partial least square,PLS)建立许氏平鲉鱼肉中脂肪和水分的定量预测模型,并对比不同光谱预处理方法、光谱范围和因子数对定量预测模型的影响。结果光谱经Savitzky-Golay(S-G)和标准正态变量变换(standardized normal variate,SNV)预处理后,在5341.85~4007.36 cm^(-1)、6556.79~5345.71cm^(-1)和8651.10~7162.33 cm^(-1)光谱范围内,选取主因子数10,建立脂肪的校正模型性能最优;光谱经过SNV预处理后,在8886.38~4061.35cm^(-1)光谱范围内,分别选取主因子数为9时,建立的水分的校正模型性能最优。脂肪和水分含量相对最优PLS模型的校正集相关系数分别为0.9918和0.9912,校正标准偏差分别为0.2680和0.3300,交叉验证相关系数分别为0.9820和0.9810,交叉验证均方差分别为0.3980和0.4850,验证集相关系数分别为0.9804和0.9798,验证集均方差分别为0.3260和0.3070。结论本方法可较为准确地预测许氏平鲉鱼肉中的脂肪和水分含量,能够满足快速分析评价许氏平鲉品质的要求。Objective To establish a method for determination of fat and moisture content inSebastes schlegeli by near infrared spectroscopy （NIRS） combined with partial least square （PLS）, so as to evaluate the quality of Sebastes schlegeli simply and quickly.MethodFat and moisture results of 70 samples were obtained by ordinary analytical methods. Meanwhile, NIRS data of these samples were investigated in order to establish quantitative prediction model forSebastes schlegeli nutrients combined with PLS. The influences of different spectra pretreatment methods, different spectra regions and the number of factors were compared.ResultsThe performance of fat content model was established in 5341.85~4007.36 cm-1, 6556.79~5345.71 cm-1and 8651.10~7162.33 cm-1after Savitzky-Golay （S-G） and standard normal variate （SNV） pretreatment, and the optimal main factor number of 10 ＆ was selected. The performance of moisture content model was established in 8886.38~4061.35 cm-1 with SNV pretreatment, and nine factors were optimal. The correlation coefficients of calibration （Rc） of fat and moisture were 0.9918 and 0.9912, and the root mean square errors of calibration （RMSEC） were 0.2680 and 0.3300, respectively. The correlation coefficients of cross validation （Rcv） were 0.9820 and 0.9810, and the root mean square errors of cross validation （RMSECV） were 0.9804 and 0.9798. The correlation coefficients of prediction （Rp） were 0.9804 and 0.9798, and the root mean square errors of prediction （RMSEP） were 0.3260 and 0.3070.ConclusionThe method has acceptable accuracy and prediction capability, which is suitable for rapid quality analysis and evaluation of Sebastes schlegeli.

关 键 词：近红外光谱法 脂肪 水分 偏最小二乘法 许氏平鲉鱼 

分 类 号：TS254.7[轻工技术与工程—水产品加工及贮藏工程] O657.33[轻工技术与工程—食品科学与工程]