摘要
为实现南极磷虾(Euphausia superba)品质的快速评定,本研究将近红外光谱技术(Near infrared spectroscopy,NIRS)与偏最小二乘法(Partial least squares,PLS)相结合,构建用于快速预测磷虾体内非蛋白氮(Non-protein nitrogen,NPN)含量和挥发性盐基氮(Total volatile base nitrogen,TVB-N)含量的近红外定量分析模型。采集近红外光谱后,通过比较决定系数(Coefficient of determination,R^(2))、校正标准偏差(Root mean square error of calibration,RMSEC)、预测标准偏差(Root mean square error of prediction,RMSEP)等模型的评价参数,选取近红外光谱定量分析模型的最佳预处理方式、特征光谱范围以及主因子数。结果显示,NPN含量模型的最佳预处理方法为多元散射校正(Multiplicative signal correction,MSC),其特征光谱范围为8887.1~7774.2 cm-1;TVB-N含量模型则采用MSC与卷积平滑(Savitzky-Golay smoothing,SG)相结合的方式进行预处理,建模范围为全波段。两个定量模型的主因子数均为5。经模型的优化与外部验证,最终构建的PLS最优模型如下:NPN含量近红外定量分析模型的R^(2)为0.9384,RMSEC为0.279,RMSEP为0.443;TVB-N含量近红外定量分析模型的R^(2)为0.8685,RMSEC为3.800,RMSEP为4.070。研究结果表明,两个模型均具有良好的预测精度(R^(2)>0.85)和稳定性,其中NPN定量分析模型的预测能力优于TVB-N定量分析模型。综上,本研究基于NIRS与PLS构建的定量分析模型能够有效预测南极磷虾体内的NPN和TVB-N含量,为南极磷虾的品质评价提供了可靠的技术支持,满足快速评定的实际应用需求。
The aim of this study was to develop a rapid quality assessment method for Antarctic krill(Euphausia superba)by integrating near-infrared spectroscopy(NIRS)with partial least squares(PLS)regression.Quantitative models were established to predict two critical quality indicators:non-protein nitrogen(NPN)and total volatile base nitrogen(TVB-N)contents.Following spectral acquisition,the key model parameters,including the preprocessing methods,characteristic spectral ranges,and principal factor numbers,were systematically optimized.Model performance was evaluated using the coefficient of determination(R^(2)),root mean square error of calibration(RMSEC),and root mean square error of prediction(RMSEP).For the NPN model,multiplicative scatter correction(MSC)was selected as the optimal preprocessing method,with a characteristic spectral range of 8887.1 to 7774.2 cm-1.The TVB-N model utilized a combination of MSC and Savitzky-Golay smoothing(SG),with the full spectral band employed for modeling.Both models adopted five principal factors.After optimization and external validation,the optimized NPN model demonstrated a robust performance,with R^(2)=0.9384,RMSEC=0.279,and RMSEP=0.443,whereas the TVB-N model achieved R^(2)=0.8685,RMSEC=3.800,and RMSEP=4.070.These results indicated that both models exhibit high predictive accuracy(R^(2)>0.85)and stability,with the NPN model outperforming the TVB-N model in terms of predictive capability.In conclusion,quantitative analysis models constructed by combining NIRS and PLS can predict the NPN and TVB-N content in Antarctic krill.This approach provides a rapid solution for Antarctic krill quality assessment,addressing the growing demand for the efficient monitoring of Antarctic krill resource utilization.
作者
李琳
孙慧慧
曹荣
孙永
张朝辉
LI Lin;SUN Huihui;CAO Rong;SUN Yong;ZHANG Zhaohui(Yellow Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences,Qingdao 266071,China;College of Food Science and Engineering,Ocean University of China,Qingdao 266003,China)
出处
《食品工业科技》
北大核心
2026年第1期318-325,共8页
Science and Technology of Food Industry
基金
国家重点研发计划项目(2023YFD2401205)。
关键词
南极磷虾
近红外光谱技术
偏最小二乘法
非蛋白氮
挥发性盐基氮
Antarctic krill
near infrared spectroscopy
partial least squares
non-protein nitrogen
total volatile base nitrogen
