摘要
以多批次黄桃脆片为分析对象,分别采集了可见/短波近红外光谱(400~1000 nm)和长波近红外光谱(1000~2500 nm)原始信息,分别采用标准正态变量变换(SNV)、多元散射校正(MSC)、移动平均平滑(MS),一阶导数(1-Der)预处理后,建立了全波段线性偏最小二乘法(PLS)和非线性支持向量机(SVM)预测模型,并结合外部试验进行可行性验证。结果表明,基于MSC-SVM的可见/短波红外光谱模型对可溶性固形物预测效果最佳,验证集的决定系数(R p)、预测均方根误差(RMSEP)、相对预测偏差(RPD)分别为0.761,1.998%和1.532;而基于MSC-SVM的长波近红外光谱模型对硬度预测效果相对最佳,对应R p、RMSEP和RPD分别为0.862,0.292 kg和1.991。基于近红外光谱系统可以实现对大批量黄桃脆片品质参数的快速无损检测。
The spectral data was collected by using two different infrared spectroscopies with 400 to 1000 nm(visible-shortwave)and 1000 to 2500 nm(longwave)from yellow peach chips.Then four mathematic algorithms,i.e.standard normal variate transformation(SNV),multiplicative scatter correction(MSC),moving-average smoothing(MS)and 1st-derivative(1-Der),were utilized in data preprocessing.Regression models by linear partial least squares(PLS)and non-liner support vector machine(SVM)were constructed for the predicting the soluble solids content(SSC)and firmness in yellow peach chips,respectively.Moreover,the feasibility analysis for prediction of SSC and firmness were vitrificated by the external experiments.The results showed that the best performance for SSC prediction was obtained with R p of 0.761,RMSEP of 1.998%and RPD of 1.532 by MSC-SVM algorithm in 400 to 1000 nm.However,the best performance for firmness prediction was obtained with R p of 0.862,RMSEP of 0.292 kg and RPD of 1.991 by MSC-SVM algorithm in 1000 to 2500 nm.All these findings demonstrated that the near-infrared spectroscopy could be utilized to monitor the quality of fruit chips with non-destructive attributes,and also positively promote the development of online automated grading system.
作者
曹念念
刘强
彭菁
屠康
赵保民
朱金星
潘磊庆
CAO Nian-nian;LIU Qiang;PENG Jing;TU Kang;ZHAO Bao-ming;ZHU Jin-xing;PAN Lei-qing(College of Food Science and Technology,Nanjing Agricultural University,Nanjing,Jiangsu 210095,China;College of Food Science and Engineering,Nanjing University of Finance and Economics,Nanjing,Jiangsu 210023,China;Collaborative Innovation Center for Modern Grain Circulation and Safety,Nanjing,Jiangsu 210023,China;Jiangsu Key Laboratory of Quality Control and Further Processing of Cereals and Oil,Nanjing,Jiangsu 210023,China;Jiangsu Palarich Food Company,Xuzhou,Jiangsu 221008,China)
出处
《食品与机械》
北大核心
2021年第3期51-57,共7页
Food and Machinery
基金
江苏省重点研发计划项目(编号:BE2019312)
国家自然科学基金项目(编号:31671926,31671925)。
关键词
黄桃
脆片
近红外光谱
无损检测
可溶性固形物
硬度
yellow peach
chip
near infrared spectroscopy
non-destructive detection
soluble solid
firmness
作者简介
曹念念,女,南京农业大学在读硕士研究生;通信作者:刘强(1991—),男,南京财经大学讲师,博士。E-mail:qiangliu@nufe.edu.cn;通信作者:潘磊庆(1980—),男,南京农业大学教授,博士生导师,博士。E-mail:pan_leiqing@njau.edu.cn。
