摘要
酚类物质是评价葡萄成熟品质的重要指标,本文利用可见-近红外光谱技术结合化学计量学定量分析方法对葡萄皮总酚、籽总酚、皮单宁和籽单宁含量开展了无损检测研究。通过手持式可见-近红外光谱仪采集巨玫瑰葡萄波长400~1029 nm范围内的漫反射光谱,采用SPXY算法将其划分为校正集和预测集,结合标准正态变换(Standard normal variate,SNV)、多元散射校正(Multiplicative scatter correction,MSC)、一阶导数(First derivative,1 D)、二阶导数(Second derivative,2 D)、Savitzky-Golay卷积平滑(Savitzky-Golay smoothing,SG)和Savitzky-Golay卷积平滑+一阶导数(SG+1D)6种预处理方法以及偏最小二乘回归(Partial least squares regression,PLSR)、支持向量机回归(Support vector machine regression,SVR)和卷积神经网络(Convolutional neural network,CNN)3种建模算法,分别建立了基于全波段和特征波长的葡萄皮总酚、籽总酚、皮单宁和籽单宁定量预测模型并进行综合对比分析。结果表明,对于皮总酚、籽总酚和籽单宁,经特征波长筛选后建立的模型效果优于全波段,而对于皮单宁,全波段建立的模型较特征波长效果更佳;因此,在预测皮总酚、籽总酚、皮单宁和籽单宁含量时,最优模型分别为RAW-CARS-SVR、1D-CARS-SVR、RAW-CNN和RAW-CARS-PLSR,校正集相关系数(Correlation coefficient of calibration set,Rc)分别为0.96、0.99、0.96和0.91,预测集相关系数(Correlation coefficient of prediction set,Rp)分别为0.95、0.99、0.83和0.89,剩余预测偏差(Residual predictive deviation,RPD)分别为3.56、7.30、1.92和2.25。因此,结合可见-近红外光谱和合适的回归模型,可以实现对巨玫瑰葡萄的皮-籽总酚、皮-籽单宁含量的无损检测。
Phenolic compounds play a crucial role in assessing the internal quality of grapes and hold significant importance in this regard.The capability of visible-near-infrared(Vis-NIR)spectroscopy combined with multivariate regression models was explored to detect the contents of total phenolics and tannins in grape skins and seeds.Reflectance spectra data of Muscat Kyoho grapes were collected within the wavelength range of 400 nm to 1029 nm,and the samples were divided into correction set and prediction set by SPXY algorithm.Six commonly used preprocessing methods such as standard normal variate(SNV),multiplicative scatter correction(MSC),first derivative(1D),second derivative(2D),Savitzky-Golay smoothing(SG)and SG+1D were applied to the spectral data,and the competitive adaptive reweighted sampling algorithm(CARS)was utilized to select informative wavelengths.The quantitative models for comprehensive analysis of total phenolics and tannins in grape skins and seeds based on full spectra and effective wavelengths were established by partial least squares regression(PLSR),support vector machine regression(SVR),and convolutional neural network(CNN).The results showed that for the total phenolics in grape skins,total phenolics and tannins in grape seeds,the models on the basis of effective wavelengths performed better than those with full spectra data.While for the tannins in grape skins,the models constructed with full spectra yielded better results than the feature wavelength-selected models.The optimal models for the total phenolics and tannins in grape skins and seeds were RAW-CARS-SVR,1D-CARS-SVR,RAW-CNN and RAW-CARS-PLSR,respectively.The correlation coefficent of calibration set(Rc)were 0.96,0.99,0.96 and 0.91,the correlation coefficent of prediction set(Rp)were 0.95,0.99,0.83 and 0.89,the residual predictive deviation(RPD)were 3.56,7.30,1.92 and 2.25,respectively.Therefore,the developed method could realize the non-destructive detection of the contents of total phenolics and tannins in grape skins and seeds.
作者
刘文政
周雪健
平凤娇
苏媛
鞠延仑
房玉林
杨继红
LIU Wenzheng;ZHOU Xuejian;PING Fengjiao;SU Yuan;JU Yanlun;FANG Yulin;YANG Jihong(College of Enology,Northwest A&F University,Yangling,Shaanxi 712100,China;College of Food Science and Pharmacy,Xinjiang Agricultural University,Urumqi 830052,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2024年第2期372-383,共12页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(32201678)
中央高校基础科研业务费专项资金项目(2452020201)。
关键词
葡萄
可见-近红外光谱
成熟度
品质检测
grape
visible-near-infrared spectroscopy
ripeness
quality detection
