摘要
利用可见/近红外半透射光谱技术对未剥皮(完整)和剥皮脐橙的可溶性固形物(SSC)进行检测,探索果皮对脐橙SSC检测精度的影响。采用QualitySpec型光谱仪获取未剥皮和剥皮脐橙在350~1 000nm波段的可见/近红外光谱,并从光谱和模型性能两方面分析果皮的影响。对未剥皮和剥皮脐橙平均光谱进行比较,并提取前20个主成分进行多元方差分析;应用偏最小二乘(PLS)回归结合不同预处理方法分别建立未剥皮和剥皮脐橙SSC的预测模型,对预测模型性能进行比较,并对预测集样本的预测残差平方进行方差分析。结果表明,在5%置信水平下,果皮对脐橙SSC检测精度的影响是显著的。未剥皮和剥皮脐橙SSC的最优PLS模型的预测集相关系数和预测均方根误差分别为0.888,0.456%和0.944,0.324%。
Visible/near infrared(Vis/NIR)spectroscopy was used to determine soluble solidcontent(SSC)of navel oranges with pericarp and without pericarp,and the effect of pericarp on prediction accuracy of SSC of navel oranges was investigated.In addition,Vis/NIR spectra of navel oranges with pericarp and without pericarp were acquired by a QualitySpec spectrometer in the wavelength range of 350~1 000 nm,andthe effect of pericarp was analyzed from two aspects of spectrum and model performance.The average spectra of navel oranges with pericarp and without pericarpwere compared,and 20 principal components that obtained were used for multivariate analysis of variance(MANOVA).Moreover,partial least squares(PLS)regressioncombined with different pretreatment methods was used to develop calibration models of SSC for navel oranges with pericarp and without pericarp.Furthermore,the performance of models was compared,and square of prediction residuals of samples in prediction set were used for analysis of variance(ANOVA).The results indicate that the effect of pericarp on prediction accuracy of soluble solid content in navel oranges is significant at 5%confidence level.The correlation coefficients of prediction set and root mean square errors of prediction(RMSEPs)of PLS of SSC for navel oranges with pericarp and without pericarp are 0.888,0.456%and0.944,0.324%,respectively.
作者
孙通
莫欣欣
刘木华
SUN Tong;MO Xin-xin;LIU Mu-hua(Key Laboratory of Jiangxi University for Optics-Electronics Application of Bio materials,College of Engineering,Jiangxi Agricultural University;Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province,Nanchang 330045,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2018年第5期1406-1411,共6页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(31401278)
江西省自然科学基金项目(20151BAB204025
20161BAB213096)
留学人员科技活动项目(2012)资助
关键词
可见/近红外
果皮影响
检测精度
可溶性固形物
方差分析
脐橙
Vis/NIR
Pericarp effect
Prediction accuracy
Soluble solid content
Analysis of variance
Navel orange