Tian Hai-Qing, Ying Yi-Bin, Lu Hui-Shan, Xu Hui-Rong, Xie Li-Juan, Fu Xia-Ping, Yu Hai-Yan
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Jun;27(6):1113-7.
Watermelon is a popular fruit in the world and firmness (FM) is one of the major characteristics used for assessing watermelon quality. The objective of the present research was to study the potential of visible/near Infrared (Vis/NIR) diffuse transmittance spectroscopy as a way for the nondestructive measurement of FM of watermelon. Statistical models between the spectra and FM were developed using partial least square (PLS) and principle component regression (PCR) methods. Performance of different models was assessed in terms of correlation coefficients (r) of validation set of samples and root mean square errors of prediction (RMSEP). Models for three kinds of mathematical treatments of spectra (original, first derivative and second derivative) were established. Savitsky-Goaly filter smoothing method was used for spectra data smoothing. The PLS model of the second derivative spectra gave the best prediction of FM, with a correlation coefficient (r) of 0. 974 and root mean square errors of prediction (RMSEP) of 0. 589 N using Savitsky-Goaly filter smoothing method. The results of this study indicate that NIR diffuse transmittance spectroscopy can be used to predict the FM of watermelon. The Vis/NIR diffuse transmittance technique will be valuable for the nandestructive detection large shape and thick peel fruits'.
西瓜是一种在全球广受欢迎的水果,硬度是评估西瓜品质的主要特性之一。本研究的目的是探究可见/近红外(Vis/NIR)漫透射光谱法作为无损测量西瓜硬度方法的潜力。使用偏最小二乘法(PLS)和主成分回归(PCR)方法建立了光谱与硬度之间的统计模型。根据验证集样本的相关系数(r)和预测均方根误差(RMSEP)评估不同模型的性能。建立了三种光谱数学处理方式(原始光谱、一阶导数光谱和二阶导数光谱)的模型。采用Savitsky-Goaly滤波平滑法对光谱数据进行平滑处理。使用Savitsky-Goaly滤波平滑法,二阶导数光谱的PLS模型对硬度的预测效果最佳,相关系数(r)为0.974,预测均方根误差(RMSEP)为0.589 N。本研究结果表明,近红外漫透射光谱法可用于预测西瓜的硬度。可见/近红外漫透射技术对于大型、形状不规则且果皮较厚的水果的无损检测具有重要价值。