猕猴桃是我国发展势头和经济效益比较突出的水果之一, 其果肉色泽是评价猕猴桃果实品质的重要指标。 利用近红外光谱技术对贮藏期猕猴桃不同深度果肉色泽的变化进行研究。 以贮藏期“哑特”猕猴桃果皮下0, 5和10 mm处果肉色泽(L*, a*和b*)为研究对象, 用近红外光谱(830~2 500 nm)结合化学计量学方法对猕猴桃果肉色泽特征进行预测分析。 通过建立基于全波段的偏最小二乘回归(PLSR)模型, 发现猕猴挑果皮下5 mm处色泽特征(L*5, a*5, b*5)所建立的校正预测模型效果好, 说明该处的色泽数据和近红外光谱信息的相关度较高。 运用竞争性自适应重加权采样法(CARS)和无信息变量消除法(UVE)两种算法从高维近红外光谱全波段信息中选取与颜色特征相关的特征波长信息, 并与猕猴桃果皮下5 mm处的色泽(L*5, a*5, b*5)分别建立PLSR和多元线性回归(MLR)预测模型。 其中对果肉色泽L*5所建立的模型中, CARS-PLSR模型的校正和预测效果均为最好, RC达到0.942 7, RMSEC为1.699 7, RP达到0.885 0, RMSEP为1.642 4； 对猕猴桃果肉色泽a*5所建立的模型中, UVE-MLR模型的校正和预测效果最好, RC达到0.946 3, RMSEC为0.342 4, RP达到0.854 9, RMSEP为0.629 6； 对猕猴桃果肉色泽b*5所建立的模型中, CARS-MLR模型的效果最好, RC达到0.944 3, RMSEC为1.010 1, RP达到0.839 8, RMSEP为1.354 3。 研究表明近红外光谱分析技术检测猕猴桃果皮下5 mm处色泽(L*5, a*5和b*5)具有良好的准确度, 为猕猴桃品质评价提供技术支撑。

Kiwifruit is one of the fruits with strong development momentum and economic benefits in China, its pulp color has become an important indicator for evaluating the quality of kiwifruit. Here, near-infrared spectroscopy was employed to study the changes in pulp color in different depths of kiwifruit during different storage periods. In this study, the “Mute” kiwifruit’s pulp color features (L*, a*, b*) in depths of 0, 5, and 10 mm under the skin wereviewed as the research object, the near-infrared spectroscopy (830～2 500 nm) was used as a technical tool, and chemometric methods were combined to analyze the pulp color features of kiwifruit. By establishing a partial least-square regression (PLSR) model based on the full-wavelengths, it found that the established model offered good results by using color features (L*5, a*5, b*5) at a depth of 5 mm, which indicated that the pulp color features and the spectrum data had a relatively high correlation. Then, the competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) algorithms were used to select the characteristic wavelengths related to color features from the high-dimensional full-wavelengths. And the PLSR and multiple linear regression (MLR) prediction models were respectively established based on the color features (L*5, a*5, b*5) and spectra at characteristic wavelengths. Results revealed that the CARS-PLSR model with the RC=0.942 7, RMSEC=1.699 7, RP=0.885 0, and RMSEP=1.642 4 has the best predictive effect for the pulp color feature L*5; the UVE-MLR model with the RC=0.946 3, RMSEC=0.342 4, RP=0.854 9, and RMSEP of 1.354 3 exhibited the best predictive results for pulp color feature a*5, the CARS-MLR model with the RC=0.944 3, RMSEC=1.010 1, RP=0.839 8, and RMSEP=1.354 3 performed best predictive results for pulp color feature b*5. The results demonstrated that the near-infrared spectroscopy technique would be employed to detect the color features at different depths of kiwifruit, which provided technical support for the quality evaluation of kiwifruit.