基于近红外透射光谱与机器视觉的蜜柚汁胞粒化分级检测

汁胞粒化是一种柑橘类水果中汁液囊的生理失调现象,表现为汁液囊变硬、干燥等,对水果内部品质产生消极影响。蜜柚是一种厚皮的柑橘类水果,很难通过外部果皮及果形,鉴定果实内部的汁胞粒化程度。采用近红外透射光谱结合机器视觉技术的快速无损检测方法,对蜜柚汁胞粒化程度进行分级检测。采集600个不同生长期的蜜柚样本在900～1700 nm的光谱数据,按果实的汁胞粒化程度将其分为5级。结合化学计量学研究由汁胞粒化引起的内部品质的化学变化,而机器视觉技术可用于研究由汁胞粒化引起的外部特征的物理变化。因此,该方法相较于传统检测方法,分级模型的预测能力更好。尤其是,连续投影-K近邻算法预测模型的准确性、敏感性和特异性分别达到0.970 0、0.923 1和0.987 4以上。结果表明:该方法可用于汁胞粒化的鉴定与评估分级,且具有巨大潜力,以期为厚皮类水果在线分选及内部品质研究提供参考和理论依据。

Detection of Honey Pomelo in Different Granulation Levels Based on Near-Infrared Transmittance Spectroscopy Combined with Machine Vision

Granulation is a physiological disorder of juice sacs in citrus fruit, which made juice sacs become hard and dry and damaged the internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is hard to identify the granulation levels by observing the outer peel and fruit shape. In this study, a rapid and non-destructive detection method based on near-infrared transmittance spectroscopy combined with machine vision technology was used to classify honey pomelo by the granulation levels. 600 honey pomelos in different growth stages were harvested and divided into five granulation levels according to the granulation changes of samples. Spectral data of samples were recorded in the range of 900~1700nm, which were combined with chemometrics to research the chemical changes of inner quality caused by granulation. Machine vision technology can be used to study the physical changes of external characteristics caused by granulation. Therefore, comparison of the traditional method, this method has better predictive performances in classification models. In particular, the predictive performances of accuracy, sensitivity, and specificity were respectively not less than 0.9700, 0.9231, and 0.9874 in the SPA-KNN (successive projections algorithm-K nearest neighbor) predicted model. The results showed that this method could be used for classification and evaluation of granulation, and had a great potential. The method provides a reference and theoretical basis for the online sorting and inner quality detecting of thick-skinned fruits.