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A non-destruction measurement system based on hyperspectral imaging for sugar content in banana (Musa sp.)

机译:基于高光谱成像的香蕉糖含量无损测量系统(Musa sp。)

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Sugar content is one of the important parameters to determine the quality of banana. In this paper, a non-destruction measurement system for sugar content is introduced using hyperspectral camera system over the VIS/NIR (400-1,000 nm) spectral range. Hyperspectral image (HSI) calibration was performed to compute reflectance value of banana surface in full wavelength range while spectral and spatial analysis was conducted using a partial least squares regression (PLSR) to create a model that computing relationship between the HSI spectra and the sugar content. The ground truth value of sugar content was measured using digital refractometer on the extracted banana sample. The proposed system was evaluated using 90 Ambon bananas (Musa acuminata Colla) which consist of 30 raw, 30 mature and 30 overripe banana. The PLSR model provided the root mean square error of 0.79 % and the correlation coefficient R2 of 0.988 in the full wavelength band. Finally, the proposed non-destruction prediction system could be implemented as an instrument for sugar content measurement of banana fruit.
机译:糖含量是确定香蕉质量的重要参数之一。在本文中,介绍了使用高光谱相机系统在VIS / NIR(400-1,000 nm)光谱范围内的糖含量无损测量系统。进行高光谱图像(HSI)校准以计算香蕉表面在整个波长范围内的反射率值,同时使用偏最小二乘回归(PLSR)进行光谱和空间分析以创建一个模型,该模型计算HSI光谱与糖含量之间的关系。使用数字折光仪在提取的香蕉样品上测量糖含量的地面真值。使用90个Ambon香蕉(Musa acuminata Colla)对提议的系统进行了评估,其中包括30个生香蕉,30个成熟香蕉和30个未熟香蕉。 PLSR模型在整个波段内的均方根误差为0.79 \%,相关系数R 2 为0.988。最后,所提出的无损预测系统可以作为香蕉果实含糖量测量的一种工具来实现。

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