利用高光谱成像技术预测苹果内部品质

摘要：糖度和硬度是重要的内部特性，对品质等级和工业生产具有重要意义。研究了高光谱成像技术在苹果糖度和硬度 预测中的应用。从 400nm 到 1000nm 之间的图像感兴趣区域提取平均光谱。经过乘性散射校正 (MSC) 预处理后，利用 偏最小二乘 (PLS) 回归系数提取特征波长。提取了糖度的 6 个特征波长 418.9、502.5、675.2、720.9、968.7nm 和硬度 的 6 个特征波长（384.2，418.9、500.0、513.5、599.9、704.4、964.7nm）。对比了全波长和特征波长下糖度和硬度预测 模型的性能，分别建立了糖度和硬度的BP人工神经网络(BP)、偏最小二乘回归(PLSR)和主成分回归(PCR)3种预测模型。 结果表明，3 种模型都能对糖度和硬度进行预测，其中基于特征波长的 BP 模型对糖度和硬度的预测效果最好，BP 预测 的相关系数和均方根误差分别为SSC的0.9225 0.137和硬度的0.9126 1.099。研究表明高光谱成像技术可以成为一种可靠 的苹果内部品质检测工具，为苹果的无损检测提供了理论参考。

关键词：高光谱成像技术；苹果；糖度；硬度

期刊：2017 International Conference on Advanced Mechatronic Systems (ICAMechS)

DOI：10.1109/ICAMechS.2017.8316516

设备：GaiaSorter

Prediction of Apple Internal Qualities Using Hyperspectral Imaging Techniques

Abstract：Sugar content and firmness are significant internal properties and have important significance for quality grade and industry production. The hyperspectral imaging techniques was investigated for prediction of sugar content and firmness in apples. Mean spectra were extracted from the regions of interest of the image between 400nm and 1000nm After multiplicative scatter correction (MSC) preprocessing, the regression coefficient of partial least squares (PLS) was used to extract feature wavelengths. 6 feature wavelengths of sugar content 418.9, 502.5, 675.2, 720.9, 968.7nm and 6 feature wavelengths of firmness (384.2，418.9, 500.0, 513.5, 599.9, 704.4, 964.7nm) were extracted. The performance of prediction model of sugar content and firmness in full wavelength and feature wavelength was compared, 3 prediction models like BP artificial neural network (BP) ,partial least squares regressions (PLSR) and principal component regression (PCR) model of sugar content and firmness were built, respectively. The results showed that the sugar content and firmness can be predicted by 3 types of models. The BP model based on feature wavelength achieved the optimal performance for sugar content and firmness. The correlation coefficient and root mean square errors of prediction by BP were 0.9225 0.137 for SSC and 0.9126 1.099 for firmness, respectively. The study demonstrated that hyperspectral imaging technique can be a reliable tool to detection of apple internal qualities, which provided a theoretical reference for nondestructive detection of apple.

Keywords：hyperspectral imaging technique; apple; sugar content; firmness