Authors

Abstract

Soluble solid content (SSC) is a major quality index of kiwifruits. Visible near-infrared (Vis/NIR) hyperspectral imaging with the genetic algorithm (GA) was adopted in this study to realize the non-destructive prediction of kiwifruit SSC. A laboratory Vis/NIR hyperspectral imaging system was established to collect the hyperspectral imaging of 120 kiwifruit samples at a range of 400–1100 nm. The average reflectance spectral data of the region of interest of the kiwifruit hyperspectral imaging were obtained after different preprocessing method, namely, Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), and their combination method. The prediction models of partial least squares regression, multiple linear regression, and least squares support vector machine (LS-SVM) were built for determining kiwifruit SSC by using the average reflectance spectral data and effective feature wavelength variables selected by GA, respectively. The results show that SG+MSC is the best preprocessing method. The precisions of the prediction models built using the effective feature wavelength variables selected by GA are higher than that established using full average reflectance spectral data. The GA-LS-SVM prediction model has a best performance with correlation coefficient for prediction (R=0.932) and standard error of prediction (SEP=0.536° Bx) for predicting kiwifruit SSC. The prediction accuracy has been improved by 5.6% compared with that of the prediction models established by using the full-band reflectance spectral data. This study provides an effective method for non-destructive detection of kiwifruit SSC.

Abstract in Chinese

可溶性固形物含量是猕猴桃的主要品质指标。本研究采用可见/近红外高光谱成像结合遗传算法实现猕猴桃可溶性固形物含量的无损检测。构建了可见/近红外高光谱成像系统，采集了120个猕猴桃样品400-1100 nm的高光谱图像。获取猕猴桃高光谱图像感兴趣区域平均反射光谱，采用Savitzky-Golay平滑、散射校正及其组合预处理方法对其进行预处理。分别利用全波段反射光谱和遗传算法选取的有效特征波长光谱建立猕猴桃可溶性固形物含量的偏最小二乘回归、多元线性回归和最小二乘支持向量机预测模型。结果表明，Savitzky-Golay组合散射校正是最好的预处理方法。利用遗传算法选择有效特征波长建立的预测模型精度高于全波段反射光谱建立的预测模型。GA-LS-SVM预测模型预测猕猴桃可溶性固形物含量的相关系数(R=0.932)和标准误差(SEP=0.536°Bx)最好，预测精度大大提高。本研究表明，利用可见/近红外高光谱成像结合遗传算法可以无损预测猕猴桃可溶性固形物含量，为无损检测水果品质提供了有效的方法。