College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2021 Jan 30;21(3):930. doi: 10.3390/s21030930.
The acidity of green plum has an important influence on the fruit's deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis-linear discriminant analysis-extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.
青梅的酸度对其深加工有重要影响。传统的物理化学分析方法对青梅酸度的检测具有破坏性、耗时且无法实现在线检测。针对这一问题，本文研究了一种基于高光谱成像技术的快速无损检测方法。目前，基于监督学习和无监督学习方法的预测性能比较研究较为热门。为了进一步提高成分预测的准确性，开发了一种新的高光谱成像系统，并提出了核主成分分析-线性判别分析-极端梯度提升算法（KPCA-LDA-XGB）模型来预测青梅的酸度。KPCA-LDA-XGB 模型是一种结合了极端梯度提升算法（XGBoost）、核主成分分析（KPCA）和线性判别分析（LDA）的监督学习模型。实验结果证明，KPCA-LDA-XGB 模型对青梅的酸度具有良好的预测效果，预测集的相关系数（R）为 0.829，均方根误差（RMSE）为 0.107。与基本的 XGBoost 模型相比，KPCA-LDA-XGB 模型的 R 提高了 79.4%，RMSE 降低了 31.2%。本文还对线性、径向基函数（RBF）和多项式（Poly）核函数进行了比较和分析，以进一步优化 KPCA-LDA-XGB 模型。
