College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, 625014, China.
Institute of Modern Agricultural Industry, China Agricultural University, Chengdu, Sichuan, 611430, China.
Talanta. 2024 Dec 1;280:126793. doi: 10.1016/j.talanta.2024.126793. Epub 2024 Aug 30.
Dry matter content (DMC), firmness and soluble solid content (SSC) are important indicators for assessing the quality attributes and determining the maturity of kiwifruit. However, traditional measurement methods are time-consuming, labor-intensive, and destructive to the kiwifruit, leading to resource wastage. In order to solve this problem, this study has tracked the flowering, fruiting, maturing and collecting processes of Ya'an red-heart kiwifruit, and has proposed a non-destructive method for kiwifruit quality attribute assessment and maturity identification that combines fluorescence hyperspectral imaging (FHSI) technology and chemometrics. Specifically, first of all, three different spectral data preprocessing methods were adopted, and PLSR was used to evaluate the quality attributes (DMC, firmness, and SSC) of kiwifruit. Next, the differences in accuracy of different models in discriminating kiwifruit maturity were compared, and an ensemble learning model based on LightGBM and GBDT models was constructed. The results indicate that the ensemble learning model outperforms single machine learning models. In addition, the application effects of the 'Convolutional Neural Network'-'Multilayer Perceptron' (CNN-MLP) model under different optimization algorithms were compared. To improve the robustness of the model, an improved whale optimization algorithm (IWOA) was introduced by modifying the acceleration factor. Overall, the IWOA-CNN-MLP model performs the best in discriminating the maturity of kiwifruit, with Accuracy of 0.916 and Loss of 0.23. In addition, compared with the basic model, the accuracy of the integrated learning model SG-MSC-SEL was improved by about 12%-20 %. The research findings will provide new perspectives for the evaluation of kiwifruit quality and maturity discrimination using FHSI and chemometric methods, thereby promoting further research and applications in this field.
干物质含量（DMC）、硬度和可溶性固形物含量（SSC）是评估猕猴桃品质属性和确定成熟度的重要指标。然而，传统的测量方法既耗时又费力，而且对猕猴桃具有破坏性，导致资源浪费。为了解决这个问题，本研究跟踪了雅安红心猕猴桃的开花、结果、成熟和采集过程，并提出了一种结合荧光高光谱成像（FHSI）技术和化学计量学的猕猴桃品质属性评估和成熟度识别的无损方法。具体来说，首先采用了三种不同的光谱数据预处理方法，并采用偏最小二乘回归（PLSR）评估了猕猴桃的品质属性（DMC、硬度和 SSC）。接下来，比较了不同模型在区分猕猴桃成熟度方面的准确性差异，并构建了一个基于 LightGBM 和 GBDT 模型的集成学习模型。结果表明，集成学习模型的性能优于单一机器学习模型。此外，还比较了不同优化算法下“卷积神经网络”-“多层感知机”（CNN-MLP）模型的应用效果。为了提高模型的鲁棒性，通过修改加速度因子引入了改进的鲸鱼优化算法（IWOA）。总体而言，在区分猕猴桃成熟度方面，IWOA-CNN-MLP 模型的性能最佳，准确率为 0.916，损失为 0.23。此外，与基础模型相比，集成学习模型 SG-MSC-SEL 的准确率提高了约 12%-20%。该研究结果将为使用 FHSI 和化学计量学方法评估猕猴桃品质和成熟度识别提供新的视角，从而促进该领域的进一步研究和应用。
