Zhou Chenxin, Zhang Xiao, Liu Ying, Ni Xiaoyu, Wang Honghong, Liu Yang
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 20037, China.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 20037, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 15;303:123151. doi: 10.1016/j.saa.2023.123151. Epub 2023 Jul 16.
Soluble solids content is an important evaluation index affecting the quality of greengage fruit. The SSC content of green plum determines the picking time of green plum and what products are finally made into the market, such as preserves or fruit wine. The traditional destructive experiment is not conducive to the subsequent processing of green plum, and the efficiency is low and the labor cost is high. In this paper, hyperspectral images of green plums are analyzed based on the DenseNet network model, and a sugar content prediction model for green plums is established. After experimental collection and screening, 366 samples were obtained for the prediction of sugar content. According to the ratio of 3:1, 274 samples were obtained for the training set and 92 samples for the test set. In the prediction of sugar content, compared with the PLSR and MobileNetV2 model, the Rp of the 1D-DenseNet121 model in this experiment increased by 8.95%, and 6.27% respectively. and the MAEp was reduced by 15.44% and 10.35% respectively. The 1D-DenseNet121 model had a faster iterative convergence rate than the MobileNetV2 model, showing better prediction performance, which is more in line with the actual demand for green plum sorting, effectively improving the low efficiency of traditional physical and chemical detection.
可溶性固形物含量是影响青梅果实品质的重要评价指标。青梅的可溶性固形物含量决定了青梅的采摘时间以及最终投放市场的产品，如蜜饯或果酒。传统的破坏性实验不利于青梅的后续加工，且效率低、人工成本高。本文基于DenseNet网络模型对青梅的高光谱图像进行分析，建立了青梅糖含量预测模型。经过实验采集与筛选，获得366个样本用于糖含量预测。按照3:1的比例，得到274个样本作为训练集，92个样本作为测试集。在糖含量预测中，与PLSR和MobileNetV2模型相比，本实验中1D-DenseNet121模型的Rp分别提高了8.95%和6.27%，MAEp分别降低了15.44%和10.35%。1D-DenseNet121模型的迭代收敛速度比MobileNetV2模型更快，表现出更好的预测性能，更符合青梅分拣的实际需求，有效改善了传统理化检测效率低的问题。
