Huang Xiao, Wang Huakun, Qu Shenchun, Luo Wenjie, Gao Zhihong
College of Horticulture Nanjing Agricultural University Nanjing China.
Technical Extension Center of Evergreen Fruit Trees in Taihu of Jiangsu Province Suzhou China.
Food Sci Nutr. 2021 Jan 29;9(3):1780-1791. doi: 10.1002/fsn3.2166. eCollection 2021 Mar.
The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12-12-1 can predict the single fruit weight with the highest accuracy ( = .91), the ANN model with the structure of 10-11-1 can predict the soluble solid content with the highest accuracy ( = .91), and the ANN model with the structure of 9-10-1 can predict the titratable acid content with the highest accuracy ( = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.
枇杷果实品质的形成与调控一直是提高果实品质、商品性和市场价值的重要研究领域。果实大小、可溶性固形物含量和可滴定酸含量是枇杷最重要的品质因素。矿质营养元素的丰缺是影响果实品质的最重要关键因素之一。在本研究中，我们使用人工神经网络（ANN）来探究土壤和叶片中的矿质营养元素对枇杷关键果实品质的影响。结果表明，结构为12-12-1的ANN模型预测单果重的准确率最高（ = 0.91），结构为10-11-1的ANN模型预测可溶性固形物含量的准确率最高（ = 0.91），结构为9-10-1的ANN模型预测可滴定酸含量的准确率最高（ = 0.95）。同时，我们还进行了敏感性分析，以分析土壤和叶片中矿质营养元素对关键果实品质的相对贡献。就相对贡献而言，土壤中的钙、铁和镁含量以及叶片中的锌、钾和钙含量对单果重的贡献相对较大，土壤中的锰、铁和镁含量以及叶片中的氮含量对可溶性固形物含量的贡献相对较大，叶片中的磷、钙、氮、镁和铁对枇杷的可滴定酸含量的贡献相对较大。所建立的人工神经网络预测模型可通过优化土壤和叶片中矿质元素的含量来提高枇杷果实品质。
