预测储存质量和多目标优化储存条件新鲜的Lycium barbarum L. 基于优化拉丁式超立方样本
Xiaobin Mou1, Xiaopeng Huang1, Guojun Ma1
1College of Mechanical and Electronical Engineering, Gansu Agricultural University, Lanzhou 730070, China.
优化新鲜狼 (Lycium barbarum) 的储存包括使用神经网络预测质量变化. 最佳条件是以60%的成熟度收获,并在10°C保存10天,以保持水果的质量.
科学领域:
- 农业科学
- 食品科学
- 生物技术
背景情况:
- 由于特征和条件之间的复杂关系,在储存期间对新鲜的Lycium barbarum (狼) 的质量控制具有挑战性.
- 了解这些关系对于保持营养价值和可销售性至关重要.
研究的目的:
- 在各种储存条件下分析的质量变化 (硬度,可溶性固体含量 (SSC),可定位酸度 (TA),维生素C (Vc).
- 开发准确的预测模型,用于存储期间的黄质量特征.
- 确定最佳的储存条件,以保持新鲜的黄.
主要方法:
- 针对辐射基函数神经网络 (RBFNNs) 和Elman神经网络使用优化拉丁式超立方采样.
- 开发硬度,SSC,TA和Vc的预测模型.
- 应用粒子群优化 (PSO) 用于存储参数的多目标优化.
主要成果:
- RBFNN模型显示TA (R2=0.99) 和Vc (R2=0.99) 的高精度,硬度 (R2=0.98) 和SSC (R2=0.94) 的略低精度.
- 已确定最佳存储条件:在成熟度≥60%时收获,在10°C存储约10天.
- 预测的最佳质量:硬度为15N,SSC为17.5%,TA为1.22%,Vc为18.5mg/100g.
结论:
- 开发的预测模型在不同的储存场景下准确地预测了wolfberry的质量.
- 最佳的储存条件显著提高了新鲜的质量.
- 这些发现为优化果和其他新鲜水果的储存提供了宝贵的见解.
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