不同保鲜袋包装对水蜜桃贮藏品质的影响

目的 研究不同保鲜袋包装对水蜜桃贮藏品质的改善效果,为水蜜桃贮藏保鲜提供技术指导.方法 以"玉露"水蜜桃为研究对象,采用聚乳酸可降解保鲜袋、纳米保鲜袋包装,在0℃下贮藏12 d后转至26℃下贮藏,以普通市售聚乙烯保鲜袋包装的水蜜桃作为空白对照.在低温贮藏期间每隔4 d测定水蜜桃的感官评价、硬度、质量损失率、呼吸强度、抗氧化酶活性等理化指标,在常温贮藏期间每天测定上述指标.结果 用市售保鲜袋包装的水蜜桃在低温下贮藏12 d就会发生严重腐烂变质,失去商业价值,转入常温后,腐烂愈发严重.采用聚乳酸可降解保鲜袋和纳米保鲜袋包装的水蜜桃在贮藏期12 d时的质量损失率分别为2.13％和1.52％,可溶性固形物含量(质量分数)分别为13.43％和13.3％,且无腐烂果实.结论 纳米保鲜袋包装可以减少水蜜桃水分的流失,保持水蜜桃的硬度、口感和风味,增加其抗氧化酶活性,提高其商品价值,有效延长水蜜桃货架期.

Effect of Different Fresh-Keeping Bags on Storage Quality of Juicy Peach

The work aims to predict and simulate the viscosity of edible ink by studying the genetic algorithm to optimize the Back Propagation (BP) neural network to establish the relationship between the independent variable and the dependent variable. Based on the previous researches on edible inks, the acetic acid concentration, chitosan dosage, alcohol dosage, and grinding speed were used as independent variables, and the obtained viscosity of edible ink as the dependent variable. The experiment was first designed by orthogonal experiments, and then the BP neural network with genetic algorithm was used to predict and simulate the viscosity of edible ink. 30 sets of experimental data were obtained by orthogonal experimental design: by using Genetic Algorithm Optimization Toolbox (GAOT) in Matlab 2018a software, a neural network with a convergence accuracy of 104 was obtained after 38 iterative trainings. The relative error between the predicted value of viscosity and the corresponding true value of viscosity was between 0.05% and 3.7%, and the R2 value of the fit was 0.8672, indicating that the BP neural network had excellent predictive ability and high predictive accuracy to predict the viscosity of edible inks. The BP neural network optimized with genetic algorithm can be used to predict and simulate the viscosity of edible inks, and extend the neural network into the evaluation system of other performances of the edible ink, thereby providing guidance for the production and application of the edible ink.