基于PCA-GA-SVR的鲜食葡萄运输过程品质建模

Modeling Table Grapes:Physicochemical Indexes and Sensory Quality Based on PCA-GA-SVR during Transportation Process

由于生鲜果蔬生产的地域性和季节性,生鲜果蔬在采摘后需要经过运输、贮藏等物流过程才可以到达消费者的手中,在此过程外界因素的影响会造成生鲜果蔬发生一系列生理变化,进而影响其口感。本研究以鲜食葡萄为研究对象,拟探索鲜食葡萄在物流运输过程中的理化指标与感官品质关系建模,基于对实际运输过程的监测在实验室开展了鲜食葡萄运输模拟实验和感官实验,获取了鲜食葡萄运输过程中理化指标和感官品质数据,并构建了不同运输模式下的鲜食葡萄品质数据集;构建了基于改进支持向量回归(Support Vector Regression Algorithm,SVR)的鲜食葡萄理化指标与感官品质建模方法,首先利用主成分分析法(Principal Component Analysis,PCA)对理化指标进行降维,再利用遗传算法(genetic algorithm,GA)优化SVR模型参数提升模型的拟合效果。利用常温运输、保冷运输和冷链运输三种运输模式以及混合数据集测试,结果表明改进的PCA-GA-SVR模型预测的准确性和精度均有显著提高,MAE、MSE、RMSE均小于0.5,R2均大于0.96,PCA-GA-SVR模型能够较好地反映鲜食葡萄理化指标与感官品质之间的非线性映射关系。同时,研究表明理化指标数据与感官品质之间的关系受到运输模式的影响较小,本研究提出的感官品质评估模型可以较好地应用在任何运输方式上,辅助物流过程中生鲜农产品的品质控制与管理。

The regionality and seasonality of fresh fruits and vegetables mean that this produce is subjected to logistics and transportation processes after harvesting and before reaching the hands of consumers.During these processes,several external factors cause physiological changes in fresh fruits and vegetables,which will affect their taste.This paper considers the case of table grapes,and conducts simulation experiments and sensory experiments in the laboratory based on monitoring of the actual transportation process.After obtaining a suitable dataset,a model of the relationship between the physicochemical indicators and the sensory quality of the grapes is constructed based on an improved support vector regression(SVR) algorithm.Principal component analysis(PCA) is used to reduce the dimensionality of the physical and chemical indicators,and a genetic algorithm(GA) is used to optimize the SVR model parameters.An examination of three transportation modes(normal-temperature transportation,cold-storage transportation,and cold-chain transportation) and mixed dataset testing shows that the improved PCA-GA-SVR model offers significantly improved accuracy and precision,reflecting the nonlinear mapping relationship between the physicochemical indexes and sensory quality of table grapes.The mean absolute error,mean squared error,and root mean squared error achieved by the PCA-GA-SVR model are all less than 0.5,and the R2 values are all greater than 0.96.This study shows that the relationship between physical and chemical index data and sensory quality is not significantly affected by the transportation mode.The sensory quality evaluation model proposed in this study can be applied to any transportation mode to assist the quality of fresh agricultural products in the control and management of the logistics process.