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首页> 外文期刊>European Food Research and Technology >Development and comparison of multivariate respiration models for fresh papaya (Carica papaya L.) based on regression method and artificial neural network
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Development and comparison of multivariate respiration models for fresh papaya (Carica papaya L.) based on regression method and artificial neural network

机译：基于回归方法和人工神经网络的新鲜番木瓜多元呼吸模型的建立和比较

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摘要

Respiration modelling is the fundamental of the packaging and storage of fresh fruit and vegetables. Previous model of respiration rate accounted for external forcing from temperature and modified atmosphere but did not attempt to predict internally generated natural variability such as maturity. We present two types of respiration models here that predict the respiration rate of fresh papaya in response to changes of temperature, CO2/O2 concentrations and maturity as well. These two models were separately developed using a quadratic polynomial with four parameters and fifteen coefficients and using an artificial neural network (ANN) model with 4-15-2 architecture trained by Levenberg–Marquardt algorithm, in which the maturity of papaya covers skin yellowing from 10 to 90% and the temperatures vary over 10–30 °C. Comparison between the two types of respiration models shows a predictive superiority of the ANN-based model over the regressive one, demonstrating that the use of ANN technique can provide a reliable and effective approach to describe papaya’s respiration rate as a function of multivariate influencing factors.
机译：呼吸建模是新鲜水果和蔬菜包装和存储的基础。先前的呼吸率模型解释了温度和改良大气导致的外部强迫，但并未尝试预测内部产生的自然变化（例如成熟度）。我们在这里提出了两种类型的呼吸模型，这些模型预测了新鲜木瓜对温度，CO 2 / O 2 浓度和成熟度变化的响应。这两个模型是分别使用具有四个参数和15个系数的二次多项式以及采用Levenberg-Marquardt算法训练的具有4-15-2体系结构的人工神经网络（ANN）模型开发的，其中木瓜的成熟度涵盖了从皮肤变黄温度在10％到90％之间，温度变化范围为10-30°C。两种类型的呼吸模型之间的比较表明，基于ANN的模型比回归模型具有预测上的优势，这表明使用ANN技术可以提供可靠且有效的方法来描述木瓜的呼吸率与多种影响因素的关系。

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