Faced with changing markets and evolving consumer demands, beef industries are investing in grading systems to maximise value extraction throughout their entire supply chain. The Meat Standards Australia (MSA) system is a customer-oriented total quality management system that stands out internationally by predicting quality grades of specific muscles processed by a designated cooking method. The model currently underpinning the MSA system requires laborious effort to estimate and its prediction performance may be less accurate in the presence of unbalanced data sets where many "muscle x cook" combinations have few observations and/or few predictors of palatability are available. This paper proposes a novel predictive method for beef eating quality that bridges a spectrum of muscle x cook-specific models. At one extreme, each muscle x cook combination is modelled independently; at the other extreme a pooled predictive model is obtained across all muscle x cook combinations. Via a data-driven regularization method, we cover all muscle x cook-specific models along this spectrum. We demonstrate that the proposed predictive method attains considerable accuracy improvements relative to independent or pooled approaches on unique MSA data sets.
翻译:面对不断变化的市场和不断演变的消费者需求,牛肉行业正在投资于分级系统,以便在整个供应链中实现价值提取最大化。澳大利亚肉类标准系统是一个面向客户的全面质量管理系统,它是一个面向客户的全面质量管理系统,通过预测通过指定烹饪方法加工的特定肌肉的质量等级而在国际上显赫。目前作为管理服务协议系统基础的模式要求作出艰苦的努力来估计,如果存在不平衡的数据集,其预测性能可能不那么准确,因为许多“肌肉x烹饪”组合没有多少观察和(或)很少有易感性预测。本文提出了一个新的牛肉食品质量预测方法,它连接了肌肉x烹饪特定模型的频谱。在一个极端的方面,每个肌肉x烹饪组合都是独立模型;在另一个极端的方面,在所有肌肉x烹饪组合中都获得一个集合的预测模型。通过一种数据驱动的正规化方法,我们覆盖了所有肌肉x烹饪特制模型。我们证明,拟议的预测方法与独特的管理服务数据集的独立或集合方法相比,具有相当大的准确性改进。