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Milk quality control requirement evaluation using a handheld Near Infrared Reflectance spectrophotometer and a bespoke mobile application
Journal of Food Composition and Analysis ( IF 4.3 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.jfca.2019.103388
Rubén Muñiz , María Cuevas-Valdés , Begoña de la Roza-Delgado

Abstract This research introduces a novel approach for real-time analysis of individual cow milk samples in order to get an estimation of required quality control parameters such as lactose, protein, fat, and solids-non-fat (SNF), in order to distinguish their concentrations in conventional cow milk. This will permit the classification of milk samples according to their quality, and help to avoid penalties over quality issues in dairy facilities. To fulfil this goal a newly developed mobile application has been implemented, along with a neural network based model fed with spectral data from a handheld near infrared reflectance (NIR) spectrophotometer. With the combination of this application and a portable NIR sensor, milk quality parameters can be estimated by dairy farms on their own premises. The model was obtained by means of the widely used machine learning framework TensorFlow provided by Google Inc. A total of 903 fresh cow milk samples collected over a 3 year period, were used to train and validate the models. The advantages provided by this mobile application at the milking stage allows us to know in real-time the quality control parameters for each cow milk sample, individually. This offers an immediate management change capability along with enhanced decision making potential at farm level, thus leading to the optimisation of the quality of milk production.

中文翻译:

使用手持式近红外反射分光光度计和定制的移动应用程序评估牛奶质量控制要求

摘要 本研究介绍了一种实时分析单个牛奶样品的新方法,以估计所需的质量控制参数,如乳糖、蛋白质、脂肪和非脂肪固体 (SNF),以便区分它们在传统牛奶中的浓度。这将允许根据质量对牛奶样品进行分类,并有助于避免因乳品设施质量问题而受到处罚。为了实现这一目标,我们实施了一个新开发的移动应用程序,以及一个基于神经网络的模型,该模型采用来自手持式近红外反射 (NIR) 分光光度计的光谱数据。结合此应用程序和便携式 NIR 传感器,奶牛场可以在自己的场所估计牛奶质量参数。该模型是通过谷歌公司提供的广泛使用的机器学习框架 TensorFlow 获得的。 在 3 年的时间里收集了 903 份新鲜牛奶样本,用于训练和验证模型。此移动应用程序在挤奶阶段提供的优势使我们能够实时了解每个牛奶样品的质量控制参数。这提供了即时的管理变革能力,并增强了农场层面的决策潜力,从而优化了牛奶生产的质量。此移动应用程序在挤奶阶段提供的优势使我们能够实时了解每个牛奶样品的质量控制参数。这提供了即时的管理变革能力,并增强了农场层面的决策潜力,从而优化了牛奶生产的质量。此移动应用程序在挤奶阶段提供的优势使我们能够实时了解每个牛奶样品的质量控制参数。这提供了即时的管理变革能力,并增强了农场层面的决策潜力,从而优化了牛奶生产的质量。
更新日期:2020-03-01
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