接近红外光谱数据集的乳制品成分在传导模式
Jose A Diaz-Olivares1, Arnout van Nuenen1, Martin J Gote1
1Department of Biosystems, Division of Animal and Human Health Engineering, KU Leuven, Geel Campus, Kleinhoefstraat 4, 2440 Geel, Belgium.
Data in brief
|December 11, 2023
概括
近红外光谱 (NIR) 提供一种非破坏性的实时方法来监测牛奶成分,改善奶牛群的健康和牛奶质量. 这项研究利用NIR光谱数据构建了关键牛奶成分的准确校准模型.
科学领域:
- 乳制品科学 乳制品科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 传统的牛奶成分分析方法往往是破坏性的,昂贵的,耗时的.
- 非破坏性近红外 (NIR) 光谱技术为牛奶分析提供了实时的农场解决方案.
- 精确监测牛奶成分对于乳牛群的健康和牛奶质量保证至关重要.
研究的目的:
- 提供近红外 (NIR) 频谱测量原牛奶样本的数据集.
- 开发和验证用于预测关键牛奶成分 (脂肪,蛋白质,乳糖),尿素和体细胞计数 (SCC) 的多变量校准模型.
- 探索监测这些预测校准模型性能的策略.
主要方法:
- 从1224个单独的生牛奶样本中获取NIR光谱数据在传输模式 (960-1690nm) 中.
- 收集脂肪,蛋白质,乳糖,尿素和体细胞计数 (SCC) 的实验室参考值.
- 使用收集的光谱和参考数据开发多变量校准模型.
主要成果:
- 该研究成功生成了NIR光谱和相应的牛奶成分参数的综合数据集.
- 建立了多变量校准模型,以高准确度预测主要的牛奶成分,尿素和SCC.
- 开发了监测校准模型预测性能的初始策略.
结论:
- 近红外光谱 (NIR) 是一种可行且高效的技术,用于在农场实时监测生奶成分.
- 开发的校准模型为改善乳制品管理实践提供了基础.
- 持续监测模型性能对于保持预测准确性和可靠性至关重要.
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