一个基于部分最小平方回归和支向量机回归的新鲜切割的番茄新鲜度预测模型
Liyan Rong1, Yajing Wang1, Yanqun Wang1
1College of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Heliyon
|May 6, 2024
概括
通过物理化学和风味分析来评估新鲜切割的番茄的质量. 支持矢量机器回归 (SVMR) 模型根据有氧板数和感觉数据准确预测了新鲜度.
科学领域:
- 食品科学 食品科学 食品科学
- 感官分析 感官分析
- 统计建模 统计建模
背景情况:
- 新鲜切割的水果,如,在储存期间容易受到质量降低.
- 客观地评估新鲜切割产品的新鲜度对于质量控制和消费者满意度至关重要.
研究的目的:
- 为了研究在4°C下储存的新鲜切割的番茄的物理化学和风味质量变化.
- 开发和比较多变量统计模型,用于预测新鲜切割的木瓜的新鲜度.
主要方法:
- 新鲜切割的木瓜在4°C下存储,并对其物理化学和风味品质进行分析.
- 使用有氧板数作为新鲜度指标.
- 部分最小平方回归 (PLSR) 和支持向量机回归 (SVMR) 算法用于构建预测模型.
主要成果:
- 通过物理化学和风味质量数据,可以区分新鲜切割的木瓜的新鲜度.
- 有氧板数量与储存时间有显著的相关性.
- 与PLSR模型相比,SVMR模型显示出更高的预测准确性.
结论:
- 将风味质量分析与多变量统计方法相结合,可以有效地评估新鲜切割的木瓜的新鲜度.
- SVMR提供了一种可靠的方法来预测新鲜切割的的保质期和质量.
- 有氧板数量作为评估储存的新鲜切割的新鲜度的可靠预测指标.
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