使用可见近红外光谱仪对食用葡萄进行质量评估和成熟度预测
Fengjiao Ping1, Jihong Yang1,2, Xuejian Zhou1
1College of Enology, Northwest A&F University, Yangling 712100, China.
Foods (Basel, Switzerland)
|June 28, 2023
概括
可见近红外 (Vis-NIR) 光谱学提供了一种快速,非破坏性的方法来评估葡萄的质量. 该技术在成熟过程中准确预测可溶性固体含量 (SSC) 和总酸度 (TA),有助于质量控制.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 包括葡萄在内的水果质量受到成熟阶段的严重影响.
- 精确监测葡萄质量参数对于商业价值和销售至关重要.
- 评估葡萄品质的传统方法往往是破坏性的和耗时的.
研究的目的:
- 调查可见近红外 (Vis-NIR) 光谱的使用,以进行葡萄成熟的非破坏性监测.
- 为关键质量参数建立光谱预测模型:可溶性固体含量 (SSC) 和总酸度 (TA).
- 为了确定有效的波长和最佳的光谱预处理方法,以进行准确的质量评估.
主要方法:
- 在四个成熟阶段探索物理化学性质 (颜色,SSC,TA,硬度).
- 使用部分最小平方回归 (PLSR) 开发光谱预测模型.
- 应用竞争适应权重算法 (CARS) 进行有效的波长选择和各种光谱预处理技术.
主要成果:
- 物理化学分析显示了成熟期间颜色,SSC和TA变化的趋势.
- 使用全频谱和第1导数预处理的PLSR模型证明了SSC和TA的高预测精度.
- 最佳模型实现了高确定系数 (R2>0.93) 和低误差 (RMSEP) 的SSC和TA.
结论:
- 视NIR光谱是一种非常有效的工具,用于快速,非破坏性地评估葡萄的可溶固体含量 (SSC) 和总酸度 (TA).
- 开发的光谱模型提供了可靠的葡萄成熟期葡萄质量的预测.
- 这项技术可以在整个供应链中对葡萄的质量控制和管理做出重大帮助.
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