评估白色草中的可溶性固体:对Vis-NIR和NIR光谱学的比较分析
Hayato Seki1, Haruko Murakami2, Te Ma2
1Institute of Agricultural Machinery, National Agricultural and Food Research Organization, 1-40-2, Nisshin-Cho, Kita-Ku, Saitama City 331-8537, Japan.
Foods (Basel, Switzerland)
|July 27, 2024
概括
可见近红外 (Vis-NIR) 和近红外 (NIR) 光谱学准确地测量白色草中的可溶性固体含量 (SSC),有助于成熟度评估. 这种非破坏性方法对于新型草品种的分类和现场质量控制至关重要.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 食品质量评估 食品质量评估
背景情况:
- 育种进步引入了白色草的低安东含量,在成熟度的确定中提出了挑战,因为它们缺乏红色的颜色.
- 溶性固体含量 (SSC) 是水果成熟度和质量的关键指标,但传统的测量方法具有破坏性.
- 对于新型草品种的高效质量控制和成熟度评估,需要使用非破坏性评估技术.
研究的目的:
- 开发和验证用于评估白色草中可溶性固体含量 (SSC) 的非破坏性方法.
- 为了比较可见近红外 (Vis-NIR) 和近红外 (NIR) 光谱在白色草中SSC预测的有效性.
- 通过使用红色和白色草的综合数据集,研究色素独立SSC预测的潜力.
主要方法:
- 可见近红外 (Vis-NIR) 光谱 (500-978 nm) 和近红外 (NIR) 光谱 (908-1676 nm) 用于数据采集.
- 部分最小平方回归 (PLSR) 用于构建SSC的预测模型.
- 从180个白色 ("Tochigi iW1 go") 和150个红色 ("Tochigi i27 go") 草样本收集了光谱数据.
主要成果:
- 白色草的Vis-NIR模型实现了高的确定系数 (R2p = 0.89) 和低的预测误差 (RMSEP = 0.40%).
- 白色草的NIR模型显示了令人满意的准确性 (R2p = 0.85,RMSEP = 0.43%),与红色草相比.
- 在Vis-NIR光谱中,在白色草中观察到与素和叶绿素相关的明显的吸收模式.
结论:
- 视NIR和NIR光谱是有效的非破坏性工具,用于评估白色草的SSC,从而能够准确地确定成熟度.
- 开发的模型提供了与红草相似的估计准确度,为各种品种的质量控制提供了便利.
- 这些发现支持开发快速水果分类和现场成熟度确定系统,用于商业化白色草.
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