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通过近红外光谱学量化分类和预测星梨的成熟度

Ruitao Lu¹, Linqian Qiu¹, Shijia Dong¹

¹College of Horticulture, Northwest A&F University, Taicheng Road No. 3, Yangling, Xianyang 712100, China.

Foods (Basel, Switzerland)

|December 17, 2024

概括

近红外光谱学提供了一种快速,非破坏性的方法来评估Starkrimson梨的成熟度. 将这种技术与视觉成熟度和成熟后分数相结合,可以准确预测用于商业用途的梨质量.

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:

准确的梨成熟度评估对于商业可行性至关重要.
像近红外光谱学 (NIRS) 这样的非破坏性方法提供了快速评估的潜力.
星梨需要可靠的成熟度评估,以获得最佳的收获和储存.

研究的目的:

开发一种有效的近红外光谱学 (NIRS) 方法来评估星梨的成熟度.
评估化学测量方法的性能,用于定量和分类分析.
确定质量指数的最佳组合,以准确预测成熟度.

主要方法:

近红外光谱学 (NIRS) 从星梨获取数据.
部分最小平方回归 (PLSR) 的应用用于定量分析.
使用五种分类方法,包括神经网络,用于成熟度分类.
采用竞争性适应性重权部分最小方程 (CAR-PLSR) 进行建模.

主要成果:

视觉成熟度指数显示使用CAR-PLSR (Rp2:0.87) 的量化建模效果最好.
一个交叉验证的神经网络模型使用视觉成熟度和后成熟度得分实现了最高的分类准确度 (88.7%).
与精选的质量指数相结合的NIRS有效评估了Starkrimson梨的成熟度.

结论:

近红外光谱 (NIRS) 是一种可行的工具,用于快速,非破坏性地评估Starkrimson梨的成熟度.
将NIRS与关键质量指数集成,可以提高成熟度评估的准确性.
这种方法支持高效的商业梨子管理和质量控制.

关键词:

期货到期时间期货到期时间接近红外光谱学近红外光谱学梨子梨子梨子定性分析是一种定性分析.定量分析量化分析

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