使用适应式提升组合模型与NIR和高频UVE选择变量相结合,预测杏仁中的总溶性固体
Feng Gao1,2, Yage Xing1,3,4, Jialong Li1,3,4
1College of Horticulture and Forestry, Tarim University, Alar, Xinjiang 843300, China.
Molecules (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种使用自适应增强 (Adaboost) 和非信息变量消除 (UVE) 的新方法,用于非破坏性杏仁质量测试. 该方法准确地预测了总溶性固体 (TSS),提高了水果质量评估.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 总溶性固体 (TSS) 是杏仁成熟度和质量的关键指标.
- 需要准确的,非破坏性的方法来及时收获和收获后管理.
研究的目的:
- 开发一个先进的框架,用于快速,非破坏性检测杏仁TSS.
- 将自适应增强 (Adaboost) 与光谱变量选择相结合,以提高精度.
主要方法:
- 获得的近红外 (NIR) 光谱 (1000-2500nm).
- 使用强大的主要成分分析 (ROBPCA) 和z-score规范化预处理的光谱.
- 应用了非信息变量消除 (UVE) 用于波长选择和Adaboost用于模型优化.
主要成果:
- 使用高频波长的模型显示出卓越的性能.
- 优化的UVE-PLS-Adaboost模型实现了高精度 (R=0.889,RMSEP=1.267,MAE=0.994) 的使用.
结论:
- 在UVE-Adaboost融合方法显著提高预测准确性和概括性.
- 该框架提供了一种可靠的方法来进行非破坏性的杏仁质量评估,并且可以应用于其他水果.
相关概念视频
Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Sensitivity, Specificity, and Predicted Value
135
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
135
Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
Precipitation Titration Curve: Analysis
960
The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
960


