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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Updated: Jun 10, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
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多阶段的玉米到糖过程监测和收益预测使用机器学习和统计方法.

Sheng-Jen Hsieh1, Jeff Hykin2

  • 1Department of Engineering Technology & Industrial Distribution and Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

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概括

机器学习模型准确地预测了半自动化食品生产的玉米糖质量 (pH和乳糖等效). 人工神经网络为过程控制提供了最高的准确性和耐噪性.

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科学领域:

  • 食品科学与技术 食品科学与技术
  • 化学工程是化学工程的重要组成部分.
  • 数据科学数据科学数据科学

背景情况:

  • 玉米糖是食品工业的重要甜味剂,但在半自动批量生产中,保持一致的质量 (德克斯特等效 - DE) 是一个挑战.
  • 现有的工艺控制方法往往侧重于连续系统,为中小型工厂留下了一个空白.
  • 开发强大的数据驱动模型对于提高效率和产品一致性至关重要.

研究的目的:

  • 开发和评估机器学习模型,以预测半自动设置中的玉米糖关键质量参数 (料pH和DE).
  • 使用相关性分析确定影响料pH和DE的关键过程参数.
  • 为了比较人工神经网络 (ANN),支持向量机 (SVM) 和线性回归 (LR) 模型的性能.

主要方法:

  • 相关系数的计算是为了确定影响料pH和DE的关键过程参数.
  • 通过使用历史过程数据构建ANN,SVM和LR模型来预测料pH和DE.
  • 模型性能根据预测准确性,噪声耐受性和有限数据的稳定性进行了评估.

主要成果:

  • 模型准确度在91%至96%之间,ANN模型的性能比SVM和LR高出1-3%.
  • 与SVM相比,ANN模型表现出优越的噪声耐受性,而SVM性能则在高维数据下降.
  • 线性回归模型表现出比ANN和SVM更高的精度变化.

结论:

  • 机器学习,特别是ANN,为半自动化玉米糖生产提供了有效的过程控制,提高了质量的一致性.
  • 开发的模型是准确和强大的,即使有有限的数据集,为中小型工厂提供实用解决方案.
  • 多阶段建模方法显示出提高准确性的潜力,尽管与单阶段方法相比存在一些权衡.