相关实验视频

Updated: Jan 12, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

使用基于树的机器学习模型预测玉米料的化学成分:模型开发,特征分析和实际应用

Huilong Chen¹, Shuyan Feng², Junliang Wan²

¹College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China.

Bioresource technology

|November 2, 2025

概括

机器学习 (ML) 模型根据原材料和发酵参数预测玉米料质量. 这项研究提供了对无氧发酵的见解,并为科学进步提供了一种工具.

科学领域:

农业科学农业科学
生物技术是生物技术.
数据科学数据科学数据科学

背景情况:

玉米的无氧发酵对于化物生产至关重要.
根据原材料和工艺参数预测料质量是复杂的.
机器学习为模拟复杂的生物过程提供了潜力.

研究的目的:

使用机器学习 (ML) 调查玉米原料/工艺参数和无氧发酵后的化学成分之间的关联.
开发高精度的ML模型,用于预测玉米料的营养和发酵质量.
通过可解释的ML模型,提供对无氧发酵过程的见解.

主要方法:

利用来自140篇学术论文的实验数据进行模型培训.
包括19个输入变量:玉米原料成分和工艺参数.
构建了11个高精度的ML模型,包括可解释的基于树的模型.

主要成果:

在预测营养成分和发酵质量方面取得了高准确性,最大R2值为0.98.8.
确定了原材料,工艺参数和料质量之间的关键关联.
开发了一个可解释的ML框架,为玉米无氧发酵提供了新的见解.

结论:

原材料和加工参数在很大程度上决定了发酵后玉米料的质量.
开发的ML模型和预测平台 (http://silagedb.com/SMART-Maize/) 促进了科学研究.
该方法框架可应用于其他无氧发酵过程.

关键词:

无氧发酵是无氧发酵的方法.特性分析特性分析在文献中挖掘数据.预测建模框架预测建模框架这就是 SMART-Maize.

更多相关视频

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

相关概念视频

Light Acquisition

Light Acquisition

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.

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Multiple Regression

Multiple Regression

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...

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.