羊羔原产地可追溯性的代谢学:基于随机森林递归特征消除的集合学习方法
Chongxin Liu1,2, Simona Grasso2, Nigel Patrick Brunton2
1Institute of Food Science and Technology, Chinese Academy of Agriculture Sciences, Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, China.
Food chemistry: X
|August 13, 2025
概括
这项研究确定了14个关键的代谢生物标志物,用于准确追踪羊羔的起源. 机器学习方法,特别是随机森林,增强了这些生物标志物的识别,以实现品种特定的可追溯性.
科学领域:
- 食品科学 食品科学 食品科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 消费者对可追溯羊肉产品的需求很高.
- 未定位的代谢学被用于肉类原产地可追溯性,但生物标志物识别具有挑战性.
- 准确的羊羔品种可追溯性对于质量保证和消费者信任至关重要.
研究的目的:
- 开发一种快速而准确的方法来识别用于羊羔来源可追溯性的代谢生物标志物.
- 评估机器学习算法在分析代谢学数据的有效性,以获得特定品种的可追溯性.
- 建立一个强大的生物标志物面板,用于地理标志的羊羔可追溯性.
主要方法:
- 应用非向代谢学来分析五种地理标志羊羔品种.
- 随机森林递归特征消除被用于从4139种代谢物中识别潜在的代谢生物标志物.
- 一个由14个代谢生物标志物组成的小组得到了改进和验证.
- 使用纯粹的贝叶斯算法来评估分类的准确性.
主要成果:
- 最初确定了29种潜在的生物标志物,具有品种特异性和环境相关的变化.
- 一个由14个代谢生物标志物组成的精细小组证明了羊羔起源追踪的高准确性和稳定性.
- 14个生物标志物的组合和天真贝叶斯算法实现了最高的分类准确性.
- 随机森林递归特征消除证明对高维代谢学数据有效.
结论:
- 一个由14个代谢生物标志物组成的小组,使用随机森林识别,显著提高了羔羊品种特定的可追溯性.
- 基于机器学习的生物标记面板为准确的肉类来源可追溯提供了强大的方法.
- 这项研究为食品行业开发先进的可追溯性系统提供了实际框架.
相关概念视频
Cluster Sampling Method
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Random Sampling Method
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Response Surface Methodology
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
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 survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...


