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相关概念视频

Data Validation01:15

Data Validation

161
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Prediction Intervals01:03

Prediction Intervals

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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. 
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Regression Analysis01:11

Regression Analysis

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Microcracking in Concrete01:20

Microcracking in Concrete

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Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...
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相关实验视频

Updated: Jun 26, 2025

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

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使用预测建模来证明一个饼干过程的不适当的验证方法.

Ian M Hildebrandt1, Linnea M Riddell1, Nicole O Hall1

  • 1Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA.

Journal of food protection
|May 11, 2024
PubMed
概括
此摘要是机器生成的。

在烤过程中沙门氏菌失活的预测模型必须考虑温度和湿度的变化. 使用基于单一水分水平的模型高估了致命性,突出了食品安全中需要验证的动态模型的需要.

关键词:
烤烤烤的方法低水分的食物食物.烤箱炉子 烤箱炉子沙门氏菌是一种沙门氏菌.验证 验证 验证 验证

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

  • 食品微生物学 食品微生物学
  • 食品工艺工程 食品工艺工程
  • 预测建模预测建模

背景情况:

  • 由于同时加热和干燥,对萨尔蒙氏菌无活化的过程验证是复杂的.
  • 现有的预测模型通常依赖于同热和单一湿度数据,这些数据可能不准确地反映动态条件.

研究的目的:

  • 用同热和单湿失活数据量化评估用于过程的预测建模方法.
  • 评估以前传播的模型是否适合在动态烤环境中验证沙门氏菌无活化.

主要方法:

  • 制造了一个用五种菌株的沙门氏菌尾酒接种的饼干面团.
  • 在56°C,60°C和63°C进行同热无活化实验,以确定沙门氏菌动力学 (D60°C = 4.6分钟,z = 4.9°C).
  • 在177°C的对流炉中进行烤实验,动态测量沙门氏菌幸存者和产品温度/湿度概况.

主要成果:

  • 异热数据产生了D和z值,用于预测动态条件下的无活化.
  • 烤实验实现了沙门氏菌的平均5日减少150秒.
  • 基于面团的模型,仅使用异热数据,过度预测了沙门氏菌的致命性,每150秒减少100多个日志.

结论:

  • 基于单一湿度的预测模型不适用于具有动态温度和湿度的过程,导致故障危险的高估.
  • 基于模型的预防性控制验证必须包含动态湿度/水活动 (aw) 数据.
  • 最终用户应谨慎使用未经验证的过程验证预测模型.