Name: sampling errors in sample preparation
Uploaded: 2024-12-05T18:31:55.000Z
Duration: 1 min 16 s
Channel: JoVE

En règle générale, un échantillon en vrac est réduit à un échantillon de laboratoire par échantillonnage, ce qui introduit des erreurs.
Les erreurs d’échantillonnage courantes comprennent la contamination et les incohérences entre la méthode d’échantillonnage et le type de mesure.
Plus précisément, des erreurs potentielles telles qu’un biais de concentration dû à une séparation inappropriée et à la contamination par des méthodes de réduction de la taille des particules peuvent se produire. Il est essentiel de minimiser ces erreurs pour obtenir des résultats précis et fiables.
En supposant la distribution normale d’une population cible, dans l’équation de l’intervalle de confiance de l’erreur d’échantillonnage, ns représente le nombre d’échantillons, et ss désigne l’écart-type d’échantillonnage. Ici, la réorganisation et le remplacement de e aident à déterminer le nombre d’échantillons requis pour l’erreur d’échantillonnage souhaitée.
Un autre aspect critique est la réduction de la variance globale de l’analyse, qui repose sur deux composantes : la méthode et l’échantillonnage.
Alors que le fait de rassembler suffisamment d’échantillons de la bonne taille améliore la variance associée à l’échantillonnage, l’augmentation du nombre d’analyses sur chaque échantillon améliore la variance de la méthode.

contaminants et erreurs

Une préparation efficace des échantillons est essentielle pour une analyse en laboratoire précise et fiable. Au cours de ce processus, deux sources d'erreur importantes peuvent survenir: un biais de concentration dû à une division incorrecte de l'échantillon et une contamination causée par des méthodes utilisées pour réduire la taille des particules, telles que le broyage ou l'homogénéisation. L'identification et la minimisation de ces erreurs potentielles sont essentielles pour garantir la validité de l'analyse.

Un autre élément clé à prendre en compte est la détermination du nombre approprié d'échantillons requis pour obtenir l'erreur d'échantillonnage souhaitée. Si la population cible suit une distribution normale, la taille de l'échantillon peut être calculée à l'aide de l'équation suivante :

Dans cette équation, la valeur t basée sur le niveau de confiance souhaité représente l'écart type d'échantillonnage, qui mesure la dispersion des échantillons par rapport à la moyenne, et dénote l'erreur d'échantillonnage acceptable. Cette formule permet de définir le nombre d'échantillons nécessaire pour atteindre un seuil d'erreur spécifique, garantissant ainsi la précision du processus d'échantillonnage.

En outre, la minimisation de la variance totale de l'analyse implique de traiter à la fois les variances de méthode et d'échantillonnage. La variance d'échantillonnage peut être réduite en collectant un nombre adéquat d'échantillons de taille correcte, tandis que la variance de méthode s'améliore lorsque plusieurs analyses sont effectuées sur chaque échantillon. En gérant efficacement ces deux variances, des résultats plus précis et plus fiables peuvent être obtenus.

Contaminants et erreurs

Une préparation efficace des échantillons est cruciale pour une analyse de laboratoire précise et fiable. Au cours de ce processus, deux sources d’erreur importantes peuvent survenir : ...

Education

Enseignement

JoVE Core

Analytical Chemistry

Chimie analytique

Unit 1

Unité 1

Method Development and Sampling Techniques

Développement de méthodes et techniques d'échantillonnage

method-development-and-sampling-techniques

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

Contaminants and Errors

sampling errors in sample preparation

Sampling errors in sample preparation can affect the accuracy of laboratory results. Two common problems are concentration bias from poor sample splitting and contamination from reducing particle size by grinding or homogenization. Recognizing these errors helps make analysis more reliable.

The number of samples also matters when a target population follows a normal distribution. In that case, the sample size ns can be found with a formula that uses the t-value for the chosen confidence level, the sampling standard deviation s, and the acceptable error e. Here, the t-value reflects confidence, s shows how spread out the samples are from the mean, and e sets the error limit.

Controlling total variance requires attention to both sampling variance and method variance. Sampling variance can be lowered by collecting enough correctly sized samples. Method variance improves when multiple analyses are performed on each sample, which helps produce more accurate results.

Sampling Errors in Sample Preparation

development of analytical methods

Analytical Methods: Technique to Protocol

Analytical methods follow a clear path from technique to protocol. In analytical chemistry, a technique is the scientific principle used to explain a chemical measurement. A method applies that technique to a sample of interest. The next steps are procedure and protocol. A procedure gives the directions for carrying out the analysis with an analytical method. A protocol is the detailed set of guidelines for that procedure, and it should be followed closely to get the desired results. <br...

quality control

Quality Control Charts and Lab Records

Quality control charts, proficiency testing, and lab records are key parts of quality control in a laboratory. These steps help keep a system under statistical control. They also help track data, show trends, and spot variations that may affect the accuracy of an analysis. One common tool is a quality control chart. This chart plots one or more analytical characteristics over time. The measured values are taken at different times during the analysis and compared with the known value of...

quality assurance

Quality Checks in Analytical Testing

Quality assurance in analytical testing brings together the checks that help a system perform properly. It includes quality control, quality assessment, and internal corrective measures. These parts work in a cycle to support reliable results. Quality control happens before and during the analysis. Quality assessment takes place during and after the investigation. Internal corrective measures are then based on the results of post-analysis quality assessment, or they may be applied before a new...

data validation

How Analytical Methods Are Checked

Method validation checks whether an analytical chemistry method gives reliable results. It is especially important when the same method is used with different sample matrices or when the procedure is changed. Validation helps show that the method still meets acceptable standards in each case. Several key checks are used in method validation. Specificity means the method can measure the target analyte without interference from other substances. Linearity means the result changes in direct...

qualitative analysis

Classical and Instrumental Sample ID

Qualitative analysis identifies the elements, ions, or compounds in an unknown sample. It is the first step in the hierarchy of analytical goals. That makes it the starting point for scientific study before quantitative analysis or other forms of analysis. Two main approaches are used in qualitative analysis: classical methods and instrumental methods. Classical methods depend on the visible or sensory result of a chemical reaction, such as a color change, a precipitate, or even a smell. These...

quantitative analysis

Measuring Analyte Concentration

Quantitative analysis measures the amount of a specific substance, called the analyte, in a sample. If the sample composition is unknown, qualitative analysis comes first. That step identifies the components so the correct substances can be measured during the quantitative phase. This method depends on two key measurements. Scientists measure the sample quantity and also a property that is proportional to the amount of analyte. Together, these measurements help determine the analyte...

instrument calibration

Calibrating Lab Instruments for Accurate Results

Calibration helps lab instruments produce accurate and consistent results. It is important in manufacturing, healthcare, testing laboratories, and scientific research. Each instrument needs a calibration process that matches its design and function. This process improves data accuracy and helps results stay reliable. An analytical balance measures mass and needs regular calibration to stay precise. It is calibrated by comparing its readings with known standard weights. These weights act as...

glassware calibration

Calibrating Lab Glassware for Accurate Volume

Calibrating lab glassware helps students measure liquids accurately in the analytical laboratory. It reduces volume errors and supports consistent results across measurements. This matters when using volumetric flasks, pipettes, burettes, and other tools that depend on precise volume readings. Volumetric flasks are used to prepare aqueous solutions with a precise final volume. They have a calibration line on the neck. To calibrate one, fill it with distilled water until the meniscus, the...

standard solutions

Preparing Standard Solutions in the Lab

Standard solutions are prepared to have a precisely known concentration or composition. In analytical chemistry, this accuracy is important because it supports reliable results. A primary standard is a highly pure, stable substance with a high molar mass. It is fully soluble in water, which is the most commonly used solvent in analytical chemistry. A primary standard solution can be used to standardize a secondary standard. Secondary standards have known concentrations, but they are usually...

blank solutions

Blank Solutions in Analytical Testing

Blank solutions are used in analytical testing to show the background signal before a sample is measured. A blank solution does not contain the analyte, which is the substance of interest being tested or measured. It is usually made with the same reagents and procedure as the sample solution, but without the analyte. The main purpose of a blank solution is to control for background interference and contamination. Reagents, solvents, or lab equipment can sometimes create a detectable signal on...

sampling methods: overview

How Sampling Shapes Lab Results

Sampling shapes lab results by determining how a smaller sample represents a larger population. In analytical chemistry, testing an entire population is often impractical or impossible, so samples are collected to make inferences about the whole group. The sampling method is the process used to choose individuals or items for that sample. Common sampling methods include random, judgemental, systematic, stratified, and cluster sampling. Each method can affect the quality of the data in a...

sampling methods: sample types

Sampling Materials in Solid, Liquid, Gas

Sampling materials are grouped as solid, liquid, or gas. The type of sample helps decide the right collection method. Solid samples can include sediments from water bodies, soil, metals, and biological tissues. Sediment sampling from water bodies can be done in two common ways. Grab sampling uses a device to collect a discrete sample from the bottom with minimal disturbance. These samples may not show the full area because conditions can vary from place to place. Piston coring collects a...

sampling plans

Choosing Samples for Reliable Results

Sampling is a key step in analytical chemistry. It helps researchers collect data that represents a larger population. The main sampling methods in this topic are random, judgmental, systematic, stratified, and cluster sampling. Random sampling gives each member of the population an equal chance of being chosen. Researchers often use a random number generator or a lottery-style method. For example, a polymer sheet can be divided into 1 cm x 1 cm sections, and 10 sections can be selected at...

sample preparation for analysis: overview

Preparing Samples by State and Form

Sample preparation depends on the state and form of the sample before analysis. It helps make the sample ready for accurate measurement. The main goal is to extract the analyte, which is the substance being measured, and remove parts that could interfere with the analysis. Solid samples often need the most handling. Large solids are usually ground, crushed, or milled to make them smaller and increase surface area. The sample is then sieved and mixed so it has a uniform composition. After that,...

sample preparation for analysis: advanced techniques

sample-preparation-for-analysis-advanced-techniques

Preparing Complex Samples for Analysis

Preparing complex samples for analysis often requires special digestion and fusion methods. Samples with inorganic or organic material can be hard to dissolve or break down. Common sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion. These steps help turn difficult samples into forms that can be tested reliably and again with similar results. Acid digestion is often used for inorganic materials that do not dissolve in water. It is useful for soil and...

sample handling

Sample Transport and Storage Basics

Sample transport and storage help keep specimens intact before testing. Moving a sample from the collection point to the laboratory requires careful handling. The sample should be sealed well and clearly labeled. These steps help prevent cross-contamination and support reliable results. Temperature control is also important during transport. Some samples travel in insulated containers, cool packs, or refrigerated vehicles so they stay within the needed temperature range. After the samples...

contaminants and errors

Sampling errors in sample preparation can affect the accuracy of laboratory results. Two common problems are concentration bias from poor sample splitting and contamination from reducing particle size by grinding or homogenization. Recognizing these errors helps make analysis more reliable. The number of samples also matters when a target population follows a normal distribution. In that case, the sample size ns can be found with a formula that uses the t-value for the chosen confidence level,...

key terms and concepts

Learn how poor sample splitting, contamination, and sample size affect lab accuracy, and how sampling and method variance can be reduced in analysis.

Sampling Errors in Sample Preparation | JoVE K12

analytical chemistry

JoVE Core Analytical Chemistry simplifies complex chemical principles by breaking them down into clear, engaging animations, making topics from SI Units to NMR spectroscopy more accessible and straightforward. Additionally, the scientist-in-action videos highlight the application of these concepts in real-world research conducted in laboratories globally.

Sampling Errors in Sample Preparation

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