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Documentation Experimental Data Analyst.

Analysis of the properties of a food material depends on the successful completion of a number of different steps: planning identifying the most appropriate analytical procedure , sample selection, sample preparation, performance of analytical procedure, statistical analysis of measurements, and data reporting. Most of the subsequent chapters deal with the description of various analytical procedures developed to provide information about food properties, whereas this chapter focuses on the other aspects of food analysis. A food analyst often has to determine the characteristics of a large quantity of food material, such as the contents of a truck arriving at a factory, a days worth of production, or the products stored in a warehouse.

Data analysis

Analysis of the properties of a food material depends on the successful completion of a number of different steps: planning identifying the most appropriate analytical procedure , sample selection, sample preparation, performance of analytical procedure, statistical analysis of measurements, and data reporting.

Most of the subsequent chapters deal with the description of various analytical procedures developed to provide information about food properties, whereas this chapter focuses on the other aspects of food analysis. A food analyst often has to determine the characteristics of a large quantity of food material, such as the contents of a truck arriving at a factory, a days worth of production, or the products stored in a warehouse.

Ideally, the analyst would like to analyze every part of the material to obtain an accurate measure of the property of interest, but in most cases this is practically impossible.

Many analytical techniques destroy the food and so there would be nothing left to sell if it were all analyzed. Another problem is that many analytical techniques are time consuming, expensive or labor intensive and so it is not economically feasible to analyze large amounts of material. It is therefore normal practice to select a fraction of the whole material for analysis, and to assume that its properties are representative of the whole material.

Selection of an appropriate fraction of the whole material is one of the most important stages of food analysis procedures, and can lead to large errors when not carried out correctly. Populations, Samples and Laboratory Samples. It is convenient to define some terms used to describe the characteristics of a material whose properties are going to be analyzed.

The sample may be comprised of one or more sub-samples selected from different regions within the population. The sample may be too large to conveniently analyze using a laboratory procedure and so only a fraction of it is actually used in the final laboratory analysis. The primary objective of sample selection is to ensure that the properties of the laboratory sample are representative of the properties of the population, otherwise erroneous results will be obtained.

Selection of a limited number of samples for analysis is of great benefit because it allows a reduction in time, expense and personnel required to carry out the analytical procedure, while still providing useful information about the properties of the population.

Nevertheless, one must always be aware that analysis of a limited number of samples can only give an estimate of the true value of the whole population. Sampling Plans. A sampling plan should be a clearly written document that contains precise details that an analyst uses to decide the sample size , the locations from which the sample should be selected, the method used to collect the sample, and the method used to preserve them prior to analysis.

It should also stipulate the required documentation of procedures carried out during the sampling process. The choice of a particular sampling plan depends on the purpose of the analysis, the property to be measured, the nature of the total population and of the individual samples, and the type of analytical technique used to characterize the samples. For certain products and types of populations sampling plans have already been developed and documented by various organizations which authorize official methods, e.

Some of the most important considerations when developing or selecting an appropriate sampling plan are discussed below. The first thing to decide when choosing a suitable sampling plan is the purpose of the analysis. Samples are analyzed for a number of different reasons in the food industry and this affects the type of sampling plan used:. Samples may be selected for official or legal requirements by government laboratories. These samples are analyzed to ensure that manufacturers are supplying safe foods that meet legal and labeling requirements.

An officially sanctioned sampling plan and analytical protocol is often required for this type of analysis. Raw materials are often analyzed before acceptance by a factory, or before use in a particular manufacturing process, to ensure that they are of an appropriate quality.

A food is often analyzed during processing to ensure that the process is operating in an efficient manner. Thus if a problem develops during processing it can be quickly detected and the process adjusted so that the properties of the sample are not adversely effected. Techniques used to monitor process control must be capable of producing precise results in a short time. Manufacturers can either use analytical techniques that measure the properties of foods on-line, or they can select and remove samples and test them in a quality assurance laboratory.

Samples of the final product are usually selected and tested to ensure that the food is safe, meets legal and labeling requirements, and is of a high and consistent quality.

Officially sanctioned methods are often used for determining nutritional labeling. Samples are analyzed by food scientists involved in fundamental research or in product development.

Once the reason for carrying out the analysis has been established it is necessary to clearly specify the particular property that is going to be measured, e. The properties of foods can usually be classified as either attributes or variables. An attribute is something that a product either does or does not have, e. On the other hand, a variable is some property that can be measured on a continuous scale, such as the weight, fat content or moisture content of a material.

Variable sampling usually requires less samples than attribute sampling. The type of property measured also determines the seriousness of the outcome if the properties of the laboratory sample do not represent those of the population. For example, if the property measured is the presence of a harmful substance such as bacteria, glass or toxic chemicals , then the seriousness of the outcome if a mistake is made in the sampling is much greater than if the property measured is a quality parameter such as color or texture.

Consequently, the sampling plan has to be much more rigorous for detection of potentially harmful substances than for quantification of quality parameters. It is extremely important to clearly define the nature of the population from which samples are to be selected when deciding which type of sampling plan to use. Some of the important points to consider are listed below:. A finite population is one that has a definite size, e. An infinite population is one that has no definite size, e.

Analysis of a finite population usually provides information about the properties of the population, whereas analysis of an infinite population usually provides information about the properties of the process. To facilitate the development of a sampling plan it is usually convenient to divide an "infinite" population into a number of finite populations, e. A continuous population is one in which there is no physical separation between the different parts of the sample, e.

A compartmentalized population is one that is split into a number of separate sub-units, e. The number and size of the individual sub-units determines the choice of a particular sampling plan. A homogeneous population is one in which the properties of the individual samples are the same at every location within the material e.

If the properties of a population were homogeneous then there would be no problem in selecting a sampling plan because every individual sample would be representative of the whole population.

In practice, most populations are heterogeneous and so we must carefully select a number of individual samples from different locations within the population to obtain an indication of the properties of the total population. The nature of the procedure used to analyze the food may also determine the choice of a particular sampling plan, e. Obviously, it is more convenient to analyze the properties of many samples if the analytical technique used is capable of rapid, low cost, nondestructive and accurate measurements.

Developing a Sampling Plan. After considering the above factors one should be able to select or develop a sampling plan which is most suitable for a particular application. Different sampling plans have been designed to take into account differences in the types of samples and populations encountered, the information required and the analytical techniques used. Some of the features that are commonly specified in official sampling plans are listed below.

Sample size. The size of the sample selected for analysis largely depends on the expected variations in properties within a population, the seriousness of the outcome if a bad sample is not detected, the cost of analysis, and the type of analytical technique used.

Given this information it is often possible to use statistical techniques to design a sampling plan that specifies the minimum number of sub-samples that need to be analyzed to obtain an accurate representation of the population.

Often the size of the sample is impractically large, and so a process known as sequential sampling is used. Here sub-samples selected from the population are examined sequentially until the results are sufficiently definite from a statistical viewpoint. For example, sub-samples are analyzed until the ratio of good ones to bad ones falls within some statistically predefined value that enables one to confidently reject or accept the population. Sample location. In homogeneous populations it does not matter where the sample is taken from because all the sub-samples have the same properties.

In heterogeneous populations the location from which the sub-samples are selected is extremely important. In random sampling the sub-samples are chosen randomly from any location within the material being tested.

Random sampling is often preferred because it avoids human bias in selecting samples and because it facilitates the application of statistics. In s ystematic sampling the samples are drawn systematically with location or time, e.

This type of sampling is often easy to implement, but it is important to be sure that there is not a correlation between the sampling rate and the sub-sample properties. In judgment sampling the sub-samples are drawn from the whole population using the judgment and experience of the analyst. This could be the easiest sub-sample to get to, such as the boxes of product nearest the door of a truck.

Alternatively, the person who selects the sub-samples may have some experience about where the worst sub-samples are usually found, e. It is not usually possible to apply proper statistical analysis to this type of sampling, since the sub-samples selected are not usually a good representation of the population. Sample collection. Sample selection may either be carried out manually by a human being or by specialized mechanical sampling devices.

Manual sampling may involve simply picking a sample from a conveyor belt or a truck, or using special cups or containers to collect samples from a tank or sack. The manner in which samples are selected is usually specified in sampling plans. Once we have selected a sample that represents the properties of the whole population, we must prepare it for analysis in the laboratory. The preparation of a sample for analysis must be done very carefully in order to make accurate and precise measurements.

The food material within the sample selected from the population is usually heterogeneous, i. The units in the sample could be apples, potatoes, bottles of ketchup, containers of milk etc.

An example of inter-unit variation would be a box of oranges, some of good quality and some of bad quality. An example of intra-unit variation would be an individual orange, whose skin has different properties than its flesh.

For this reason it is usually necessary to make samples homogeneous before they are analyzed, otherwise it would be difficult to select a representative laboratory sample from the sample. A number of mechanical devices have been developed for homogenizing foods, and the type used depends on the properties of the food being analyzed e.

Homogenization can be achieved using mechanical devices e. Reducing Sample Size. Once the sample has been made homogeneous, a small more manageable portion is selected for analysis. This is usually referred to as a laboratory sample, and ideally it will have properties which are representative of the population from which it was originally selected. Sampling plans often define the method for reducing the size of a sample in order to obtain reliable and repeatable results.

Preventing Changes in Sample. Once we have selected our sample we have to ensure that it does not undergo any significant changes in its properties from the moment of sampling to the time when the actual analysis is carried out, e. There are a number of ways these changes can be prevented. Many foods contain active enzymes they can cause changes in the properties of the food prior to analysis, e.

If the action of one of these enzymes alters the characteristics of the compound being analyzed then it will lead to erroneous data and it should therefore be inactivated or eliminated.

Title: Basic Statistics and Data Presentation Page 1 of 28

The proper understanding and use of statistical tools are essential to the scientific enterprise. This is true both at the level of designing one's own experiments as well as for critically evaluating studies carried out by others. Unfortunately, many researchers who are otherwise rigorous and thoughtful in their scientific approach lack sufficient knowledge of this field. This methods chapter is written with such individuals in mind. Although the majority of examples are drawn from the field of Caenorhabditis elegans biology, the concepts and practical applications are also relevant to those who work in the disciplines of molecular genetics and cell and developmental biology. Our intent has been to limit theoretical considerations to a necessary minimum and to use common examples as illustrations for statistical analysis. Our chapter includes a description of basic terms and central concepts and also contains in-depth discussions on the analysis of means, proportions, ratios, probabilities, and correlations.

Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data. Missing data or missing values is defined as the data value that is not stored for a variable in the observation of interest.

Data analysis

Item analysis is a process which examines student responses to individual test items questions in order to assess the quality of those items and of the test as a whole. Item analysis is especially valuable in improving items which will be used again in later tests, but it can also be used to eliminate ambiguous or misleading items in a single test administration. This report has two parts. The first part assesses the items which made up the exam. The second part shows statistics summarizing the performance of the test as a whole.

Data analysis is a process of inspecting, cleansing , transforming , and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Her eyes were lit, and she was still about to really screw everything up, she did, but it was too badly damaged, but I work in a nursing home now, where he grunted in an unfortunate manner during lunch hour. He pulled her in, she thunked her head against it. He would never be able to get her away from him to safety.

Item Statistics

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Она вцепилась Беккеру в плечо, заставив его подняться - как раз в тот момент, когда губы старика шевельнулись. Единственное сорвавшееся с них слово фактически не было произнесено. Оно напоминало беззвучный выдох-далекое чувственное воспоминание. - Капля Росы… Крик медсестры гнал его прочь. Капля Росы.

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2 Response
  1. Julie F.

    The third part introduces the readers to some basic data analysis procedures: He could only pray that the reward would not prove to be out of his reach. the covers arranged just so. spectrum reading grade 2 pdf Well, answering one of.

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