Sample types crops

In contrast to the requirements for enforcement methods and to ensure sufficient quality of the generated data, validation data should be submitted for all types of crop samples to be analyzed. However, matrix comparability and a reduced validation data set may be considered where two or more very similar matrices are to be analyzed (e.g., cereal grain). A reduced sample set may also be acceptable (two levels, at least three determinations and an assessment of matrix interference) provided that the investigated samples belong to the same crop group as described in SANCO/825/00 (see also Section 4.2.1). 

In the case of processing study samples, the samples to be processed should be collected fresh and remain fresh (never frozen) in the type of bag, container, etc., normally used for transportation of these crops from the field in commercial practice, e.g., paper bags, net bags, boxes, and trays. This should prevent deterioration of the samples and mimic commercial practices. 

The quantity, quality and purity of the template DNA are important factors in successful PGR amplification. The PGR is an extremely sensitive method capable of detecting trace amounts of DNA in a crop or food sample, so PGR amplification is possible even if a very small quantity of DNA is isolated from the sample. DNA quality can be compromised in highly processed foods such as pastries, breakfast cereals, ready-to-eat meals or food additives owing to the DNA-degrading action of some manufacturing processes. DNA purity is a concern when substances that inhibit the PGR are present in the sample. For example, cocoa-containing foodstuffs contain high levels of plant secondary metabolites, which can lead to irreversible inhibition of the PGR. It is important that these substances are removed prior to PGR amplification. Extraction and purification protocols must be optimized for each type of sample. 

Samples were collected over 1974 euid 1975 crop years, are the average of all samples of a given type. 

Dimension 2 differentiated samples based on tobacco type. Bright and oriental were found to be most different from each other. Blend 2 (60(A), 30(B), 10(C)) was most similar to bright, while blend 1 (33.3(A), 33.3(B), 33.3(C)) lost bright character and was more similar to burley and oriental. There were more differences in tobacco type between uncased samples than those which had been cased. That is, most of the differences seen between uncased bright and burley were lost when casing was added. Tobacco types from different crop years were very similar indicating more differences due to tobacco type than crop year. 

Problems of access affect any effort to obtain information about Antarctic coal. Coal commonly crops out in inaccessible steep slopes swept clean by the wind and, even where accessible, the climate and the occurrence of permafrost are not favorable for sampling coal beds according to accepted standards. Coal samples obtained in the Antarctic generally are not standard. Even though modem standards of analysis have been used in treating the samples (14), no amount of laboratory technique can compensate for inadequacy in sampling. Nearly all of the available analytic data are based on analyses of the available coal specimens which were exposed on surfaces of outcrops. This is the type of sample that can be obtained practically under commonly existing conditions of climate and transport. 

In summary, for each trap or standing-crop sampling point, eight particle-size fractions were created with nominal cutoffs of 508, 212, 114, 63, 19, 8.2, 1.0, and 0.4 pm. The 600 standing-crop and 1000 trap-mass fractions were chemically analyzed for phosphorus and major and trace elemental composition. Major particle types were identified and enumerated by optical microscopy. 

Field Locations. For each field test location a variety of information is collected and recorded in addition to the samples which are collected. Seven general categories of information can be defined. Three simple ones are 1) test design or plot plan, 2) location and 3) field use history for several years. Field soil characterization 4) includes screen analysis (soil type), pH measurement, and organic matter content. Weather information 5) includes daily temperatures and rainfall and/or irrigations during the test. Application related data 6) consists of dates, application modes, weather conditions at application, calculations and calibrations. Harvest information 7) includes crop name, part, amount, date, and collector. 

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