{"id":10282,"date":"2021-10-29T07:57:42","date_gmt":"2021-10-29T07:57:42","guid":{"rendered":"https:\/\/www.zeulab.com\/centro-de-conocimiento\/understanding-qualitative-test-results-leads-to-a-simple-interpretation-of-the-results\/"},"modified":"2022-05-18T10:49:00","modified_gmt":"2022-05-18T10:49:00","slug":"understanding-qualitative-test-results-leads-to-a-simple-interpretation-of-the-results","status":"publish","type":"post","link":"https:\/\/www.zeulab.com\/en\/centro-de-conocimiento\/understanding-qualitative-test-results-leads-to-a-simple-interpretation-of-the-results\/","title":{"rendered":"Understanding qualitative test results leads to a simple interpretation of the results"},"content":{"rendered":"

[vc_row css_animation=”” row_type=”row” use_row_as_full_screen_section=”no” type=”full_width” angled_section=”no” text_align=”left” background_image_as_pattern=”without_pattern”][vc_column][vc_column_text]Some food chain analyses are performed with qualitative or binary response tests<\/strong>; that is, they provide a positive or negative (compliant or non-compliant) result for a sample, with respect to its limit of detection. Some well -known examples are microbial inhibition tests (Eclipse)<\/a> or rapid tests<\/a> (e.g. Proteon<\/a>, Duplex BT Scan or IC<\/a>).<\/p>\n

Despite the fact that the interpretation of these tests is simple (+\/-), sometimes the analyst may find situations that are more difficult to understand. This is the case with samples that contain the analyte at a concentration close to the test detection limit, when the result may be positive or negative if the test is repeated several times.<\/p>\n

This is not a reflection on the performance of the test or the quality of the product, but rather due to a universal principle<\/strong> on which multiple events in nature are based (e.g. chemical reactions, freezing and thawing processes, the growth of organisms or the evolution of a pandemic or neuronal transmission). All of these processes, as well as the response of qualitative tests, follow a sigmoidal or \u201cS \u2013shaped\u201d curve model<\/strong>. Understanding this curve helps us to easily understand the response of the tests.<\/p>\n

The example below is the response of the Eclipse Farm 4G<\/a> test for detecting antibiotic residues in milk. This is equally valid for rapid tests, with only the analyte dose on the x-axis having to be changed in the range of its detection limit.[\/vc_column_text][vc_single_image image=”10222″ img_size=”full” qode_css_animation=””][vc_column_text]Figure<\/em><\/strong>: Graph of the Eclipse test response (positive\/negative) with increasing doses of amoxicillin in a milk sample being analysed. The ordinate (y) axis represents the % positive results obtained in the milk sample at a given amoxicillin concentration, represented along the abscissa (x) axis.<\/em>[\/vc_column_text][vc_empty_space height=”15″][vc_column_text]After looking at the graph, the following can be concluded:<\/p>\n