Scientific Detection

Scientific Detection

Detection Methods

  • Spectroscopy: A technique that allows the identification and measurement of elements and compounds, particularly important in food quality control.
  • Gas Chromatography-Mass Spectrometry (GC-MS): Used to analyse volatile substances in food samples, and can identify specific compounds within a sample.
  • ELISA Tests: Immunoassays like ELISA (Enzyme-Linked Immunosorbent Assay) are utilised to detect allergens, toxins or any foreign substance present in food.

Detection Parameters

  • pH Measurement: Helps in determining the acidity or alkalinity of food, important for assuring the safety, taste, and shelf-life of the products.
  • Total Dissolved Solids (TDS): Measurement of the total amount of mobile charged ions in a sample, including minerals, salts, and metals.
  • Nutritional Content: Detection methods like titration, colorimetry, and chromatography are used to determine levels of fats, proteins, vitamins, and minerals.

Food Labelling and Detection Importance

  • Allergen Detection: Crucial to identify any potential allergens in food products to ensure the safety of consumers with food allergies.
  • Pathogen Detection: Techniques like polymerase chain reaction (PCR) help in early detection of food-borne pathogens to prevent food-borne illnesses.
  • Date Marking and Storage Instructions: Use of indicators and sensors that respond to temperature and time, helping to ensure that food has not spoiled or become unsafe.
  • Country of Origin: Techniques like DNA barcoding and isotope ratio mass spectrometry (IRMS) can verify the authenticity of the origin of food products.

Limitations and Errors in Detection

  • False Positives/Negatives: A test can wrongly indicate the presence/absence of a factor, leading to inaccurate information and potential risks.
  • Limit of Detection (LOD): The smallest measurement that can be reliably detected which varies between different testing equipment and procedures.
  • Matrix Interferences: Other compounds in the sample can interfere with the detection process, leading to inaccurate results.
  • Sampling Errors: Errors can occur if the sample taken is not representative of the whole, leading to inaccurate conclusions.