Identify starch, reducing sugars, proteins and lipids using chemical reagents. This separate-biology core practical detects nutrient classes, not the identity of a whole food.Labelled apparatus schematic; not to scale. Follow the measurements and connections, not the drawn dimensions.
Crush a sample with distilled water and filter if needed. Use separate fresh portions for each test; reagents from one test can interfere with another.
Use known positive controls and distilled-water negative controls. Match sample/reagent volumes, label tubes and use clean separate pipettes.
Wear eye protection: alkaline Biuret reagent and iodine can irritate, copper sulfate is harmful, hot water burns and ethanol is flammable. Never eat laboratory samples.
Four test methods
Starch: add iodine. Orange-brown becoming blue-black is positive; unchanged colour means starch was not detected.Positive food-test appearances and required treatment. Benedict colours vary with reducing-sugar concentration; lipid emulsions are cloudy.
Reducing sugar: add Benedict's solution and heat in a hot water bath for a fixed time. Blue changing to green/yellow/orange or brick-red precipitate is positive; the intensity depends on amount and conditions.
Benedict’s solution tests for reducing sugars, not every carbohydrate. Sucrose is a non-reducing sugar and needs to be broken down first (hydrolysed) for this test to detect it. A negative result does not prove that no sugar is present.
Protein: add potassium hydroxide then a small quantity of copper sulfate as instructed, or use prepared Biuret reagent. Lilac/purple is positive; blue is negative.
Lipid: shake a sample with ethanol to extract lipids, then add the ethanol extract to water. A cloudy white emulsion is positive. No heating is required; keep ethanol away from flames.
Allow insoluble solids to settle or filter before transferring an ethanol extract, so debris is not mistaken for an emulsion. A precipitate is an insoluble reaction product, not merely a dissolved colour.
Precision and interpretation
Record test, starting colour, final observation and what the observation tells you for every sample. A food can contain several nutrients; a protein-positive result does not rule out starch.
Use a fixed bath temperature and heating time for Benedict's. A cold test may be falsely negative; label tubes and do not point them at anyone.
Use the specified small Biuret copper-sulfate amount: excess blue reagent obscures a positive colour. Fresh reagents and controls help identify tests that have not worked properly.
Negative means not detected under the conditions, not proof of complete absence. Dilution, poor extraction and sensitivity limits affect interpretation.
These tests normally show whether a nutrient is present, not its exact amount: they are qualitative tests. To find concentration, you need a checked measurement method and standards of known concentration for comparison. Uncalibrated colours alone do not give an exact concentration.
Repeat tests on separate portions and compare colours under the same lighting. A colorimeter can make colour readings less dependent on personal judgement if the method has been checked. Cloudiness and precipitates can affect how much light passes through, complicating the readings.
Explain improvements through their purpose: equal volumes enable comparisons, clean pipettes reduce contamination and controlled heating improves consistency.
Exam skills: planning, precision and evaluation
State what you change (the independent variable), what you measure (the dependent variable) and what you keep the same (control variables). Explain how you keep each control variable constant, rather than just saying “make it fair”.
Accuracy means how close a result is to the true value. Precision means how close repeated measurements are to each other. Resolution is the smallest change an instrument can show. More digits on a display do not automatically mean a more accurate result.
Repeat measurements for each condition, calculate a mean and describe how spread out the results are. This helps assess and reduce the effect of random errors. Repeating cannot fix an error that pushes results consistently in one direction (a systematic error), such as contaminated reagent stock.
Repeatability means getting similar results when the same person repeats the same method with the same equipment. Reproducibility means getting similar results when someone else, or different suitable equipment, repeats the experiment. Results can be consistent but still inaccurate.
Check that instruments read zero correctly and are calibrated where needed. Read scales at eye level: looking from an angle can give a wrong reading (parallax error). Choose suitable ranges, measurement intervals and scale divisions (resolution).
Write down the original readings straight away in a table, with units in the headings. Use decimal places that match the instrument’s resolution. Keep the original data and round only when needed. Do not discard a result just because it differs from your prediction.
An anomalous result does not fit the pattern of the other results. Repeat that measurement and check the method. Only leave it out of a mean if you have a clear reason; state which result you excluded and why.
For continuous variables, plot the independent variable on the horizontal axis and the dependent variable vertically. Use sensible scales, units and a best-fit line or curve; do not automatically join every point or force the graph through zero.
Find the gradient of a straight best-fit line using a large triangle: vertical change ÷ horizontal change. For a curve, draw a tangent to estimate the gradient at one point. Explain what the gradient shows in this experiment, include its units and use measured values to support your conclusion.
Uncertainty describes the possible range around a measurement. For one reading on a scale, half the smallest division is a useful classroom estimate unless the question says otherwise. If you subtract two readings, both have uncertainty. Percentage uncertainty = absolute uncertainty ÷ measured value × 100. Follow the method specified in the question.
Use results as evidence and then explain what they mean. A pattern linking variables (a correlation) does not prove that one causes the other. If the ranges of repeat results overlap, a claimed difference may be less convincing. Keep conclusions within the range tested and suggest an improvement that tackles a specific error.
Mr Kidman's Classroom