UV-C Control of Strawberry Gray Mold

ISEF Category: Plant Sciences

Subcategory: Pathology  ·  Difficulty: Intermediate  ·  Setup: School Lab  ·  Time: 1 to 2 Months

The Hook

A strawberry can look fine one day and turn fuzzy by the next. That is bad news for farms, grocery stores, and food waste. UV-C light can slow some fungi, but the dose has to be right. Too little may do nothing, and too much can harm the fruit.

What Is It?

This project tests whether UV-C light can slow gray mold on strawberries. Gray mold comes from Botrytis, a fungus that spreads fast on soft fruit after harvest. You are asking a simple question: if you give the fruit a controlled UV-C treatment, does the mold spread more slowly than it does on untreated fruit?

Think of UV-C like a harsh flashlight for microbes. It damages their genetic material, so they cannot grow and spread as well. But strawberries are living tissue, too. Your job is to find a dose that hurts the fungus more than the fruit.

Why This Is a Good Topic

This is a strong science fair topic because you can change one clear variable, UV-C dose, and measure one clear result, lesion growth over time. It connects to real problems in food storage, crop loss, and shipping fresh produce. You can also learn basic experimental design, image analysis, and statistics without needing a full research lab.

Research Questions

• How does UV-C dose affect the rate of gray-mold lesion growth on strawberries?
• What is the effect of UV-C exposure on the final lesion area after storage?
• Does a single UV-C treatment reduce mold spread more than no treatment?
• To what extent does the distance from the lamp change the effect of the same exposure time?
• Which UV-C dose gives the best balance between slowing mold and avoiding fruit damage?
• How does the size of the initial wound or infection spot affect lesion growth after UV-C treatment?

Basic Materials

• Fresh strawberries of similar size and ripeness.
• Cheap UV-C germicidal lamp with known output rating.
• Opaque safety goggles rated for UV protection.
• Nitrile gloves.
• Digital kitchen scale with 0.1 g accuracy.
• Ruler or calipers.
• Smartphone or digital camera with fixed settings.
• Tripod or phone stand.
• Notebook or spreadsheet for data logging.
• Clear plastic storage containers with lids.
• White background board for imaging.
• ImageJ or similar image analysis software.

Advanced Materials

• UV-C radiometer to measure lamp output.
• Controlled growth chamber or incubator with stable temperature and humidity.
• Stereo microscope for early lesion scoring.
• Analytical balance.
• Data-logging environmental sensor for temperature and humidity.
• Fluorescent-safe UV shielding enclosure.
• Petri dishes or sterile trays for standardized placement.
• PCR or plating setup for confirming Botrytis presence, if a school or partner lab supports it.
• ImageJ with calibration targets for pixel-to-area conversion.
• R or Python for mixed-effects or survival analysis.

Software & Tools

• ImageJ: Measures lesion area from photos and helps you track spread over time.
• Google Sheets: Organizes treatment groups, image labels, and summary tables.
• R: Runs statistical tests and plots dose-response trends.
• Python: Automates image processing and batch file naming if you collect many photos.
• GeoGebra: Helps you sketch and compare curve shapes when planning your dose-response model.

Experiment Steps

1. Define your response variable, then decide whether you will measure lesion area, lesion diameter, or growth rate from images.
2. Choose a dose range that creates a clear low, medium, and high UV-C comparison without changing other conditions.
3. Plan your control groups, including untreated fruit and any lamp-only control that checks for handling effects.
4. Standardize your image setup so every photo uses the same scale, angle, and background.
5. Build an analysis plan that turns each image into one numeric value and compares change over time.
6. Decide in advance how you will handle outliers, fruit-to-fruit variation, and any sample that dries out or collapses.

Common Pitfalls

• Mixing strawberries of very different ripeness, which makes mold speed vary for reasons unrelated to UV-C.
• Taking photos under changing room light, which makes lesion boundaries harder to trace consistently.
• Using a lamp distance that changes between trials, which changes the real UV-C dose.
• Letting condensation build up in the containers, which can speed fungal spread and mask the treatment effect.
• Measuring only the final mold patch, which can hide differences in how fast the lesion grew each day.

What Makes This Competitive

A stronger version of this project goes beyond a simple treated-versus-untreated comparison. You can test a real dose-response curve, quantify uncertainty, and separate UV-C effects from fruit damage or storage conditions. A competitive entry also uses careful imaging, clear controls, and a statistic that matches the data instead of a quick average.

Project Variations

• Test UV-C on a different soft fruit, such as blueberries or raspberries, to see whether skin texture changes mold spread.
• Compare UV-C with another post-harvest treatment, such as cold storage or edible coating, to see which slows lesion growth best.
• Analyze lesion shape instead of area alone, then ask whether UV-C changes how evenly the mold spreads from the infection point.

Learn More

• USDA ARS fruit pathology resources: Search the USDA Agricultural Research Service site for post-harvest fruit disease and gray mold background.
• PubMed: Search review articles on Botrytis cinerea, UV-C treatment, and post-harvest disease control.
• NIH PubChem: Look up UV-C related compounds and basic chemical safety context if you compare treatments.
• NASA Earth Observatory: Use the site for clear explanations of radiation types and how wavelength affects living tissue.
• ImageJ documentation: Find the official user guides for measuring area, setting scale, and analyzing images.
• MIT OpenCourseWare, Biology and Statistics courses: Search for free lecture notes on experimental design, data analysis, and plant-microbe interactions.