Flavor Chemistry Lab: Exploring the Pandan Negroni in Food Science Class

Hook: Turn student overwhelm into hands-on mastery with one iconic cocktail

Students and instructors in culinary classrooms and food science labs struggle with two recurring problems: abstract flavor chemistry that feels disconnected from real food, and sensory analysis exercises that lack clear structure or meaningful outcomes. Use Bun House Disco’s pandan negroni as a focused, safe, and science-rich lab activity that teaches flavor chemistry, solubility, the role of bitters and botanicals, and rigorous sensory analysis—all aligned with 2026 trends in sustainable extraction and AI-assisted sensory modeling.

Why the pandan negroni is perfect for a food science lab in 2026

The pandan negroni combines a small set of well-defined variables: a pandan-infused rice gin, white vermouth, and green Chartreuse. That simplicity makes it ideal for a controlled experiment: students can manipulate infusion time, solvent polarity, temperature, and dilution, then measure both analytical and sensory outcomes.

• Focused chemistry: Pandan contains fragrant molecules (notably 2-acetyl-1-pyrroline) that pair well with spirit matrices; the experiment highlights volatile vs. nonvolatile extraction.
• Clear variables: Infusion mass, solvent strength (ethanol %), temperature, and time—easy to isolate and test.
• Sensory-ready: The drink’s herbal-bitter balance is ideal for training descriptive sensory panels, threshold testing, and preference mapping.
• 2026 relevance: Modern extraction methods (ultrasonic, sous-vide), portable GC-MS and AI-supported sensory prediction tools are now affordable for teaching labs, making this experiment timely and practical.

Learning objectives (course-aligned)

• Explain how solvent polarity influences extraction of aroma compounds from botanicals.
• Design and run a controlled infusion experiment with pandan, comparing methods (cold maceration, sous-vide, ultrasonication).
• Perform basic analytical checks (refractive index, density, headspace analysis where available) and document color, turbidity, and odor attributes.
• Conduct and analyze a sensory panel (triangle test, descriptive analysis, and preference testing) and apply basic statistics (ANOVA or nonparametric tests).
• Discuss ethics and safety of working with alcohol in an educational setting and design non-alcoholic alternatives for inclusive instruction.

Materials, reagents and equipment

Consumables

• Fresh pandan leaf (green parts only) — 10 g per infusion (per Bun House Disco recipe).
• Rice gin or neutral spirit (175 ml for infusion batch). For non-alcoholic labs, food-grade glycerol or 40% propylene glycol mix can be used to mimic solvation (see safety notes).
• White vermouth, green Chartreuse (or analytical-grade herbal liqueur if not available).
• Filter paper, muslin, fine sieve.
• Graduated cylinders, pipettes, digital scale (0.01 g), beakers, amber storage bottles.

Instruments

• Blender or homogenizer (for rapid maceration).
• Immersion circulator for sous-vide infusion (optional but highly effective).
• Ultrasonic bath (optional for ultrasonic-assisted extraction).
• Portable GC-MS or headspace GC (if available in 2026 teaching labs).
• Refractometer, pH meter, colorimeter (optional).

Step-by-step lab protocol: pandan-infused gin and negroni assembly

Below is a classroom-ready protocol based on Bun House Disco’s recipe, adapted for reproducibility and data collection.

Part A — Prepare pandan-infused gin (two methods)

Method 1: Cold maceration (classic, low-tech)

1. Weigh 10 g of fresh pandan leaves (green parts only). Rinse and roughly chop.
2. Combine with 175 ml rice gin in an amber jar. Seal and agitate gently.
3. Store at room temperature; sample at 1 h, 4 h, 12 h, and 24 h to study kinetics. Record color, turbidity, and aroma intensity.
4. After desired infusion time, strain through muslin and fine sieve into amber bottles. Label with time and date.

Method 2: Sous-vide infusion (fast, reproducible)

1. Place pandan and gin in vacuum-sealable bag or heat-resistant jar. Remove air if using bag.
2. Immersion-circulatory at 50°C for 30–60 minutes. Note: 50°C minimizes thermal degradation of volatile aromatics while accelerating extraction.
3. Cool, filter, and bottle. Compare sensory and analytical data to cold maceration samples.

Part B — Prepare pandan negroni samples for sensory testing

1. Use recipe proportions: 25 ml pandan-infused gin, 15 ml white vermouth, 15 ml green Chartreuse per sample (total 55 ml).
2. Prepare replicate samples using different infusion methods and times (e.g., cold 24 h vs. sous-vide 60 min).
3. Label samples with randomized codes for blind sensory testing.

Part C — Non-alcoholic (inclusion) variant

To include minors or non-drinkers, prepare mock versions by extracting pandan into a water:glycerol (70:30 w/w) mix or 40% propylene glycol solution to approximate mouthfeel and extraction of less volatile compounds. Make clear to students that these are sensory analogs—not chemically identical.

Key chemistry concepts to teach and test

Solvent polarity and partitioning

Explain that ethanol–water mixtures act as mixed-polarity solvents, extracting both polar phenolics and moderately nonpolar terpenes. Use the infusion comparisons to show how solvent strength and temperature change extraction profiles. For example, green Chartreuse (~55% ABV) can act as a co-solvent when mixed into the drink, shifting perceived aroma by changing solubility of some volatiles.

Volatile vs. nonvolatile compounds

Have students sniff headspace (carefully) and compare immediate aroma (volatiles) with taste and aftertaste (nonvolatile glycosides, bitter principles). Highlight 2-acetyl-1-pyrroline (2-AP), the compound associated with aromatic rice and pandan’s popcorn/roasted note—it’s a potent low-threshold volatile worth discussing in molecular detail.

Bitters and balance

Use the framework of a Negroni to discuss the functional roles of each component: spirit (solvent and alcohol heat), vermouth (sweetness, tannins, aromatics), and Chartreuse (herbal density and bitterness). Assign students to probe which molecules could be contributing to bitterness (gentian, quinine-like alkaloids) and how bitterness interacts with aroma perception.

Sensory analysis: design and statistics (2026-ready)

Panel type and size

For a classroom: 12–20 semi-trained student panelists yield useful data for descriptive tasks. For triangle tests, 15–30 panellists is typical for statistical power. Where possible in 2026 labs, pair human panels with AI-assisted sensory prediction tools to pre-screen samples (edge/embedded AI options).

Tests to run

• Triangle test: Determine whether two infusion methods produce perceivable differences (three samples: two identical, one different).
• Descriptive analysis: Build a mini flavor wheel for pandan negroni attributes (pandan aroma, herbal complexity, bitterness, sweetness, mouthfeel, green/vegetal note, aftertaste duration). Use 15-cm line scales or 9-point intensity scales.
• Preference test: Pairwise or ranking to assess overall liking.

Data analysis and interpretation

Teach students to run simple statistics: chi-square for triangle test, ANOVA for descriptive attributes (or Kruskal–Wallis if nonparametric). Show how to visualize results with spider/radar plots and principal component analysis (PCA) for multivariate relationships. In 2026, many teaching labs include cloud-based analytics tools that integrate sensory ratings with chemical data (GC-MS peak areas), enabling correlation analysis and machine-learning models—pair those tools with robust data practices (data engineering patterns) and safe data workflows (automated backups/versioning).

Troubleshooting common issues

• Cloudiness after infusion: Fine filter through muslin and then filter paper; cold-settle overnight. Emulsified oils may cause persistent haze—centrifugation or gentle chilling helps.
• Bitter over-extraction: Reduce infusion time or decrease the plant mass; bitter compounds often extract more slowly and at higher ethanol concentrations.
• Loss of green color: Chlorophyll can degrade with heat and light. For vibrant color, minimize heat exposure and store in amber bottles. Color is not a proxy for aroma potency—use sensory checks.
• Low aroma intensity: Increase surface area (chop or bruise leaves), extend infusion time, or use ultrasonic assistance to release volatiles.

Safety, ethics and inclusivity

Alcohol safety: follow institutional policies. Use small portions for tasting (20–30 ml) and provide non-alcoholic alternatives. Ensure participants are of legal age and not intoxicated. Label samples and provide spit buckets and palate cleansers (water, unsalted crackers). Maintain clear documentation for biosafety and allergen awareness—pandan is generally safe but always check for allergies.

Assessment ideas and rubrics

Design rubrics that evaluate experimental design, execution, data recording, analysis, sensory interpretation, and communication. A sample weighting could be:

• Experimental design and hypothesis: 20%
• Execution and lab notebook quality: 25%
• Sensory protocol and data collection: 20%
• Data analysis and interpretation: 20%
• Report and presentation (scientific and culinary framing): 15%

Worked example: concentration math and dilution effects

Students should practice basic concentration calculations to understand how infusion strength translates to final drink intensity.

Example: If you infuse 10 g pandan in 175 ml gin (batch), the initial mass per volume is ~57.1 g/L (10 g / 0.175 L). When using 25 ml of that infused gin in a 55 ml cocktail, the infusion fraction is 25/55 ≈ 0.455. Thus the pandan-derived solutes are diluted by ~0.455 in the finished drink. Teach students to express sensory concentration in mg/L (if analytical data is available) and relate it to detection thresholds.

Connecting to 2026 trends and future predictions

In late 2025 and into 2026, three trends shape how this lab can be taught and what students learn:

• Affordable analytical tools: Portable GC-MS and headspace analyzers are now within reach for many universities and community labs—use them to tie sensory results to chemical fingerprints. Plan for instrument power and field use (field power solutions).
• AI-assisted flavor modeling: Cloud platforms can now predict perceived aroma intensity from GC peak patterns. Pair human panels with model predictions to discuss model limits and explainability; consider edge deployment options for on-site inference (deploying generative AI on Raspberry Pi 5).
• Sustainable extraction methods: Ultrasonic, microwave-assisted, and sous-vide extractions reduce solvent use and energy; compare green metrics (energy, waste) across methods as part of the lab deliverable (CES-worthy kitchen tech & extraction tools).

Prediction: by 2028, integrated sensory-chemo labs using AI will be a standard capstone in many food science programs; this pandan negroni module is an early, low-cost building block for that competency.

Teacher tip: Combine chemical analysis, sensory panels, and creative brief: task students with reformulating a zero-proof pandan negroni that maximizes pandan character while keeping bitterness balanced—this synthesizes technical and culinary skills.

Extension activities and research ideas

• Compare pandan sourced from different regions: does terroir alter volatile profile?
• Use GC-MS to identify and quantify 2-AP and other volatiles; correlate with sensory intensity.
• Develop natural bitters using local botanicals, then test them as substitutes for Chartreuse to teach formulation and flavor matching.
• Run shelf-life studies: how does pandan aroma and color change over 1, 3, and 6 months?

Practical takeaways for instructors

• Start small: one infusion method vs. another allows students to observe clear differences and learn experimental controls.
• Use replicates and randomized blind tasting to reduce bias.
• Integrate new 2026 tools: portable GC for chemical backing and AI for modeling—students gain both experimental and data-science skills.
• Plan inclusive alternatives so the lab teaches broadly applicable concepts without requiring alcohol tasting.

Sample lab report outline for students

1. Introduction: chemistry of pandan and function of negroni components.
2. Hypothesis and variables.
3. Methods: detailed protocol with times, temperatures, and volumes.
4. Results: sensory tables, statistical analysis, and any chemical data.
5. Discussion: interpretation, troubleshooting, limitations, and sustainability considerations.
6. Conclusion and recommendations for future work.

Closing: teach flavor chemistry that sticks

Using Bun House Disco’s pandan negroni as a lab experiment transforms abstract concepts into tangible, testable learning. Students practice experimental design, understand solvent–solute behavior, learn to quantify and qualify flavor, and connect culinary creativity with rigorous food science methods. With accessible 2026 tools—portable analytics, AI modeling, and green extraction techniques—this module prepares learners for modern food R&D roles and culinary innovation.

Ready to run this lab? Download our free lab worksheet, sensory templates, and grading rubric tailored for the pandan negroni experiment. Implement one week-long module or a semester-long research project—either way, your class will leave with practical skills in flavor chemistry, sensory science, and sustainable formulation.

Call-to-action: Click to download the ready-to-use lab packet, or subscribe to receive updated 2026 curriculum packs integrating portable GC workflows and AI-sensory labs. Bring flavor chemistry to life in your classroom this term.

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