Date of Thesis

Spring 2026

Description

Roasted coffee undergoes continuous chemical and physical degradation after roasting, yet industry freshness standards remain empirically defined rather than grounded in measurable parameters. This study developed a multi-parameter framework for quantifying roasted coffee freshness by integrating gas-phase and solid-phase analysis, backed by human sensory evaluation, across three roast levels — light (City+), medium (Full City), and dark (Vienna) — over a four-month storage period. CO2 off-gassing was monitored continuously for 30 day, gas chromatography-mass spectrometry (GC-MS) tracked volatile compound changes in headspace gas, and Fourier transform infrared-diffuse reflectance spectroscopy (FTIR-DRIFTS) quantified oxidation-associated functional group changes, and microindentation assessed bean stiffness.

Methylfuran concentration decreased across all roast levels while methanethiol accumulated, and the FTIR-derived symmetric-to-asymmetric aliphatic C-H ratio increased with sample age, consistent with progressive structural changes in the bean matrix. Microindentation showed no statistically significant change over the storage period. A gradient boosting regression model trained on 13 instrumental features achieved a test R2 of 0.994 for predicting coffee age, with SHAP analysis identifying methylfuran and CO2 off-gassing as the dominant predictors that can predict accurately as a 2-feature model to R2 of 0.951. Sensory evaluation with trained coffee professionals confirmed that age-related quality differences were perceptible, though panelists were unable to reliably rank samples in chronological order within narrower age windows, indicating that sensory discrimination does not map linearly onto chemical degradation timelines. This work demonstrates that coffee freshness can be quantified with high accuracy from instrumental measurements and that these chemical changes correspond to detectable, if imprecisely ordered, shifts in sensory quality.

Keywords

coffee freshness, volatile organic compounds, microindentation, FTIR-DRIFTS, machine learning, sensory evaluation

Access Type

Honors Thesis

Degree Type

Bachelor of Science in Chemical Engineering

Major

Chemical Engineering

First Advisor

Katsuyuki Wakabayashi

Second Advisor

Jonathan Scholnick

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