ABSTRACT
This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.
Subject(s)
Chemometrics , Spectroscopy, Near-Infrared , Ethiopia , Furans , Gas Chromatography-Mass SpectrometryABSTRACT
OBJECTIVES: To assess the performance sentinel lymph node (SLN) biopsy and effect of ultrastaging in clinically early stage endometrial cancer. METHODS: Patients with endometrial cancer prospectively enrolled after informed consent was obtained. The cervix was injected superficially with 1â¯mL of ISB and 1â¯mL of ICG (diluted 1:25) at 3 and 9 o'clock each. SLN biopsy was followed by complete pelvic lymphadenectomy (aortic lymphadenectomy at the discretion of the surgeon). Lymph nodes (LNs) were analyzed by standard sectioning with H&E; ultrastaging of SLN was done retrospectively and blinded to treating physicians. RESULTS: 204 patients received dye injections. In 184 (90.2%) patients at least one SLN was identified. Of all patients, 138 (68%) had bilateral mapping. In the patients with successful mapping of a hemipelvis, ICG detected SLNs in 83% and ISB in 64% of cases (pâ¯<â¯0.0001). Median BMI (kg/m2) for patients with successful mapping was 35.7 compared to 40.1 for those who did not map (pâ¯=â¯0.01). Twenty-three (11.3%) patients had positive LNs. Applying the SLN algorithm, positive nodes were detected in 21/23 (91.3%). The negative predictive value (NPV) was 98.9% (95% CI: 96.01% to 99.71%). Eleven patients had positive SLN with isolated tumor cells (ITCs) or micrometastases detected on ultrastaging. Including these patients, 34 (17%) had positive LNs, increasing the NPV to 99% and sensitivity to 94%. There were no recurrences in patients with ITCs only. CONCLUSIONS: SLN assessment in endometrial cancer is feasible and safe with high NPV (99%). ICG was more effective in detecting SLN compared to ISB. Although ultrastaging detected additional positive LNs, treatment based on standard sectioning appears reasonable but further research is needed.
