Analysis of Aroma Components in Milk Using Smart Aroma Database

Importance of the Topic

Aroma compounds are key determinants of milk flavor and consumer perception. They occur at trace levels influenced by geographical origin, animal diet, processing and storage methods. High-sensitivity profiling of these volatiles supports quality assurance, product development and authentication across dairy industries.

Goals and Study Overview

This study aimed to demonstrate the combined use of a solid phase microextraction device and a targeted aroma database for comprehensive analysis of milk volatiles. Six commercial milk samples with distinct provenance and sterilization treatments were compared to reveal compositional differences.

Used Instrumentation

• Gas chromatograph–mass spectrometer: GCMS-QP2020 NX
• Smart Aroma Database™: proprietary library of ~500 volatile compounds
• Autosampler: AOC-6000 Plus equipped for SPME Arrow
• Capillary column: InertCap Pure Wax (30 m × 0.25 mm I.D., 0.25 µm film)
• Extraction device: SPME Arrow fiber (1.1 mm O.D., DVB/Carbon WR/PDMS, 120 µm)

Methodology

Each milk sample (3 mL) was sealed in a vial, incubated at 40 °C with agitation to release volatiles. The SPME Arrow was exposed for 30 min to adsorb aroma components, then thermally desorbed in the GC inlet at 250 °C. GC separation used a temperature program from 50 °C to 250 °C. Mass spectra were acquired in scan mode under electron ionization.

Main Results and Discussion

A total of 45 aroma compounds were identified by matching retention and spectral data against the Smart Aroma Database. Principal component analysis distinguished notable aroma profiles, particularly separating sample 2 and sample 6. Sample 2 was enriched in C5–C7 alcohols and carbonyls (e.g., 1-pentanol, 1-hexanol, hexanal), while sample 6 showed higher ester content (e.g., ethyl hexanoate, butyl acetate). Chromatogram comparisons highlighted differences in hexanal and ethyl hexanoate abundances between these samples.

Benefits and Practical Applications

• High sensitivity extraction via SPME Arrow enables detection of trace volatiles in complex matrices.
• Comprehensive database-driven identification accelerates qualitative profiling.
• Multivariate analysis supports differentiation of milk origin and processing method.
• Approach applicable to other food products for quality control and authentication.

Future Trends and Possibilities

Advances may include expansion of aroma libraries to cover diverse matrices, integration with automated sample handling for high-throughput screening, coupling with olfactometry for sensory correlations, and application in real-time monitoring of production lines. Machine learning on large volatile datasets could enable predictive quality assessment and fraud detection.

Conclusion

The combination of SPME Arrow extraction and a dedicated aroma database enabled effective profiling of milk volatile components at trace levels. The approach provided clear discrimination among milk samples and offers a robust platform for dairy quality evaluation and research.