풍미체학

기술 사양

TECHNICAL SPECIFICATIONS

Item	Details
Platform	Comprehensive two-dimensional GC-TOF MS (GC×GC-TOF MS) for volatiles boiling at 50–260 °C at atmospheric pressure
Identification	ChromaTOF annotation with dual verification — standard spectral-library matching plus retention index (RI) calculated from C7–C30 n-alkanes — yielding Level 1 identifications
Quantification	Single-point semi-quantification with isotope internal standards; relative content from peak-area ratios (μg/g solids, μg/mL liquids) with normalization
ROAV Odor Activity	ROAV calculated from odor thresholds and relative content to identify the compounds that truly drive flavor — not merely the most abundant ones
Sensory-Flavor Mapping	Sensory flavor notes annotated via the Flavordb database, translating "compound differences" into "flavor differences"

분석 내용

ANALYSIS MODULES

Module	Analysis Content
Data Pre-analysis	1D / 2D / 3D total ion chromatograms (TIC) · annotation and identification counts · intergroup Venn diagrams · relative-content analysis · odor thresholds and ROAV analysis
Basic Analysis	Compound-class annotation (hydrocarbons/aldehydes/esters/acids/ketones/alcohols/ethers/phenols/heterocycles; PubChem + ClassyFire) · HMDB classification · KEGG annotation and functional classification · PCA / PLS-DA / OPLS-DA (permutation tests, S-plots, VIP) · cluster heatmaps and sample hierarchical clustering
Differential Analysis	Differential compound screening (P < 0.05 and VIP > 1) · Venn diagrams · heatmaps and correlation heatmaps · K-means clustering · Z-score plots · volcano plots · bar / box / violin plots
Sensory Analysis	Sensory-note radar charts (frequency / content) · ROAV scatter plots of differential flavor compounds · sensory-note–compound association networks · flavor-note Sankey diagrams · flavor wheels of differential compounds
Advanced Analysis	KEGG pathway enrichment (bar / bubble plots, pathway networks, differential enrichment scores) · machine-learning biomarker screening (random-forest feature importance)

결과 예시

SELECTED FIGURES FROM ACTUAL REPORTS

3D 총이온크로마토그램

GC×GC-TOF MS의 3D TIC 파노라마로, 종합 2차원 플랫폼의 피크 용량과 분리능을 시각화합니다.

ROAV 산점도

냄새 역치와 상대 함량으로 산출한 ROAV로, 실제로 풍미를 결정하는 핵심 휘발성 성분을 찾아냅니다.

차이 화합물 클러스터 히트맵

상위 50개 차이 화합물의 클러스터 히트맵으로, 그룹 간 함량 패턴 차이를 즉시 확인할 수 있습니다.

향미 노트 레이더 차트

상위 10개 향미 노트의 그룹 간 함량 차이를 레이더 차트로 표현하여 풍미 프로파일 변화를 직접 보여줍니다.

향미 노트–화합물 네트워크

향미 노트와 차이 풍미 화합물의 연관 네트워크로, 화합물 차이를 풍미 차이로 해석합니다.

차이 화합물 풍미 휠

상위 10개 향미 노트와 해당 차이 화합물의 풍미 휠로, 내부와 외부 고리가 노트와 화합물을 대응시킵니다.

다중 모델 ROC 곡선

풍미 마커로 구축한 여러 머신러닝 모델의 ROC 곡선으로 마커의 판별력을 평가합니다.

적용 분야

APPLICATIONS

식품 풍미 품질 분석가공이 풍미에 미치는 영향원산지 & 품종 판별풍미 마커 발굴신제품 R&D 지원

FAQ

How long does a Flavoromics project take?

Mature targeted panels typically take 1-3 weeks; untargeted projects take 2-4 weeks (counted from sample receipt and QC approval). The exact timeline is confirmed in writing before project start, and progress is trackable on the OmicsHub cloud platform.

What if my sample amount is below the recommendation?

Please do not dilute your samples. Low-input samples can often be accommodated by adjusting the preparation workflow — tell us the actual amount and our technical team will assess feasibility and note adjustments in the study plan.

Can I add targets to an existing panel?

Yes. Mature panels support on-demand additions; novel analytes outside the list can be implemented through custom method development (requires standards and extra validation time) — see our Custom Targeted Metabolomics service.

What samples do you need for Flavoromics?

Serum/plasma ≥100 μL (fasting, no haemolysis), tissue ≥50 mg (snap-frozen), cells ≥1×10⁶ (medium washed off), urine ≥200 μL. Keep fasting time consistent, aliquot into single-use volumes and avoid freeze–thaw. Full guidance: Technical Resources → Sample Guide.