Fatty Acids Analysis Service - Creative Proteomics Metabolomics

Q: What is the difference between free fatty acid and total fatty acid analysis?

Free fatty acid analysis measures non-esterified, unbound fatty acids — the biologically active pool for signaling and β-oxidation. Total fatty acid analysis hydrolyzes all esterified lipids and measures free + esterified FAs — reflecting dietary intake and membrane composition. Our dual-panel service provides both from a single sample submission.

Q: How many fatty acids can you quantify in a single analysis?

Our free FA panel covers 30+ FFAs (C2-C26) by LC-MS/MS. Our total FA panel covers 40+ FAMEs (C2-C26) by GC-MS, including cis/trans isomers, omega-3/6/9 positional isomers, and branched-chain species.

Q: Can you distinguish between omega-3, omega-6, and omega-9 fatty acid isomers?

Yes. Our 100 m polar GC column achieves baseline separation of positional isomers (e.g., α-linolenic C18:3n3 vs. γ-linolenic C18:3n6), cis/trans pairs (oleic vs. elaidic), and conjugated linoleic acid isomers.

Q: What enzyme activity indices do you calculate from fatty acid data?

We pre-calculate SCD1 (C16:1/C16:0), Δ6-desaturase (C18:3n6/C18:2n6), D5-desaturase (C20:4n6/C20:3n6), Elovl5, and Elovl2 indices. Each reported as a product-to-precursor ratio.

Q: How should I prepare samples for free fatty acid vs. total fatty acid analysis?

For free FAs: snap-freeze immediately, store at −80°C, never thaw before extraction — lipolysis artificially inflates free FA concentrations. For total FAs: less critical since all pools are measured together.

Fatty Acids — Why Targeted Profiling Matters for Metabolic, Nutrition, and Microbiome Research

Fatty acids are carboxylic acids with aliphatic chains that range from 2 to 26+ carbons, classified by chain length (SCFA, MCFA, LCFA, VLCFA) and saturation (saturated, monounsaturated, polyunsaturated). They serve as energy substrates, membrane phospholipid components, signaling molecules, and precursors to eicosanoids, resolvins, and other lipid mediators. Their biological roles are chain-length-specific and saturation-state-specific — C16:0 palmitate and C16:1 palmitoleate have opposing metabolic effects despite differing by a single double bond.

For researchers, the analytical challenge is threefold: (1) SCFAs are volatile and require specialized extraction or derivatization to avoid loss during sample preparation; (2) free fatty acids exist at trace concentrations (nM–μM) and require sensitive, underivatized LC-MS/MS methods to distinguish from the far more abundant esterified pool; and (3) total fatty acid profiling requires hydrolysis and derivatization to fatty acid methyl esters (FAMEs) followed by GC-MS — a completely different workflow. Most services offer one or the other. We offer both from a single sample submission.

Targeted fatty acid profiling by GC-MS and LC-MS/MS enables you to:

(i) quantify free fatty acids at endogenous concentrations without derivatization bias, using LC-MS/MS with isotope-labeled internal standards

(ii) profile total fatty acid composition after hydrolysis and FAME derivatization by GC-MS, covering the full C2–C26 range with isomer-resolved separation of cis/trans, omega-3/6/9, and branched-chain species

(iii) compare free vs. total pools — for example, the ratio of free arachidonic acid to total arachidonic acid reflects phospholipase A2 activity and is a direct measure of eicosanoid precursor mobilization.

What Problem Do We Solve?

Most fatty acid analysis services force a choice — free or total, GC or LC, short-chain or long-chain. This fragments your data and doubles sample requirements. Creative Proteomics resolves this with a dual-platform service designed to capture the complete fatty acid profile from one submission:

Free + total from one sample: Your sample is split at intake — one portion undergoes direct extraction for LC-MS/MS free fatty acid quantification, the other undergoes hydrolysis and FAME derivatization for GC-MS total fatty acid profiling. Two datasets, one submission, no doubled sample requirements.
Full chain-length range, no gaps: From acetate (C2:0) to cerotic acid (C26:0). SCFAs via specialized extraction with internal standards; MCFAs and LCFAs by both GC-MS and LC-MS/MS for cross-platform validation; VLCFAs with optimized chromatography for the C22–C26 range where diagnostic ratios matter.
Isomer-resolved quantification: cis/trans separation (e.g., cis-C18:1 oleic acid vs. trans-C18:1 elaidic acid), omega-3/6/9 positional isomer distinction (e.g., C18:3n3 α-linolenic vs. C18:3n6 γ-linolenic), and branched-chain iso/anteiso resolution — all with per-analyte calibration curves, not single-surrogate quantification.

Fatty Acid Detection Panels — Free and Total Profiling

Creative Proteomics offers two complementary fatty acid panels — free and total — deployable independently or together from a single sample submission. Each panel includes class-specific isotopically labeled internal standards and per-analyte multi-point calibration.

Free Fatty Acid Panel (LC-MS/MS, Underivatized)

FA Class	Chain Range	Representative Analytes	Biological Context
Short-Chain Fatty Acids (SCFA)	C2–C6	Acetate, propionate, butyrate, isobutyrate, valerate, isovalerate, hexanoate	Gut microbial metabolites, GPCR signaling (GPR41/43), HDAC inhibition
Medium-Chain Fatty Acids (MCFA)	C8–C12	Caprylic (C8:0), capric (C10:0), lauric (C12:0)	Dietary MCTs, ketogenesis, antimicrobial lipids
Saturated Fatty Acids (SFA)	C14–C22	Myristic (C14:0), palmitic (C16:0), stearic (C18:0), arachidic (C20:0), behenic (C22:0)	De novo lipogenesis markers, membrane rigidity, lipotoxicity
Monounsaturated Fatty Acids (MUFA)	C14–C22	Palmitoleic (C16:1n7), oleic (C18:1n9), cis-vaccenic (C18:1n7), erucic (C22:1n9)	SCD1 activity index (C16:1/C16:0), cardiometabolic protection
Polyunsaturated Fatty Acids — Omega-6 (PUFA n-6)	C18–C22	Linoleic (C18:2n6), γ-linolenic (C18:3n6), dihomo-γ-linolenic (C20:3n6), arachidonic (C20:4n6), adrenic (C22:4n6)	Eicosanoid precursors, pro-inflammatory signaling, Δ6-desaturase activity
Polyunsaturated Fatty Acids — Omega-3 (PUFA n-3)	C18–C22	α-Linolenic (C18:3n3), eicosapentaenoic (C20:5n3 EPA), docosapentaenoic (C22:5n3 DPA), docosahexaenoic (C22:6n3 DHA)	Resolvin/protectin precursors, anti-inflammatory signaling, neuronal membrane function
Trans Fatty Acids (TFA)	C16–C18	trans-Palmitoleic (C16:1n7t), elaidic (C18:1n9t), trans-vaccenic (C18:1n7t), linoelaidic (C18:2n6t)	Dietary intake biomarkers, CVD risk assessment, industrial hydrogenation markers
Very-Long-Chain Fatty Acids (VLCFA)	C22–C26	Lignoceric (C24:0), nervonic (C24:1n9), cerotic (C26:0)	Peroxisomal β-oxidation disorders, C24:0/C22:0 and C26:0/C22:0 diagnostic ratios

Total Fatty Acid Panel (GC-MS, FAME Derivatization)

Parameter	Specification
Method	Base- or acid-catalyzed hydrolysis followed by BF3/MeOH or acetyl chloride/methanol derivatization to fatty acid methyl esters (FAMEs); GC-MS analysis on Agilent 7890B-5977A with DB-23 or SP-2560 column (100 m × 0.25 mm × 0.20 μm) for high-resolution isomer separation
Coverage	40+ fatty acids, C2:0–C26:0, including odd-chain (C15:0, C17:0) and branched-chain (iso/anteiso C15:0, C17:0) species
Isomer Resolution	Baseline separation of cis/trans isomers, omega-3/6/9 positional isomers, and conjugated linoleic acid (CLA) isomers (cis-9,trans-11 vs. trans-10,cis-12)
Quantification	Absolute (mg/g or % of total fatty acids) with C13:0, C17:0, C19:0, C21:0, and C23:0 internal standards; response-factor-corrected against authentic FAME standard mixtures (Supelco 37-Component FAME Mix, GLC-674)

Custom panels: add specific FAs of interest, expand VLCFA coverage, or integrate with lipid metabolism analysis or fatty acid metabolism profiling for comprehensive lipidomics. Contact us with your target list during study design.

GC-MS vs LC-MS/MS for Fatty Acid Analysis

Researchers have two primary platforms for fatty acid analysis — but they answer different questions. The table below clarifies which platform fits your research goal.

Dimension	GC-MS (FAME Derivatization) — Total FA	LC-MS/MS (Underivatized) — Free FA
What is measured	Total fatty acids (free + esterified) after hydrolysis and derivatization to FAMEs	Free (non-esterified) fatty acids at endogenous concentrations, without derivatization
Chain-length range	C2–C26; excellent for SCFA through VLCFA in a single run	C8–C26; SCFAs require separate extraction (volatile, poor ESI ionization)
Isomer resolution	Excellent — 100 m polar columns resolve cis/trans, omega-3/6/9, CLA isomers, and branched-chain FAs at baseline	Good — reversed-phase C18 separates by chain length and unsaturation; isomer pairs may co-elute without specialized columns
Quantification	Absolute (% composition or mg/g) with odd-chain FAME internal standards; response-factor-corrected	Absolute (nmol/mL or pmol/mg) with per-class isotopically labeled internal standards (e.g., D31-palmitate, D8-arachidonate)
Biological question answered	"What is the total fatty acid composition of this sample?" — dietary intake, membrane composition, oil/food quality	"How much free arachidonic acid is available for eicosanoid synthesis?" — signaling, enzyme activity indices, lipolysis
Best for	Nutritional profiling, food/oil analysis, total FA composition, omega-3/6 ratio, trans FA quantification	Metabolic signaling, NEFA/FFA panels, phospholipase activity markers, enzyme activity indices (SCD1, Δ6D, Elovl5/2)

Not sure which panel fits your study? Most projects benefit from both — the free/total ratio itself is a biologically meaningful parameter. Contact us during study design and we will recommend the optimal configuration for your sample type and research question.

Why Choose Our Fatty Acids Analysis Service?

Free + Total from One Sample Submission
Your sample is split and processed through two optimized workflows — LC-MS/MS for free fatty acids and GC-MS FAME derivatization for total fatty acids. Two complementary datasets, one submission, no extra sample requirements.
Full Chain-Length Coverage with No Platform Gaps
SCFA (C2–C6) by specialized extraction-GC-MS, MCFA through VLCFA (C8–C26) by both platforms for cross-validation. No "sorry, we don't do short-chain" or "we only go up to C22."
Isomer-Level Resolution That Matters Biologically
Our 100 m polar GC columns resolve cis-C18:1 from trans-C18:1, α-linolenic (n3) from γ-linolenic (n6), and conjugated linoleic acid isomers — distinctions that a 30 m column or reversed-phase LC alone cannot make.
Per-Analyte Calibration with Authentic Standards
Every analyte is quantified against its own calibration curve with class-matched isotopically labeled internal standards. Your palmitate data is calibrated against a palmitate standard curve — not estimated from a C17:0 surrogate.
Publication-Ready Data Package
Quantitative tables (.xlsx) with per-analyte concentrations, QC metrics, calibration documentation, FAME chromatograms with peak assignments, and a methods appendix formatted for direct inclusion in your manuscript.

Instrumentation and Method Performance

Analytical Platform

GC-MS (Total Fatty Acids — FAME Method)

Mass Spectrometer: Agilent 7890B GC coupled to 5977A MSD (single quadrupole)

Column: Agilent DB-23 or SP-2560 (100 m × 0.25 mm × 0.20 μm) for high-resolution cis/trans and positional isomer separation

Ionization: EI 70 eV, full-scan (m/z 50–550) + SIM for quantification; NIST/Wiley library confirmation

Derivatization: BF3/MeOH or acetyl chloride/methanol → FAME; odd-chain IS (C13:0, C17:0, C19:0, C21:0, C23:0) spiked pre-derivatization

LC-MS/MS (Free Fatty Acids — Underivatized)

Mass Spectrometer: SCIEX QTRAP 6500+ or Agilent 6495C Triple Quadrupole

Ionization: ESI Negative Mode for underivatized FFAs; scheduled MRM with polarity switching

LC System: Waters ACQUITY UPLC with C18 column (100 × 2.1 mm, 1.7 μm) for reversed-phase separation

Method Performance

Parameter	Typical Range
Linearity (R²)	≥ 0.995 (GC-MS FAMEs); ≥ 0.992 (LC-MS/MS FFAs)
LOD (GC-MS)	0.1–5 ng on-column (FAME, analyte-dependent)
LOD (LC-MS/MS)	0.01–1 pmol on-column (FFA, analyte-dependent)
Intraday Precision	CV ≤ 10% for the majority of analytes
Interday Precision	CV ≤ 15% across qualified matrices
Recovery (Spike)	80–120% for most analytes in qualified matrices

Internal Standards and Calibration Strategy

GC-MS FAME: Odd-chain fatty acid internal standards (C13:0, C17:0, C19:0, C21:0, C23:0) spiked pre-derivatization; response-factor correction against certified FAME reference mixtures (Supelco 37-Component FAME Mix, GLC-674, GLC-481); individual FAME calibration for CLA isomers and trans FAs.
LC-MS/MS FFA: Per-class isotopically labeled internal standards (D31-palmitate for SFAs, D8-arachidonate for PUFAs, D5-oleate for MUFAs, 13C2-acetate for SCFAs) spiked at extraction; 6–8 point calibration curves per analyte in surrogate or matrix-matched matrix.
QC Monitoring: Pooled QC every 8 injections; NIST SRM 1950 (Metabolites in Frozen Human Plasma) or NIST SRM 3278 (Tocopherols in Edible Oils) as applicable; Westgard multi-rule evaluation; LOESS signal drift correction; flagged analytes (RSD > 30%) reported.

Agilent 7890B-5977A GC-MS (Figure from Agilent)

SCIEX Triple Quad 6500+ (Figure from Sciex)

Waters ACQUITY UPLC System (Figure from Waters)

Fatty Acid Analysis Workflow

Study Design and Panel Configuration

We define the panel configuration with you — free FAs only, total FAs only, or the dual-panel Free + Total approach. Internal standard suite selection, sample amount requirements, and data output format are aligned to your research goals and biological matrix.

Sample Preparation and Lipid Extraction

Sample is split at intake. For free FAs: Folch or MTBE lipid extraction at 4°C under nitrogen; isotopically labeled IS spiked at extraction; underivatized extracts analyzed directly by LC-MS/MS. For total FAs: base hydrolysis (NaOH/MeOH) to release esterified FAs; BF3/MeOH FAME derivatization; odd-chain IS spiked pre-derivatization; hexane extraction of FAMEs for GC-MS.

GC-MS and LC-MS/MS Acquisition

FAMEs analyzed on Agilent 7890B-5977A GC-MS with 100 m polar column for cis/trans and positional isomer resolution. Free FAs analyzed on SCIEX QTRAP 6500+ or Agilent 6495C with scheduled MRM in negative ion mode. Pooled QC injections every 8 samples; system suitability tests per batch.

Quantification and Quality Review

Per-analyte concentrations calculated via multi-point calibration curves (6–8 points) with class-matched internal standards. Data reviewed for linearity (R² ≥ 0.992), precision (CV ≤ 15%), ion ratio confirmation (LC-MS/MS), retention time locking (GC-MS), and carryover. LOESS signal drift correction and Westgard multi-rule evaluation applied.

Data Delivery and Publication Support

Complete data package including quantitative tables (.xlsx) with per-analyte concentrations and QC flags, calibration and QC reports, FAME chromatograms with peak assignments, raw data files (.d/.wiff), and a methods appendix formatted for direct manuscript inclusion.

Sample Collection and Preparation Guidelines

Sample Type	Minimum Amount	Preparation	Storage and Shipping
Plasma / Serum	≥ 100 μL (free FA); ≥ 50 μL (total FA); ≥ 150 μL (both)	Collect in EDTA tube, centrifuge at 4°C within 30 min, aliquot, avoid hemolysis. Fasting samples recommended for free FA panels (postprandial FFAs spike acutely)	−80°C; ship on dry ice
Tissue (liver, muscle, adipose, brain)	≥ 30 mg wet weight	Snap-freeze in liquid N₂ immediately after collection; record wet weight. Avoid thawing before extraction — FFAs are rapidly released post-mortem via lipolysis	−80°C; ship on dry ice
Cell Pellets	≥ 1 × 10⁶ cells	Wash twice with cold PBS; centrifuge at 4°C; aspirate supernatant completely; snap-freeze pellet. Include medium blank for secreted FAs	−80°C; ship on dry ice
Feces / Cecal Content	≥ 100 mg	Collect fresh, snap-freeze immediately. SCFAs are produced by ongoing microbial fermentation — any delay at room temperature alters the SCFA profile	−80°C; ship on dry ice
Food / Oil / Plant Material	≥ 200 mg (solid); ≥ 100 μL (oil)	Homogenize solids; protect oils from oxidation (flush headspace with N₂, seal). Record processing history for food samples	−20°C or −80°C; ship on dry ice

Critical Notes:

Free fatty acids are acutely sensitive to lipolysis — samples for free FA analysis must be frozen immediately and never thawed before extraction. Even brief thawing releases FFAs from triglycerides and phospholipids, artificially inflating measured concentrations.
Polyunsaturated fatty acids (especially DHA, EPA, arachidonic acid) are susceptible to oxidation. Minimize air exposure during collection. For long-term storage, consider adding BHT (0.01% w/v) as antioxidant if compatible with your downstream analysis.
Short-chain fatty acids (acetate, propionate, butyrate) are volatile. For feces and cecal samples, discuss acidified extraction protocols during study design to ensure quantitative SCFA recovery.

Deliverables

Quantitative Data Tables (.xlsx/.csv)
Absolute concentrations (nmol/mL, pmol/mg, or mg/g as applicable), % composition of total FAs, QC flags, and calculated indices — SCD1 (C16:1/C16:0), Δ6-desaturase (C18:3n6/C18:2n6), Elovl5 (C20:5n3/C20:4n3), omega-3 index (EPA+DHA as % of total).

QA/QC Report
Calibration linearity, internal standard recovery, pooled QC RSD, batch trend plots, Westgard rule compliance summary, NIST SRM performance (if applicable).

Chromatograms with Peak Assignments
Annotated FAME chromatograms (GC-MS) showing peak identification and resolution of key isomer pairs. MRM chromatograms (LC-MS/MS) with retention time markers.

Raw Data Files
Vendor-native files (.d for GC-MS, .wiff for LC-MS/MS) and open formats (.mzML, .csv) upon request.

Methods Appendix
Extraction protocol, derivatization conditions, GC oven program, MRM transitions, MS parameters — formatted for direct inclusion in your manuscript methods section.

FAME chromatogram showing baseline separation of 37 fatty acid methyl esters with peak annotations

Representative FAME chromatogram on a 100 m DB-23 column: baseline separation of C16:0, C16:1n7, C18:0, C18:1n9 (oleic), C18:1n7 (cis-vaccenic), C18:2n6 (linoleic), C18:3n3 (α-linolenic), and C18:3n6 (γ-linolenic).

Free fatty acid profiling in plasma: comparison of SFA, MUFA, and PUFA concentrations between control and high-fat diet groups, with enzyme activity indices (SCD1, Δ6D) calculated from product/precursor ratios.

Applications of Fatty Acid Profiling

Our fatty acid analysis service supports researchers across disciplines where lipid metabolism data drives decisions:

Metabolic Disease and Obesity

Quantify NEFA/FFA profiles, SCD1 and D6-desaturase activity indices, omega-3/6 ratios, and total FA composition in diabetes, NAFLD, and cardiovascular studies

Microbiome and Gut Health

Profile SCFAs (acetate, propionate, butyrate) in feces and cecal content as quantitative readouts of gut microbial fermentation and fiber metabolism

Food Science and Nutrition

Determine total fatty acid composition of foods and oils, quantify omega-3/6 ratios, detect trans fatty acids, and validate nutritional labeling claims

Pharmacology and Toxicology

Monitor drug-induced changes in fatty acid metabolism, assess hepatotoxicity via NEFA/FFA profiles, and use FA-based enzyme activity indices as pharmacodynamic biomarkers

Plant and Agricultural Science

Profile fatty acid composition of oilseed crops, assess genetic modification and breeding outcomes on oil quality, and quantify VLCFA for cuticular wax and stress tolerance studies

Case Study: Free Fatty Acid Profiling Across 4,900 Participants Identifies Early Markers of Diabetes Risk

Prospective Association of Fatty Acids in the De Novo Lipogenesis Pathway with Risk of Type 2 Diabetes: The Cardiovascular Health Study

Ma, W., Wu, J. H. Y., Wang, Q., Lemaitre, R. N., Mukamal, K. J., Djousse, L., King, I. B., Song, X., Biggs, M. L., Delaney, J. A., Kizer, J. R., Siscovick, D. S., and Mozaffarian, D. | The American Journal of Clinical Nutrition, 2015, 101(1), 153–163

DOI: 10.3945/ajcn.114.092601

Background

Type 2 diabetes develops over years, but identifying at-risk individuals before glycemic markers become abnormal remains challenging. Fatty acid profiles — particularly free fatty acids involved in de novo lipogenesis (DNL) — reflect hepatic metabolic state and may serve as early biomarkers of diabetes risk before conventional clinical markers change.

Challenge: Prospectively assess whether circulating fatty acid concentrations — measured years before disease onset — predict incident type 2 diabetes in a large, diverse cohort, independent of standard risk factors.

Key Findings

Metric	Finding
Study design	Prospective cohort; 4,904 participants; median follow-up 13 years; 577 incident T2D cases
Palmitic acid (C16:0) — highest vs. lowest quintile	HR = 2.10 (95% CI: 1.55–2.85); strongest association among all FAs tested
Stearic acid (C18:0) — highest vs. lowest quintile	HR = 1.61 (95% CI: 1.19–2.18)
SCD1 activity index (C16:1n7/C16:0)	Significantly associated with incident T2D; HR = 1.48 per SD
DNL fatty acids (16:0, 16:1n7, 18:0, 18:1n9)	Combined DNL score associated with 50% higher T2D risk (HR = 1.50; 95% CI: 1.29–1.74)

What This Means for Your Fatty Acid Research

Free fatty acid profiles predict disease risk years before onset. The DNL score — calculated from just four free fatty acid concentrations and one enzyme index — identified individuals at elevated diabetes risk independent of BMI, fasting glucose, and triglycerides. Our free FA panel provides the same per-analyte quantitative data needed to compute these indices.
Product-to-precursor ratios are more informative than individual concentrations. The SCD1 index (C16:1/C16:0 ratio) was a stronger predictor than either fatty acid alone. Our data package includes pre-calculated enzyme activity indices for every sample.
Chain-length specificity matters. Palmitic acid (C16:0) was the single strongest predictor, while longer-chain SFAs showed weaker or null associations. A "total SFA" measurement would have diluted this signal — underscoring the value of per-analyte, chain-length-resolved quantification.

Conclusion

This study demonstrates that targeted free fatty acid profiling — with per-analyte quantification and calculated enzyme activity indices — provides actionable metabolic insight that total or class-summed measurements miss. Our dual-platform service delivers the same quantitative rigor: per-analyte FFA concentrations by LC-MS/MS with class-matched internal standards, plus total FA composition by GC-MS — all with pre-calculated desaturase and elongase indices ready for statistical analysis.

Read the full paper: Ma et al., The American Journal of Clinical Nutrition, 2015

What is the difference between free fatty acid and total fatty acid analysis?

Free fatty acid (FFA) analysis measures the non-esterified, unbound fatty acids circulating in plasma or present in tissue — the biologically active pool available for cellular uptake, β-oxidation, and signaling. Total fatty acid analysis hydrolyzes all esterified lipids (triglycerides, phospholipids, cholesterol esters) and measures the sum of free + esterified fatty acids — reflecting dietary intake, membrane composition, and long-term fatty acid status. The free/total ratio itself is informative: a high free arachidonic acid/total arachidonic acid ratio indicates active phospholipase A2-mediated release and eicosanoid precursor mobilization.

How many fatty acids can you quantify in a single analysis?

Our free fatty acid panel covers 30+ FFAs from C2 (acetate) to C26 (cerotic acid) by LC-MS/MS with isotopically labeled internal standards. Our total fatty acid panel covers 40+ FAMEs from C2:0 to C26:0 by GC-MS with odd-chain internal standards, including cis/trans isomers, omega-3/6/9 positional isomers, and branched-chain species. The combined Free + Total panel provides the most complete fatty acid profile available from a single CRO submission.

Can you distinguish between omega-3, omega-6, and omega-9 fatty acid isomers?

Yes. Our GC-MS method uses a 100 m polar column (DB-23 or SP-2560) that achieves baseline separation of positional isomers — for example, α-linolenic acid (C18:3n3), γ-linolenic acid (C18:3n6), and α-eleostearic acid (C18:3n5) elute at distinct retention times. The same column resolves cis/trans isomer pairs (oleic vs. elaidic acid) and conjugated linoleic acid (CLA) isomers. This resolution is not achievable with shorter columns or reversed-phase LC alone.

What are the LOD and LOQ for fatty acid quantification?

For GC-MS FAME analysis, typical LOD is 0.1–5 ng on-column, depending on the specific FAME and the complexity of the chromatographic region. For LC-MS/MS free fatty acid analysis, typical LOD is 0.01–1 pmol on-column. SCFAs by specialized extraction-GC-MS achieve LOD of 0.1–1 μM in biological matrices. Exact per-analyte LOD/LOQ values are reported in your data package.

How should I prepare samples for free fatty acid vs. total fatty acid analysis?

For free fatty acid analysis, the critical requirement is preventing lipolysis. Samples must be snap-frozen immediately after collection, stored at −80°C, and never thawed before extraction. Even brief thawing releases FFAs from triglycerides and phospholipids, artificially inflating free concentrations. For total fatty acid analysis, this is less critical since all esterified and free FAs are measured together. If requesting both panels, we split your sample at intake and apply the appropriate protocol to each portion.

Can you analyze short-chain fatty acids (SCFAs) together with long-chain fatty acids?

SCFAs (C2–C6) require a different analytical workflow than MCFAs and LCFAs because they are volatile and poorly retained on standard reversed-phase LC columns. For total FA panels, SCFAs are derivatized to FAMEs and analyzed by GC-MS alongside longer-chain FAMEs — this is straightforward. For free FA panels, SCFAs are analyzed by a specialized extraction-GC-MS method rather than LC-MS/MS. Our dual-platform approach ensures complete chain-length coverage without compromising data quality for any carbon range.

What enzyme activity indices do you calculate from fatty acid data?

We pre-calculate the most commonly used fatty acid-based enzyme activity indices from your quantitative data: SCD1 (C16:1n7/C16:0 and C18:1n9/C18:0), Δ6-desaturase (C18:3n6/C18:2n6), D5-desaturase (C20:4n6/C20:3n6), Elovl5 (C20:5n3/C20:4n3 and C22:5n3/C22:4n3), and Elovl2 (C22:6n3/C22:5n3). Each is reported as a product-to-precursor ratio with per-sample values. Additional indices can be calculated on request.

Can you analyze fatty acids in food and oil samples?

Yes. Our GC-MS FAME method is well-suited for food and oil fatty acid profiling. We can report results as mg fatty acid per g sample or as % of total fatty acids (relative composition). For edible oils, we follow AOAC 996.06 methodology and can provide full nutritional labeling support including saturated fat, monounsaturated fat, polyunsaturated fat, and trans fat per serving.

What is the advantage of the Free + Total dual-panel approach?

The dual-panel approach provides two complementary datasets from one sample submission. The free FA panel tells you what is biologically active right now — the fatty acids available for β-oxidation, signaling, and eicosanoid synthesis. The total FA panel tells you the overall fatty acid composition of the sample — reflecting diet, membrane remodeling, and long-term metabolic status. Together, the free/total ratio for individual fatty acids (e.g., free arachidonic acid / total arachidonic acid) reveals enzyme activities like phospholipase A2 that would be invisible from either panel alone.

Do you analyze branched-chain and odd-chain fatty acids?

Yes. Our GC-MS method quantifies odd-chain fatty acids (C15:0, C17:0, C19:0, C21:0, C23:0) — which serve as biomarkers of dairy fat intake and are inversely associated with type 2 diabetes risk — and branched-chain fatty acids (iso-C15:0, anteiso-C15:0, iso-C17:0, anteiso-C17:0) which reflect bacterial fermentation in the rumen (for dietary assessment) and gut microbial metabolism. These are included in both our total FA panel and, for the longer-chain species, our free FA panel.

Prospective Association of Fatty Acids in the De Novo Lipogenesis Pathway with Risk of Type 2 Diabetes: The Cardiovascular Health Study

Ma, W., Wu, J. H. Y., Wang, Q., et al.

Journal: The American Journal of Clinical Nutrition, 2015, 101(1), 153–163

Prospective cohort (n = 4,904; 577 incident T2D cases over 13 years). Palmitic acid (C16:0) was the strongest FA predictor of incident diabetes (HR = 2.10). DNL fatty acid score associated with 50% higher T2D risk. Demonstrates the predictive value of per-analyte free fatty acid quantification.

Applications of Ion-Mobility Mass Spectrometry for Lipid Analysis

Paglia, G., Kliman, M., Claude, E., Geromanos, S., and Astarita, G.

Journal: Journal of Lipid Research, 2015, 56(7), 1305–1319

Review of ion-mobility MS for lipid analysis including fatty acids. Covers CCS database development, IM-MS for isomer separation, and integration with LC-MS lipidomics workflows.

Fatty Acidomics: Global Analysis of Non-Esterified Fatty Acids in Biological Samples

Quehenberger, O., and Dennis, E. A.

Journal: Progress in Lipid Research, 2016, 61, 80–91

Comprehensive review of free fatty acid analysis methods. Emphasizes the importance of underivatized LC-MS/MS for free FA quantification to avoid derivative-related artifacts and the need for isotopically labeled internal standards for accurate absolute quantification.

Fatty Acid Biomarkers of Dairy Fat Consumption and Incidence of Type 2 Diabetes: A Pooled Analysis of Prospective Cohort Studies

Imamura, F., Fretts, A., Marklund, M., et al.

Journal: Advances in Nutrition, 2018, 9(1), 41–50

Pooled analysis of 16 prospective cohorts (n = 63,682). Odd-chain fatty acids (C15:0, C17:0) as biomarkers of dairy fat intake were inversely associated with T2D incidence. Demonstrates the diagnostic value of minor fatty acid species beyond the standard panel.

Gas Chromatography-Mass Spectrometry of Fatty Acids: A Review of Current Practices

Chiu, H. H., and Kuo, C. H.

Journal: Journal of Chromatography A, 2017, 1506, 1–17

Comprehensive review of GC-MS methods for fatty acid analysis. Covers FAME derivatization strategies, column selection for isomer resolution (100 m polar columns for cis/trans and positional isomers), and response factor correction with certified reference mixtures.

The Role of Short-Chain Fatty Acids in the Interplay Between Diet, Gut Microbiota, and Host Energy Metabolism

Canfora, E. E., Jocken, J. W., and Blaak, E. E.

Journal: Nature Reviews Gastroenterology and Hepatology, 2017, 14(10), 577–591

Review of SCFA biology and analytical methodology. Establishes acetate, propionate, and butyrate as the three principal gut microbial SCFAs and discusses their roles in GPR41/43 signaling, HDAC inhibition, and host energy metabolism.