High-Throughput, Automated Food-Flavor Analysis Using SIFT-MS

Detection of volatile organic compounds (VOCs) is vitally important in the food and flavor industry for several reasons. For example, VOCs can function individually as markers of food quality and freshness, and together as an overall volatile “fingerprint” that can enable determination of product origin or differentiate between commercial products. Furthermore, these VOCs can play a major part in aroma sensory experience.

Analysis of aroma compounds in both quality assurance and new product development applications benefits significantly from analysis of larger sample numbers to support effective decision-making. However, conventional analytical techniques used for VOC analysis—most notably gas chromatography-mass spectrometry (GC-MS)—are poorly equipped to address changing industry needs. Factors that preclude high-throughput and real-time analysis using GC-MS include (i) the chromatographic separation itself, which results in extended analysis times; (ii) discrimination of the chromatographic column against certain chemical functionalities, which can make sensory correlated analysis difficult; (iii) preconcentration to detect important odor-active compounds; and (iv) moisture removal prior to analysis.

Selected ion flow tube mass spectrometry (SIFT-MS) can overcome these limitations, by providing robust high-throughput and high-time-resolution VOC analysis with simplicity and flexibility.^1,2 This article provides a brief review of the application of automated SIFT-MS instruments to high-throughput product quality and sensory-correlated analysis, origin, varietal, and authentication screening, and product identification and matching. For a more comprehensive review of food-flavor applications of SIFT-MS, see Langford and associates’ paper on the topic.³

SIFT-MS, Its Automation, and Data Acquisition

The SIFT-MS technique has been described in detail elsewhere,^2,4 and its diverse applications in gas analysis have been reviewed recently.¹ Briefly, SIFT-MS uses soft chemical ionization (CI) in the form of highly controlled ion-molecule reactions (IMRs) to quantify a very wide range of VOCs and certain inorganic gases directly in the gas phase with high sensitivity. Up to eight standard ions (so-called reagent ions⁵) are available on modern commercial instruments (Syft Technologies), providing both broad-spectrum and high-specificity real-time analysis because they have multiple ionization mechanisms and are very rapidly switched.^2,5

Automation of SIFT-MS Instruments

Automated-SIFT-MS analysis is readily achieved using “xyz” robotic autosamplers (for example, those manufactured by CTC Analytics and Gerstel) that utilize syringe-based sample injection.⁶ Various headspace approaches are compatible with automated SIFT-MS instruments, including static headspace analysis (SHA), the method of standard additions (MoSA), and multiple headspace extraction (MHE). Advanced software enables efficient workflows to be achieved through optimized sample scheduling.⁶

Targeted and Untargeted Approaches

SIFT-MS can be utilized for both targeted (selected ion monitoring [SIM]) and untargeted (full scan) analysis, each with distinct benefits. SIM mode enables direct, quantitative analysis of targeted aroma compounds, whereas full-scan mode provides a broader view of the sample and can be used to detect volatiles that are outside a target compound suite—albeit with a modest reduction in sensitivity and response time. Using full-scan mode with multivariate statistical analysis enables mass spectral “fingerprinting” to be conducted, supporting quality control workflows and other functions.⁷

Multivariate Statistical Analysis of SIFT-MS Data

Many food products have a complex VOC profile. To simplify differentiation of samples with varying characteristics and accommodate correlation with human sensory perception, multivariate statistical analysis is often applied to SIFT-MS data. Soft independent modeling by class analogy (SIMCA),⁸ an enhanced form of principal component analysis (PCA), has been most widely utilized.³ The SIMCA interclass distance metric is used to assess the ability of SIFT-MS to distinguish between pre-defined sample classes and should have a value greater than three.⁹

Product Quality and Sensory-Correlated Analysis

Product quality, which impacts food safety and sensory experience in terms of volatiles, is a very important consideration for food manufacturers. Human sensory analysis benchmarks consumer acceptance of food and fragrance products, but it is expensive, panelists are subject to fatigue, and it has been very difficult to conduct on the process line. With sensitive, broad-spectrum, and rapid analysis, SIFT-MS is an effective technique for both real-time and high-throughput quality analysis.

Depending on the complexity of the aroma, there are several possible approaches for instrument-based assessment of product quality. Three examples are provided here.

Edible Oil Oxidation

First, for systems with a relatively simple headspace, quantitation of quality markers versus simple concentration thresholds is a straightforward approach. For example, determination of the extent of edible oil oxidation can be achieved by targeting VOCs—primarily saturated and unsaturated aldehydes that are indicative of lipid oxidation.¹⁰ Analysis of each sample takes less than 1 minute, supporting high-throughput screening—whether of ingredients or finished products. Furthermore, the high sensitivity of SIFT-MS analysis enables oxidation products to be detected early.

Parmesan Cheese

SIFT-MS can also be used to target dominant odor-active volatiles (OAVs) in a food product. This approach was first utilized with SIFT-MS in a 2012 study comparing four genuine Italian and four imitation New Zealand Parmesan cheeses.¹¹ Discrimination of the individual products, in addition to more general origin determination, was achieved based on OAVs. Subsequent work using automated headspace analysis¹² demonstrated improved repeatability compared to the earlier manual headspace work. Retail products from several Italian manufacturers were differentiated (Figure 1), since all interclass distances were greater than three.

Beef Aroma Quality

The most comprehensive approach to correlating SIFT-MS measurements with those obtained by a human sensory panel involves combining data from both sources using multivariate statistical analysis. This approach enables the creation of a model that correlates the SIFT-MS data with the odor descriptor assigned by the panelists. The model can then be utilized to predict sensory panel results based on SIFT-MS measurements.

Figure 2 shows SIFT-MS concentration data for beef samples that have prime and defective (eight) beef aromas, as judged by a trained sensory panel¹³. Combining sensory data with measurements from SIFT-MS, multivariate statistical analysis using SIMCA enables differentiation of all sensory classes instrumentally. Therefore, SIFT-MS can rapidly grade beef aroma quality from sample VOC profiles, providing an objective, rapid sensory test that enables many more samples to be graded per day than a traditional sensory panel.

Origin, Varietal, and Authenticity Determination

For certain food products or ingredients, volatile compound profiles can be used to screen for origin and/or variety.

Coffee Bean Origin

Coffee beans have distinct regional aroma variations arising from different relative concentrations of volatiles. SIFT-MS has potential to be utilized for confirmation of origin both for single-origin coffee products and for quality assessment of blends.

Green coffee beans of five origins—Brazil, Colombia, Ethiopia, Guatemala, and Sumatra—were analyzed using automated headspace-SIFT-MS.¹⁴ Multivariate statistical analysis using the SIMCA algorithm yielded the interclass distances shown in Table 1, indicating that all coffee origins can be distinguished. Dryahina and associates¹⁵ have also demonstrated that SIFT-MS combined with PCA can classify by origin after roasting.

Vanilla Extract Origin

Vanilla extracts from India, Indonesia, Madagascar, Papua New Guinea, and Uganda, supplied by a well-known United States ingredient brand, were distinguished using SIFT-MS headspace analysis and SIMCA multivariate statistical analysis.¹⁶ Vanillin, anise alcohol, and 4-methylguaiacol were the three most significant volatiles contributing to differentiation of the extracts.

High-Value Natural Edible and Cosmetic Oils

High-value oils, such as Argan oil and olive oil, are vulnerable to fraudulent activities, including falsified origin labeling and adulteration. As a result, assuring their integrity is important to genuine producers.

Vercammen and co-workers pioneered both untargeted SIFT-MS food analysis in the literature and demonstrated that the approach could be utilized for high-throughput origin authentication of high-value Argan and olive oils.^17,18 Using this approach, they subsequently demonstrated adulteration detection in Argan oil.¹⁹ Ozcan-Sinir²⁰ demonstrated that adulteration of olive oil by commodity edible oils (corn and sunflower) is detectable using SIFT-MS combined with multivariate statistical analysis.

Product Identification and Matching

The sensitive and broad-spectrum aroma compound detection provided by SIFT-MS confers the ability to rapidly identify and match products. This can be achieved using targeted and untargeted approaches. Here, untargeted analysis is illustrated using two examples: beer and strawberry flavor mixes.

Beer

Beer, with typical alcohol levels of 5–7%, is readily analyzed using automated headspace-SIFT-MS following 10-fold dilution in water, coupled with typically 10-fold dilution of headspace in make-up gas during injection into the instrument inlet.⁶ Figure 3 shows the results obtained from applying SIMCA data processing to full-scan SIFT-MS data (H₃O⁺ reagent ion) obtained for 12 beer products.²¹ The interclass distances demonstrate that all products are distinguished using SIFT-MS.

Strawberry Flavor Mixes

A similar analytical approach has been utilized for classification of commercial strawberry flavor mixes for batch-to-batch and inter-mix variations,²² demonstrating potential for flavor matching. SIFT-MS can be utilized to confirm the integrity of products or ingredients, and, by extension, provide rapid assessment of similarity in aroma matching.

Conclusion

The SIFT-MS technique has found broad application in detection and quantitation of diverse aroma volatile compounds since it was introduced in the mid-1990s. This article has used brief case studies to demonstrate that automated SIFT-MS instruments can empower both new product development and accelerate routine testing, including for sensory screening. Applications of automated headspace-SIFT-MS include freshness and quality screening, raw material origin and varietal authentication, and identification and matching of products. Through rapid, sensitive, and comprehensive analysis, automated SIFT-MS instruments provide new opportunities for aroma analysis at low cost per sample.

References

1. Smith, D.; Španěl, P.; Demarais, N. et al. Recent Developments and Applications of SIFT-MS. Mass Spec. Rev. 2025, 44, 101–134. DOI: 10.1002/mas.21835
2. Langford, V.S.; Perkins, M.J. SIFT-MS: From Method Concept to Routine Analysis. Practical and Technical Guides for Laboratory-Based Chemists. RSC Books, 2025. DOI: 10.1039/9781837677917-FP001
3. Langford, V. S.; Padayachee, D.; McEwan, M. J. et al. Comprehensive Odorant Analysis for On‐Line Applications Using Selected Ion Flow Tube Mass Spectrometry (SIFT-MS). Flavour Fragr. J. 2019, 34, 393–410. DOI: 10.1002/ffj.3516
4. Smith, D.; Španěl, P. Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) for On-Line Trace Gas Analysis. Mass Spec. Rev. 2005, 24, 661–700. DOI: 10.1002/mas.20033
5. Langford, V. S.; Dryahina, K.; Španěl, P. Robust Automated SIFT-MS Quantitation of Volatile Compounds in Air Using a Multicomponent Gas Standard. J. Am. Soc. Mass Spectrom. 2023, 34, 2630–2645. DOI: 10.1021/jasms.3c00312
6. Langford, V. S.; Perkins, M. J. Improved Volatiles Analysis Workflows Using Automated Selected Ion Flow Tube Mass Spectrometry (SIFT-MS). Anal. Methods 2024, 16, 8119–8138. DOI: 10.1039/d4ay01707b
7. Langford, V. S.; Perkins, M. J. Rapid Untargeted Screening of Food and Ingredient Aroma Using Direct-Injection Mass Spectrometry. Column 2023, 19 (6), 23-26. www.chromatographyonline.com/view/rapid-untargeted-screening-of-food-and-ingredient-aroma-using-direct-injection-mass-spectrometry
8. Wold, S. Pattern Recognition by Means of Disjoint Principal Components Models. Pattern Recognition 1976, 8, 127-139. DOI: 10.1016/0031-3203(76)90014-5
9. Kvalheim, O. M.; Karstang, T. V. SIMCA — Classification by Means of Disjoint Cross Validated Principal Components Models. In Multivariate Pattern Recognition in Chemometrics, Illustrated by Case Studies. Brereton, R.G., Ed.; Elsevier, 1992; pp 209-238.
10. Syft Technologies. Simple, Instant Detection of Fish Oil Oxidation. Syft Technologies Application Note, 2016. https://bit.ly/4lQJK2B
11. Langford, V. S.; Reed, C.J.; Milligan, D. B.; McEwan, M. J.; Barringer, S.A.; Harper, J. Headspace Analysis of Italian and New Zealand Parmesan Cheeses. J. Food Sci. 2012, 77, C719-C726. DOI: 10.1111/j.1750-3841.2012.02730.x
12. Perkins, M. J.; Padayachee, D.; Langford, V. S. Rapid Parmesan Classification Using Automated Static Headspace-SIFT-MS Analysis. Syft Technologies Application Note. 2021. https://bit.ly/4d7zXAi
13. Langford, V. S.; McEwan, M. J.; Cummings, T. et al. Rapid Classification of Beef Aroma Quality Using SIFT‐MS. Adv. Food Bev. Anal. (Supplement to LCGC North America), 2018, 1, 8-15. https://www.chromatographyonline.com/view/rapid-classification-beef-aroma-quality-using-sift-ms
14. Syft Technologies. Rapid Determination of Coffee Origin. Syft Technologies Application Note, 2017. https://bit.ly/4jJm7HF
15. Dryahina, K.; Smith, D.; Španěl, P. Quantification of Volatile Compounds Released by Roasted Coffee by Selected Ion Flow Tube Mass Spectrometry. Rapid Commun Mass Spectrom. 2018, 32, 739-750. DOI: 10.1002/rcm.8095
16. Sharp, M. D.; Kocaoglu-Vurma, N.A.; Langford, V. et al. Rapid Discrimination and Characterization of Vanilla Bean Extracts by Attenuated Total Reflection Infrared Spectroscopy and Selected Ion Flow Tube Mass Spectrometry. J. Food Sci. 2012, 77, C284-C292. DOI: 10.1111/j.1750-3841.2011.02544.x
17. Kharbach, M.; Kamal, R.; Mansouri, A. M. et al. Selected-Ion Flow-Tube Mass-Spectrometry (SIFT-MS). Fingerprinting Versus Chemical Profiling for Geographic Traceability of Moroccan Argan Oils. Food Chem. 2018, 263, 8-17. DOI: 10.1016/j.foodchem.2018.04.059
18. Bajoub, A.; Medina‐Rodriguez, S.; Ajal, E. A. et al. Metabolic Fingerprinting Approach Based on Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) and Chemometrics: A Reliable Tool for Mediterranean Origin‐Labelled Olive Oils Authentication. Food Res. Int. 2018, 106, 233-242. DOI: 10.1016/j.foodres.2017.12.027
19. Kharbach, M.; Yu, H. W.; Kamal, R. et al. Authentication of Extra Virgin Argan Oil by Selected-Ion Flow-Tube Mass-Spectrometry Fingerprinting and Chemometrics. Food Chem. 2022, 383, 132565. DOI: 10.1016/j.foodchem.2022.132565
20. Ozcan-Sinir, G. Detection of Adulteration in Extra Virgin Olive Oil by Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) and Chemometrics. Food Control 2020, 118, 107433. DOI: 10.1016/j.foodcont.2020.107433
21. Perkins, M. J.; Padayachee, D.; Langford, V. S. Rapid Classification of Beer Using Untargeted SIFT-MS Headspace Analysis. Syft Technologies Application Note. 2021. http://bit.ly/40n3LT4
22. Langford, V. S.; Bell, K. J. M. Rapid Determination of Strawberry Flavour Integrity Using Static Headspace-SIFT-MS. Chromatogr. Today 2019, 12(3), 2-6. https://www.chromatographytoday.com/article/gc-ms/46/syft-technologies-ltd/rapid-determination-of-strawberry-flavour-integrity-using-static-headspace-selected-ion-flow-tube-mass-spectrometry/2558/download

Vaughan Langford is Senior Principal Scientist at Syft Technologies in New Zealand. He joined Syft in late 2002 after completing his Ph.D. in Physical Chemistry at the University of Canterbury, New Zealand, and several post-doctoral fellowships. He has 45 peer-reviewed publications on a wide range of SIFT-MS applications and is co-author of the textbook SIFT-MS: From Method Concept to Routine Analysis, published in 2025. Direct correspondence to: [email protected]