SIFT-MS Application Note: Rapid Parmesan Classification using Automated Static Headspace-SIFT-MS

Anatune

17th March 2022

Automated Analysis, Automated Dynamic Headspace, Food Analysis, odour analysis, parmesan,

Rapid Parmesan Classification using Automated Static Headspace-SIFT-MS Part One

Authors: Vaughn S Langford (Principal Scientist, Syft Technologies), Diandree Padayachee (Product Manager, Syft Technologies), Mark J Perkins (Senior Applications Chemist, Anatune)

Automated static headspace analysis using selected ion flow tube mass spectrometry (SIFT- MS) provides rapid and economic screening of food products and ingredients. This application note describes how SIFT-MS coupled with multivariate statistical analysis rapidly classifies genuine Italian Parmesan cheeses by product and manufacturer via the most important odour- impact compounds.

Selected ion flow tube mass spectrometry (SIFT-MS) has been shown previously to readily discriminate genuine Italian Parmesan cheeses from imitation New Zealand variants, including when narrowing the target compound list to the most significant odour-active species identified by Qian and Reineccius.

This application note revisits Parmesan cheese analysis using SIFT-MS, but applies the more recent automated headspace variant. Since our previous study demonstrated that foreign imitations were poor, in this study the ability of SIFT-MS to differentiate Parmesan products from three Italian manufacturers by targeting the odour-active compounds is examined instead.

Automated headspace-SIFT-MS analysis determines factory of origin at throughputs of 12 samples per hour, offering great potential for rapid product screening.

Part One Conclusions

This study demonstrates that automated SIFT-MS coupled with multivariate statistical analysis can rapidly analyse and classify genuine Parmesan cheese products based on the most significant odour-active volatiles. There is also potential for classification of products according to the manufacturer.

The combined instrumental and statistical approach utilised here facilitates enhanced origin and quality control screening of Parmesan.

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Automated Static Headspace SIFT-MS Parmesan Classification Part One

parmesan, cheese, automated, static, headspace, analysis

Rapid Parmesan Classification using Automated Static Headspace-SIFT-MS Part Two

Authors: Vaughn S Langford (Principal Scientist, Syft Technologies), Diandree Padayachee (Product Manager, Syft Technologies), Mark J Perkins (Senior Applications Chemist, Anatune)

Automated, direct headspace analysis using selected ion flow tube mass spectrometry (SIFT- MS) provides rapid and economic screening of food products, ingredients, and packaging materials.

This application note describes how SIFT-MS coupled with multivariate statistical analysis rapidly classifies genuine Italian Parmesan cheeses by product and manufacturer via an untargeted “fingerprinting” approach (i.e. utilising SIFT-MS SCAN mode). Automated headspace- SIFT-MS analysis determines factory of origin at throughputs of 12 samples per hour, offering great potential for rapid product screening.

Selected ion flow tube mass spectrometry (SIFT-MS) has been shown previously to readily discriminate genuine Italian Parmesan cheeses from imitation New Zealand variants. Recently it was demonstrated that the same methodology is readily transferred to automated SIFT-MS, with successful classification of six Italian Parmesans via the odour-active volatiles. The present application note utilises the same Parmesan cheese data set, but applies a fingerprinting approach based on full-scan SIFT-MS analysis coupled with multivariate statistical analysis.

Previous work on argan and olive oils, strawberry flavour mixes, and beer have demonstrated the efficacy of this approach using positively charged reagent ions. Here, in addition to positively charged reagent ions we examine the utility of negatively charged reagent ions for classification of the six Parmesan products (by individual products and manufacturer) using untargeted analysis.

Part Two Conclusions

This study demonstrates that automated SIFT-MS analysis, using an untargeted approach coupled with multivariate statistical analysis, can rapidly analyse and classify genuine Parmesan cheese products both individually and by manufacturer. Positively charged reagent ions perform better than negatively charged reagent ions for Parmesan, with NO+ being preferred overall because it alone classifies each manufacturer completely.

The combined instrumental and statistical approach utilised here facilitates enhanced origin and quality control screening of Parmesan cheese with throughputs of 12 samples/hr achievable using currently available automation technology.