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Cheese Testing by Isotope Ratio Mass Spectroscopy Report

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Updated: May 11th, 2021


Food fraud is a pervasive malpractice in the food industry. Isotopic composition analysis is used to identify cases of food fraud by quantifying the stable isotopic ratio of elements such as carbon, nitrogen, hydrogen, oxygen and sulphur in food. This experiment aimed at finding differences in isotopic percentages of 15nitrogen, 13carbon and 2hydrogen in six pre-prepared cheese types using isotope ratio mass spectroscopy (IRMS).

The analysis was conducted using IRMS coupled with an elemental analyser. The lowest δ15N (‰) of 4.74 was recorded in Italian cheese, whereas cheese from Ireland and England had the highest δ15N (‰) values of 7.2. Cheese from goat and cow milk had different isotopic compositions even though they originated from the same place. The δ13C was between -22 and -28 whereas δ2H ranged from -76 to -120 for all the samples. Post-analysis chemometrics was not done, which limited the ability to group the cheese samples into various geographical regions, geological settings and agricultural systems.


Food fraud involves the deliberate altering, substitution or contamination of food products within the production, processing and marketing chain. Presenting wrong information regarding the actual contents of food on its label also constitutes food fraud. Additionally, claiming that a commodity comes from a specific geographical region that is renowned for producing high quality items can be considered food fraud.

Financial gain is the main motivation for food scams where unscrupulous businessmen engage in the practice to lower the production costs or increase the apparent value of food (Camin et al. 2017). Passing off substandard food commodities as high quality has moral and ethical implications. However, interfering with the composition of food may pose additional health risks to consumers. This form of deception holds back market growth by damaging consumers’ trust in food commodities.

The prevalence of food fraud has prompted regulatory bodies to conduct certain tests to ascertain farmers’ and manufacturers’ claims about food commodities. Methods used to fight food fraud fall into various categories such as chromatographic, DNA-based, spectroscopy and isotope fingerprints. Chromatographic techniques include gas chromatography and liquid chromatography, whereas spectroscopic methods encompass Raman infrared, vibrational, Fourier-transform infrared and nuclear magnetic resonance (Hong et al. 2017).

Some of the techniques that use DNA include, DNA hybridization, polymerase chain reaction, Randomly Amplified Polymorphic DNA (RAPD), DNA barcoding, Sequence Characterized Amplified Region (SCAR) techniques and many others. Chemometric procedures are often conducted to increase the robustness of these analytical techniques and facilitate the grouping of different samples based on predefined parameters.

Isotope ratio mass spectrometry (IRMS) is a commonly used isotope and elemental procedure in food analysis. This method facilitates the accurate and precise measurement of isotopic ratios in food items (Manca et al. 2006). Data generated from this analysis can be used to authenticate food items and ascertain its geographical origin to counteract food fraud (Luo et al. 2015). Numerous studies to combat food fraud in the dairy industry have used IRMS successfully (Capici et al. 2015).

The basis of isotopic analysis in food science is the fact that what a living organism consumes is reflected in their physical, chemical and biological features, which generate a unique isotopic signature that is referred to as Terroir. The purpose of this experiment was to apply IRMS in the discrimination of different cheese samples based on isotopic disparities. Carbon dioxide and nitrogen concentrations were used to measure the ratios of 13carbon and 15nitrogen ratios in various cheese samples.


The experiment used six cheese samples from various geographical regions in Europe, including Dunmanus, Co. Cork (Ireland), Parmigiano Reggiano (Italy), Kidderton Ash Goats Cheese (England), Somerset Brie (England), Gruyère (Switzerland) and Ballyblue (Northern Ireland). These samples were pre-prepared in IGFS before bringing them to the lab. However, a defatting step was needed before subjecting the samples to IRMS analysis. This process was done as described by Camin et al. (2012).

Frozen cheese was grated into fine pieces and allowed to freeze-dry overnight. Three different extractions were done on approximately 4g of the dried sample in a 50 ml centrifuge tube using 30 ml of a mixture of petrol ether and diethyl ether in the ratio of 2:1. The pellets following each centrifugation step were washed two times using 30 ml of MilliQ water. The first and second extractions were done by centrifuging the cheese solvent mixture at 13.500 rpm while the third extraction involved vortexing the two substances at 2.500 rpm for 3 minutes. At each step, the pellet contained casein, which was finally recovered by centrifuging at 4.100 rpm for 5 minutes. This deposit was lyophilized, ground by hand and kept at room temperature prior to the analysis.

In the IRMS analysis, the first step involved combustion to ascertain the isotopic number. Approximately 0.5g of each cheese sample was weighed and put in a capsule. A flash elemental analyser was used to determine the proportion of carbon, hydrogen and nitrogen (in percentages). The other equipment that were used were a TC-EA to analyse water and GC-C IRMS- CSIA. Separation in the chromatographic columns was achieved at two temperatures of 1020oC and 650oC though other procedures were conducted at 41oC.

The machines were calibrated using positive and negative standards. However, helium gas acted as a negative control due to its own distinct isotopic signature. To ensure the accuracy of the experimental procedure, high standards of cleanliness were maintained by cleaning all equipment to avoid contamination by extraneous sources of carbon and nitrogen. The following equation was used to compute isotope values that were then tabulated in the results section.


In the above equation, R stands for the ratio between heavy and light isotopes.


The readings of the standards were plotted into a two-point carbon calibration curve and a three-point nitrogen calibration curve as indicated in Figures 1 and 2. These linear curves were then used to determine the δ13C (‰) and δ15N (‰) by substituting into the equations appropriately to produce the values of corrected δ15N, δ13C and δ2H as shown in Table 1. The key software used to produce the plots and summary table was Microsoft Office Excel.

The data were not subjected to further chemometric analysis. However, instrument errors were factored into the readings by making adjustments using the means. The data showed that Parmigiano Reggiano cheese from Italy had the lowest δ15N (4.74) followed by Gruyère cheese from Switzerland with δ15N of 5.78. The highest δ15N values of 7.2 and 7.22 were observed in cheese from Ireland. A distinct observation was variations of δ15N values (6.8 and 7.22) between Kidderton Ash Goats Cheese and Somerset Brie from England. The δ13C and δ2H values differed from the δ15N values in the cheese types.

Carbon 2-point calibration curve.
Figure 1: Carbon 2-point calibration curve.
Nitrogen 3-point calibration curve.
Figure 2: Nitrogen 3-point calibration curve.
Adjusted δ15N (‰), δ13C (‰) and δ2H (‰)
Table 1: Adjusted δ15N (‰), δ13C (‰) and δ2H (‰) readings for the 6 cheese samples from different geographical regions.


Geographical, soil, climatic, botanical, geological and farming factors play a role in the stable isotope ratios (SIR) of biological samples. Changes in SIR are usually assimilated into animal tissue in the course of drinking, eating, inhalation and environmental interactions. SIR assessment can be used to ascertain the geographical source, animal food and production systems by relating the observed SIRs with the physical processes that influence the composition of isotopes (Valenti et al. 2017). Combining SIR and elemental data is suggested to be a more reliable way of distinguishing different types of cheese from Italian Parmigiano Reggiano cheese than SIR analysis alone (Bontempo et al. 2012; Camin et al. 2012).

Agricultural practices, which goes hand in hand with animal diet, influence the δ13C and δ15 N values of plants and corresponding animal products such as milk, meat and cheese. For instance, animals that feed mainly on C4 plants such as maize and other grasses tend to have high δ13C values (Camin et al. 2004). In this study, the highest δ13C value of -27.61 was observed in Dunmanus, Co. cheese from Cork, Ireland, suggesting that maize was the main feed in this region.

In contrast, the δ15N of casein in dairy commodities is usually a reflection of the isotopic constituents of soil, which is in turn affected by aspects such as farming practices, climatic (precipitation) and geographical conditions (Camin et al. 2016). The extensive use of organic fertilizers, enhanced salinity, aridity and proximity to water bodies such as seas usually elevate this isotopic ratio in the soil, plants and subsequent animal commodities (Stevenson, Desrochers & Hélie 2015).

In this study, cheese from Northern Ireland and England had the highest δ15N values, thereby suggesting that organic farming was practiced in those areas. Furthermore, Northern Ireland is surrounded by three large water bodies: the Irish Sea from the east, the Atlantic Ocean from the west and the Celtic Sea to the south. This observation supports the hypothesis that closeness to water bodies affects the δ15N of soil and the ensuing food items.

The δ15C (‰) values ranged from -22 to -27, which corresponded to δ15C values that are normally observed in pure casein. Casein from cheese usually has δ15C of about -18 (Camin et al. 2004). This observation suggested that the cheese samples were enriched with pure casein. Camin et al. (2016) reported that milk produced through organic farming ought to have δ13C and δ15N of -26.5 ‰ and +5.5 ‰, respectively. Of the experimental samples, cheese from Switzerland and Italy met these conditions.

The literature values for δ15N in cheese from Italy and Northern Ireland is 4.5 and 7.3, whereas the experimental values were 4.74 and 7.22, in that order. Therefore, the difference between the experimental and accepted nitrogen values was not statistically significant, which partly confirmed the reliability of the assay. Geology affects the δ34S of casein in dairy products such as cheese (Camin et al. 2016). However, this parameter was not measured in the experiment.


The key objective of this study was to use IRMS to differentiate six cheese samples according to their isotopic proportions of 13carbon, 15nitrogen and 2hydrogen. The IRMS analysis yielded varying values of δ15N, δ13C and δ2H. For the δ13N parameter, Italian cheese had the lowest value of 4.74, which did not differ significantly from the literature value of 4.5. The highest δ15N values of 7.2 and 7.22 were from Ireland cheese.

It was not possible to use the δ15N, δ13C and δ2H values to separate the cheese samples according to strictures such as climate, geography, soil type, geology and farming practice due to the lack of chemometric analysis. Future investigations should consider using IRMS alongside chemometrics to derive more meaning from the isotopic composition data.

Reference List

Bontempo, L, Lombardi, G, Paoletti, R, Ziller, L & Camin, F 2012, ‘H, C, N and O stable isotope characteristics of alpine forage, milk and cheese’, International Dairy Journal, vol. 23, no. 2, pp. 99-104.

Camin, F, Boner, M, Bontempo, L, Fauhl-Hassek, C, Kelly, S, Riedl, J & Rossmann, A 2017, ‘Stable isotope techniques for verifying the declared geographical origin of food in legal cases’, Trends in Food Science & Technology, vol. 61, pp. 176-187.

Camin, F, Bontempo, L, Perini, M & Piasentier, E 2016, ‘Stable isotope ratio analysis for assessing the authenticity of food of animal origin’, Comprehensive Reviews in Food Science and Food Safety, vol. 15, no. 5, pp. 868-877.

Camin, F, Wehrens, R, Bertoldi, D, Bontempo, L, Ziller, L, Perini, M, Nicolini, G, Nocetti, M & Larcher, R 2012, ‘H, C, N and S stable isotopes and mineral profiles to objectively guarantee the authenticity of grated hard cheeses’, Analytica Chimica Acta, vol. 711, pp. 54-59.

Camin, F, Wietzerbin, K, Cortes, A, Haberhauer, G, Lees, M & Versini, G 2004, ‘Application of multielement stable isotope ratio analysis to the characterization of French, Italian, and Spanish Cheeses’, Journal of Agricultural and Food Chemistry, vol. 52, no. 21, pp. 6592-6601.

Capici, C, Mimmo, T, Kerschbaumer, L, Cesco, S & Scampicchio, M 2015, ‘Determination of cheese authenticity by carbon and nitrogen isotope analysis: Stelvio cheese as a case study’, Food Analytical Methods, vol. 8, no. 8, pp. 2157-2162.

Hong, E, Lee, SY, Jeong, JY, Park, JM, Kim, BH, Kwon, K & Chun, HS 2017, ‘Modern analytical methods for the detection of food fraud and adulteration by food category’, Journal of the Science of Food and Agriculture, vol. 97, no. 12, pp. 3877-3896.

Luo, D, Dong, H, Luo, H, Xian, Y, Guo, X & Wu, Y 2015, ‘Multi-element (C, N, H, O) stable isotope ratio analysis for determining the geographical origin of pure milk from different regions’, Food Analytical Methods, vol. 9, no. 2, pp. 437-442.

Manca, G, Franco, MA, Versini, G, Camin, F, Tola, A 2006, ‘Correlation between multielement stable isotope ratio and geographical origin in Peretta cows’ milk cheese’, Journal of Dairy Science, vol. 89, no. 3, pp. 831-839.

Stevenson, R, Desrochers, S & Hélie, J 2015, ‘Stable and radiogenic isotopes as indicators of agri-food provenance: insights from artisanal cheeses from Quebec, Canada’, International Dairy Journal, vol. 49, pp. 37-45.

Valenti, B, Biondi, L, Campidonico, L, Bontempo, L, Luciano, G, Di Paola, F, Copani, V, Ziller, L & Camin, F 2017, ‘Changes in stable isotope ratios in PDO cheese related to the area of production and green forage availability. The case study of Pecorino Siciliano’, Rapid Communications in Mass Spectrometry, vol. 31, no. 9, pp. 737-744.

van Ruth, SM, Huisman, W & Luning, PA 2017, ‘Food fraud vulnerability and its key factors’, Trends in Food Science & Technology, vol. 67, pp. 70-75.

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