Post on 10-Feb-2022
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Cheddar Facts
Country of origin: Somerset, England
Made from cow’s milk (whole milk)
Orange Cheddars are coloured with annatto
(natural dye)
As Cheddar aged moisture is lost
becomes dry &
crumbly
As Cheddar aged, taste and flavours develop
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Analytical Tools Used
Method Pros Cons
GC-MS i. Able to identify
compounds with library
software
i. Expensive
E-nose i. Non-destructive to
samples
ii. Real-time Analysis
iii. Efficient, informative
i. Expensive
ii. No identification of
compounds
Sensory Panel i. Interactive
ii. Can be trained
i. Expensive
ii. Time-consuming
iii. Not done at real time
pH, Texture
analysis
i. Low cost
ii. Simple to execute
i. Lack information
Flavour Generation Pathways
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Casein
Lactose
Triglycerides
Amino Acids
Lactic Acids
Fatty Acids
Citrate
Acetate Diacetyl
Proteolysis by
Chymosin, Plasmin
Lipolysis by Lipases
Metabolism
Fermentation by
LAB
Aims
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1. Evaluate GC-MS and direct injection APCI-MS for the ability to identify & characterise
aroma volatiles of Cheddar Cheese
2. Predict the age of Cheddar Cheese using proposed PLS models
Analysis of Aroma by HS-SPME GC-MS
Samples preparation: Five commercial Cheddar cheese
brands (V, W, X, Y, Z) - Mild (MI), Medium (ME), Mature (M),
Extra Mature (EM) & Vintage (V) were grated
(i) GC-MS: SPME StableFlex fibre (50/30 μm
DVB/CAR/PDMS)
ZB-Wax capillary column
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(ii) APCI-MS: Scanning at FULL SCAN mode (m/z: 40-200) for
Cheddar Cheese volatiles
Static headspace
Analysis of Aroma by APCI-MS
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Statistical Analysis
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i. Principal Component Analysis (PCA)
ii. Partial Least Square Regression (PLS-1) with full cross
validation
Cheddar Cheese Aroma Profile
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Table1. Volatile compounds in Cheddar identified in the
headspace using SPME-GC-MS
Aroma Volatiles
(MW)
Description Aroma Volatiles (MW) Description
Acetonitrile (41) Solvent-like Heptanal (114) Fatty, oily
Acetic acid (60) Vinegar Hexanoic acid (116) Cheese, fatty
Diacetyl (86) Buttery Octanal (128) Fatty, citrus
2-methyl-2-buten-1-
ol (86)
Green, fruity Octanoic acid (144) Cheese, oily
Butyric acid (88) Rancid cheese 2-decenal (154) Fatty, green
Acetoin (88) Butter, creamy δ-nonalactone (156) Butter, meaty
Methional (104) Meaty, creamy 2-undecanone (170) Citrus, fruity
2-heptanone (114) Banana, spicy n-decanoic acid (172) Fatty, citrus
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Figure 1. PCA on the data obtained by GC-MS and APCI-MS headspace
analysis of the 42 grated Cheddar cheeses.
Correlation of Maturity with Headspace
Maturity
V
EM
M
ME
MI
(28%)
(7%)
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Maturity
Changes in Aroma with Maturity
V
EM / V MI / ME
M
Figure 2. PCA biplot on the obtained by headspace analysis of the
grated Cheddar cheeses .
Acids
Ketones
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Figure 3. PLS-DA loadings for the first two factors of the classification
models based on headspace data
Key Aromas Driving Maturity in Cheddar
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Figure 4. Age of Cheddar cheeses indicated by the manufacturer
(labeled as ‘Actual’) vs predictive values from model (labeled as
‘Predicted’)
Predicted vs Actual Cheddar Age
R² = 0.85
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Pre
dic
ted
Ag
e (
mo
nth
s)
Actual Age (months)
Conclusion
• GC-MS and APCI-MS headspace analysis were efficient
techniques for determining aroma compounds relevant to
Cheddar Cheese
• Cheddar cheese maturity could be predicted using APCI-
MS and GC-MS headspace analysis combined with
chemometric data pre-treatment
• PLS models were able to predict Cheddar cheeses
maturity (R2 = 0.85)
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References
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• Fox, P.F., ed Cheese: Chemistry, Physics and Microbiology. 2004. 3rd Ed.
Elsevier Academic Press, Amsterdam.
• Law, B.A. ed. Microbiology and Biochemistry of Cheese and Fermented Milks.
1997. 2nd Ed. Blackie Academic and Professional, London.
• McSweeney, P.L.H., Sousa, M.J. (2000). Biochemical pathways for the
production of flavour compounds in cheeses during ripening: A review. Lait 80,
293-324
• Biasioli, F., Gasperi, F., Aprea, E., Endrizzi, I., Framondino, V., Marini, F.,
Mott, D., & Mark, T. D. (2006). Correlation of PTR-MS spectral fingerprints
with sensory characterisation of flavour and odour profile of 'Trentingrana'
cheese. Food Quality and Preference, 17, 63-75
• Curionia, P. M. G., & Bosset, J. O. (2002). Key odorants in various cheese
types as determined by GCO.pdf. International Dairy Journal, 12, 959-
984.Fatma A. M. Hassan, Mona A M*. Abd El- Gawad, A. K. Enab. 2003. Flavour
Compounds in Cheese. Research on Precision Instrument and Machinery, 2
(2),15-29
• Whetstine, M. E. C., Drake, M. A., Nelson, B. K., & Barbano, D. M. (2006).
Flavour Profiles of Full-Fat and Reduced-Fat Cheese and Cheese Fat Made
from Aged Cheddar with the Fat Removed Using a Novel Process. Journal of
Dairy Science, 89, 505-517.
Acknowledgements
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• Bingnan Yan (Msc student, Division of Food Sciences,
The University of Nottingham)
• Fisk Ian, (Associate Prof, Division of Food Sciences,
The University of Nottingham)
HEADSPACE TECHNIQUES HAVE BEEN EXTENSIVELY EMPLOYED IN FOOD ANALYSIS TO MEASURE VOLATILE COMPOUNDS, WHICH PLAY A CENTRAL ROLE IN THE PERCEIVED QUALITY OF FOOD. IN THIS RESEARCH ATMOSPHERIC PRESSURE CHEMICAL IONISATION-MASS SPECTROMETRY (APCI-MS), COUPLED WITH GC-MS, WAS USED TO INVESTIGATE THE COMPLEX MIX OF VOLATILE COMPOUNDS PRESENT IN CHEDDAR CHEESE OF DIFFERENT YEAST STRAINS, PROCESSING AND RECIPES TO ENABLE CHARACTERIZATION OF THE CHEESES. PARTIAL LEAST SQUARE-LINEAR DISCRIMINANT ANALYSIS (PLS-LDA) PROVIDED A 70% SUCCESS RATE IN CORRECT CLASSIFICATION OF THE CHEESE VARIETIES BASED ON HEADSPACE VOLATILE PROFILES. THE ANALYTICAL RESULTS COUPLED WITH SENSORY EVALUATION OFFERED A MORE RAPID AND DETAILED PROFILING OF THE CHEESES, WHICH COULD ADD VALUE TO FURTHER PRODUCT DEVELOPMENT RESEARCH WORK.
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