Monitoring composition and flavor quality of Cheddar cheese during ripening using a rapid spectroscopic method
MetadataShow full item record
Series/Report no.:2008 Edward F. Hayes Graduate Research Forum. 22nd
About 9.13 billion pounds of cheese is produced in the US every year, of which 34% is cheddar cheese. Cheddar cheese composition and flavor quality, which influence the consumer acceptance, price and food processing application, develop during the ripening process. However, ripening is not well understood due to complexity of the process and heterogeneous nature of cheese. Rapid monitoring of composition and flavor quality of cheese and understanding cheese ripening hold many advantages for the cheese-maker. Cheese composition and quality are currently determined using chromatographic techniques and trained human expert sensory panels, which are expensive and time consuming. Rapid methods capable of simultaneous monitoring of multiple components can save time and money. Fourier transform Infrared (FT-IR) spectroscopy, which monitors the light absorbing properties of chemical compounds, can be used as a rapid, inexpensive, and sensitive method to analyze cheese quality. Unlike many chromatographic techniques, FT-IR spectroscopy provides unique overall chemical fingerprints of cheese samples that can be analyzed through multivariate statistical techniques to rapidly determine cheese composition and flavor quality. Hence the objective of this research was to develop a rapid method based on FT-IR spectroscopy to monitor composition and flavor quality of Cheddar cheese during ripening. Twelve different Cheddar cheese samples ripened for a period of 73 days were provided by a commercial cheese manufacturer, along with their final moisture, pH, salt, fat content and flavor quality data. Samples were collected on days 7, 15, 30, 45 and 73 during ripening and analyzed for organic acid content using chromatographic techniques (reference method). For FT-IR analysis the samples were treated using organic solvents and the extracts were dried on zinc selenide crystal and scanned in the spectrometer (4000-700 wavenumbers). Infrared profiles (spectra) of the samples were matched with their composition and quality data to develop multivariate statistical regression and classification models. The infrared spectra of the samples were well defined, highly consistent within each sample and distinct from other samples. The regression models showed excellent fit (r-value>0.95) and could determine moisture, pH, salt, fat, organic acid contents in less than 20 min, which is significantly less than the current methods. Furthermore, cheeses could also be classified based on their flavor quality (sour, whey taint, good cheddar, etc.). The discrimination of the samples was due to organic acids, amino acids and short chain fatty acids (1800 to 900 cm-1), which are known to contribute significantly to cheese flavor. FT-IR spectroscopy based method shows great promise as a rapid, simple and cost-effective analytical and quality control tool for the industry. It will enable monitoring and controlling cheese ripening process to produce cheese of desired quality. For the cheese industry, this can be an extremely valuable tool as it will enable identification of quality defects early in the ripening process. The identification of defects will assist in deciding whether the cheese is marketable prior to incurring storage and interest charges associated with aging as well as deciding the future application of the cheese.
FAES and Human Ecology: 3rd Place (The Ohio State University Edward F. Hayes Graduate Research Forum)
A five-year embargo was granted for this item.
Items in Knowledge Bank are protected by copyright, with all rights reserved, unless otherwise indicated.