Rapid Prediction of Composition and Flavor Quality of Cheddar Cheese Using ATR-FTIR Spectroscopy

• Published in: Journal of food science Vol. 74; no. 3; pp. C292 - C297
• Main Authors: Subramanian, A, Harper, W.J, Rodriguez-Saona, L.E
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.04.2009; Wiley Subscription Services, Inc
• Subjects: amino acids; Cheddar cheese; Cheese; Cheese - analysis; classification; Fats - analysis; flavor; flavor compounds; Flavors; food analysis; food composition; food quality; Food science; Fourier transform infrared spectroscopy; Fourier transforms; Hydrogen-Ion Concentration; infrared spectroscopy; lipid content; mathematical models; mineral content; organic acids and salts; prediction; rapid analysis; rapid methods; Regression Analysis; sensory properties; short chain fatty acids; sodium; Sodium Chloride - analysis; Spectroscopy, Fourier Transform Infrared - methods; Spectrum analysis; Taste; validity; Water - analysis; water content
• ISSN: 0022-1147; 1750-3841
• DOI: 10.1111/j.1750-3841.2009.01111.x

Multiple methods are required for analysis of cheese flavor quality and composition. Chromatography and sensory analyses are accurate but laborious, expensive, and time consuming. A rapid and simple instrumental method based on Fourier transform infrared (FTIR) spectroscopy was developed for simultaneous analysis of Cheddar cheese composition and flavor quality. Twelve different Cheddar cheese samples ripened for 67 d were obtained from a commercial cheese manufacturer along with their moisture, pH, salt, fat content, and sensory flavor quality data. Water-soluble components were extracted from the cheese, dried on zinc selenide FTIR crystal and scanned (4000 to 700 cm⁻¹). Infrared spectra of the samples were correlated with their composition and flavor quality data to develop multivariate statistical regression and classification models. The models were validated using an independent set of ten 67-d-old test samples. 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 > 0.92) and could accurately determine moisture, pH, salt, and fat contents as well as the flavor quality rating in less than 20 min. Furthermore, cheeses could also be classified based on their flavor quality (slight acid, whey taint, good cheddar, and so on). 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. The results show that this technique can be a rapid, inexpensive, and simple tool for predicting composition and flavor quality of cheese.