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Premium Application of Mid‐Infrared Spectroscopy to the Prediction of Maturity and Sensory Texture Attributes of Cheddar Cheese
Fagan C.C.,
O'Donnell C.P.,
O'Callaghan D.J.,
Downey G.,
Sheehan E.M.,
Delahunty C.M.,
Everard C.,
Guinee T.P.,
Howard V.
Publication year2007
Publication title
journal of food science
Resource typeJournals
PublisherBlackwell Publishing Inc
ABSTRACT:  The objective of this study was to determine the potential of mid‐infrared spectroscopy in conjunction with partial least squares (PLS) regression to predict various quality parameters in cheddar cheese. Cheddar cheeses ( n = 24) were manufactured and stored at 8 °C for 12 mo. Mid‐infrared spectra (640 to 4000/cm) were recorded after 4, 6, 9, and 12 mo storage. At 4, 6, and 9 mo, the water‐soluble nitrogen (WSN) content of the samples was determined and the samples were also evaluated for 11 sensory texture attributes using descriptive sensory analysis. The mid‐infrared spectra were subjected to a number of pretreatments, and predictive models were developed for all parameters. Age was predicted using scatter‐corrected, 1st derivative spectra with a root mean square error of cross‐validation (RMSECV) of 1 mo, while WSN was predicted using 1st derivative spectra (RMSECV = 2.6%). The sensory texture attributes most successfully predicted were rubbery, crumbly, chewy, and massforming. These attributes were modeled using 2nd derivative spectra and had corresponding RMSECV values in the range of 2.5 to 4.2 on a scale of 0 to 100. It was concluded that mid‐infrared spectroscopy has the potential to predict age, WSN, and several sensory texture attributes of cheddar cheese.
Subject(s)analytical chemistry (journal) , artificial intelligence , chemistry , chromatography , computer science , derivative (finance) , economics , financial economics , food science , image (mathematics) , infrared spectroscopy , mathematical analysis , mathematics , near infrared spectroscopy , optics , organic chemistry , partial least squares regression , physics , quantum mechanics , second derivative , spectroscopy , statistics , texture (cosmology)
SCImago Journal Rank0.772

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