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Evaluating Mid-infrared Spectroscopy as a New Technique for Predicting Sensory Texture Attributes of Processed Cheese

C. C. Fagan^*,1, C. Everard^*, C. P. O’Donnell^*, G. Downey, E. M. Sheehan, C. M. Delahunty and D. J. O’Callaghan^||

^* Biosystems Engineering, UCD School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Earlsfort Terrace, Dublin 2, Ireland
Teagasc, Ashtown Food Research Centre, Dublin 15, Ireland
Department of Nutritional Sciences, University College Cork, Cork, Ireland
Department of Food Science, University of Otago, PO Box 56, Dunedin 9015, New Zealand
^|| Teagasc, Moorepark Food Research Centre, Fermoy, Co. Cork, Ireland

¹ Corresponding author: colette.fagan{at}ucd.ie

The objective of this study was to investigate the potential application of mid-infrared spectroscopy for determination of selected sensory attributes in a range of experimentally manufactured processed cheese samples. This study also evaluates mid-infrared spectroscopy against other recently proposed techniques for predicting sensory texture attributes. Processed cheeses (n = 32) of varying compositions were manufactured on a pilot scale. After 2 and 4 wk of storage at 4°C, mid-infrared spectra (640 to 4,000 cm^–1) were recorded and samples were scored on a scale of 0 to 100 for 9 attributes using descriptive sensory analysis. Models were developed by partial least squares regression using raw and pretreated spectra. The mouth-coating and mass-forming models were improved by using a reduced spectral range (930 to 1,767 cm^–1). The remaining attributes were most successfully modeled using a combined range (930 to 1,767 cm^–1 and 2,839 to 4,000 cm^–1). The root mean square errors of cross-validation for the models were 7.4 (firmness; range 65.3), 4.6 (rubbery; range 41.7), 7.1 (creamy; range 60.9), 5.1 (chewy; range 43.3), 5.2 (mouth-coating; range 37.4), 5.3 (fragmentable; range 51.0), 7.4 (melting; range 69.3), and 3.1 (mass-forming; range 23.6). These models had a good practical utility. Model accuracy ranged from approximate quantitative predictions to excellent predictions (range error ratio = 9.6). In general, the models compared favorably with previously reported instrumental texture models and near-infrared models, although the creamy, chewy, and melting models were slightly weaker than the previously reported near-infrared models. We concluded that mid-infrared spectroscopy could be successfully used for the nondestructive and objective assessment of processed cheese sensory quality.

Key Words: descriptive sensory analysis • processed cheese • mid-infrared spectroscopy • chemometrics

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