Journal of Dairy Science
Volume 89, Issue 5 , Pages 1407-1412 , May 2006

Rapid Determination of Swiss Cheese Composition by Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

  • L.E. Rodriguez-Saona

      Affiliations

    • Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, Columbus 43210-1007
    • ,
  • N. Koca

      Affiliations

    • Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, Columbus 43210-1007
    • Department of Food Engineering, Ege University, Izmir, Turkey
    • ,
  • W.J. Harper

      Affiliations

    • Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, Columbus 43210-1007
    • ,
  • V.B. Alvarez

      Affiliations

    • Department of Food Science and Technology, The Ohio State University, 110 Parker Food Science and Technology Building, Columbus 43210-1007
    • Corresponding Author InformationCorresponding author.

Received 5 October 2005 ,Accepted 1 December 2005.

  • Image Result

    Midinfrared spectra of Swiss cheese using an attenuated total reflectance accessory equipped with a 3-bounce diamond plate. A) Midinfrared absorbance spectrum collected in the 4,000 to 700cm−1 range;

    Midinfrared spectra of Swiss cheese using an attenuated total reflectance accessory equipped with a 3-bounce diamond plate. A) Midinfrared absorbance spectrum collected in the 4,000 to 700cm−1 range; B) Second-derivative (5-point window) spectral transformation collected in the 1,800 to 700cm−1 range.

  • Image Result
    Partial least squares regression (PLSR) calibration and validation plots for A) moisture, B) protein, and C) fat determination in Swiss cheese samples. Second-derivative (δ2A/δλ2) spectral transformat

    Partial least squares regression (PLSR) calibration and validation plots for A) moisture, B) protein, and C) fat determination in Swiss cheese samples. Second-derivative (δ2A/δλ2) spectral transformation was used for the multivariate analysis.

  • Image Result
    A) Soft independent modeling of class analogy (SIMCA) class projections of transformed (second derivative, 5-point window) midinfrared spectra of Swiss cheese samples with known degree of maturation;

    A) Soft independent modeling of class analogy (SIMCA) class projections of transformed (second derivative, 5-point window) midinfrared spectra of Swiss cheese samples with known degree of maturation; B) Discriminative power between Swiss cheese samples based on the SIMCA model. The data comprised repeated observations (3 to 4) collected from cheese slices (∼0.5g). Samples from different production lots with different aging times were supplied by local (Ohio) Swiss cheese manufacturers (sources A, B, and C).

PII: S0022-0302(06)72209-3

doi: 10.3168/jds.S0022-0302(06)72209-3

Journal of Dairy Science
Volume 89, Issue 5 , Pages 1407-1412 , May 2006

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