Literature DB >> 22060647

Multivariate techniques in the analysis of meat quality.

T N˦s1, P Baardseth, H Helgesen, T Isaksson.   

Abstract

The present paper discusses the use of multivariate statistical methods in meat science. Three examples are given illustrating some of the most important areas of use. The three examples are i) interpretation of large data matrices, ii) prediction of chemical constituents from multivariate spectral data and iii) finding relationships between multivariate data matrices.

Year:  1996        PMID: 22060647     DOI: 10.1016/0309-1740(96)00061-7

Source DB:  PubMed          Journal:  Meat Sci        ISSN: 0309-1740            Impact factor:   5.209


  3 in total

1.  Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

Authors:  Debasree Ghosh; Parimal Chattopadhyay
Journal:  J Food Sci Technol       Date:  2011-02-18       Impact factor: 2.701

2.  Sensory characterization of doda burfi (Indian milk cake) using Principal Component Analysis.

Authors:  Rekha Chawla; Girdhari Ramdas Patil; Ashish Kumar Singh
Journal:  J Food Sci Technol       Date:  2011-09-16       Impact factor: 2.701

3.  Principal component analysis of physicochemical and sensory characteristics of beef rounds extended with gum arabic from Acacia senegal var. kerensis.

Authors:  Johnson K Mwove; Lilian A Gogo; Ben N Chikamai; Mary Omwamba; Symon M Mahungu
Journal:  Food Sci Nutr       Date:  2018-01-16       Impact factor: 2.863

  3 in total

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