Authentication and discrimination of tissue origin of bovine gelatin using combined supervised pattern recognition strategies

[Display omitted] •A combined classification strategy for improving the efficiency of authentication has been used.•A stable and reliable classification model was developed using dd-SIMCA and PLS-DA method.•Combination of Raman spectroscopy and chemometric methods.•Bovine gelatin source can be predi...

Full description

Saved in:
Bibliographic Details
Published in:Microchemical journal 2023-04, Vol.187, p.108417, Article 108417
Main Authors: Forooghi, Elaheh, Vali Zade, Somaye, Sahebi, Hamed, Abdollahi, Hamid, Sadeghi, Naficeh, Jannat, Behrooz
Format: Article
Language:English
Subjects:
Authentication
Bovine bone gelatin
Bovine skin gelatin
Chemometrics
Discrimination
Raman spectroscopy
Supervised pattern recognition
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:[Display omitted] •A combined classification strategy for improving the efficiency of authentication has been used.•A stable and reliable classification model was developed using dd-SIMCA and PLS-DA method.•Combination of Raman spectroscopy and chemometric methods.•Bovine gelatin source can be predicted with great accuracy using this method. Gelatin is a protein-based product derived from bovine and porcine bone and skin. Gelatin is increasingly used in food and non-food products, but there are errors in labeling concerning the origin of commercial gelatin raw materials. These mislabeling may negatively impact religious or safety communities. In gelatin studies, species identification is common, but determining the origin of the tissues, bone and skin, is also crucial. The current study demonstrated the use of Raman spectroscopy as a rapid and economical method coupled with chemometrics to differentiate tissue origins of bovine gelatin. The proposed technique could also detect adulteration of bone origin gelatin with skin gelatin with 100% sensitivity and specificity. Accordingly, Raman spectroscopy, along with DD- SIMCA and PLS-DA methods, provides a reliable technique that can easily be applied to detecting the tissue source of bovine gelatin. Also, two- step classification method with goal of finding a suitable authentication of target classes have been applied. dd-SIMCA as class modeling for authentication and PLS-DA for discrimination of groups have been combined in this strategy.
ISSN:0026-265X
1095-9149
DOI:10.1016/j.microc.2023.108417