Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats-Reference-Cited by-同舟云学术

Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats

Published:2020-02-04 Issue:4 Volume:13 Page:970-981
ISSN:1936-9751
Container-title:Food Analytical Methods
language:en
Short-container-title:Food Anal. Methods

Author:

Rady Ahmed,Adedeji Akinbode A.^ORCID

Funder

National Institute of Food and Agriculture

Publisher

Springer Science and Business Media LLC

Subject

Safety Research,Safety, Risk, Reliability and Quality,Applied Microbiology and Biotechnology,Food Science,Analytical Chemistry

Link

http://link.springer.com/content/pdf/10.1007/s12161-020-01719-1.pdf

Reference39 articles.

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2. American Meat Science Association (AMSA) (2012) Meat color measurement guidelines: AMSA. American Meat Science Association

3. Boyacı IH, Temiz HT, Uysal RS, Velioğlu HM, Yadegari RJ, Rishkan MM (2014) A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem 148:37–41

4. Boysworth MK, Booksh KS (2001) Aspects of multivariate calibration applied to near-infrared spectroscopy. Pract Spec Series 27:209–240

5. Brehmer H, Schleicher S, Borowski U (1999) Determination of soya protein, pea protein and gluten in frankfurter-type sausages by means of an enzyme-linked-immunosorbent-assay (ELISA). Fleischwirtschaft (German)

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