Insausti Barrenetxea, Kizkitza

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/49281

Birth Date

Job Title

Last Name

Insausti Barrenetxea

First Name

Kizkitza

person.page.departamento

Agronomía, Biotecnología y Alimentación

person.page.instituteName

IS-FOOD. Research Institute on Innovation & Sustainable Development in Food Chain

ORCID

0000-0002-3821-814X

person.page.observainves

88593

person.page.upna

2554

Name

Filters

20191
Reset filters

Settings

Author: Arazuri Garín, Silvia × Subject: Authentication ×

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    PublicationOpen Access
    Detection of minced lamb and beef fraud using NIR spectroscopy
    (Elsevier, 2019) López Maestresalas, Ainara; Insausti Barrenetxea, Kizkitza; Jarén Ceballos, Carmen; Pérez Roncal, Claudia; Urrutia Vera, Olaia; Beriain Apesteguía, María José; Arazuri Garín, Silvia; Ingeniaritza; Agronomia, Bioteknologia eta Elikadura; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Agronomía, Biotecnología y Alimentación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    The aim of this work was to investigate the feasibility of near-infrared spectroscopy (NIRS), combined with chemometric techniques, to detect fraud in minced lamb and beef mixed with other types of meats. For this, 40 samples of pure lamb and 30 samples of pure beef along with 160 samples of mixed lamb and 156 samples of mixed beef at different levels: 1-2-5-10% (w/w) were prepared and analyzed. Spectral data were pre-processed using different techniques and explored by a Principal Component Analysis (PCA) to find out differences among pure and mixed samples. Moreover, a PLS-DA was carried out for each type of meat mixture. Classification results between 78.95 and 100% were achieved for the validation sets. Better rates of classification were obtained for samples mixed with pork meat, meat of Lidia breed cattle and foal meat than for samples mixed with chicken in both lamb and beef. Additionally, the obtained results showed that this technology could be used for detection of minced beef fraud with meat of Lidia breed cattle and foal in a percentage equal or higher than 2 and 1%, respectively. Therefore, this study shows the potential of NIRS combined with PLS-DA to detect fraud in minced lamb and beef.