Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/7157
Title: Analisis Rendang Daging Sapi dan Daging Babi Hutan Menggunakan Metode Spektroskopi FTIR Kombinasi Kemometrik untuk Autentikasi Halal
Authors: Andayani, Regina
Kesumaningrum, Ditya
Nisa, Tadzkia
Husni, Elidahanum
Suryati
Syofyan
Dachriyanus
Keywords: authentication
beef rendang
wild boar
FTIR
chemometrics
Issue Date: Apr-2023
Publisher: Jurnal Sains Farmasi & Klinis
Citation: Original Article
Abstract: Rendang is one of West Sumatra's traditional foods made from processed beef. The high price of beef encourages counterfeiting using wild boar meat. This study aims to analyze adulterated beef rendang with wild boar meat using FTIR spectroscopy in combination with chemometrics for halal authentication. The sample used were a mixture of beef and boar meat made in 11 concentrations (training data set) and restaurant rendang (testing data set). The rendang fat component was extracted using the Folch and Bligh Dyer methods and then analyzed by FTIR spectroscopy. The resulting FTIR spectra were used as a chemometric modelling variable. To group rendang, a discriminant analysis (DA) model was used. Folch's method on wave numbers 1800-1600 cm-1 and 1250-950 cm-1, and the Bligh Dyer method on wave numbers 1800-650 cm-1. For quantification purposes, the Folch method uses the partial least squares (PLS) model at wave numbers 1250-950 cm- with a calibration R2 value of 0.9946, predicted R2 of 0.9954, RMSEC 0.0328, RMSEP 0.0402. Meanwhile, the Bligh Dyer method uses a principle component regression (PCR) model at wave numbers 1800-500 cm-1 with a calibration R2 value of 0.9940, predicted R2 of 0.9919, RMSEC 0.0345, and RMSEP 0.0457. The results showed that the chemometric combination FTIR spectroscopy method was a fast, simple and inexpensive screening method for halal authentication in the analysis of beef rendang and wild boar meat.
URI: http://localhost:8080/xmlui/handle/123456789/7157
Appears in Collections:VOL 10 NO 1 2023

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