Identification of Lard on Processed Products in Medan City Using Uv Spectrophotometer With Linear Discriminant Analysis and Principal Component Analysis Methods

Abstract

Objective: Processed meat products are highly popular among the community. However, deceptive traders sometimes adulterate these products with pork elements, necessitating thorough inspections. The qualitative detection of lard in processed products can be analyzed using UV spectrophotometry with chemometric techniques such as Linear Discriminant Analysis and Principal Component Analysis. These methods facilitate data analysis derived from spectra and wavelengths, enabling the categorization of objects and providing high accuracy.
 Methods: This study aimed to determine whether processed products in Medan contain lard using UV spectrophotometry, Linear Discriminant Analysis, and Principal Component Analysis methods.
 Results: The highest fat yield was obtained from lard at 14.24%, while the lowest was from chicken fat at 7.00%. The maximum wavelength results for control samples were 234 nm for chicken fat, 237 nm for beef fat, and 268 nm for lard. Data processing using Linear Discriminant Analysis and Principal Component Analysis showed that the processed products of three random samples, nugget, meatball, and sausage type A and C, fell within the same quadrant as chicken fat. Meatball and sausage type B were in the same quadrant as beef fat.
 Conclusion: Based on the identification of lard in processed products in Medan City using UV spectrophotometer by LDA and PCA, all random samples of nuggets, meatballs, and sausages do not contain lard, and this method can classify chicken fat, beef fat, lard well.