Species Identification in Sausage Samples Using Near-Infrared Spectroscopy and Linear Discriminant Analysis Publisher



M Pirhadi MOHADESEH ; N Shariatifar NABI ; G Jahed Khaniki GHOLAMREZA ; M Amanlou MASSOUD ; P Sadighara PARISA ; E Molaeeaghaee EBRAHIM ; S Pirhadi SOHRAB
Source: Discover Sustainability Published:2025

Abstract

The demand for meat products has increased significantly due to changes in people’s lifestyles and rising meat prices. The study aims to investigate the potential use of an accurate and reliable method for detecting adulteration and ensuring the quality of meat products. The sausages are made with various meat from 5 to 100% pure meat. The samples are then analyzed using Fourier-transform Near-Infrared (FT-NIR) spectroscopy. This research used a NIR spectrophotometer, Principal component analysis (PCA), and Linear discriminant analysis (LDA) to identify beef, chicken, donkey, and horse handmade sausage samples. Using PCA, two principal components were extracted that covered 99 percent of the data variance and accurately distinguished sausage samples from different animal meats. Following this, the LDA model can confidently classify the sausage samples and achieved 100% accuracy in classifying all test samples. To conclude, NIR spectroscopy is a reliable tool for sausage quality evaluation and species identification, which can identify different animal species in meat products. © 2025 Elsevier B.V., All rights reserved.