Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef.
Chemometric MID-FTIR methods were developed to detect and quantify the adulteration of mince meat with horse meat, fat beef trimmings, and textured soy protein. Also, a SIMCA (Soft Independent Modeling Class Analogy) method was developed to discriminate between adulterated and unadulterated samples. Pure mince meat and adulterants (horse meat, fat beef trimmings and textured soy protein) were characterized based upon their protein, fat, water and ash content. In order to build the calibration models for each adulterant, mixtures of mince meat and adulterant were prepared in the range 2-90% (w/w). Chemometric analyses were obtained for each adulterant using multivariate analysis. A Partial Least Square (PLS) algorithm was tested to model each system (mince meat+adulterant) and the chemical composition of the mixture. The results showed that the infrared spectra of the samples were sensitive to their chemical composition. Good correlations between absorbance in the MID-FTIR and the percentage of adulteration were obtained in the region 1800-900 cm(-1). Values of R(2) greater than 0.99, standard errors of calibration (SEC) in the range to 0.0001-1.278 and standard errors of prediction (SEP estimated) between 0.001 and 1.391 for the adulterant and chemical parameters were obtained. The SIMCA model showed 100% classification of adulterated meat samples from unadulterated ones. Chemometric MID-FTIR models represent an attractive option for meat quality screening without sample pretreatments which can identify the adulterant and quantify the percentage of adulteration and the chemical composition of the sample.
Meza-Márquez
Ofelia G
OG
Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, IPN Prolongación de Carpio y Plan de Ayala S/N Col Santo Tomás CP 11340, DF México, Mexico.
Gallardo-Velázquez
Tzayhrí
T
Osorio-Revilla
Guillermo
G
D000465:Algorithms; D000818:Animals; D002417:Cattle; D005506:Food Contamination / Q000032:analysis*; D006736:Horses; D016018:Least-Squares Analysis; D008461:Meat Products / Q000032:analysis*; D015233:Models, Statistical; D015999:Multivariate Analysis; D012015:Reference Standards; D030262:Soybean Proteins; D013055:Spectrophotometry, Infrared; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*
D030262:Soybean Proteins