Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef

• Published in: Food and bioprocess technology Vol. 8; no. 5; pp. 1054 - 1062
• Main Authors: Kamruzzaman, Mohammed, Makino, Yoshio, Oshita, Seiichi, Liu, Shu
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.05.2015; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Beef; Biotechnology; Calibration; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Food Science; Horsemeat; Hyperspectral imaging; I.R. radiation; Image processing; Infrared imaging; Mathematical models; Meat; Minced meat; Multivariate analysis; Original Paper; Pretreatment; Regression analysis; Regression coefficients; Spectra; Wavelengths; Minced beef; Adulteration; Hyperspectral imaging; Horsemeat; Partial least-squares regression
• ISSN: 1935-5130; 1935-5149
• DOI: 10.1007/s11947-015-1470-7

For the first time, a visible near-infrared (Vis-NIR) hyperspectral imaging system (400–1000 nm) was investigated for rapid and non-destructive detection of adulteration in minced beef meat. Minced beef meat samples were adulterated with horsemeat at levels ranging from 2 to 50 % ( w / w ), at approximately 2 % increments. Calibration model was developed and optimized using partial least-squares regression (PLSR) with internal full cross-validation and then validated by external validation using an independent validation set. Several spectral pre-treatment techniques including derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied to examine the influence of spectral variations for predicting adulteration in minced beef. The established PLSR models based on raw spectra had coefficients of determination ( R 2 ) of 0.99, 0.99, and 0.98, and standard errors of 1.14, 1.56, and 2.23 % for calibration, cross-validation, and prediction, respectively. Four important wavelengths (515, 595, 650, and 880 nm) were selected using regression coefficients resulting from the best PLSR model. By using these important wavelengths, an image processing algorithm was developed to predict the adulteration level in each pixel in whole surface of the samples. The results demonstrate that hyperspectral imaging coupled with multivariate analysis can be successfully applied as a rapid screening technique for adulterate detection in minced meat.