Characterization of different blue cheeses using a custom-design multispectral imager

The present study was conducted to determine whether multispectral imagery combined with chemometrics could accurately distinguish and classify different blue cheeses. The images of the pre-packed PDO Bleu d'Auvergne (n = 12) and Fourme d'Ambert (n = 23) blue cheeses were acquired using a custom-design multispectral imager. The image acquisition was conducted in the ultraviolet (360 nm, 370 nm and 400 nm), visible (470 nm, 568 nm and 625 nm) and near- infrared (875 nm and 950 nm) spectral regions. The spectral functions of image texture based on the Fourier spectrum and image weights were extracted from the raw multivariate images using an image processing tool and a method of simultaneous decomposition of covariance matrices, respectively. Principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) of the spectrum functions showed a reliable discrimination of the Bleu d'Auvergne and Fourme d'Ambert blue cheeses. Examination of the image weights using PLSDA allowed the pre- diction of the producers of the blue cheeses. Our data demonstrated the ability of the multispectral imagery combined with chemometrics to characterize the quality and identity of the blue cheeses in a rapid and inexpensive manner. blue cheese / characterization / multispectral imagery / image texture / chemometrics