Prof. Marena Manley Profile

Prof. Marena Manley

at Stellenbosch Univ

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Proceedings Article | 23 April 2020 Presentation

Classification of game meat with NIR hyperspectral imaging (Rising Researcher) (Conference Presentation)

Paul Williams, Qhakazile Makhubo, Marena Manley, Louwrens Hoffman

Proceedings Volume 11421, 114210A (2020) https://doi.org/10.1117/12.2557252

KEYWORDS: Near infrared, Hyperspectral imaging, Data analysis, Reflectivity, Line scan image sensors, Image segmentation, Principal component analysis

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In this study, NIR hyperspectral imaging and multivariate data analysis were used to classify game meat species, namely Springbok (Antidorcas marsupialis) and Blesbok (Damaliscus pygargus phillipsi). The animals (6 blesbok and 6 springbok) were harvested in Witsand and Elandsberg in the Western Cape, South Africa. Longissimus thoracis et lumborum (LTL) muscles were excised and left to bloom for ca. 30 minutes prior to imaging. Thereafter, the moisture was wiped off the surface to avoid specular reflectance. NIR hyperspectral images were collected with a linescan system (HySpex SWIR-384) in the spectral range 950 – 2500 nm. Data were pre-processed with Savitzky-Golay smoothing and derivatives (2nd order polynomial, 2nd derivative, 15 point smoothing) and noisy regions in the spectra, specifically between 1884.9nm -2500nm, were removed. In addition, two data analysis methods, the pixel and object wise approach, were evaluated. In the object wise approach the muscles were segmented into ca 2 cm ROI’s, of which the mean was computed. For both pixel and object wise approaches, there was no distinct separation of the species with PCA. When PLS-DA models were developed, the object wise approach proved to be superior with a classification accuracy of 96%, whereas that of the pixel wise approach was 62%. It is evident that NIR hyperspectral imaging can be used to distinguish between the two species, with the object wise being the optimal option of the two approaches, as it represents the mean spectra of each object.

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