Discrimination between Barley and Corn finished Beef by visible and near infrared spectroscopy
Discrimination between Barley and Corn finished Beef by visible and near infrared spectroscopy. W. Barragan, J.L. Aalhus, G.B. Penner, M.E.R. Dugan, M. Juárez, Ó. López-Campos, P. Vahmani, J. Segura, J. Angulo, and N. Prieto. CMSA-ASCV 2020
Barley production is well suited to western Canada and provides feedstuffs (i.e., grain and silage) found in most beef finisher diets. The recent development of low heat unit hybrid corn has, however, allowed the production of this crop in Alberta and Saskatchewan regions, providing a practical alternative to barley. With the availability of both corn and barley for inclusion in finishing diets, an opportunity exists for differentiation and value addition for target markets, and branding of barley-fed beef. Previous research has shown visible and near-infrared spectroscopy (vis-NIRS) as a successful technology to classify beef based on quality characteristics, animal feeding regime, geographical origin and species. The objective of this study was to test if visible and near-infrared spectroscopy (vis-NIRS) could be used to rapidly classify beef based on grain source (barley vs. corn) in finishing diets.
A total of 85 in-bone ribeyes from barley (n=29) and corn (n=27) grain-fed steers from a larger study were shipped refrigerated to the Lacombe Research and Development Centre (Agriculture and Agri-Food Canada, Canada). After 15 d of ageing, two 2.5-cm steaks were fabricated from each ribeye for subsequent analyses. Sensory descriptive evaluation and fatty acid analysis were performed on the longissimus thoracis (LT), whereas colour (L*, a*, b*) was measured on both LT and subcutaneous fat using a Minolta CR-300 (Minolta Canada Inc., Mississauga, ON). Vis-NIR spectra were collected on subcutaneous fat and intact (LT), and then LT was ground (Robot Coupe Blixir BX3) and scanned again. All spectra were collected at the laboratory using a portable LabSpec®4 Standard-Res spectrometer (Analytical Spectral Device-ASD Inc., Boulder, CO, USA) from 350 to 2500 nm (vis-NIR range). For the spectra collection on the intact and ground LT, the spectrometer was fitted with a 20 mm ASD fibre-optic high-intensity contact probe and scanned 50 times per reading. When subcutaneous fat was scanned, a hand-held ASD fibre-optics pro-reflectance probe was used. Colour, fatty acids and sensory data were analyzed using the MIXED model procedure of SAS (Version 9.4 Institute Inc., Cary, NC). Grain was used as a fixed effect, and pen (and trained panellist for sensory analysis) as the random effect. Partial least square discriminant analysis (PLS-DA) was applied to the spectra regions (vis-NIRS: 350-2500 nm; vis: 350-750 nm; NIRS: 750-2500 nm) from the three tissues (fat, intact LT and ground LT) for source of grain-fed discrimination, using the software R-Project (R Development Core Team 2009).
When the subcutaneous fat samples were scanned, 100% of the samples from barley-fed cattle were correctly classified, regardless of the wavelength range used for the spectra collection, and the fat samples from corn-fed steers were discriminated with 93-100% accuracy. When these calibration models were cross-validated, 100 and 93% of the fat samples from barley and corn-fed cattle, respectively, were correctly classified regardless of the region used in the analyses. Hence, 97% overall fat samples were correctly discriminated between barley and corn-fed cattle. When the spectra were collected on intact LT, 100% of intact LT samples from both barley and corn-fed cattle were correctly classified using the NIR region. In the cross-validation, however, the highest percentage of samples correctly discriminated was found when the vis-NIR region was considered in the analysis with 96 and 85% of intact LT samples from barley and corn-fed cattle correctly classified, respectively (91% overall accuracy). When the spectra were collected on ground LT samples, the PLS-DA accuracy in the calibration process along the different spectral regions ranged from 71 to 82%, and 66 to 85%, in barley and corn grain samples, respectively. In the cross-validation, 66 and 67% of the ground LT samples were correctly classified from barley and corn-fed cattle, respectively, using the NIR region. Overall, ground LT samples were classified with <70% overall accuracy.
Vis-NIRS measurements on fat and intact loin can be used to accurately discriminate between corn and barley-fed beef, providing a rapid authentication tool for branded barley-fed beef for target markets.