The effect of three pork backfat levels (0% vs. 10% vs. 30%) on chemical composition, fatty acid profile and sensory properties on sheep and goat meat sausages was studied. All physicochemical parameters were affected by the addition of pork backfat in both types of sausages. Sausages manufactured with 30% of pork backfat showed the lowest moisture and protein contents and the highest total fat content. The lower a w values in sausages manufactured with higher fat content while in pH happened the reverse situation. The addition of pork backfat modified the total fatty acid profile, prompting a significant drop in the relative percentages of C14:0, C16:0, C17:0, C17:1, C18:0 and TVA (trans-vaccenic acid), together with a marked increase in oleic and linoleic acids. Finally, in goat sausages, the fat content significantly affected sensory parameters: taste, texture and overall acceptability (P b 0.05). As expected, all physicochemical parameters were affected by the addition of pork backfat in both types of sausages.

The present study was a result of a project in co-promotion between 2 breeder associations, an industry unit and a research center. Results indicated that the meat from animals out of quality commercial brands could be useful as processed meat in a product with consumer acceptability. Also these new meat products brought diversity to meat industry to reach new markets and originating 2 new meat brands recorded at INPI (Instituto Nacional da Propriedade Industrial—Natl. Industrial Property Inst.) with the numbers of 489664 and 489662 of National Brands of sheep and goat meat, respectively.

a b s t r a c tThe ability of near infrared reflectance spectroscopy (NIRS) to estimate the protein, moisture, connective tissue and ash content in the Longissimus thoracis et lumborum (LTL) muscle of goat was studied. Samples (n = 240) of the LTL muscle were taken from the 8th to 13th rib cut of goat carcasses. Samples were scanned in a FT-NIR Master TM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm −1 with a resolution of 4 cm −1 . It was collected 3 spectra per sample and subsequently, chemical analyses were performed at the Carcass and Meat Quality Laboratory of ESA-IPB. Using NirCal 1.5 it was developed a PLS regression model assaying, first and second derivatives as math treatment and multiplicative scatter correction for minimizing scattering effect on the spectra database recorded (n = 240). The best calibrations' models show relatively good predictability for protein (standard error of prediction SEP = 0.43; coefficient of determination R 2 = 0.91), moisture (SEP = 0.48; R 2 = 0.92). Calibrations' models obtained are important as a first attempt to predict the chemical composition of goat meat by NIRS. More experimental data are needed to improve the accuracy of these calibrations.

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Sixty-one intact meat samples from Asturcelta autochthonous swine breed were scanned in the slaughterhouse in reflectance mode. A handheld microelectromechanical system digital transform (Phazir1624, Polychromix Inc.), with a window sampling area of 0.8×1 cm and wavelengths ranging from 1,600 to 2,400 nm, was used. With the spectra database recorded were developed different chemometrical models assaying first and second derivatives as math treatment and standard normal variate (SNV) and multiplicative scatter correction for minimizing scattering effect. The greatest predictive capacity was achieved after applying SNV and first derivative for moisture, intramuscular fat (IMF) content, and pH parameters and second derivative for CIE L*, a*, b* colorimetric values, and the Warner-Bratzler force (instrumental texture). The coefficients of determination for calibration ranged from 0.63 to 0.89. The ratio between the standard error of the laboratory and the standard error of calibration ranged from 0.8 to 2.5 for all parameters (1.7 on average) with the exception of b and pH with ratios of 3.5 and 4.1, respectively. The statistical values obtained for the models developed to estimate IMF, CIE L*, a*, b*, moisture, and pH, displayed acceptable predictive capacity. For instrumental texture, the model could be able to discriminate among tender, medium, and hard meat in carcasses for characterization slaughter purposes.