Milk and meat fatty acids from sheep fed a plantain-chicory mixture or a grass-based permanent sward.

Plantain and chicory are interesting forage species since they present good nutritional quality and are more resistant to drought than many temperate grasses. The fatty acid (FA) profile in milk and meat is related to a growing concern for the consumption of healthy foods, that is, with a lower content of saturated FA, higher polyunsaturated FA (PUFA) and a favourable n-6 : n-3 FAs ratio. Our objective was to evaluate the FA content in ewe's milk and lamb's meat fed a plantain-chicory mixture (PCH) or a grass-based permanent sward (GBS) dominated by perennial ryegrass. Eighteen Austral ewes in mid-lactation were allocated to PCH and GBS treatments. Milk samples were obtained during September (spring). Thirty weaned lambs were finished on both treatments from November to December (7 weeks), slaughtered and their meat sampled. Fat from milk and meat samples was extracted and stored until analysed by gas chromatography. Milk fat from GBS was higher than from PCH (P < 0.05) in C18:0 (11 385 v. 5874 mg/100 g FA), 9c-18:1 (15 750 v. 8565 mg/100 g FA), 11 t-18:1 (4576 v. 2703 mg/100 g FA) and 9c,11 t-18:2 (1405 v. 921 mg/100 g FA) and lower in 18:2n-6 (827 v. 1529 mg/100 g FA) and 18:3n-3 (943 v. 1318 mg/100 g FA) FA. Total mono-unsaturated FA was higher in GBS than PCH (P < 0.05). Meat fat from PCH swards presented a higher (P < 0.05) content than GBS for 18:2n-6 (46.8 v. 28.2 mg/100 g FA), linolenic (24.6 v. 14.2 mg/100 g FA), polyunsaturated FA (119.7 v. 73.4 mg/100 g FA), n-6 (65.9 v. 40.8 mg/100 g FA) and n-3 (53.8 v. 32.5 mg/100 g FA), respectively. No effect of treatment (P > 0.05) was detected for 9c-18:1 (283.9 v. 205.8 mg/100 g FA), 11 t-18:1 (26.2 v. 19.3 mg/100 g FA) and 9c,11 t-18:2 (10.1 v. 7.6 mg/100 g FA), for PCH and GBS. These results suggest that grazing a PCH mixture results in a higher concentration of PUFA in ewes' milk and in lambs' fat, as compared to a GBS sward.

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy.

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible-near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R 2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R 2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 µm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R 2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 µm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.

Prediction of immunoglobulin G content in bovine colostrum by near-infrared spectroscopy.

The objective of this work was to assess the potential of near infrared spectroscopy to predict the immunoglobulin G (IgG) content in bovine colostrum. Liquid colostrum samples (n=157) were collected from Holstein cows from 2 dairy farms in southern Chile. Samples were obtained within 1h of parturition and scanned in folded transmission (transflectance) in the visible-near infrared range. Multivariate regression models (modified partial least squares) were developed with spectral data against IgG content measured by radial immunodiffusion. The best calibration included a mathematical treatment of the spectra by a second derivative plus standard normal variate and detrending. The best equation explained a high proportion of the variation in IgG content (R(2) of 0.95 in calibration and 0.94 in cross-validation). Average (91.5 g/L), standard deviation (37.6g/L), and range, as highest minus lowest values (171.9 g/L) of reference values were 10.1, 4.2, and 19 times the value of the root mean square error of cross-validation (9.03 g/L) respectively. Near-infrared spectroscopy, scanned in folded transmission, is an effective tool to predict the IgG content in liquid colostrum.

Identification of cattle, llama and horse meat by near infrared reflectance or transflectance spectroscopy.

Visible and near infrared reflectance spectroscopy (VIS-NIRS) was used to discriminate meat and meat juices from three livestock species. In a first trial, samples of Longissimus lumborum muscle, corresponding to beef (31) llamas (21) and horses (27), were homogenised and their spectra collected in reflectance (NIRSystems 6500 scanning monochromator, in the range of 400-2500 nm). In the second trial, samples of meat juice (same muscle) from the same species (20 beef, 19 llama and 19 horse) were scanned in folded transmission (transflectance). Discriminating models (PLS regression) were developed against "dummy" variables, testing different mathematical treatments of the spectra. Best models indentified the species of almost all samples by their meat (reflectance) or meat juice (transflectance) spectra. A few (three of beef and one of llama, for meat samples; one of beef and one of horse, for juice samples) were classified as uncertain. It is concluded that NIRS is an effective tool to recognise meat and meat juice from beef, llama and horses.

Evaluation of the chemical composition of dry dogfoods commercialized in Chile used for growing dogs / Avaliação da composição química de rações comerciais no Chile, usadas para filhotes

The nutritional quality of dry dogfood commercialized in Chile for growing dogs was studied. Samples from at least three different batches of 26 dogfood brands were mixed. The resultant samples (n=26) were chemically analyzed to determine their concentrations of dry matter (DM), gross energy, fiber, ash, crude protein, essential amino acids, total fat, linoleic acid and minerals. The metabolizable energy (ME) content of each sample was estimated using modified atwater factors. The results from the chemical analyses were compared with the nutrient profiles published by the American Association of Feed Control Officials (AAFCO). Dogfoods that were found to contain an estimated ME of over 4,000kcal/kg DM were corrected for their high energy density before comparison. All of the dogfoods contained adequate levels of protein, total fat, linoleic acid, iron, copper, manganese and selenium. The concentration of tryptophan was adequate in 92.3 percent of the samples. All of the other essential amino acids were present in adequate quantities. However, the situation was different for many of the minerals. Only 92.3 percent of the dogfoods contained an adequate Ca:P ratio. A total of 96.2 percent of the dogfoods contained an adequate level of Ca, 96.2 percent for P, 96.2 percent for Mg, 92.3 percent for I, 88.5 percent for Cl, 80.8 percent for Na, 80.8 percent for Zn and only 34.6 percent were adequate for K content. Overall, only 23 percent of the dogfoods evaluated in this study fulfilled all of the requirements established by the AAFCO in terms of their content of crude protein, amino acids, total fat, linoleic acid, and minerals. It appears that the majority of the dogfoods evaluated in this study (77 percent) would not satisfy all nutritional requirements of the growing dog.

Determinou-se a qualidade nutricional de 26 rações para filhotes caninos comercializadas no Chile. As rações foram analisadas quimicamente e comparadas com as recomendações da American Association of Food Control Officials (AAFCO). Para as análises, utilizou-se uma amostra de cada ração, composta de pelo menos três lotes diferentes. Para cada amostra, foram determinados os conteúdos de matéria seca (MS), fibra, proteína bruta, aminoácidos essenciais, gordura, ácido linoléico e minerais. A energia metabolizável foi determinada mediante os fatores de conversão de Atwater e corrigida por sua densidade quando ultrapassava 4000kcal/kgMS. Todas as rações apresentaram conteúdos adequados de proteína, gordura, ácido linoléico, ferro, cobre, manganês e selênio. A concentração de triptofano foi adequada em 92,3 por cento das rações, e a dos demais aminoácidos essenciais foi maior que a mínima recomendada. As maiores irregularidades foram encontradas no conteúdo de minerais, 92,3 por cento das rações apresentaram uma adequada relação Ca:P. Foram observados níveis adequados de Ca, P e Mg em 96,2 por cento das rações, de I em 92,3 por cento, de Cl em 88,5 por cento, de Na e Zn em 80,8 por cento e de K em 34,6 por cento. Em relação às concentrações de proteína, aminoácidos, gordura, ácido linoléico e minerais, somente 23 por cento das rações apresentavam todas as especificações recomendadas pela AAFCO. A maioria das rações analisadas, (77 por cento) apresentavam concentrações de nutrientes inferiores ao requerimento de filhotes caninos.

Nutritional evaluation of commercial dry dog foods by near infrared reflectance spectroscopy.

Near infrared reflectance spectroscopy (NIRS) was used to predict the nutritional value of dog foods sold in Chile. Fifty-nine dry foods for adult and growing dogs were collected, ground and scanned across the visible/NIR range and subsequently analysed for dry matter (DM), crude protein (CP), crude fibre (CF), total fat, linoleic acid, gross energy (GE), estimated metabolizable energy (ME) and several amino acids and minerals. Calibration equations were developed by modified partial least squares regression, and tested by cross-validation. Standard error of cross validation (SE(CV)) and coefficient of determination of cross validation (SE(CV)) were used to select best equations. Equations with good predicting accuracy were obtained for DM, CF, CP, GE and fat. Corresponding values for and SE(CV) were 0.96 and 1.7 g/kg, 0.91 and 3.1 g/kg, 0.99 and 5.0 g/kg, 0.93 and 0.26 MJ/kg, 0.89 and 12.4 g/kg. Several amino acids were also well predicted, such as arginine, leucine, isoleucine, phenylalanine-tyrosine (combined), threonine and valine, with values for and SE(CV) (g/kg) of 0.89 and 0.9, 0.94 and 1.3, 0.91 and 0.5, 0.95 and 0.9, 0.91 and 0.5, 0.93 and 0.5. Intermediate values, appropriate for ranking purposes, were obtained for ME, histidine, lysine and methionine-cysteine. Tryptophan, minerals or linoleic acid were not acceptably predicted, irrespective of the mathematical treatment applied. It is concluded that NIR can be successfully used to predict important nutritional characteristics of commercial dog foods.

Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS).

Near infrared reflectance spectroscopy (NIRS) was evaluated as a tool to segregate different types of bovine meat and predict several chemical fractions on samples from two breeds, three muscles and six grading (Chilean system) categories. Samples previously minced, frozen and thawed, were scanned (400-2500 nm) and then analyzed for dry matter, crude protein, ether extract, total ash and collagen content, after freeze drying. Discriminant analysis using a partial least squares regression technique and cross validation, correctly identified breed and muscle type for most samples, but carcass grades, with the exception of samples from calves, were not successfully predicted. Best calibrations for chemical composition tested by cross-validation, showed R(2) and standard errors of cross validation of 0.77 and 0.58% (dry matter), 0.82 and 0.48% (crude protein), 0.82 and 0.44% (ether extract). Calibrations for total ash showed a poor, and for collagen, a very poor prediction ability.