Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d'Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLS-DA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).

The one-humped camel: The animal of future, potential alternative red meat, technological suitability and future perspectives

The 2020 world population data sheet indicates that world population is projected to increase from 7.8 billion in 2020 to 9.9 billion by 2050 (Increase of more than 25%). Due to the expected growth in human population, the demand for meats that could improve health status and provide therapeutic benefits is also projected to rise. The dromedary also known as the Arabian camel, or one-humped camel ( Camelus dromedarius), a pseudo ruminant adapted to arid climates, has physiological, biological and metabolic characteristics which give it a legendary reputation for surviving in the extreme conditions of desert environments considered restrictive for other ruminants. Camel meat is an ethnic food consumed across the arid regions of Middle East, North-East Africa, Australia and China. For these medicinal and nutritional benefits, camel meat can be a great option for sustainable meat worldwide supply. A considerable amount of literature has been published on technological aspects and quality properties of beef, lamb and pork but the information available on the technological aspects of the meat of the one humped camel is very limited. Camels are usually raised in less developed countries and their meat is as nutritionally good as any other traditional meat source. Its quality also depends on the breed, sex, age, breeding conditions and type of muscle consumed. A compilation of existing literature related to new technological advances in packaging, shelf-life and quality of camel meat has not been reviewed to the best of our knowledge. Therefore, this review attempts to explore the nutritional composition, health benefits of camel meat, as well as various technological and processing interventions to improve its quality and consumer acceptance. This review will be helpful for camel sector and highlight the potential for global marketability of camel meat and to generate value added products.

Near-Infrared Reflectance Spectroscopy for Predicting the Phospholipid Fraction and the Total Fatty Acid Composition of Freeze-Dried Beef

Research on fatty acids (FA) is important because their intake is related to human health. NIRS can be a useful tool to estimate the FA of beef but due to the high moisture and the high absorbance of water makes it difficult to calibrate the analyses. This work evaluated near-infrared reflectance spectroscopy as a tool to assess the total fatty acid composition and the phospholipid fraction of fatty acids of beef using freeze-dried meat. An average of 22 unrelated pure breed young bulls from 15 European breeds were reared on a common concentrate-based diet. A total of 332 longissimus thoracis steaks were analysed for fatty acid composition and a freeze-dried sample was subjected to near-infrared spectral analysis. 220 samples (67%) were used as a calibration set with the remaining 110 (33%) being used for validation of the models obtained. There was a large variation in the total FA concentration across the animals giving a good data set for the analysis and whilst the coefficient of variation was nearly 68% for the monounsaturated FA it was only 27% for the polyunsaturated fatty acids (PUFA). PLS method was used to develop the prediction models. The models for the phospholipid fraction had a low R²p and high standard error, while models for neutral lipid had the best performance, in general. It was not possible to obtain a good prediction of many individual PUFA concentrations being present at low concentrations and less variable than other FA. The best models were developed for Total FA, saturated FA, 9c18:1 and 16:1 with R²_p greater than 0.76. This study indicates that NIRS is a feasible and useful tool for screening purposes and it has the potential to predict most of the FA of freeze-dried beef.

Prediction of the intramuscular connective tissue components of fresh and freeze-dried samples by near infrared spectroscopy

This study compared the performance of near-infrared spectroscopy (NIRS) models on fresh and freeze-dried beef muscle samples to predict intramuscular connective tissue (IMCT) components and to determine whether the accuracy of the models differed among different muscles from beef cattle. The hypothesis was that the water content of muscle samples would negatively influence the accuracy of the models, which would differ among muscles. Fresh and freeze-dried samples (n = 171) of four muscles were used to develop NIRS models to predict the contents IMCT. For the total collagen content, the standard error of cross validation (SECV) for model using freeze-dried samples (0.75 mg OH-prol/g DM) was lower than that for model using fresh samples (0.84 mg OH-prol/g DM). For cross-links and proteoglycans, the SECV for models using fresh sample spectra was lower than that for models using freeze-dried sample spectra. The accuracy of the prediction of the models also differed among predicted muscle types.