Assessing the quality of dry sausages using fluorescence spectroscopy, physico-chemical and dynamic testing rheology: A preliminary study

The viscoelastic and the structure properties of three brands of dry sausages (Auvergne, Beef-poultry, and Galbanetto) were studied using dynamic rheology and fluorescence spectroscopy. The storage and the loss modulus of sausage samples showed a viscoelastic character. The principal component analysis (PCA) carried out on the normalised rheological and physico-chemical parameters allowed to differentiate the sausage samples into 3 groups according to their brands. This trend was confirmed by the factorial discriminant analysis (FDA) where 74.44% of correct classification was obtained. The emission spectra acquired after excitation set at 290, 340, and 360 nm and excitation spectra scanned after emission set at 410 nm allowed clear differentiation between the three brand samples. The obtained results were confirmed following the application of partial least squares regression (PLSR) to the fluorescence and physico-chemical parameters since an excellent prediction of moisture content was obtained from the excitation spectra set at 340 nm (R²  = 0.99) and 360 nm (R²  = 0.99). The protein content of dry sausages was well predicted after excitation set at 290 and 340 nm with R² of 0.96 and 0.97, respectively, while the fat level was well estimated after excitation set at 340 and 360 nm and emission set at 410 nm (R²  = 0.96, 0.96 and 0.94, respectively). The obtained results showed the potential use of fluorescence spectroscopy as a rapid technique for evaluating the quality of dry sausages. This article is protected by copyright. All rights reserved.

Review: Quality of animal-source foods

This article critically reviews the current state of knowledge on the quality of animal-source foods according to animal production and food processing conditions, including consumer expectations-behaviours and the effects of consumption of animal-source foods on human health. Quality has been defined through seven core attributes: safety, commercial, sensory, nutritional, technological, convenience, and image. Image covers ethical, cultural and environmental dimensions associated with the origin of the food and the way it is produced and processed. This framework enabled to highlight the priorities given to the different quality attributes. It also helped to identify potential antagonisms and synergies among quality attributes, between production and processing stages, and among stakeholders. Primacy is essentially given to commercial quality attributes, especially for standard commodity animal-source foods. This primacy has strongly influenced genetic selection and farming practices in all livestock commodity chains and enabled substantial quantitative gains, although at the expense of other quality traits. Focal issues are the destructuration of chicken muscle that compromises sensory, nutritional and image quality attributes, and the fate of males in the egg and dairy sectors, which have heavily specialised their animals. Quality can be gained but can also be lost throughout the farm-to-fork continuum. Our review highlights critical factors and periods throughout animal production and food processing routes, such as on-farm practices, notably animal feeding, preslaughter and slaughter phases, food processing techniques, and food formulation. It also reveals on-farm and processing factors that create antagonisms among quality attributes, such as the castration of male pigs, the substitution of marine-source feed by plant-based feed in fish, and the use of sodium nitrite in meat processing. These antagonisms require scientific data to identify trade-offs among quality attributes and/or solutions to help overcome these tensions. However, there are also food products that value synergies between quality attributes and between production and processing phases, particularly Geographical Indications, such as for cheese and dry-cured ham. Human epidemiological studies have found associations between consumption of animal-source foods and increased or decreased risk for chronic non-communicable diseases. These associations have informed public health recommendations. However, they have not yet considered animal production and food processing conditions. A concerted and collaborative effort is needed from scientists working in animal science, food process engineering, consumer science, human nutrition and epidemiology in order to address this research gap. Avenues for research and main options for policy action are discussed.

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.

Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches

This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.

Preliminary investigation for the prediction of intramuscular fat content of lamb in-situ using a hand- held NIR spectroscopic device

Intramuscular fat (IMF) content is critical in the determination of eating quality. At present the Australian lamb industry has no ability to measure IMF as carcases are not split and processing speeds of up to 15 animals per minute prohibit the use of traditional methods. Consequently, the potential for a hand-held Near- Infrared (NIR) device to predict the IMF content of lamb topside in-situ was investigated. Models demonstrated that there is an ability to predict the IMF content of topside (R² = 0.58, RMSEP = 0.85) using NIR spectra collected at 24 h post-mortem and loin (R² = 0.50, RMSEP = 0.91). However, the models were limited by the range and distribution of the lamb population measured. Thus, further research is required to determine whether these models can be improved by increasing the range of data in the calibration models and considering alternate methods of analysis which are suitable for skewed populations.

Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms

This study evaluated the performance of classical front face (FFFS) and synchronous (SFS) fluorescence spectroscopy combined with Partial Least Square Discriminant Analysis (PLSDA), Support Vector Machine associated with PLS (PLS-SVM) and Principal Components Analysis (PCA-SVM) to discriminate three beef muscles (Longissimus thoracis, Rectus abdominis and Semitendinosus). For the FFFS, 5 excitation wavelengths were investigated, while 6 offsets were studied for SFS. Globally, the results showed a good discrimination between muscles with Recall and Precision between 47.82 and 94.34% and Error ranging from 6.03 to 32.39%. For the FFFS, the PLS-SVM with the 382nm excitation wavelength gave the best discrimination results (Recall, Precision and Error of 94.34%, 89.53% and 6.03% respectively). For SFS, when performing discrimination of the three muscles, the 120nm offset gave the highest Recall and Precision (from 57.66% to 94.99%) and the lowest Error values (from 6.78 to 8.66%) whatever the algorithm (PLSDA, PLS-SVM and PCA-SVM).

Recent advances in rapid and nondestructive determination of fat content and fatty acids composition of muscle foods

Conventional methods for determining fat content and fatty acids (FAs) composition are generally based on the solvent extraction and gas chromatography techniques, respectively, which are time consuming, laborious, destructive to samples and require use of hazard solvents. These disadvantages make them impossible for large-scale detection or being applied to the production line of meat factories. In this context, the great necessity of developing rapid and nondestructive techniques for fat and FAs analyses has been highlighted. Measurement techniques based on near-infrared spectroscopy, Raman spectroscopy, nuclear magnetic resonance and hyperspectral imaging have provided interesting and promising results for fat and FAs prediction in varieties of foods. Thus, the goal of this article is to give an overview of the current research progress in application of the four important techniques for fat and FAs analyses of muscle foods, which consist of pork, beef, lamb, chicken meat, fish and fish oil. The measurement techniques are described in terms of their working principles, features, and application advantages. Research advances for these techniques for specific food are summarized in detail and the factors influencing their modeling results are discussed. Perspectives on the current situation, future trends and challenges associated with the measurement techniques are also discussed.