Marlon M. Reis

Early on-line classification of beef carcasses based on ultimate pH by near infrared spectroscopy

Prediction of ultimate pH (measured 48 h post mortem; pH(u)) in beef from Visible-near infrared (VIS-NIR) spectra collected 20 to 40 min post mortem was assessed. Spectra were collected from carcasses (cows: n = 86, bulls: n = 170, steers: n = 363, and heifers: n = 38) in a commercial hot boning abattoir under routine conditions. Partial Least Squares (PLS) models showed limited accuracy with RMSE for validation equal to 0.26, 0.20 and 0.36 for the All-animals, Non-bulls and Bulls models, respectively. The pH(u)-PLS-predicted values were used to segregate carcasses as normal (pH(u)<5.8) or high (pH(u) ≥ 5.8) showing better performance, by correctly classifying at least 90% of high pH(u) carcasses. The Non-bulls model was equivalent to the current technology used in the abattoir to classify carcasses based on pH(u). Thus near infrared spectroscopy (NIRS) could be used for on-line classification of beef carcasses based on pH(u).

Direct analysis of fatty acid profile from milk by thermochemolysis–gas chromatography–mass spectrometry

The fatty acid composition of milk is of considerable interest due to their nutritional and functional properties. Although rapid milk fat separation and transesterification procedures have been developed, the overall procedure remains time consuming, specially, for the analysis of a large number of samples. In this work, a fast and simple method for direct profiling of fatty acids from milk using thermochemolysis has been developed. This method has the capability of directly analyse fatty acids from one drop of milk without fat extraction or cleanup. Our approach for thermochemolysis is based on thermal desorption integrated with a cold trap inlet. The optimized method does not present isomerisation/degradation of polyunsaturated fatty acid and shows milk fatty acid profiles comparable to the conventional method based on fat extraction and alkaline transesterification. Overall, this method has demonstrated significant potential for high throughput analysis of fatty acids in milk.

Impact of dietary dairy polar lipids on lipid metabolism of mice fed a high-fat diet

The effect of milk polar lipids on lipid metabolism of liver, adipose tissue, and brain and on composition of intestinal microbiota was investigated. C57BL/6J mice were fed a high-fat diet (HFD) for 5 weeks, followed by 5 weeks with HFD without (control) or supplemented with total polar lipids (TPL), phospholipids (PL), or sphingolipids (SPL). Animals fed SPL showed a tendency for lower triglyceride synthesis (P = 0.058) in the liver, but not in adipose tissue. PL and TPL reduced de novo hepatic fatty acid biosynthesis. The ratio of palmitoleic to palmitic acid in the liver was lower for animals fed SPL or TPL compared to control. There was little effect of the supplementation on the cecal microbiota composition. In the brain, DHA (C22:6) content correlated negatively with tetracosanoic acid (C24:0) after TPL supplementation (-0.71, P = 0.02) but not in control (0.26, P = 0.44). Arachidonic acid (C20:4) was negatively correlated with C24:0 in both groups (TPL, -0.77, P = 0.008; control, -0.81, P = 0.003).

Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi.

The potential of near infrared (NIR) spectroscopy as an on-line method to quantify glycogen and predict ultimate pH (pH(u)) of pre rigor beef M. longissimus dorsi (LD) was assessed. NIR spectra (538 to 1677 nm) of pre rigor LD from steers, cows and bulls were collected early post mortem and measurements were made for pre rigor glycogen concentration and pH(u). Spectral and measured data were combined to develop models to quantify glycogen and predict the pH(u) of pre rigor LD. NIR spectra and pre rigor predicted values obtained from quantitative models were shown to be poorly correlated against glycogen and pH(u) (r(2)=0.23 and 0.20, respectively). Qualitative models developed to categorize each muscle according to their pH(u) were able to correctly categorize 42% of high pH(u) samples. Optimum qualitative and quantitative models derived from NIR spectra found low correlation between predicted values and reference measurements.

Characterization of volatile metabolites associated with confinement odour during the shelf-life of vacuum packed lamb meat under different storage conditions.

Confinement odour was investigated. Volatiles were extracted directly from the pack, using solid phase microextraction and analysed by gas chromatography-mass spectrometry. Sensory evaluation and microbiological analysis of the meat surface were also performed. Commercial samples of vacuum packed lamb legs (n=85), from two meat processing plants, were kept for 7weeks at -1.5°C then at different regimes of temperature (-1.5 to +4°C) until 11, 12 or 13weeks. Persistent odour was observed in 66% of samples, confinement odour in 24% and no odour in 11%. Volatiles associated with confinement odour (3-methyl-butanal, 3-hydroxy-2-butanone and sulphur dioxide) corresponded with end/sub products of glucose fermentation and catabolism of amino acids by bacteria (all bacteria naturally found in meat and do not represent a risk to health). Confinement odour could indicate a stage at which the environment for bacteria growth is becoming favourable for the production of volatiles with strong odours that are noticed by the consumer.

Effect of Dietary Complex Lipids on the Biosynthesis of Piglet Brain Gangliosides

Gangliosides, found in mammalian milk, are known for their roles in brain development of the newborn. However, the mechanism involved in the impact of dietary gangliosides on brain metabolism is not fully understood. The impact of diets containing complex lipids rich in milk-derived ganglioside GD3 on the biosynthesis of gangliosides (assessed from the incorporation of deuterium) in the frontal lobe of a piglet model is reported. Higher levels of incorporation of deuterium was observed in the GM1 and GD1a containing stearic acid in samples from piglets fed milk containing 18.2 μg/mL of GD3 compared to that in those fed milk containing 25 μg/mL of GD3. This could suggest that the gangliosides from the diet may be used as a precursor for de novo biosynthesis of brain gangliosides or lead to the reduction of de novo biosynthesis of these gangliosides. This effect was more pronounced in the left compared to that in the right brain hemisphere.

Aplicação do modelo Tucker-3 para a análise da biodegradação de diesel

Tucker-3 model offers several advantages for analysis of environmental data but its interpretation is still challenging. A Tucker-3 model was applied to a biodegradation experiment involving a large number of overlapped chromatographic peaks and a temporal variation. The Tucker-3 model allowed the data to be decomposed in two processes: evaporation and biodegradation. The results suggest that linear hydrocarbons were those biodegraded first and demonstrate that the data analysis can be simplified by interpreting the elements of the core array. The approach discussed in this work can be applied in similar problems involving multi-way data in other areas of chemistry.

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

Detection of Adulteration in Red Meat Species Using Hyperspectral Imaging.

This paper reports the performance of hyperspectral imaging for detecting the adulteration in red-meat species. Line-scanning images are acquired from muscles of lamb, beef, or pork. We consider the states of fresh, frozen, or thawed meat. For each case, packing and unpacking the sample with a transparent bag is considered and evaluated. Meat muscles are defined either as a class of lamb, or as a class of beef or pork. For visualization purposes, fat regions are also considered. We investigate raw spectral features, normalized spectral features, and a combination of spectral and spatial features by using texture properties. Results show that adding texture features to normalized spectral features achieves the best performance, with a 92.8% overall classification accuracy independently of the state of the products. The resulting model provides a high and balanced sensitivity for all classes at all meat stages. The resulting model yields 94% and 90% average sensitivities for detecting lamb or the other meat type, respectively. This paper shows that hyperspectral imaging analysis provides a rapid, reliable, and non-destructive method for detecting the adulteration in red-meat products.

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat

Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat structure. The use of HSI in the meat and food industry has observed a significant growth in the last decade, yet its use for assessment of meat it is not optimal yet. The application of HSI for assessment of meat is reviewed with focus on its ability to capture meat unique chemical and structural characteristics. While HSI is widely used for assessment of chemical composition, a limited number of evidences on its ability to handle the effect of different sources of variation on the assessment is found. The use of spatially resolved spectroscopy has been able to detect structural information related to animal background, muscle type, rigor process and ageing. Similarly the use of texture features seem to capture unique characteristics of meat.