Modeling of Ethiopian Beef Meat Marbling Score Using Image Processing for Rapid Meat Grading.

Meat characterized by a high marbling value is typically anticipated to display enhanced sensory attributes. This study aimed to predict the marbling scores of rib-eye, steaks sourced from the Longissimus dorsi muscle of different cattle types, namely Boran, Senga, and Sheko, by employing digital image processing and machine-learning algorithms. Marbling was analyzed using digital image processing coupled with an extreme gradient boosting (GBoost) machine learning algorithm. Meat texture was assessed using a universal texture analyzer. Sensory characteristics of beef were evaluated through quantitative descriptive analysis with a trained panel of twenty. Using selected image features from digital image processing, the marbling score was predicted with R2 (prediction) = 0.83. Boran cattle had the highest fat content in sirloin and chuck cuts (12.68% and 12.40%, respectively), followed by Senga (11.59% and 11.56%) and Sheko (11.40% and 11.17%). Tenderness scores for sirloin and chuck cuts differed among the three breeds: Boran (7.06 ± 2.75 and 3.81 ± 2.24, respectively), Senga (5.54 ± 1.90 and 5.25 ± 2.47), and Sheko (5.43 ± 2.76 and 6.33 ± 2.28 Nmm). Sheko and Senga had similar sensory attributes. Marbling scores were higher in Boran (4.28 ± 1.43 and 3.68 ± 1.21) and Senga (2.88 ± 0.69 and 2.83 ± 0.98) compared to Sheko (2.73 ± 1.28 and 2.90 ± 1.52). The study achieved a remarkable milestone in developing a digital tool for predicting marbling scores of Ethiopian beef breeds. Furthermore, the relationship between quality attributes and beef marbling score has been verified. After further validation, the output of this research can be utilized in the meat industry and quality control authorities.

Trends and Determinants of Dietary Diversity in Children Aged 6-59 Months in Ethiopia: Analysis of 2005-2016 Demographic and Health Survey.

Background: Dietary diversity may be associated with health and optimum growth in children. Objectives: In this study we analyzed the trends and determinants of minimum dietary diversity (MDD) among Ethiopian children aged 6 to 59 mo. Methods: Ethiopian Demographic Health Survey (EDHS) data of 3 consecutive years (2005, 2011, and 2016) were analyzed. A total of n = 2396 (2005), n = 3385 (2011), and n = 3723 (2016) children aged 6 to 59 mo were included for measurement of trends and identification of the determinants of MDD. The associations between the study factors and MDD were investigated using multiple logistic regression analysis. Results: The proportion of children who fulfilled the MDD decreased from 2.46% in 2005 to 1.57% in 2011 but sharply increased to 7.82% in 2016. Adjusted regression analysis revealed that exposure of mothers to media, particularly watching television, maternal education, and household wealth were associated with a greater likelihood of mothers providing diversified diets to their children across the 3 y of EDHS data. Conclusions: A decrease in MDD was observed from the years 2005 to 2011, after which a sharp increase was noted in 2016. In all 3 y of the EDHS, media exposure, maternal education, and household wealth were the consistent factors positively affecting dietary diversity among children aged 6 to 59 mo. Future intervention programs to increase dietary diversity in children should emphasize improving access to media exposure, education, and antenatal care visits.

A feasibility study on nondestructive classification of frozen Atlantic salmon (Salmo salar) fillets based on temperature history at the logistics using NIR spectroscopy.

Temperature fluctuation commonly occurs in the cold chain leading to complete or partial thawing and refreezing of frozen products resulting in a multifrozen product. Such oscillation of temperature could cause significant quality reduction compared to single frozen products. This study was designed to differentiate frozen Atlantic salmon fillets based on the level of temperature fluctuation. Near-infrared spectroscopy (NIRS) coupled with chemometrics was used to classify the frozen fillets stored at no fluctuation (NF), low fluctuation (LF), high fluctuation (HF), and very high fluctuation (VF) temperature. Using spectral profiles obtained at both frozen and thawed states, fillets were classified based on the level of temperature fluctuation by partial least squares discriminant analysis (PLS-DA). The thawed samples showed better classification accuracy (71%) than frozen samples (66%) in a four-class model. Considering the small variation within the first two (NF, LF) and the last two (HF, VF) groups, a two-class classification model was developed using thawed samples, and the obtained model correctly classified the two groups ([NF, LF] and [HF, VF]) with 100 % classification accuracy. Protein- and water-related changes were found important to distinguish the fillets. Based on these findings, the four-class prediction model is found insufficient to be used for nondestructive determination of temperature history of frozen fillets. However, the two-class prediction model with further external validation can be applied to determine the level of temperature fluctuation particularly using fillets scanned at thawed state. PRACTICAL APPLICATION: NIR spectroscopy can be used to evaluate the degree of temperature fluctuation and thus related quality loss throughout the logistics of frozen Atlantic salmon fillets. Researchers, food control authorities, and the retail industry could be the primary beneficiaries of this research output.