Feasibility of Image Analysis Coupled with Machine Learning for Detection and Quantification of Extraneous Water in Milk

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.

How Chemometrics Can Fight Milk Adulteration

Adulteration and fraud are amongst the wrong practices followed nowadays due to the attitude of some people to gain more money or their tendency to mislead consumers. Obviously, the industry follows stringent controls and methodologies in order to protect consumers as well as the origin of the food products, and investment in these technologies is highly critical. In this context, chemometric techniques proved to be very efficient in detecting and even quantifying the number of substances used as adulterants. The extraction of relevant information from different kinds of data is a crucial feature to achieve this aim. However, these techniques are not always used properly. In fact, training is important along with investment in these technologies in order to cope effectively and not only reduce fraud but also advertise the geographical origin of the various food and drink products. The aim of this paper is to present an overview of the different chemometric techniques (from clustering to classification and regression applied to several analytical data) along with spectroscopy, chromatography, electrochemical sensors, and other on-site detection devices in the battle against milk adulteration. Moreover, the steps which should be followed to develop a chemometric model to face adulteration issues are carefully presented with the required critical discussion.