Aloe Vera leaves have a great potential as an economic supplement with an adequate nutritional profile. In this study, Aloe Vera leaf gel (AVG) powder was used to fortify plain cakes. Freeze drying of AVG was performed for the production of Aloe Vera powder (ALP) and four plain cakes were prepared with different proportions of ALP for further investigation. Analysis suggested that ALP contained significantly (p<0.05) higher amount of protein (22.23 vs 12.24), ash (19.83 vs 0.64) and iron (175 vs 3.05) content than refined wheat flour (RWF). ALP also contained significant amount of total polyphenols and antioxidant. Moisture, protein, ash, weight, and minerals (Fe, Ca) content were higher (p<0.05) in ALP-cakes; whereas fat, volume, specific volume, height, baking loss, and total carbohydrate content were higher (p<0.05) in RWF-cakes. Incorporation of 6 and 8% ALP in the formulation increased the total polyphenols and anti-oxidant activity in plain cakes. Texture analysis revealed that hardness and chewiness increased in ALP-cakes but decreased in RWF-cakes, however, cohesiveness, springiness, and chewiness decreased in ALP-cakes. Sensory attributes suggested that 4% ALP incorporated cake was attributed as the best formulation. In conclusion, ALP can be supplemented in cakes up to 8% to improve the nutrient value.

The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the “you only look once (YOLO) v5” principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. The dataset was trained using the pre-trained YOLOv5 algorithm. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.