Tanga City is the region with several micro-and small-scale pasta processing companies in the country. Therefore, the purpose of this study was to assess the quality and safety of folded vermicelli produced by the small-scale processors in Tanzania. Samples of 1 kg folded vermicelli were collected from 14 processing companies, by the intentional cluster sampling technique. The samples were analysed for aflatoxin and microbiological (Escherichia coli, Aspergillus flavus, and Aspergillus parasiticus) quality. Moreover, physico-chemical quality was assessed in terms of diameter by using a digital calliper, moisture content by oven-drying method at 110℃± 5℃, breaking strength by the texture analyzer, and colour by colourimeter (Chroma Meter CR-400) of the collected samples were determined. In terms of microbial quality, the results indicated contamination by E. coli (1.25-3.00 Log CFU.g^-1 in 8/14 samples), A. flavus (2.23-2.83 Log CFU.g^-1 in 12/14 samples), and A. parasiticus (1.22-2.75 Log CFU.g^-1 in 2/14 samples) as they are beyond the set limits. The diameter varied between 0.90 mm to 1.73 mm in 9/14 samples and moisture content were 10.61% to 12.65% in 13/14 samples, being within the established parameters. The samples indicated low breaking strength with levels between 6.79x10⁵ N.m^-2 to 3.75x10⁶ N.m^-2 in 11/14 samples. The result of brightness (L*) were between 53.03 to 72.14 and yellowness (b*) between 13.68 to 19.48 indices, indicating that there was no significant difference at the 5% level, respectively, although 2/14 samples had red (a*) colour values (-1.32 – +0.56). However, 4/14 samples were detected with aflatoxin B₁ (0.60-0.70 μg.kg^-1), they are within the recommended level (5 μg.kg^-1). The study underscores the need for concerted efforts to enhance production and hygiene practices to ensure consistent compliance with quality and safety standards.

This study assessed the handling and processing practices of 30 small-scale folded vermicelli processors in Tanga, specifically in urban areas of Tanga City, Tanzania. However, the micro- and small-scale processors were producing in unhygienic way because they are lacking facilities and equipment to process and handle the product hygienically. Multistage sampling design was adopted for this study and face-to-face interviews were conducted to collect data from all processing units through nine streets using semi-structured questionnaires and observation checklists. Data were analyzed using Statistical Package for Social Sciences, where the statistics aspect was determined from the results obtained. The processors found across various streets (ranging from 3.3% in Kwaminchi Street to 23.3% in Mabawa Street), exhibited diverse demographics, with 53.3% being owner-operators and 40% and 6.7% in labourer and supervisor roles, respectively. A significant portion (53.3%) had 1-3 years of experience, and a small portion (10%) attended formal training in pasta processing. Despite 73.3% possessing food manufacturing licenses, many were unfamiliar with legal requirements, lacking documentation and standardized processes, raising concerns about food safety. Raw materials were sourced locally, but 56.7% lacked storage facilities. Hygienic practices varied, with 43.3% undergoing periodic medical check-ups, 70% using protective gear, and 60% had hand washing facilities. Sun drying was the sole method employed, with 86.7% placed drying trays on rooftops. Packaging practices raised concerns, as 93.3% reused woven polypropylene bags, potentially impacting product quality. Awareness of aflatoxin and its health implications was lacking in 90% of the processors. Overall, the study highlighted gaps in awareness, training, and adherence to standards among processors, posing potential risks to food safety and quality. Encourage them to adhere with Tanzania Bureau of Standards requirements and formalize their quality control practices.

Recent technologies have made life smarter. vehicles are vital components in daily life that are getting smarter for a safer environment. Advanced Driving Assistance Systems (ADAS) are widely used in today's vehicles. It has been a revolutionary approach to make roads safer by assisting the driver in difficult situations like collusion, or assistance in respecting road rules. ADAS is composed of a huge number of sensors and processing units to provide a complete overview of the surrounding objects to the driver. In this paper, we introduce a road signs classifier for an ADAS to recognize and understand traffic signs. This classifier is based on a deep learning technique, and, in particular, it uses Convolutional Neural Networks (CNN). The proposed approach is composed of two stages. The first stage is a data preprocessing technique to filter and enhance the quality of the input images to reduce the processing time and improve the recognition accuracy. The second stage is a convolutional CNN model with a skip connection that allows passing semantic features to the top of the network in order to allow for better recognition of traffic signs. Experiments have proved the performance of the CNN model for traffic sign classification with a correct recognition rate of 99.75% on the German traffic sign recognition benchmark GTSRB dataset.