Microbiological quality of food sold by street vendors in Kisangani, Democratic Republic of Congo

Classification of multicategory edible fungi based on the infrared spectra of caps and stalks

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect.

Bacteriological profile, antimicrobial susceptibility patterns of the isolates among street vended foods and hygienic practice of vendors in Gondar town, Northwest Ethiopia: a cross sectional study

Background

There are numerous advantages offered by street vended foods, but evidence exists that foods exposed for sale on the road side may be contaminated by pathogenic microorganisms. However, information on the bacteriological profile, bacterial load and antimicrobial susceptibility patterns of bacterial isolates from street food in Gondar town are lacking. The aim of this study was to assess bacterial profile, bacterial load, and antimicrobial susceptibility patterns of bacterial isolates among street vended foods and also the hygienic practice of vendors in Gondar town, Northwest Ethiopia.

Methods

Socio-demographic characteristics and the hygienic practices of 24 vendors were collected using structured questionnaire. A total of 72 food samples from four different food items were analyzed and counted by standard aerobic plate count method. Ten grams of each food sample was transferred in to 90 ml of buffered peptone water and homogenized. The homogenates were serially dilute and a volume of 0.1 ml dilution was spread on solid media and incubated at 35-37 °C for 24 h. Antibiotic susceptibility testing was done for isolated species using Muller Hinton agar and data was entered and analyzed by using SPSS version 20.0.

Results

Seventy two food samples of street vended food were analysed for bacterial pathogens. 44/72 tested positive, a total of 63 isolates were identified as 19 samples contained two pathogens. The total mean aerobic bacterial count was 6.64 × 10⁴ CFU/g which is varied from 1 × 10⁴–1.86 × 10⁵ CFU/g. S. aureus is the most frequent isolate 34 (53.96%) followed by E.coli 15(23.8%), Enterobacter species 10(15.87%) and Citrobacter species 4(6.3%). Gentamycin, chloramphenicol, tetracycline, ciprofloxacin, and trimethoprim-sulfamethoxazole were found to be the most effective antimicrobials against all isolates but the enterobactereaceae were resistant to ampicillin and Ceftaziidime and S.aureus were resistant to penicillin.

Conclusion

The results of this study showed that, the majority of street-vended food items in Gondar were contaminated with one or more different pathogenic bacteria. The presence of these bacteria in foods could lead to potential health problems for consumers. Therefore, health education as well as training in food safety and hygienic handling is required for food handlers to minimize contamination and the likelihood of people falling ill.

What are the key factors influencing consumers' preference and willingness to pay for meat products in Eastern DRC?

Dietary patterns for consumers among the elite and middle-income classes in developing countries are shifting rapidly toward the consumption of more animal-based products. Although this shift presents opportunities, there are significant market failures affecting their preferences and willingness to pay (WTP). This study used a multistage sample survey of 309 consumers from three different communities of Bukavu, Eastern DRC, to examine the effect of socioeconomic/socio-demographic characteristics and quality attributes on consumers' purchasing decisions and WTP for meat products. The results suggested that about 53% of the respondents were dissatisfied with meat products in the market due to their high price, low quantity, unhealthiness, and harmful effects. Older female respondents living in urban areas were more likely to purchase meat products. Their WTP was significantly determined by attributes such as color, in-mouth texture, and availability. Nutrition, harmful effects, and availability of meat products are the important factors that influence purchasing decisions among higher income groups. Addressing these market failures could have an impact on the meat market, improving the nutrition of low-income consumers and ensuring food safety standards in DRC and other developing countries with similar challenges.

Antibiotic Use in Agriculture and Its Consequential Resistance in Environmental Sources: Potential Public Health Implications

Due to the increased demand of animal protein in developing countries, intensive farming is instigated, which results in antibiotic residues in animal-derived products, and eventually, antibiotic resistance. Antibiotic resistance is of great public health concern because the antibiotic-resistant bacteria associated with the animals may be pathogenic to humans, easily transmitted to humans via food chains, and widely disseminated in the environment via animal wastes. These may cause complicated, untreatable, and prolonged infections in humans, leading to higher healthcare cost and sometimes death. In the said countries, antibiotic resistance is so complex and difficult, due to irrational use of antibiotics both in the clinical and agriculture settings, low socioeconomic status, poor sanitation and hygienic status, as well as that zoonotic bacterial pathogens are not regularly cultured, and their resistance to commonly used antibiotics are scarcely investigated (poor surveillance systems). The challenges that follow are of local, national, regional, and international dimensions, as there are no geographic boundaries to impede the spread of antibiotic resistance. In addition, the information assembled in this study through a thorough review of published findings, emphasized the presence of antibiotics in animal-derived products and the phenomenon of multidrug resistance in environmental samples. This therefore calls for strengthening of regulations that direct antibiotic manufacture, distribution, dispensing, and prescription, hence fostering antibiotic stewardship. Joint collaboration across the world with international bodies is needed to assist the developing countries to implement good surveillance of antibiotic use and antibiotic resistance.