1 

UNIVERSITY OF GHANA 

COLLEGE OF HEALTH SCIENCES 

 

 

 

ANTIBIOTIC RESIDUES AND MULTIDRUG-RESISTANT GRAM-NEGATIVE 

BACTERIA CONTAMINATING RAW MEAT SOLD IN ACCRA, GHANA 

 

  

 

BY 

DERIC ASINOR BAAH 

(10805460) 

 

 

 

THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON, IN 

PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF 

MPHIL MEDICAL MICROBIOLOGY DEGREE. 

JULY, 2021 

 

  

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DECLARATION 

I, Deric Asinor Baah, hereby declare that except for references to the literature and works of 

other researchers which have been duly acknowledged, the work in this dissertation is the result 

of my work put together and that I have never before submitted it to any other tertiary institution 

for a degree. 

 

 

 

…………………………………….                                                           24/02/2023 

Deric Asinor Baah 

(Student) 

 

 

………………………………………                                                         24/02/23 

Prof. Eric Sampane-Donkor 

(Supervisor) 

 

……… …….…………….                                                         24/02/23 

Prof. Nicholas T. K. D. Dayie 

(Co-Supervisor) 

 

 

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DEDICATION 

I dedicate this piece of work to my lovely dad and mom for sacrificing their comfort for my 

education. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

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ACKNOWLEDGEMENT 

My immediate thanks go to the Almighty God for guiding and providing me with wisdom and 

understanding throughout this period. My sincere gratitude also goes to the extremely talented 

and able supervisor Prof. Eric Sampane-Donkor and co-supervisor, Dr. Nicholas T. K. D. 

Dayie, for their continuous guidance and dedication to making this work materialize. I am also 

grateful to Mr. Fleischer C. N. Kotey for his immense contribution and supervision to bring to 

fruition this work. I am also indebted to my work colleagues, especially Sandra Wireko 

Attakorah, for helping me during the laboratory analysis of the meat samples. I am also grateful 

to my twin brother, Mr. Eric Asinor Baah, for his sacrifices towards this work. I also wish to 

extend my gratitude to my beloved wife, Gloria Asinor Baah, for being understanding 

throughout this period.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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ABSTRACT 

Background: Antimicrobial resistance (AMR) is considered by the World Health 

Organization as one of the greatest threats to health and economies in recent times. Multidrug 

resistance limits treatment options and has serious implications for human and animal health. 

Efforts to combat AMR should be based on the One Health approach and involve human health, 

animal health, and the environment. In Ghana, previous studies on AMR have given little 

attention to animal source food, which is a major route of transmission of antibiotic-resistant 

zoonotic pathogens. 

Aim: The aim of the study was to investigate the occurrence of antibiotic residues and 

multidrug-resistant bacteria in meat sold in Accra. 

Methods: This was a cross-sectional study in which 270 meat samples were collected. The 

presence of antibiotic residues in the meat samples was detected using microbiological 

inhibition assays. Standard microbiological methods were employed in cultural isolation and 

identification of bacterial pathogens present in the meat samples. Bacteria isolated from the 

samples were subjected to antimicrobial susceptibility testing (using the Kirby-Bauer method) 

against the following antimicrobials: amikacin (30 µg), ampicillin (10 µg), 

amoxicillinclavulanate (20/10 µg), cefuroxime (30 µg), ceftriaxone (30 µg), ceftazidime (30 

µg), cefepime (30 µg), ciprofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25/23.75 µg), 

ertapenem (10 µg), meropenem (10 µg), imipenem (10 µg), tigecycline (15 µg), and gentamicin 

(10 µg). 

Results: The prevalence of antibiotic residues among the meat samples was 7.7% [beef (0.0%), 

goat meat (0.0%), and chicken (23.3%, n = 21)]. Furthermore, thirty-two (32) different types 

of bacterial agents, totaling 588, were isolated from the samples. The predominant ones were 

Escherichia coli [262; Beef = 30.5%, n = 80; Goat meat = 30.5%, n = 80; Chicken = 38.9%, n 

= 102], Aeromonas hydrophila [117; Beef = 35.9%, n = 42; Goat meat = 53.0%, n = 62; 

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Chicken = 11.1%, n = 13], Vibrio cholerae [20; Beef = 50.0%, n = 10; Goat meat = 50.0%, n 

= 10; Chicken = 0.0%, n = 0], Aeromonas veronii [19; Beef = 63.1%, n = 12; Goat meat = 

36.8%, n = 7; Chicken = 0.0%, n = 0], and Klebsiella pneumoniae [18; Beef = 22.2%, n = 4; 

Goat meat = 16.7%, n = 3; Chicken = 61.1%, n = 11]. The prevalence of MDR among the 

contaminating bacteria was 14.9% (n = 83), and the distribution was beef (3.8%, n = 21), goat 

meat = (5.0%, n = 28), and chicken (6.1%, n = 34). Also, the MDR distribution among the 

predominant bacteria was E. coli (Overall = 18.7%, n = 49; Beef = 5.7%, n = 15; Goat meat = 

5.7%, n = 15; Chicken = 7.3%, n = 19), A. hydrophila (Overall = 11.1%, n =13; Beef = 2.3%, 

n = 3; Goat meat = 7.7%, n = 9; Chicken = 0.9%, n = 1), V. cholerae and A. veronii (0.0% 

each), and K. pneumoniae (Overall = 5.6%, n = 1; Beef = 0.0%, n = 0; Goat meat = 0.0%, n = 

0; Chicken = 5.6%, n = 1). Moreover, 2.0% (n = 11) of the contaminating bacteria were ESBL 

producers, all of which occurred in 11 of the chicken samples, and their distribution was: E. 

coli (1.3%, n = 7), K. pneumoniae, Pantoea spp., E. cloacae, and Serratia plymuthica (0.2% 

each, n = 1). 

Conclusion: The prevalence of antibiotic residues in the meat samples was low, and the 

occurrence was restricted to chicken. The major bacterial contaminants were E. coli, A. 

hydrophila, V. cholerae, A. veronii, and K. pneumoniae. The prevalence of multidrug resistance 

was moderate, while that of ESBL producers was low. 

 

 

 

 

 

 

 

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LIST OF TABLES 

Table 1. A summary of the number of individual bacteria isolated per sample………………49 

Table 2. Distribution of the bacteria contaminating the meat sold in the markets…………..50 

Table 3. Antimicrobial resistance rates of the bacterial species contaminating the meat 

samples……………………………………………………………………………………….53 

Table 4. Multidrug resistance rates among the bacterial contaminants………………………55 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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LIST OF FIGURES 

Figure 1. A map showing the study sites…………………………………………………….43 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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LIST OF ABBREVIATIONS 

MDR: Multidrug-resistant organisms 

AST: Antibiotic susceptibility testing 

WHO: World Health Organisation 

CDC: Centre for Disease Control 

UTI: Urinary tract infection 

STC: Shigella toxin-producing E. coli    

CLSI: Clinical Laboratory Standards Institute 

EUCAST: European committee on Antimicrobial susceptibility testing 

LA-MRSA: Livestock Associated- Methicillin Resistant Staphylococcus aureus 

MIC: Minimum inhibitory Concentration 

ESBL: Extended spectrum beta-lactamase 

LAMP: Loop-mediated Isothermal Amplification 

VRE: Vancomycin resistant enterococcus 

MRSA: Methicillin-Resistant Staphylococcus aureus 

DNA: Deoxyribonucleic acid 

PCR: Polymerase Chain Reaction 

XLD: Xylose Lysine Deoxycholate  

TCBS: Thiosulphate-Citrate-Bile Salt-Sucrose agar 

MAR: Multiple Antibiotic Resistance index 

GDP: Gross Domestic Product 

USDA: United States Department of Agriculture  

VIS: Veterinary Services  

TLC: Thin Layer Chromatographer 

ELISA: Enzyme-Linked Immunosorbent Assay 

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1 Contents 

DECLARATION ....................................................................................................................... 2 

DEDICATION ......................................................................................................................... 3 

ACKNOWLEDGEMENT .......................................................................................................... 4 

ABSTRACT ............................................................................................................................. 5 

LIST OF TABLES ..................................................................................................................... 7 

LIST OF FIGURES ................................................................................................................... 8 

LIST OF ABBREVIATIONS ....................................................................................................... 9 

1.4 Aim ....................................................................................................................................... 17 

1.5 Specific Objectives ................................................................................................................ 17 

CHAPTER TWO .................................................................................................................... 18 

2.0 LITERATURE REVIEW ............................................................................................................. 18 

2.1 Antibiotics: Their history and use in food production ........................................................... 18 

2.2 Nutritional value and consumption of animal source foods in Africa ................................... 19 

2.3 Food safety and antibiotic residues in animal source foods .................................................. 20 

2.4 Analytical methods for determination of antibiotic residues in meats ................................. 22 

2.5 Emergence of antibiotic resistance as a global threat ........................................................... 23 

2.6 Factors contributing to the emergence of antibiotic resistance ............................................ 24 

2.7 Multidrug-resistant organisms of public health importance isolated in animal-source foods
 ................................................................................................................................................... 25 

2.8 Mechanisms of antibiotic resistance ..................................................................................... 28 

2.8.1 Inhibition of antibiotic penetration and efflux ..................................................... 28 

2.8.2 Modification of the antibiotic target .................................................................... 30 

2.8.3 Inactivation of the antibiotic molecule ................................................................ 31 

2.8.4 Inactivation of antibiotics by hydrolysis .............................................................. 31 

2.8.5 Chemical alterations of the antibiotic molecule ................................................... 33 

2.9 Techniques for determining the susceptibility of antibiotics ................................................ 33 

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2.9.1 Manual methods for determination of antibiotic susceptibility ............................ 35 

2.9.2 Automated and newer methods of antibiotic susceptibility testing ....................... 37 

2.10 A review of some studies conducted on the safety of animal-source foods in Ghana......... 38 

CHAPTER THREE.................................................................................................................. 42 

3.0 METHODOLOGY ............................................................................................................ 42 

3.1 Study design and site ............................................................................................................ 42 

3.2 Sample size determination and sample collection ................................................................ 42 

3.3 Laboratory processing........................................................................................................... 43 

3.3.1 Sample preparation ............................................................................................. 43 

3.3.2 Isolation and Identification of bacteria in the meat samples ................................. 43 

3.3.3 Antibiotic susceptibility testing ........................................................................... 45 

3.3.4 Phenotypic detection of ESBL-producing Enterobacteriaceae ............................. 46 

3.3.5 Microbial inhibition plate test for determination of antibiotic residues ................ 46 

3.4 Statistical analysis ................................................................................................................. 46 

CHAPTER FOUR ................................................................................................................... 48 

4.0 RESULTS ................................................................................................................................ 48 

4.1 Prevalence of antibiotic residues in the meat samples ......................................................... 48 

4.2 Spectrum of bacterial pathogens contaminating the meats ................................................. 48 

Table 1. A summary of the number of individual bacteria isolated per sample. ................ 48 

Table 2. Distribution of the proportion of bacteria contaminating the meat sold in the 

markets. ............................................................................................................................. 49 

4.3 Antimicrobial resistance rates among the bacterial Contaminants of the Meats ................. 50 

Table 3. Antimicrobial resistance rates of the bacterial species contaminating the meat 

samples. ............................................................................................................................. 51 

Table 4. Multidrug resistance rates among the bacterial contaminants. ........................... 53 

CHAPTER FIVE ..................................................................................................................... 55 

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5.0 DISCUSSION .................................................................................................................. 55 

CHAPTER SIX ....................................................................................................................... 62 

6.0 CONCLUSIONS, RECOMMENDATIONS, AND LIMITATIONS ........................................... 62 

6.1 Conclusions ........................................................................................................................... 62 

6.2 Recommendation ................................................................................................................. 62 

REFERENCES ....................................................................................................................... 64 

APPENDIX I ......................................................................................................................... 73 

APPENDIX II ........................................................................................................................ 74 

APPENDIX III ....................................................................................................................... 75 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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CHAPTER ONE 

1.0 INTRODUCTION 

1.1 Background 

The quality of food is generally affected by the presence of chemicals and infectious agents 

such as aflatoxins, antibiotic residues, pesticides, and pathogenic microorganisms. Food-borne 

diseases which result from the intake of food contaminated with bacteria, viruses, parasites, 

and or chemicals are major causes of mortality and morbidity due to their broad spectrum and 

quick rate of transmission (Smith, M’ikanatha, & Read, 2015). Animal-source foods are 

protein-dense foods of animal origin such as meat, yogurt, cheese, egg, and honey. Although 

they have a high nutritional content, the increased occurrence of food-borne diseases has been 

associated with their consumption (Todd, 2014).  

Globally, the emergence of antibiotic resistance among isolates of bacteria has led to 

discussions on the appropriate and judicious use of antibiotics, not only in human medicine but 

also in agriculture and veterinary medicine. The use of antibiotics in the production of food 

from animal sources is not only limited to the treatment and prevention of diseases among farm 

animals but also has non-therapeutic purposes of increasing yield by serving as feed proficiency 

enhancers and growth promoters. Moreover, the classes of antibiotics used in sustaining the 

health and productivity of animals are similar to those used clinically to treat human infections.   

Although antibiotic use in livestock production has profound economic gains (Durso & Cook, 

2014), the negative effects of exposing consumers to antibiotic residues and the emergence of 

antibiotic resistance cannot be overemphasized. This can worsen the public health concerns of 

antibiotic resistance as zoonotic pathogens can be potential carriers of resistance genes. 

Moreover, it could lead to the presence of antibiotic residues, which could occur due to a 

myriad of factors, such as non-observance of withdrawal time, little veterinarian supervision, 

and inappropriate use of antibiotics (Agmas & Adugna, 2018). Exposure of consumers to 

unacceptable levels of antibiotic residues in animal-source foods poses a  serious public health 

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concern due to the potential health risk of eliciting unwanted immunological responses such as 

allergies, toxicity, and the emergence of antibiotic-resistant strains (Bacanlı & Başaran, 2019). 

Foodstuffs of animal origin are usually contaminated with bacteria, hence can be a major route 

of transmission of multidrug-resistant bacteria and resistance genes to humans (Djordjevic, 

Stokes, & Chowdhury, 2013; Exner et al., 2017; Marshall & Levy, 2011). Thus, the widespread 

applications of antibiotics in the rearing of farm animals can encourage the emergence of novel 

antibiotic-resistant bacteria strains in humans (Koch, Hungate, & Price, 2017). 

Besides the issue of antibiotic residues, several studies conducted globally have identified 

multidrug-resistant bacteria on meat products, some of which include methicillin-resistant 

Staphylococcus aureus (MRSA), extended-spectrum beta-lactamase (ESBL)-producing 

Enterobacteriaceae, multidrug-resistant Salmonella, and multidrug-resistant E. coli 0157 

(Djeffal, Mamache, Elgroud, Hireche, & Bouaziz, 2018). Most of these bacteria are important 

pathogenic bacteria isolated in a wide array of human infections, and their presence in meat 

highlights the fact that the challenge of ensuring the safety of meats is multifaceted, as is the 

largely unending problem of antimicrobial resistance.  

In Ghana, recent studies on AMR have given less attention to food from animal sources, which 

cannot be underestimated as a  major route of spread of antibiotic-resistant zoonotic pathogens 

(Addae-Nuku et al., 2022; Afum et al., 2022; Agyepong, Govinden, Owusu-Ofori, & Essack, 

2018; Ahmed et al., 2022; Anning et al., 2022; Dayie, Bannah, Dwomoh, Kotey, & Donkor, 

2022; Donkor et al., 2019). Furthermore, the large application of antibiotics in animal 

husbandry and the paradoxical paucity of AMR surveillance data regarding animal-source 

foods suggest that data generated on AMR are far from robust. (Ellen De Leener et al., 2005; 

Vishnuraj, Kandeepan, Rao, Chand, & Kumbhar, 2016) 

 

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Routine surveillance of antimicrobial resistance and antibiotic residues in animal source foods 

will serve as a “double-edged sword” that helps to address microbial food safety issues as well 

as the antimicrobial resistance menace. 

 

1.2 Problem statement 

The safety of food is under immense threat globally, but this threat is disproportionately higher 

in poor-resource settings. To illustrate, the World Health Organization (WHO) estimated that 

globally, 600 million people are affected annually by infections due to food-borne diseases, 

leading to annual mortality of 420,000 and economic loss of US$110 billion in low- and 

middle-income countries (World Health Organisation, 2018). Meat is an important food item 

whose safety is in peril. Among other things, it is highly vulnerable to contamination with 

microbes that cause infections and spoilage as it is an ideal medium for their growth due to the 

high content of moisture, vitamins, proteins, minerals, and other growth factors (Bhaisare, 

Thyagarajan, Churchil, & Punniamurthy, 2014). Contaminated meats are one of the major 

causes of food-borne illnesses and an important conduit of zoonotic diseases (Ali, Farooqui, 

Khan, Khan, & Kazmi, 2010). Besides microbial contamination, the non-observance of 

withdrawal periods and inappropriate use of antibiotics for the treatment and prevention of 

infectious diseases, as well as growth promotion in animal production may lead to antibiotic 

residues in meats (Muaz, Riaz, Akhtar, Park, & Ismail, 2018; Shareef, Jamel, & Yonis, 2009). 

Of concern, the presence of antibiotics in human diets has been linked to adverse effects on 

human health comprising direct toxicity, allergic reactions, neurological disorders, 

gastrointestinal disturbance, tissue damage, and disruption of the intestinal microbiome (E De 

Leener, 2005; Mund, Khan, Tahir, Mustafa, & Fayyaz, 2017; Vishnuraj et al., 2016). Most 

importantly, it could further exacerbate the already huge menace of antimicrobial resistance (E 

De Leener, 2005; Mund et al., 2017; Vishnuraj et al., 2016). Meanwhile, it is estimated that by 

2050, the lack of effective planned actions against antibiotic resistance will culminate in an 

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annual 10 million mortality rate and a loss of US$ 100 trillion (Jonas, Irwin, Berthe, Le Gall, 

& Marquez, 2017). Moreover, it is expected from the projections that low-income countries 

will be largely affected in terms of lowering economic growth than wealthy countries. 

 

In Ghana, according to a comprehensive review report, bacterial isolates of clinical relevance 

from human, food, and environmental samples have been shown to have a high level of 

resistance to E. coli (62.2%), Klebsiella spp. (60.4%), and Pseudomonas spp. (52.1%) (García-

Vello, González-Zorn, & Saba, 2020). According to their report, most of the isolates showed a 

greater level of resistance to clinically useful antibiotics such as ampicillin, cefadroxil, 

cefotiam, cloxacillin, cotrimoxazole, erythromycin, penicillin, and trimethoprim. To help 

tackle the antimicrobial resistance menace and improve upon public health interventions, it is 

important to conduct continuous surveillance on the occurrence of antibiotic residues and 

multidrug-resistant pathogens in animal-source foods.  

 

1.3 Justification 

The enormity of the antimicrobial resistance menace requires that remedial efforts be based on 

the One Health approach and involve human health, animal health, and the environment. In 

Ghana, previous studies on AMR have given little attention to animal-source food, which is a 

major route of transmission of antibiotic-resistant zoonotic pathogens (Asafo-Adjei, Mensah, 

Labi, Dayie, & Donkor, 2018; Borquaye et al., 2019; Labi et al., 2021; Opintan et al., 2015). 

Furthermore, very few of the studies conducted on animal-source foods in the country have 

evaluated them for the occurrence of antibiotic residues (Addo, Adjei, Mensah, & Jackson-

Sillah, 2015; Donkor et al., 2011; Ekli, 2019; Mingle et al., 2021). Moreover, the wide usage 

of antibiotics in animal husbandry and the paradoxical paucity of AMR surveillance data 

regarding animal source foods means that data generated on antimicrobial resistance are far 

from robust (E De Leener, 2005; Mund et al., 2017; Vishnuraj et al., 2016). This study is 

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designed to help fill these knowledge gaps by determining the prevalence of antibiotic residues, 

a spectrum of bacterial pathogens contaminating three types of meat sold in Accra – beef, goat 

meat, and chicken – as well as the occurrence of multidrug-resistant bacteria in them. This is 

especially important, as antimicrobial resistance in animal-source foods needs continuous 

monitoring, just as is being done in humans. 

 

1.4 Aim  

To investigate the occurrence of antibiotic residues and multidrug-resistant bacteria in meats 

(beef, goat meat, and chicken) sold in Accra 

1.5 Specific Objectives 

 

The specific objectives were: 

 To determine the prevalence of antibiotic residues in the meats 

 To determine the spectrum of bacterial pathogens contaminating the meats 

 To determine the prevalence of multidrug resistance among the contaminating bacteria 

and the antimicrobial resistance patterns of the bacteria 

 

  

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CHAPTER TWO 

2.0 LITERATURE REVIEW 

 

2.1 Antibiotics: Their history and use in food production 

Antibiotics are an important class of drugs that act by killing or inhibiting the growth of 

bacteria. They are used both in animals and humans for the treatment and prevention 

of infections. The discovery of penicillin in 1928 by Sir Alexander Fleming was a major 

transformational event in the treatment of infectious diseases which has saved the lives of 

millions of patients with sepsis, pneumonia, severe wound infections, and other life-threatening 

diseases caused by bacteria.  Apart from antibiotics being used to cure diseases caused by 

pathogenic bacteria, they have contributed to the success and significant risk reduction of 

important clinical interventions and procedures, such as organ transplantation, cancer 

treatment, and open-heart surgery (Laxminarayan et al., 2013). Antibiotics have not only been 

useful in humans – they have had significant usage in agricultural settings, and in crop and 

animal production. The focus of current Agribusiness aimed at increasing cost efficiency has 

led to the proliferation of antibiotic use in animal husbandry. Apart from antibiotics being used 

therapeutically for treatment and disease prevention, their application as production tools for 

growth promotion has gained significant prominence among farmers across the world 

including Ghana (Graham, Boland, & Silbergeld, 2007; Paintsil et al., 2021; Phares, Danquah, 

Atiah, Agyei, & Michael, 2020).  

  

The positive relationship between the introduction of sub-therapeutic levels of antibiotics and 

weight gain of farm animals such as poultry and beef date back to the 1950s  (Libby & Schaible, 

1955). This led to the commercial application of antibiotics to enhance the market weight of 

farm animals. Although several justifiable concerns are being raised by WHO on the use of 

antibiotics in animal husbandry, the economic gains enjoyed by farmers involved in animal 

husbandry in terms of cost reduction are encouraging the use of antibiotics (Casewell, Friis, 

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Marco, McMullin, & Phillips, 2003). Globally, 73% of antibiotics used in the universe are 

applied in the rearing of farm animals (Van Boeckel et al., 2019).  In the US, more than 50% 

of antibiotics are used in livestock (Center for disease control, 2013). The use of antibiotics 

among farmers in Ghana is also widespread. According to a recent study conducted in Ghana, 

it was observed that 94.7% of farmers in Ghana applied antibiotics in livestock production, 

with a significant proportion (86.3%) of the farmers administering antibiotics to prevent and 

treat diseases, whiles 13% of them used antibiotics to enhance the growth of the farm animals 

(Phares et al., 2020). Although antibiotics are used in Agricultural settings in Ghana, relatively 

less attention has been given to how antibiotic application in farm animals is contributing to 

the menace of antibiotic resistance. 

 

Frequently used antimicrobials in animal rearing include β-lactams, tetracyclines, 

aminoglycosides, lincosamines, macrolides, pleuromutilins, and sulfonamides (Lee et al., 

2020). These same classes of antibiotics are used clinically to treat infections in humans. In 

Africa, large amounts of antibiotics, particularly tetracycline, aminoglycoside, and penicillin, 

are applied in the rearing of farm animals (Kimera, Mshana, Rweyemamu, Mboera, & Matee, 

2020).  The animal carcasses and the resulting foodstuffs after the application of antibiotics to 

farm animals may contain traces of antibiotics (Wassenaar, 2005). Consumption of such foods 

could result in the accumulation of these sub-therapeutic levels of antibiotics among 

consumers, and render these drugs less effective when subsequently used in therapy. This has 

been a key underlying factor via which the antimicrobial resistance menace has exacerbated 

and become entrenched. 

 

2.2 Nutritional value and consumption of animal source foods in Africa 

 

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Animal source foods are foodstuffs derived from animal origin and comprise fish, milk, meat, 

eggs, honey, cheese, and yogurt. Meats are the comestible part of domestic, farmed, and wild 

animals, including cattle, sheep, goats, pigs, and poultry. They are rich sources of protein, 

different types of fats comprising omega-3 polyunsaturated fatty acids, zinc, iron, selenium, 

potassium, magnesium, sodium, vitamin A, B-complex vitamins, and folic acid. Animal-source 

foods such as milk have a significant impact. The significant impact of the consumption of 

meals containing animal-source foods is enormous. In malnourished children, nutrients from 

animal-source foods like protein have been shown to enhance cognitive function, and 

anthropometric indicators, and consequently reduced death and morbidity (Dror & Allen, 

2011). Some negative health outcomes associated with the intake of diets lacking animal-

source foods include rickets, anemia, impaired cognitive abilities, poor growth, neuromuscular 

deficits, and even death (Murphy & Allen, 2003). 

For the last 50 years, the global production of meats has tripled, and in 2018, the production of 

meats was about 340 million tons, with about 80 billion animals killed every day for 

consumption (Ritchie & Roser, 2019). This has led to an increase in the commercialization of 

animal husbandry to balance the high demand for meat consumption. In developing countries, 

such as countries of Sub-Saharan Africa, where there is significant growth of populations and 

economies, the per capita meat and fish consumption is expected to rise by 54–69% if the GDP 

of Sub-Saharan Africa doubles (Desiere, Hung, Verbeke, & D’Haese, 2018). Thus, there will 

be a concomitant increase in livestock rearing to meet demands. 

 

2.3 Food safety and antibiotic residues in animal source foods 

Application of antibiotics among animals may lead to the presence of antibiotic residues in 

foods of animal origin and raises concerns regarding the safety of animal-source foods (Bacanlı 

& Başaran, 2019). Food safety encompasses zoonotic diseases and acute and chronic ingestion 

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of natural and synthetic xenobiotic which greatly affect the quality of food concerning its 

detrimental effects on human health. Some of the concerns of end users of food derived from 

animal sources are the presence of antibiotic residues, which can be hazardous to human health. 

In developing countries, it has been suggested that the presence of antibiotic residues in meats 

above the threshold concentrations is due to a lack of knowledge and information among 

farmers, coupled with negligence among regulating authorities (Muaz et al., 2018). 

A withdrawal period is usually set to prevent exposure of humans to antibiotics used during 

animal rearing. The withdrawal time refers to the interval between the stoppage of antibiotic 

usage on the farm animal and the defined time of slaughter, with the sole aim of reaching safe 

and tolerable concentrations in the animal carcasses (Nisha, 2008). The application of heat 

during cooking can significantly reduce the risks of exposure to residues of antibiotics like 

sulphonamides, tetracyclines, and fluoroquinolones, but does not assure the entire destruction 

of the residues of the antibiotics in meats (Muaz et al., 2018). 

Adverse effects posed by the presence of antibiotic residues in foods of animal origin are 

numerous and include allergic reactions, transfer of antibiotic resistance genes, nephrotoxicity 

(gentamicin), bone marrow toxicity (chloramphenicol), carcinogenicity, mutagenicity, and 

hepatotoxicity (Nisha, 2008). Also, the misapplication of antibiotics may result in the 

emergence of resistant strains of bacteria which reduces the efficiency of antibiotics and this is 

likely to culminate in the treatment failure of infected animals (Laxminarayan et al., 2013). 

Owing to the dangers associated with the presence of antimicrobial residues in foods and other 

food-safety-related threats, in most developed countries around the world, surveillance systems 

are institutionalized to monitor the presence of veterinary drugs in foodstuffs from animals to 

ensure the safety of their livestock and their products. In the USA for instance, the U.S. 

Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), and 

Veterinary Services (VIS) are responsible institutions to undertake these monitoring works 

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(Animal and Plant Health Inspection Service, 2020). In the UK, operation of residue 

monitoring is being conducted by Veterinary Medicine Directorates. In Ghana, there seem to 

be lapses in monitoring and surveillance of antibiotic residues in foods among regulatory 

institutions, and this is a major drawback to the insurance of the safety of foods marketed in 

the country. 

 

2.4 Analytical methods for determination of antibiotic residues in meats 

 

The detection of antibiotic residues in meats falls under three (3) broad analytical procedures, 

which include qualitative, quantitative, and semi-quantitative (Chen, Ying, & Deng, 2019).  

The qualitative methods involve the categorization of samples as positive or negative based on 

a threshold reference value relative to specific antibiotic concentrations, whiles the quantitative 

determination requires the response of specific antibiotics taking into consideration 

extrapolation from a standard curve of a standard antibiotic substance which serves as a 

positive control. The quantitative methods require instruments with hypersensitivity and 

response values. The semi-quantitative methods are similar to the quantitative analysis, but the 

test results are interpreted about a range of drug concentrations (e.g., negative, low positive, 

and high positive). 

Before the 21st century, some of the detection methods applied for the determination of 

antibiotic residues in foodstuffs of animal origin include microbial inhibition assays, microbial 

receptor assays, enzymatic colorimetric assays, receptor binding assays, chromatographic 

methods, and immunoassays (Pikkemaat, 2009). Most of these methods fall under the 

qualitative and semi-quantitative methods. Recent advancements in detection employ 

qualitative and sophisticated hypersensitive chemistry analyzers, such as liquid 

chromatography-mass spectrophotometry for detection, which reduces the number of false 

positives and targets a wide range of antibiotics (Dasenaki & Thomaidis, 2015). In a study 

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conducted in South Africa comparing thin layer chromatography (TLC), enzyme-linked 

immunosorbent assay (ELISA), and high-performance liquid chromatography (HPLC) for 

antibiotic detection, immunoassays were affirmed sensitive and selective method for 

monitoring antibiotic residues in foodstuffs of animal origin (Ramatla, Ngoma, Adetunji, & 

Mwanza, 2017).  

 

2.5 Emergence of antibiotic resistance as a global threat 

Antibiotic resistance is present throughout the world and is one of the major public health crises 

in this current dispensation. In fact, due to the emergence of antibiotic resistance, the likelihood 

of the vast majority of infectious diseases lacking effective antibiotic therapy is fast 

approaching normalcy (Morehead & Scarbrough, 2018). 

The concept of antibiotic resistance came to light immediately after the discovery of penicillin, 

and resistance of Staphylococcus aureus to penicillin developed even before the mass 

production of antibiotics in 1943 (Hwang & Gums, 2016). Since then, over 20 000 likely genes 

conferring resistance to antibiotics have been found in about 400 different types of bacteria 

(Davies & Davies, 2010). Concerning antibiotic resistance, infections caused by organisms that 

were susceptible to antibiotics are becoming difficult and almost impossible to treat and cure. 

Thus, healthcare is fast approaching post-antibiotic times where simple infections that were 

easily treated will lead to significant morbidity and mortality. Antibiotic resistance is also 

posing a damaging reality to people, healthcare and veterinary services as well as Agro-

businesses.  

 

In the US, it is suggested that the economic impacts of antibiotic resistance in terms of direct 

cost to healthcare and loss of productivity will be higher than $20 billion and $35 billion 

respectively (Center for disease control, 2013).  Moreover, it is suggested that by 2050, the 

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lack of effective planned actions against antibiotic resistance will culminate in an annual death 

toll of 10 million and a loss of US$ 100 trillion (O’Neill et al., 2014). A recent study by the 

World Bank presumes antibiotic resistance can potentially affect the economic fortunes of 

developing countries by increasing the rate of poverty(The world Bank, September 20, 2016).  

Antibiotic-resistant organisms have been implicated in a myriad of infections such as UTIs, 

sepsis, upper and lower respiratory infections, catheter-associated infections, eye infections, 

ear infections, and wound infections. The impacts of infections caused by antibiotic-resistant 

organisms on patients include increased costs of healthcare, prolonged hospitalization, higher 

mortality, and treatment failures (Aslam et al., 2018; Prestinaci, Pezzotti, & Pantosti, 2015). 

2.6 Factors contributing to the emergence of antibiotic resistance  

Several factors have been enumerated as the cause of the rapid emergence of antibiotic 

resistance in both developed and developing countries. One of the contributing factors 

accelerating the emergence of antibiotic resistance is the overuse and inappropriate use (in 

choice, dose, patient adherence, etc.) of antibiotics. In developing countries, the less restriction 

and almost no regulation on the availability and usage of antibiotics are leading to self-

medication (Morgan, Okeke, Laxminarayan, Perencevich, & Weisenberg, 2011). This situation 

is worse in Africa, as it has been suggested that in most countries on the continent, the majority 

of antibiotics used are not prescribed (Prestinaci et al., 2015) This causes less adherence to 

dosing and duration, which compounds the problem of unnecessary usage. 

Many pharmaceutical companies involved in drug development, research, and production have 

collapsed their antibiotic department due to business financial losses in antibiotic production 

(Fair & Tor, 2014). This has led to a slower rate of research conducted on the development of 

antibiotic resistance. 

 

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One of the main areas for the large and inappropriate use of antibiotics, which may be an 

important contributing factor to the spread of antibiotic resistance is the modernization of 

agribusiness which aids in the large-scale production of meats, eggs, and dairy products. This 

has been noted as a major contributing factor for the spread of antibiotic resistance. As 

recommended by WHO, farmers and food industries have been cautioned to stop using 

antibiotics routinely to enhance growth and also prevent growth in health animals (World 

Health Organisation, 2017). The harrowing fact that most of the classes of antibiotics used in 

agricultural settings and veterinary medicine are of similar types, applications, and modes of 

action as those prescribed for human consumption aggravates the problem of antibiotic 

resistance (Islam, Shiraj-Um-Mahmuda, & Hazzaz-Bin-Kabir, 2016). The close linkage of the 

use of antibiotics in Agriculture leading to the emergence of life-threatening infections caused 

by multidrug-resistant organisms has been suggested (Landers, Cohen, Wittum, & Larson, 

2012). With much focus on the development of antibiotic resistance in developing countries, It 

has been suggested that immoderate antibiotic usage in food-producing animals plays a key 

role in the emergence of antibiotic-resistant organisms and should be given similar 

consideration as poor regulation of antibiotic use in healthcare centers (Chokshi, Sifri, 

Cennimo, & Horng, 2019).  

 

2.7 Multidrug-resistant organisms of public health importance isolated in animal-source 

foods 

Some bacteria, such as Salmonella spp. and Campylobacter spp., are important zoonotic 

pathogens that can be transferred to humans through the food chain, and resistance of these 

organisms to antibiotics can challenge their treatment. In the case of Salmonella, there are many 

serovars of Salmonella spp. enterica. The non-typhoidal serovars are transmitted to humans 

through the food chain whiles the typhoidal serovar which causes typhoid fever is limited to 

human-to-human transmission (Boyen et al., 2008). The majority of non-typhoidal infections 

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from foodstuffs are self-limiting, but others can lead to life-threatening systemic infections and 

demonstrate resistance against drugs of choice such as fluoroquinolones and second-generation 

cephalosporins will exacerbate the infections. There are suggestions that multidrug-resistant 

Salmonella spp. could have adverse effects on human health via the food chain (Lai et al., 

2014; L. Zhang et al., 2018). Across the globe, extended-spectrum beta-lactamase-producing 

Salmonella has been identified in animal-source foods (Djeffal et al., 2017; Guo et al., 2021; 

Ibrahim, Dodd, Stekel, Ramsden, & Hobman, 2016). Also, several studies have isolated 

fluoroquinolone-resistant Salmonella spp. in meats (Cui et al., 2019; Tadesse, Tessema, 

Beyene, & Aseffa, 2018).  

The genus Campylobacter which comprises two species – Campylobacter jejuni and 

Campylobacter coli – is found in the gastrointestinal tracts of poultry (Bolton, 2015). They 

cause campylobacteriosis, which is the leading cause of gastroenteritis in humans. Although 

the majority of infections are self-limiting and mild, antibiotics such as macrolides and 

fluoroquinolones are warranted in prolonged and severe infections. Campylobacter spp. has 

long become increasing resistant to antibiotics, thereby limiting the effectiveness of medical 

therapy (Engberg, Aarestrup, Taylor, Gerner-Smidt, & Nachamkin, 2001).   

 

Some antibiotic-resistant organisms contaminating ready-to-eat dairy products such as cheese 

and milk can colonize the gastrointestinal tracts of humans and transfer genes to the numerous 

microbiota present in the gut (Giraffa, Carminati, & Neviani, 1997). One classical example of 

such contaminants capable of transferring resistance genes is Enterococcus spp. Studies have 

suggested that multidrug-resistant Enterococcus spp. which are implicated organisms for 

urinary tract infections and endocarditis can harbor genes conferring resistance to linezolid and 

vancomycin, and can transfer these genes to receptive human microbiota (Chajęcka-

Wierzchowska, Zarzecka, & Zadernowska, 2021). 

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Some subtypes of Escherichia coli are part of the organisms causing food-borne illnesses. In 

the USA, the Centers for Disease Control and Prevention, 2019, reported a sudden occurrence 

of  Shigella toxin-producing E. coli strain (STEC) 0103 infections linked to the consumption 

of ground beef (Center for Disease Control, 2019). Usually, the presence of E. coli as a 

contaminant in meat is mainly due to the delay in the slaughtering, transportation, and selling 

of meats to potential buyers (Ritchie & Roser, 2019).  

 

Staphylococcus aureus is also one of the organisms causing food poisoning and is an important 

consideration of major public health programs globally (Hennekinne, De Buyser, & Dragacci, 

2012). One of the frequent causes of outbreaks of food-borne diseases caused by Staphylococci 

is the improper handling of foods in the retail end of the food chain (Lues & Van Tonder, 

2007). Staphylococcal food-borne disease is caused by eating food containing pre-formed 

staphylococcal enterotoxins. The determinant of the severity of this disease is directly 

associated with the number of toxins ingested and the health status of the patient (Schelin, 

Wallin-Carlquist, Thorup Cohn, Lindqvist, & Barker, 2011). The majority of the patients 

suffering from food poisoning caused by Staphylococcus aureus include vomiting, diarrhea, 

abdominal pain, and nausea with a rapid onset. The loss of large amounts of fluids leads to 

physical signs of dehydration and hypotension (Chaibenjawong & Foster, 2011). Several kinds 

of foods, such as meat, unpasteurized milk, poultry, and egg products are foodstuffs that 

provide conducive environments for the growth of Staphylococcus aureus (Le Loir, Baron, & 

Gautier, 2003). Currently, a newer strain, livestock-associated methicillin-resistant 

Staphylococcus aureus (LA-MRSA) is contributing to the emergence of human infections 

(Price et al., 2012). Several studies across the globe have isolated MRSA in meat products 

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across the globe, which poses potential threats to consumers and meat handlers (De Boer et al., 

2009; Mkize, Zishiri, & Mukaratirwa, 2017; Syed et al., 2018).  

 

2.8 Mechanisms of antibiotic resistance 

The resistance of bacteria to antibiotics can be naturally inherent as well as via the acquisition 

of antibiotics resistance through mutations in the chromosomal genes and by horizontal 

transfer. The structural and functional features of some bacterial organisms make them inhibit 

the bacteriostatic and or bactericidal action of antibiotics. One classical example is daptomycin, 

which is a lipopeptide that has significant action against only Gram-positive bacterial 

organisms but does not have any effectiveness against Gram-negative organisms. This can be 

attributed to differences in the composition of the cytoplasmic membrane in Gram-positive and 

Gram-negative organisms. Invariably, the action of daptomycin is mediated by Ca 2+, which 

facilitates its uptake into the cytoplasmic membrane. The lower amount of anionic 

phospholipids in Gram-negative organisms relative to Gram-positive organisms reduces the 

insertion of daptomycin into the cytoplasm. 

Another mechanistic means of bacterial resistance apart from intrinsic resistance is the 

acquisition of resistance which can be broadly divided into three main phenomena: first, 

reduction in the concentration of antibiotics due to poor penetration; second, modification of 

the antibiotic target by genetic mutation or post-translational modification of the target; third, 

inactivation of the antibiotic by hydrolysis or modification. 

2.8.1 Inhibition of antibiotic penetration and efflux 

Several antibiotics have their target situated inside bacterial cells or found in the cytoplasmic 

membrane (usually among Gram-negative bacteria). Thus, the antibiotic must penetrate the 

outer and or cytoplasmic membrane of bacterial cells to exhibit its antibiotic action. Generally, 

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relative to Gram-positive bacteria, Gram-negative bacteria have poor penetrability to several 

antibiotics due to the structural presence of the outer membrane which forms a permeability 

barrier (Kojima & Nikaido, 2013). Hydrophilic antibiotics such as tetracycline, β-lactams, and 

some fluoroquinolones transverse the outer membranes via water-filled diffusion channels 

called outer membrane porin proteins (Pagès, James, & Winterhalter, 2008). Hence, the down 

regulation of porins or the substitution of porins with more selective channels will limit the 

penetrability of the outer membrane which will culminate in the reduction of antibiotic entry 

into Gram-negative bacterial cells ((Blair, Webber, Baylay, Ogbolu, & Piddock, 2015). Apart 

from the commonest way of resistance to carbapenems due to enzymatic degradation, 

Enterobacteriaceae, Acinetobacter spp., and Pseudomonas spp. can be resistance to 

carbapenems due to the mutations in porin genes or genes that regulate porin expressions 

(Tamber & Hancock, 2003; Tängdén, Adler, Cars, Sandegren, & Löwdin, 2013). Some of the 

best-characterized porins include the Pseudomonas aeruginosa OprD and three major proteins 

produced by E. coli which are ompF, ompC, and PhoE. Reduction in the amount of porins 

expressions and faulty porin functions can lead to resistance. Studies suggest that treatment of 

infections caused by Pseudomonas aeruginosa has resulted in mutations in the oprD gene 

during therapy (Quinn, Dudek, DiVincenzo, Lucks, & Lerner, 1986). 

 

One of the ways access to antibiotics in bacterial cells is denied is through the action of efflux 

pumps which actively extrude antibiotics out of the cell. Some efflux pumps may have fewer 

substrate specificity such as tet determinants for tetracyclines and mef genes for macrolides in 

pneumococci. Other efflux pumps can transport a plethora of structurally different substrates 

and are known as multidrug resistance efflux pumps. One important example of multidrug 

resistance efflux pumps is LmrS in Staphylococcus aureus (Kim et al., 2013). Heightened 

expression of efflux pumps observed in multidrug-resistant organisms can be due to mutations 

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in the regulatory arms influencing efflux pumps production and inducement from 

environmental signals. Multidrug resistance efflux pumps such as acrAB genes in E. coli and 

Salmonella spp. can be triggered by indole and bile produced during infections (Baucheron et 

al., 2014).  

 

2.8.2 Modification of the antibiotic target  

 

The successful action of most antibiotics upon entry into bacterial cells requires specific high-

affinity interaction with their target which is usually an important enzyme or ribosomal site. 

Alteration in the target structure will reduce the inhibitory effects of the antibiotic by 

preventing effective antibiotic binding whiles the functionality of the antibiotic is maintained. 

Substitution of a single amino acid at a specific point of the amino acid sequence of a protein 

target can change its binding affinity to its corresponding antibiotic without affecting the action 

of the target. This alteration of the target comprises (a) point mutation in the genes encoding 

the target site; (b) changes of the binding site due to the action of enzymes; (c) substitution of 

the original target. 

 

One of the examples of resistance due to mutation in the genes involves mechanisms of 

resistance to fluoroquinolone. The intracellular target of fluoroquinolones such as 

ciprofloxacin and nalidixic acid is two important enzymes: DNA gyrase and topoisomerase IV 

involved in replication (Gibson, Ashley, Kerns, & Osheroff, 2018). These enzymes are 

important in the removal of positive and negative super helical twists and knots in DNA which 

is crucial in DNA replication. Mutations in the gyrase genes (gyr A, gyr B) and topoisomerase 

IV genes (par C, par D) can lead to lower sensitivity to fluoroquinolones. 

 

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Enzymatic modification of the target sites of antibiotics is responsible for resistance observed 

among certain antibiotics. The addition of one or two methyl groups of an adenine residue in 

position A2058 of domain V of 23rRNA of the 50s ribosomal subunits is mediated by the 

catalytic action of enzymes encoded by erm genes (erythromycin ribosomal methylation). Due 

to this modification in the structure of the target sites, the interaction of the antibiotic with its 

target is compromised. Since macrolides, lincosamides, and streptogramins share the same 

binding sites in the 23sRNA, the expression of these genes causes resistance to antibiotics in 

the MLSB group. An example of this mechanism is seen in MRSA, in which resistance occurs 

by the acquisition of the staphylococcal cassette chromosome mec (SCCmec) element. This 

carries the mecA gene which encodes PBP 2a which is different in structure from the wild-type 

peptidoglycan transpeptidase penicillin-binding proteins (PBPs). This enables cell wall 

synthesis to occur whiles native PBPs are destroyed by β-lactams antibiotics (Katayama, Ito, 

& Hiramatsu, 2000). It is worth noting that PBP2a harbors a transpeptidase domain, but it does 

not function as a transglycosylase (class B PBP), so the action of other native PBPs to perform 

transglycosylation is important for robust peptidoglycan (Munita & Arias, 2016).  

 

2.8.3 Inactivation of the antibiotic molecule  

One of the effective ways bacteria renders antibiotic ineffective is by producing enzymes that 

inactivate the antibiotic by breaking down its active component or chemically modifying and 

or disrupting the structure of the drug. The resultant antibiotic, thus, is unable to bind to its 

target leading to resistance. 

 

2.8.4 Inactivation of antibiotics by hydrolysis 

After the discovery of penicillin in 1940, thousands of enzymes have been isolated that can 

break open and change antibiotics of different groups including β-lactams, aminoglycosides, 

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phenicols, and macrolides.  β-Lactamase is a principal term given to bacterial enzymes that can 

disintegrate the amide bond of β -lactam ring. The various subclasses of the  

β -lactam antibiotics such as penicillin, cephalosporins, carbapenems, and monobactams are 

destroyed by a wide array of β-Lactamases (Woodford, Turton, & Livermore, 2011). After the 

discovery of penicillin, the first β-Lactamase to be discovered when penicillin-resistant strains 

of S. aureus became widespread was penicillinase which was able to inactivate penicillin in 

vitro.  

Over the years, the extension of antibiotics of the β -lactam class with improved antimicrobial 

properties has led to the concomitant emergence of β-Lactamases that have effective action 

against novel antibiotics discovered and introduced for usage (Munita & Arias, 2016). Thus, 

the substrate specificity and physical properties of every β-Lactamases are unique. The 

development of extended-spectrum β-Lactamases (ESBLs) that have wider spectra of activities 

came after following β-Lactamases that were limited in their activity against first-generation 

β-Lactams. ESBLs can hydrolyze penicillin, third-generation cephalosporins, and 

monobactams but have no activity against cephamycins and carbapenems. Carbapenems which 

are essentially the last line of drugs for the treatment of infections caused by ESBL-producing 

bacteria are progressively being rendered useless due to the development of carbapenemases. 

Carbapenemases which have “versatile potential” can cleave penicillin, cephalosporins, 

monobactams, and carbapenems, and organisms harboring carbapenemases can render almost 

all the classes of β-Lactams ineffective leading to serious infections (Queenan & Bush, 2007). 

The harboring of wide array ESBLs and carbapenemases such as IMP (imipenemase), VIM 

(Verona integron encoded metallo β-lactamase), K. pneumoniae carbapenemase (KPC), OXA 

(oxacillinase) and NDM enzymes in organisms including K. pneumoniae, E. coli, P. 

aeruginosa, and A. baumannii has negative consequences in the treatment of severe infections 

in the hospital settings (Lynch III, Clark, & Zhanel, 2013). 

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2.8.5 Chemical alterations of the antibiotic molecule 

One common means bacteria acquire resistance to antibiotics is the production of modifying 

enzymes that alters the chemical structure of the antibiotic by the addition of chemical moieties 

to susceptible sites. This causes drug resistance by inhibiting the antibiotic from interacting 

with its target as a result of steric hindrance. The most frequent biochemical reactions leading 

to alteration in the structure of antibiotics include acetylation which causes associated with 

aminoglycosides, streptogramin, and chloramphenicol resistance; phosphorylation, which 

leads to resistance in aminoglycosides and chloramphenicol and adenylation which causes 

resistance in aminoglycosides and lincosamines. The functional groups that are transferred by 

these modifying enzymes include acyl, phosphate, nucleotidyl, and ribotoyl groups. 

One of the antibiotics that are vulnerable to alteration is aminoglycosides such as amikacin, 

gentamycin, and tobramycin which have numerous hydroxyl and amide groups exposed. They 

are modified by three principal groups of enzymes such as acetyltransferase, 

phosphotransferase, and nucleotidyltransferase. These enzymes can modify the structure of 

aminoglycosides since their active sites have a resemblance to the target area of the ribosomal 

binding clefts (Romanowska, Reuter, & Trylska, 2013). Also, genomic islands, which harbor 

multiple aminoglycosides resistance genes that encode aminoglycosides modifying enzymes 

have been identified in Campylobacter coli strains in broiler Chickens in China (Qin et al., 

2012). 

 

2.9 Techniques for determining the susceptibility of antibiotics  

One of the ways the evolutionary potentials of bacterial resistance to antibiotics can be 

suppressed in favor of humans is by performing Antibiotic Susceptibility Testing (AST). AST 

is broadly laboratory based in vitro activity that evaluates the antibiotic resistance profiles of 

bacterial organisms. Thus, it determines whether a bacterial isolate is susceptible, intermediate, 

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or resistant to an antibiotic. AST which are used to determine the minimum inhibitory 

concentration (MIC) is usually a standardized method requiring measured inoculum of the 

bacteria and appropriate growth condition in the area of appropriate medium, incubation 

temperature, and length of time of incubation. 

Any antibiotic approved for medical application has been shown in vitro to inhibit some 

particular group of bacteria at a concentration within the acceptable limit of toxicity. MIC is 

the lowest concentration of antibiotic that prevents the growth of an organism and it is used to 

evaluate whether a bacterial isolate is susceptible or resistant to an antibiotic (Bauer, Perez, 

Forrest, & Goff, 2014).  

Susceptibility to an antibiotic denotes the antibiotic is effective against the bacteria isolates 

whiles resistant means the bacteria can grow in the presence of the antibiotic and thus 

ineffective. The borderline between resistance and susceptibility is termed “intermediate” 

which implies higher concentration is required to inhibit the growth of the bacteria. A 

breakpoint refers to the concentration of an antibiotic that helps categorize the results of AST 

concerning bacterial organisms as susceptible, intermediate, or resistant (Wiegand, Hilpert, & 

Hancock, 2008). Clinical breakpoints for a wide array of antibiotics and bacteria isolates are 

yearly assessed and updated by institutions including the Clinical Laboratory Standards 

Institute (CLSI) in the USA and the European Committee on Antimicrobial Susceptibility 

Testing (EUCAST) which serve as useful references and guides for antibiotic reporting. The 

usefulness of MIC is not only limited to selecting appropriate antibiotics for the treatment of 

patients but can also be applied in epidemiological monitoring of the evolution of multidrug-

resistant organisms, evaluating the effectiveness of novel antibiotics, and standardizing new 

techniques for AST (Syal et al., 2017). 

 

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The laboratory techniques involved in determining MIC are usually based on determining 

bacterial growth in the presence of antibiotics using manual methods such as agar dilution 

assays (E-test and disc diffusion), broth dilution assays, and automated systems. 

 

2.9.1 Manual methods for determination of antibiotic susceptibility 

Most clinical laboratories employ the use of disc diffusion for determining the susceptibility of 

rapidly growing organisms (Reller, Weinstein, Jorgensen, & Ferraro, 2009). In this 

conventional method, a standardized suspension of the test bacteria is evenly spread on a sterile 

agar surface by swabbing, and filter paper discs impregnated with a predetermined 

concentration of the antibiotic are placed on an agar surface. The plate is then incubated at 

37oC for a period of 16 to 24 hours to allow the antibiotic-impregnated disc to diffuse through 

the agar. The inhibitory effect of the antibiotic leads to a clear, visible circular area around the 

disc. The zone of clearance around the disc is a direct measure of the susceptibility of the 

bacteria to the antibiotic. The measured diameter is compared to the CLSI reference table to 

determine whether the test isolate is susceptible, intermediate, or resistant to the antibiotic 

tested. The merits associated with disc diffusion in estimating AST are: multiple antibiotics 

can be tested on one plate, are relatively simple to perform and interpret, and are less costly. 

The demerit includes providing qualitative results, not MIC values which are essential in 

inpatient treatment. 

 

Dilution methods measure MIC instantly by utilizing serial dilution of the antibiotic in broth 

or agar within the range of medical relevant concentration spectrum. In the broth dilution 

method, antibiotics are diluted two folds in a liquid growth medium (broth) and incubated after 

inoculation with a standard bacterial suspension. The method employs multiple tubes or 

microdilution wells and dilution in these tubes or wells is repeated by applying a base of 1 

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μg/mL (0.25, 0.5, 1, 2, 4, 8, and so on). . Thus, they are broadly categorized into broth macro-

dilution method and broth micro-dilution method. For the broth macro-dilution method, the 

volume used per tube is usually 1ml or higher whiles for the broth micro-dilution method, the 

volume utilized is in the microliter scale (~100 μl) (Ferraro, 2000). Pure colonies of the test 

bacteria are suspended in the media, diluted appropriately, and placed in each tube to obtain a 

concentration of ~5*105 CFU/ml based on CLSI guidelines. After 24 hours of incubation at 

37oC, the tubes containing serial dilutions of antibiotics and test isolates are examined for 

turbidity produced by the growth of bacteria. The first tube or microwell at which no visible 

growth is seen infers the lowest concentration of each antibiotic and equates to MIC. The 

advantages accompanying this method include reproducibility and cost-effectiveness however 

it is labor-intensive and time-consuming. 

Another well-characterized method that utilizes the principle of dilution is the agar dilution 

method. This method involves preparing a sequence of agar plates containing antibiotics in 

increasing concentrations frequently in doubling dilutions (i.e., 1, 2, 4, 8, 16, 32 μg ml− 1, 

etc.)(Schmidt, 2019). 0.5 McFarland standard of the tested bacterial isolate is prepared and 1–

5 μl suspension is placed on each of the series of the plate with increasing concentration of the 

antibiotics. The area on the agar plate where bacterial suspensions are placed is marked as a 

spot on the agar surface typically 5-8mm in diameter. After an incubation period of 16-20 

hours, the lowest concentration of antibiotic in which no bacterial growth is observed is the 

MIC value. In this method, several organisms can be tested on a single plate but only one 

antibiotic concentration can be tested on a single plate. 

 

Another method that utilizes the combined principle of dilution and diffusion method is the 

antibiotic gradient method also known as E-test to determine MIC. (Van Belkum & Dunne, 

2013).  The E-test comprises a thin plastic reagent strip impregnated with a continuous 

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lowering concentration of antibiotic. The procedure involves initially making a confluent 

growth of the adjusted bacterial suspension on an agar surface. After overnight incubation, the 

aftermath interaction of the antibiotic gradient and the test organism leads to the formation of 

elliptical-shaped inhibitory zones. the intersection of the growth inhibition ellipse and the strip 

is determined as the MIC value of the antibiotic relative to the tested bacterial isolate (Balouiri, 

Sadiki, & Ibnsouda, 2016).  

 

2.9.2 Automated and newer methods of antibiotic susceptibility testing 

Most of the automation in antibiotic susceptibility testing are technologies adapted from micro-

dilution assays which provide relatively precise, reliable, and quantitative MIC values. The 

commercially available fully automated and semi-automated instruments are Microscan 

walkaway, Vitek-2, BD phoenix and Sensititre, Micronaut (Merlin, Berlin, Germany) (1990), 

the advantage test (Abbott Laboratories, Irving, Texas, USA) (1980) (Khan, Siddiqui, & Park, 

2019; Syal et al., 2017). Another newest instrument reducing time for AST is the  MALDI 

Biotyper antibiotic susceptibility test rapid assay (MBT-ASTRA) used for MIC determination 

(Zimmermann & Burckhardt, 2017). Apart from the MBT-ASTRA, this automated instrument 

utilizes either the principle of fluorescent emission or turbidity to determine the susceptibility 

pattern of the antibiotics 

 

Molecular and genotypic assays are also highly sensitive and specific methods that reduce 

laborious procedures of bacterial cultures and the long incubation involved in the performance 

of antibiotic susceptibility. These methods include polymerase chain reaction (PCR), DNA 

microarray and DNA chips, and loop-mediated isothermal amplification (LAMP). Assessment 

of the genetic changes associated with Methicillin-Resistant Staphylococcus aureus (MRSA), 

Vancomycin Resistant Enterococcus, Extended Spectrum Beta Lactamase (ESBL), 

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Carbapenem resistance Enterobacteriaceae, Multidrug-Resistance Mycobacterium 

tuberculosis has all been done using molecular methods. 

 

PCR seems to be the widely used molecular method used for the identification of resistance 

genes. The overall procedures of PCR include cycles of denaturation, annealing of the primers, 

and elongation of the primers by a heat-stable DNA polymerase called taq polymerase in a 

buffered pH environment containing nucleotides, ions, and others. Each cycle of amplification 

doubles the target DNA molecule. The amplified target can be confirmed for the presence of 

resistance genes through electrophoresis, southern blotting, restriction fragment-length 

polymorphism, single-strand conformation polymorphism (SSCP), DNA fingerprinting, 

molecular beacons, and other DNA sequencing analysis methods (Miller & Tang, 2009)  

 

To address the demerits associated with conventional methods of ASTs which includes labor 

intensiveness, imprecision, and complexity, microfluidics has been created for rapid 

phenotypic and molecular analysis of antibiotic resistance (K. Zhang, Qin, Wu, Liang, & Li, 

2020). This method reduces the time of ASTs to 1-3 hours and is less labor-intensive. 

 

2.10 A review of some studies conducted on the safety of animal-source foods in Ghana 

 

Several studies have been conducted on the microbial safety of animal-source foods in the 

country, and have employed a variety of methodological approaches, such as determining 

microbial contamination or load with or without evaluation of the presence of antibiotic 

residues. A variety of animal-source foods, spanning different meat types, as well as egg and 

milk, have been the targets of these studies. For example, in the study of Aning et al. (2007), 

the researchers investigated antibiotic residue occurrence in 394 milk samples and reported the 

prevalence to be 35.5% (Aning et al., 2007). In another study conducted in 2011 which 

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investigated the occurrence of antibiotic residues in a variety of animal source foods – egg, 

pork, mutton, chevon, and beef – in a composite of 634 samples, the prevalence of antibiotic 

residues in the meat samples was reported to be 21%, with the distribution being egg (6.8%), 

pork (28.6%), mutton (24%), chevon (29.3%), and beef (30.8%) (Donkor et al., 2011). 

In a similar study, but which additionally quantified the occurring antimicrobial residues using 

HPLC in association with a triple quadrupole mass spectrometer, 63% of the 144 samples 

studied had antibiotic residues occurring in them (Mingle et al., 2021). The researchers further 

reported that the mean amounts of antibiotic residues were chlortetracycline (234.43 μg/kg), 

oxytetracycline (76.94 μg/kg), tetracycline (81.35 μg/kg), penicillin G (41.02 μg/kg), cefazolin 

(47.02 μg/kg), amoxicillin (35.76 μg/kg), sulfathiazole (68.63 μg/kg), sulfadoxine (46.05 

μg/kg), sulfamethoxazole (103.98 μg/kg), enrofloxacin (30.19 μg/kg), haloperidol (9.62 

μg/kg), ketoprofen (14.94 μg/kg), prednisone (23.66 μg/kg), erythromycin (77.18 μg/kg), and 

salbutamol (6.32 μg/kg). The researchers aptly concluded that consumers of these animal-

source foods were at risk of compromising their well-being. 

Furthermore, in the study involving sampling 200 cattle slaughtered from the University of 

Ghana (UG) Farms and slaughterhouses at Amasaman, Accra, Tema (GIHOC), and Madina, 

the researchers reported an antibiotic residue prevalence of 18%, the distribution being 12% 

and 6% for the kidney and liver samples, respectively (Addo et al., 2015). Moreover, they 

recovered 43 different pathogens, with the distribution of the key ones being E. coli (69.76%), 

S. aureus (18.69%), Listeria monocytogenes (4.65%), Salmonella typhimurium (4.65%), and 

Yersinia enterocolitica (2.3%). Other organisms recovered were Klebsiella spp., Aeromonas 

spp., Pseudomonas aeruginosa, Enterobacter spp., and Bacillus spp.. 

 

In a recent study conducted in Accra by Dsani et al. (2020), but on a mixture of 205 carcasses 

of mutton (n = 16), chevon (n = 108), and beef (n = 81), and focused on E. coli, the researchers 

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reported an E. coli prevalence of 48%. The resistance rates of the organism increased across 

sulphamethoxazole/trimethoprim (SXT) (17%), cefuroxime (21%), tetracycline (45%), and 

ampicillin (57%), and the susceptibility rates increased across amikacin (92%), ciprofloxacin 

(92%), gentamicin (97%), chloramphenicol (97%), cefotaxime (98%), and ceftriaxone (99%), 

and no meropenem resistance was recorded. Also, the MDR prevalence was 22.4% (n = 22), 

and that of ESBL producers was 14.3% (n = 14), with four of these harboring the blaTEM gene.  

Similarly, in another similar study conducted to evaluate the microbial quality of raw beef and 

chevon from selected markets in Cape Coast, Ghana – Abura, Kotokuraba, and Science, the 

outcome of the study revealed the Science Market recorded the highest contamination of 

chevon, with mean highest bacterial counts of 1.67 x 108 and 7.10 x 107 CFU/ml in nutrient 

agar and MacConkey agar media, respectively, the Kotokuraba Market recorded the highest 

contamination of beef, at respective mean highest bacterial counts of 1.15 x 108 and 9.40 x 107 

CFU/ml for nutrient agar and MacConkey agar (Yafetto, Adator, Ebuako, Ekloh, & Afeti, 

2019). Moreover, the fungal counts in potato dextrose agar were the least recorded for the two 

meat types at the various markets sampled. 

In a similar study conducted in the Wa Municipality of Ghana, the researchers, with the aid of 

the USA-FDA Bacteriological Analytical Manual, evaluated the prevalence of resistant 

Salmonella spp. in cattle liver and beef, as well as antibiotic residues present in the meats (using 

the Premi®Test Kit) (Ekli, 2019). The antibiotic-resistant pattern of the various organisms was 

determined by the CLSI guidelines via disc diffusion. The researchers reported a higher 

prevalence of Salmonella in the liver (32%, n = 16) than in the beef (30%, n = 15). The 

organism demonstrated a high resistance towards teicoplanin (96.77%), but high susceptibility 

towards sulphamethoxazole/trimethoprim (100%), tetracycline (100%), ciprofloxacin (100%), 

chloramphenicol (100%), ceftriaxone (93.55%), Amoxycillin/clavulanic acid (93.55%), and 

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gentamicin (83.87%). Moreover, antibiotic residues were detected in 20% of the meat samples 

– 17% in the liver and 3% in the beef. 

Moreover, in the study conducted in the Tamale Metropolis which determined the prevalence 

of  E. coli and antimicrobial resistance patterns in a variety of meats (chevon, local chicken, 

beef, guinea fowl, and mutton), the researchers reported the prevalence among the meats to be 

84%, and the distribution across the meat types was chevon (75.56%), local chicken (80.00%), 

beef (86.67%), guinea fowl (88.89%), and mutton (88.89%) (Adzitey et al., 2020). The chicken 

samples were reported to have the least mean coliform count (3.23 log CFU/cm2), whereas 

guinea fowl recorded the highest (4.94 log CFU/cm2). The commonest resistance pattern was 

ampicillin-tetracycline-erythromycin, and the rates recorded against these antibiotics were 

ampicillin (71.67%), tetracycline (73.33%), and erythromycin (85.00%). Moreover, the MDR 

prevalence was 68.33% (n = 41). 

In another study that evaluated the microbiological quality of chicken imported from Brazil, 

the Netherlands, and the USA and sold in the Kumasi Metropolis, the researchers reported the 

distribution of the contaminating bacteria and fungi as follows: bacteria (Klebsiella spp. 

[13.8%], Salmonella spp. [24.6%], E. coli [26.2%], and Staphylococcus spp. [35.4%]) and 

fungi (Cladosporium spp. [15.2%], Penicillium spp. [24.2%], Rhizopus spp. [27.3%], 

Aspergillus spp. [33.3%]) (Yar et al., 2020). 

The findings of these studies emphasize the value of continuous monitoring of the microbial 

safety of animal-source foods in the country to inform policies and public health interventions. 

 

 

 

 

 

 

 

 

 

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CHAPTER THREE 

3.0 METHODOLOGY 

3.1 Study design and site  

This study was a cross-sectional study involving the collection of meat samples from selected 

vending shops in Accra which is the capital of Ghana with a total land area covering 225.67km2 

and an estimated population of 1,665,08 (Ghana Statistical Service, 2012). In Accra,  a greater 

number of butchers handle and process meats inconsistent with microbiological food safety 

standards according to Ghana Standards Authority and Food and Drugs Authority (Soriyi, 

Agbogli, & Dongdem, 2008). This predisposes consumers to unacceptable bacterial load and 

multidrug-resistant bacteria. Selected areas for the collection of the meat sample included meat 

vending shops at East Legon, Agblobloshie Market, and Madina where carcasses from these 

areas are consumed in the homes and eateries and also serve a greater population of the 

populace in Accra 

 

3.2 Sample size determination and sample collection  

A sample size of 270 meat was utilized taking into consideration the survey formula by (Kish, 

1965); n=z2 p(1−p)/d 2, where z=Z score for 95% confidence interval which is equal to 1.96, 

p= prevalence, and d=acceptable error (5%). The prevalence rate of 22% was used according 

to a previous study (Dsani et al., 2020). The meat samples were evenly distributed across the 

three meat types - 90 each of goat, beef, and chicken were collected usually in the afternoon 

from 12 pm to 4 pm which is the time most consumers and customers buy their meat samples 

from the meat vendors. The meat vending shops in Accra were selected by simple random 

sampling. All collected meat samples were tightly sealed with sterile plastic wrap and placed 

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in a cold box at 4°C and then transported to the research laboratory of department of 

Microbiology at the University of Ghana Medical School for microbiological analysis within 

24 hours. 

 

Figure 1. A map showing the study sites. 

 

3.3 Laboratory processing 

3.3.1 Sample preparation 

Five to ten grams of the meat was mixed with 10ml of peptone water (0.1%) and then the 

homogenized suspension was prepared using a sterilized pestle and mortar. 

3.3.2 Isolation and Identification of bacteria in the meat samples 

Homogenized samples were plated on MacConkey with crystal violet agar (Oxoid Ltd., 

Basingstoke, UK), blood agar, (Oxoid Ltd., Basingstoke, UK), and Xylose Lysine 

Deoxycholate Ltd agar (Oxoid., Basingstoke, UK), and incubated for 16-18 hours at 37°C. The 

plates were subsequently inspected for growth, lactose fermentation, hydrogen sulfide 

production, and characteristic colony morphology. Sub-culturing was done, following which 

the resultant pure cultures were identified based on Gram stain, oxidase test, indole test, and 

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API 20E (Biomerieux SA, Marcy-l’Étoile, France) and MALDI-TOF (Bruker Daltonics GmbH 

& Co., Bremen, Germany, MBT Compass IVD Ver. 4.2.100). 

For Salmonella isolates, serological identification was also employed, using the slide 

agglutination test. During the test, two separate drops (40 μl each) of saline were placed on a 

glass slide. Portions of the isolate were emulsified with a loop to give a smooth, fairly dense 

suspension. One drop (40 μl) of saline was added to saline and mixed and one drop of 

polyvalent O serum (Thermo Fisher Diagnostics BV, Landsmeer, The Netherlands) was added 

to the other slide and it was repeated for polyvalent H serum(Thermo Fisher Diagnostics BV, 

Landsmeer, The Netherlands) and polyvalent Vi serum (Thermo Fisher Diagnostics BV, 

Landsmeer, The Netherlands). Salmonella typhimurium 4,512:1:1, 2 NCTC 3048 was used as 

a positive control, and Hafnia alvei NCTC 8535 was also used as a negative control. 

In the case of Vibrio cholera, homogenized samples were cultured on Thiosulphate citrate bile 

salt agar (TCBS) (Mast Group Ltd., Liverpool, Merseyside, UK)   and inoculated in alkaline 

peptone water. TCBS culture plates were incubated at 16-18 hours at 37°C and alkaline peptone 

water was incubated for 4 hours at 37°C and sub-cultured at 37°C for 16-18 hours. 

Identification of Vibrio cholerae was done via Gram stain, indole and oxidase tests, and API 

20E (Biomerieux SA, Marcy-l’Étoile, France). For serological identification, two separate 

drops (40 μl each) were placed on the glass slide. The isolate was emulsified in each drop of 

saline to give a smooth, fairly dense suspension. To one suspension as a control, one drop (40 

μl) of saline was added and one drop (40 μl) of undiluted serum of Vibrio cholerae O1 (Vibrio 

cholerae Remel TM, Dartford, Kent, UK) was added and mixed. The two slides were rocked for 

one minute and observed for agglutination by viewing against a dark background. Known 

positive and known negative cultures were used as quality control checks 

Serological identification of species of Shigella was done as follows: about 200 μl of saline 

was placed into a tube. Two colonies from an overnight culture were emulsified in the saline 

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to produce a homogenized suspension. The latex reagents (WellcolexTM Colour Shigella, 

Thermo Fisher Diagnostics BV, Landsmeer, The Netherlands) were shaken intensely for some 

seconds and the latex reagent was dispensed into a separate circle on a flat reaction card. Using 

an applicator stick, the contents of each circle were mixed and spread to cover the whole area 

of the circle. The card was placed on a rotator and shaken at 150 rpm for 2 minutes. The results 

were read using the Wellcolex* Colour Shigella Reading Guide.  

3.3.3 Antibiotic susceptibility testing 

Antibiotic susceptibility testing was performed according to guidelines set by the Clinical and 

Laboratory Standards Institute (CLSI, 2021)using Kirby Bauer’s disc diffusion method on 

Muller-Hilton agar (Oxoid, Basingstoke, UK). Antibiotics discs, such as amikacin (30 μg), 

ampicillin (10 μg), ceftriaxone (30 μg), ciprofloxacin (10 μg), cotrimoxazole (trimethoprim/ 

sulfamethoxazole, 1.25 μg/23.75 μg), meropenem (10 μg), imipenem (10 μg), ertapenem (10 

μg), gentamycin (10 μg), tetracycline (15 μg), and amoxicillin-clavulanic acid (25 μg) were 

selected for testing.  

Pure colonies of isolates were inoculated in peptone water (Oxoid Limited, Basingstoke, UK). 

The turbidity was adjusted to 0.5 McFarland standard using sterile peptone water and swabbed 

on Müller Hinton Agar (Oxoid, Basingstoke, UK) in a manner allowing for semi-confluent 

growth post-incubation. Incubation was carried out at 37°C for 24 hours. After incubation, the 

inhibition zones were measured, and the results were interpreted using the CLSI (2021) 

guidelines. Isolates showing resistance to three or more antibiotic classes were considered 

multidrug-resistant (MDR) (Magiorakos et al., 2012). Moreover, the multiple antibiotic 

resistance index (MAR) was computed for each bacterial isolate as the fraction of the number 

of antibiotics to which an isolate displayed resistance out of the total number of antibiotics 

against which the susceptibility of the isolate was evaluated (Krumperman, 1983). 

 

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3.3.4 Phenotypic detection of ESBL-producing Enterobacteriaceae 

 

Mueller Hinton agar (MHA) (Oxoid, Basingstoke, UK) was inoculated with standard inoculum 

(0.5 McFarland) of the test isolate, similar to the description in the preceding subsection 

“Antibiotic susceptibility testing”. Ceftazidime (30 μg) and ceftazidime-clavulanic acid (30 

μg/10 μg) discs were placed on the surface of the agar and incubated at 37°C for 16–18 hours. 

An increase in zone diameter of ≥5 mm after incubation in the presence of clavulanic acid than 

ceftazidime alone was interpreted as an ESBL producer.  E. coli ATCC 25922 and Klebsiella 

pneumoniae ATCC 700603 were used as negative and positive control strains, respectively. 

3.3.5 Microbial inhibition plate test for determination of antibiotic residues  

 

The meat samples were screened for the presence of antibiotic residues using procedures 

outlined by Koenen-Dierick et al. (1995), but with some modifications using saline and 

antibiotics as controls (Koenen‐Dierick et al., 1995). A 0.5 McFarland standard of Bacillus 

subtilis ATCC 65313 was prepared and swabbed on Mueller Hinton agar plates. A sterile 8 

mm diameter cork borer was utilized to make a circular-shaped meat sample of the thickness 

of 2mm and placed on the surface of the Mueller Hinton agar (Oxoid, Basingstoke, UK) and 

incubated at 18 to 24 hours at 37°C. After incubation, samples whose zones of inhibition were 

greater than or equal to 10 mm were considered positive for the microbial inhibition assay, and 

those with zones of inhibition less than 10 mm were interpreted as negative for the assay. A 10 

μg ciprofloxacin disc and a filter paper impregnated with distilled water were utilized as 

positive and negative controls respectively. 

3.4 Statistical analysis 

The laboratory data collected were entered into STATA 14 (Strata Corp, College Station, TX, 

USA) for analysis. Descriptive statistics were used to summarize the data on the spectrum of 

bacterial pathogens contaminating the meat samples and their AMR rates, MDR prevalence, 

and MAR indices. 

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CHAPTER FOUR 

4.0 RESULTS 

 

4.1 Prevalence of antibiotic residues in the meat samples 

Of the 270 meat samples evaluated for the presence of antibiotic residues, 21 (7.7%), all being 

chicken, showed positive results. No antibiotic residues were detected in the other meat types. 

Hence the distribution of the prevalence of antibiotic residues among the meat samples was 

beef (0.0%), goat meat (0.0%), and chicken (23.3%, n = 21). 

 

4.2 Spectrum of bacterial pathogens contaminating the meats 

All the individual meat samples had bacterial contamination—5.9% (n = 16) with one 

bacterium, 83.3% (n = 225) with two bacteria, 8.9% (n = 24) with three bacteria, and 1.9% (n 

= 5) with four bacteria. A summary of the number of individual bacteria isolated per sample is 

presented in Table 1. 

 

Table 1. A summary of the number of individual bacteria isolated per sample. 

Number of Individual Bacterial 

Contaminants Per Sample 

Number of Samples 

All Meat Types Beef Goat Chicken 

One 16 (5.9%) 0 (0.0%) 1 (1.1%) 15 (16.7%) 

Two 225 (83.3%) 85 (94.4%) 77 (85.6%) 63 (70.0%) 

Three 24 (8.9%) 4 (4.4%) 12 (13.3%) 8 (8.9%) 

Four 5 (1.9%) 1 (1.1%) 0 (0.0%) 4 (4.4%) 

The spectrum of the bacterial contaminants was broad, involving 32 different types of bacteria 

totaling 558. The predominant ones were E. coli [262; beef = 30.5%, n = 80; goat meat = 

30.5%, n = 80; chicken = 38.9%, n = 102], Aeromonas hydrophila (A. hydrophila) [117; beef 

= 35.9%, n = 42; goat meat = 53.0%, n = 62; chicken = 11.1%, n = 13], Vibrio cholerae (V. 

cholerae) [20; beef = 50.0%, n = 10; goat meat = 50.0%, n = 10; chicken = 0.0%, n = 0], 

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Aeromonas veronii (A. veronii) [19; beef = 63.1%, n = 12; goat meat = 36.8%, n = 7; chicken 

= 0.0%, n = 0], and K. pneumoniae [18; beef = 22.2%, n = 4; goat meat = 16.7%, n = 3; chicken 

= 61.1%, n = 11]. The distribution of the bacterial contaminants is presented in Table 2. 

 

Table 2. Distribution of the proportion of bacteria contaminating the meat sold in 

the markets. 

Isolated Bacterium Total Number (n, %) * 

Distribution of the Bacterium Across the Meat 

Types # 

Beef (n, %) Goat (n, %) Chicken (n, %) 

Escherichia coli 262 (47.0%) 80 (30.5%) 80 (30.5%) 102 (38.9%) 

Aeromonas hydrophila 117 (21.0%) 42 (35.9%) 62 (53.0%) 13 (11.1%) 

Vibrio cholera 20 (3.6%) 10 (50.0%) 10 (50.0%) 0 (0.0%) 

Aeromonas veronii 19 (3.4%) 12 (63.1%) 7 (36.8%) 0 (0.0%) 

Klebsiella pneumonia 18 (3.2%) 4 (22.2%) 3 (16.7%) 11 (61.1%) 

Serratia plymuthica 14 (2.5%) 1 (7.1%) 2 (14.3%) 11 (78.6%) 

Pantoea spp. 13 (2.3%) 1 (7.7%) 1 (7.7%) 11 (84.6%) 

Moellerella wisconsensis 10 (1.8%) 7 (70.0%) 3 (30.0%) 0 (0.0%) 

Acinetobacter baumannii 9 (1.6%) 1 (11.1%) 1 (11.1%) 7 (77.8%) 

Vibrio spp. 9 (1.6%) 4 (44.4%) 4 (44.4%) 1 (11.1%) 

Enterobacter cloacae 7 (1.3%) 2 (28.6%) 0 (0.0%) 5 (71.4%) 

Vibrio alginolyticus 6 (1.1%) 1 (16.7%) 3 (50.0%) 2 (33.3%) 

Pseudomonas luteola 6 (1.1%) 3 (50.0%) 3 (50.0%) 0 (0.0%) 

Proteus mirabilis 6 (1.1%) 4 (66.7%) 0 (0.0%) 2 (33.3%) 

Salmonella enteritidis 6 (1.1%) 2 (33.3%) 2 (33.3%) 2 (33.3%) 

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Citrobacter koseri 5 (0.9%) 3 (60.0%) 1 (20.0%) 1 (20.0%) 

Yersinia enterolytica 5 (0.9%) 0 (0.0%) 0 (0.0%) 5 (100.0%) 

Shigella flexneri 3 (0.5%) 0 (0.0%) 0 (0.0%) 3 (100.0%) 

Enterobacter aerogenes 3 (0.5%) 2 (66.7%) 1 (33.3%) 0 (0.0%) 

Citrobacter freundii 3 (0.5%) 2 (66.7%) 1 (33.3%) 0 (0.0%) 

Rahnella aqualitis 2 (0.4%) 0 (0.0%) 0 (0.0%) 2 (100.0%) 

Serratia odorifera 2 (0.4%) 0 (0.0%) 0 (0.0%) 2 (100.0%) 

Citrobacter youngae 2 (0.4%) 1 (50.0%) 1 (50.0%) 0 (0.0%) 

Klebsiella oxytoca 2 (0.4%) 2 (100.0%) 0 (0.0%) 0 (0.0%) 

Providencia rettgeri 2 (0.4%) 1 (50.0%) 1 (50.0%) 0 (0.0%) 

Acinetobacter iwoffi 1 (0.2%) 1 (100.0%) 0 (0.0%) 0 (0.0%) 

Serratia rubidaea 1 (0.2%) 1 (100.0%) 0 (0.0%) 0 (0.0%) 

Kluyvera spp. 1 (0.2%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 

Pasteurella multocida 1 (0.2%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 

Yersinia ruckeri 1 (0.2%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 

Stenotrophomonas 

maltophilia 

1 (0.2%) 0 (0.0%) 1 (100.0%) 0 (0.0%) 

Pasteurella 

pneumotropica 

1 (0.2%) 0 (0.0%) 0 (0.0%) 1 (100.0%) 

Total 558 (100%) 186 (33.3%) 191 (34.2%) 181 (32.4%) 

* The proportions were computed using the total number of bacteria as the denominator; # the proportions were 

computed using the total number of each bacterium as the denominator. 

4.3 Antimicrobial resistance rates among the bacterial Contaminants of the Meats 

Almost all the meat samples (96.7%; n = 261) were contaminated with antibiotic-resistant 

bacteria—beef (97.8%; n = 88), goat (97.8%; n = 88), and chicken (94.4%; n = 85). When the 

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resistance data for all the bacteria are put together, the highest resistance rate was recorded 

against ampicillin (83.3%), followed by amoxicillin-clavulanate (36%). The rates of 

cefuroxime, ceftriaxone, ceftazidime, cefepime, ciprofloxacin, trimethoprim-

sulphamethoxazole, ertapenem, meropenem, and tetracycline ranged between 1.3% and 19.5%, 

whereas no resistance was recorded against either of amikacin, imipenem, and gentamicin for 

any of the bacterial contaminants. In E. coli, the antibiotics whose resistance rates were the 

highest were ampicillin (81.3%), trimethoprim-sulphamethoxazole (26.3%), and amoxicillin-

clavulanate (25.2%). Concerning A. hydrophila, the antibiotics whose resistance rates were the 

highest were ampicillin (94%), amoxicillin-clavulanate (62.4%), and cefuroxime (10.3%). 

Concerning V. cholerae, the antibiotics whose resistance rates were the highest were ampicillin 

(90%) and trimethoprim-sulphamethoxazole (25%), with no resistance recorded against any of 

the remaining antibiotics that were tested. In A. veronii, all the organisms were resistant to 

ampicillin and amoxicillin-clavulanate but not to any of the remaining antibiotics that were 

tested; these rates were identical, regardless of whether the meat type was beef, goat, or 

chicken. For K. pneumoniae, the antibiotics whose resistance rates were the highest were 

ampicillin (94.4%) and trimethoprim-sulphamethoxazole (27.8%). Details of the AMR rates of 

the bacterial contaminants are presented in Table 3. 

Table 3. Antimicrobial resistance rates of the bacterial species contaminating the 

meat samples. 

Organisms/Antibiotics AMP AMC CEFU CEFT CFTZ CEFP CIP TMS ERT MEM 

All Bacteria (n = 558) 83.3 36 9.1 2.2 2.2 2.3 5.7 19.5 1.3 1.3 

All E. coli (n = 262) 81.3 25.2 7.6 2.7 2.7 3.1 9.5 26.3 1.1 1.1 

Beef E. coli (n = 80) 78.8 42.5 3.8 0 0 1.3 10 28.8 1.3 1.3 

Goat E. coli (n = 80) 77.5 23.8 6.3 0 0 0 11.3 12.5 0 0 

Chicken E. coli (n = 102) 86.3 12.7 11.8 6.9 6.9 6.9 7.8 26.5 2 2 

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All A. hydrophila (n = 117) 94 62.4 10.3 0 0 0 3.4 6 0 0 

Beef A. hydrophila (n = 42) 100 88.1 2.4 0 0 0 3.2 1.6 0 0 

Goat A. hydrophila (n = 62) 88.7 53.2 17.7 0 0 0 3.2 3.2 0 0 

Beef A. hydrophila (n = 13) 100 23.1 0 0 0 0 0 30.8 0 0 

All V. cholerae (n = 20) 90 0 0 0 0 0 0 25 0 0 

Beef V. cholerae (n = 10) 100 0 0 0 0 0 0 0 0 0 

Goat V. cholerae (n = 10) 80 0 0 0 0 0 0 50 0 0 

All A. veronii (n = 19) 100 100 0 0 0 0 0 0 0 0 

Beef A. veronii (n = 12) 100 100 0 0 0 0 0 0 0 0 

Goat A. veronii (n = 7) 100 100 0 0 0 0 0 0 0 0 

All K. pneumoniae (n = 18) 94.4 11.1 5.6 5.6 5.6 5.6 0 27.8 0 0 

Beef K. pneumoniae (n = 4) 75 0 0 0 0 0 0 25 0 0 

Goat K. pneumoniae (n = 3) 100 0 0 0 0 0 0 66.7 0 0 

Chicken K. pneumoniae (n = 

11) 

100 18.2 9.1 9.1 9.1 9.1 0 18.2 0 0 

 

Amikacin, imipenem, tigecycline, and gentamicin are not included in the table, due to absence 

of resistance to any of them; AMP = Ampicillin; AMC = Amoxicillin-clavulanate; CEFU = 

Cefuroxime; CEFT = Ceftriaxone; CFTZ = Ceftazidime; CEFP = Cefepime; CIP = 

Ciprofloxacin; TMS= Trimethoprim-sulphamethoxazole; ERT = Ertapenem; MEM = 

Meropenem. 

As observed in Table 4, the prevalence of MDR among the contaminating bacteria was 14.9% 

(n = 83)—11.3% (n = 21) in beef, 14.7% (n = 28) in goat meat, and 18.8% (n = 34) in chicken. 

Additionally, the MDR distribution among the predominant bacteria was E. coli (18.7%, n = 

49), A. hydrophila (11.1%, n = 13), V. cholerae and A. veronii (0.0% each), and K. pneumoniae 

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(5.6%, n = 1) (Table 4). Moreover, the mean MAR index, as a composite, was 0.12 ± 0.09 (beef 

= 0.11 ± 0.08; goat meat = 0.11 ± 0.07; chicken = 0.13 ± 0.12), with 15.23% (n = 85) (beef = 

10.8% [n = 20]; goat meat = 15.71% [n = 30]; chicken = 19.33% [n = 35]) of the bacteria 

recording a MAR index greater than 0.2 (Table 4). Additionally, 2.0% (n = 11) of the 

contaminating bacteria were ESBL producers, all of which occurred in 11 of the chicken 

samples, and their distribution was: E. coli (1.3%, n = 7), K. pneumoniae, Pantoea spp., 

Enterobacter cloacae, and Serratia plymuthica (0.2% each, n = 1). 

Table 4. Multidrug resistance rates among the bacterial contaminants. 

Bacteria/Meat Types MDR Prevalence MAR Index 

All Bacteria (from all meat types) (n = 558) 14.9% (n = 83) 0.12 ± 0.09 

All Bacteria from Beef (n = 186) 11.3% (n = 21) 0.11 ± 0.08 

All Bacteria from Goat (n = 191) 14.7% (n = 28) 0.11 ± 0.07 

All Bacteria from Chicken (n = 181) 18.8% (n = 34) 0.13 ± 0.12 

All E. coli (n = 262) 18.7% (n = 49) 0.11 ± 0.10 

Beef E. coli (n = 80) 18.8% (n = 15) 0.12 ± 0.09 

Goat E. coli (n = 80) 18.8% (n = 15) 0.10 ± 0.08 

Chicken E. coli (n = 102) 18.6% (n = 19) 0.12 ± 0.12 

All A. hydrophilia (n = 117) 11.1% (n = 13) 0.13 ± 0.05 

Beef A. hydrophilia (n = 42) 7.1% (n = 3) 0.14 ± 0.03 

Goat A. hydrophilia (n = 62) 14.5% (n = 9) 0.12 ± 0.06 

Chicken A. hydrophilia (n = 13) 7.7% (n = 1) 0.11 ± 0.05 

All V. cholerae (n = 20) 0.0% (n = 0) 0.08 ± 0.04 

Beef V. cholerae (n = 10) 0.0% (n = 0) 0.09 ± 0.03 

Goat V. cholerae (n = 10) 0.0% (n = 0) 0.08 ± 0.05 

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All A. veronii (n = 19) 0.0% (n = 0) 0.14 ± 0.00 

Beef A. veronii (n = 12) 0.0% (n = 0) 0.14 ± 0.00 

Goat A. veronii (n = 7) 0.0% (n = 0) 0.14 ± 0.00 

All K. pneumoniae (n = 18) 5.6% (n = 1) 0.12 ± 0.10 

Beef K. pneumoniae (n = 4) 0.0% (n = 0) 0.11 ± 0.04 

Goat K. pneumoniae (n = 3) 0.0% (n = 0) 0.12 ± 0.04 

Chicken K. pneumoniae (n = 11) 9.1% (n = 1) 0.12 ± 0.13 

MDR= Multidrug resistance; MAR = Multiple antibiotic resistance index. 

 

 

 

 

 

 

 

 

 

 

 

 

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CHAPTER FIVE 

5.0 DISCUSSION 

The purpose of the current study was to investigate the occurrence of antibiotic residues and 

multidrug-resistant bacteria in meats (beef, goat meat, and chicken) sold in Accra. One major 

focus of it was to determine the prevalence of antibiotic residues in the meat samples. Very 

few of the studies conducted on animal-source foods in the country have evaluated them for 

the occunce of antibiotic residues (Addo et al., 2015; Aning et al., 2007; Donkor et al., 2011; 

Mingle et al., 2021). This study thus fills an important knowledge gap. As observed, the 

prevalence of antibiotic residues in the meat samples was 7.7%, and these occurred in chicken 

exclusively (23.3%). This prevalence in chicken is comparable to those reported in mutton 

(24%), pork (28.6%), chevon (29.3%), and beef (30.8%), but higher than that reported in eggs 

(6.8%) in the study conducted by Donkor et al. (2011) that evaluated the risk of exposure to 

egg- and meat-contained antibiotic residues in the country. It is also comparable to the 

prevalence of 35.5% reported by Aning et al. (2007) in marketed milk in the country. Addo et 

al. (2014) also reported a prevalence of 18% in raw beef, and Mingle et al. (2021) reported a 

prevalence of 63% in beef, chicken, and eggs. Moreover, in the study of Ekli et al. (2019), 

antibiotic residues were detected in 20% of the meat samples – 17% in the liver and 3% in the 

beef. 

In many other countries particularly developing ones, a high prevalence of antibiotic residues 

has been reported in a variety of animal-source foods. For example, a study conducted in 2005 

in Kenya reported the occurrence of antibiotic residues in meats to be 16% (Kang'ethe et al., 

2005). Another similar study conducted in Tanzania reported a high prevalence of antibiotic 

residues to be 36% in the same sample types (Kurwijila, Omore, Staal, & Mdoe, 2006).  

 

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The exclusive occurrence of antibiotic residues in chicken, but not beef and goat meat, suggests 

that generally, withdrawal periods are probably observed by cattle and goat farmers in the 

country. It is difficult to ascribe the occurrence of antibiotic residues in the chicken to non-

observance of withdrawal periods by poultry farmers in the country, as most of the chicken 

seems to be imported. That notwithstanding, given that the chicken samples, like the other meat 

types, were obtained from markets that receive high patronage, the high prevalence of antibiotic 

residues in them raises several safety concerns. First, consumers may be at a high risk of 

exposure to the residues, and this risk could further increase with repeated patronage and 

antibiotic residue accumulation (Hosain, Kabir, & Kamal, 2021; Ngangom, Tamunjoh, & 

Boyom, 2019). Health impacts that the consumers could be exposed to include direct toxicity, 

allergic reactions, neurological disorders, gastrointestinal disturbance, tissue damage, and 

disruption of the intestinal microbiome (E De Leener, 2005; Mund et al., 2017; Vishnuraj et 

al.,