eChapters

Hazard Analysis Critical Control Points (HACCP) is an internationally recognized method of identifying and managing food safety related risk and, when central to an active food safety program, can provide customers, the public and regulatory agencies assurance that a food safety program is well managed. HACCP is a management system in which food safety is addressed through the analysis and control of biological, chemical, and physical hazards from raw material production, procurement and handling, to manufacturing, distribution and consumption of the finished product. (FAO, 2003)

Mycotoxin hazard analysis and identification of possible control measures

I. Hazard Analysis

a) Identification of mycotoxin hazard

For a given commodity system in a particular location, the HACCP team need to first consider which, if any, of the mycotoxins known to constitute a food safety hazard are likely to be present. Over 300 mycotoxins are known, but only a relatively few of these are widely accepted as presenting a significant food or animal feed safety risk.

The following mycotoxins have regulatory limits set by one or more countries: the aflatoxins (including aflatoxin M₁), ochratoxin A, zearalenone, patulin, ergot alkaloids, and deoxynivalenol. Guideline limits exist for fumonisin B₁ and regulatory limits are likely to be set in the near future. The regulatory limits are taken as the target levels and should be included in the Product Description table. Mycotoxin limits can also be set by the customer in specific contracts and it is possible that these may include mycotoxins not subject to regulatory limits.

Introduction
Antimicrobial resistance (AMR) has emerged as a critical global health threat, diminishing the effectiveness of antibiotics against bacterial infections. Conventional strategies to counter AMR—including antibiotic stewardship programs and the development of new drugs—are often slow, expensive, and labor intensive. However, artificial intelligence (AI) and machine learning (ML) present ground breaking alternatives to address this challenge. By leveraging advanced computational techniques, AI can significantly speed up the discovery of novel antibiotics, enabling researchers to identify promising drug candidates more efficiently than traditional methods. Additionally, ML algorithms enhance treatment optimization by analyzing patient data to recommend personalized antibiotic regimens, reducing misuse and resistance risks. AI also strengthens surveillance systems by processing vast datasets— such as genomic sequences and electronic health records—to detect emerging resistance patterns in real time. These capabilities allow for proactive interventions, helping healthcare providers and policymakers stay ahead of AMR outbreaks. Together, AI and ML offer a transformative approach to mitigating AMR, combining speed, precision, and scalability to combat one of the most pressing medical challenges of our time.
AI in Antibiotic Discovery
One of the most promising applications of AI in AMR mitigation is in antibiotic discovery. Traditional drug development is costly and time-consuming, often taking over a decade [1]. Machine learning models can analyse vast datasets of chemical compounds to predict their antibacterial properties. For example, researchers at MIT used deep learning to identify halicin, a novel antibiotic effective against drug-resistant bacteria. AI-driven platforms can also repurpose existing drugs, reducing the time needed for clinical trials [2].
Fig. 1: AI cyclic interface in antibiotic discovery
Predictive Analytics for Resistance Patterns AI-powered predictive analytics enables early detection of antimicrobial resistance (AMR) patterns by processing clinical, genomic, and epidemiological data. Machine learning models analyse electronic health records (EHRs) [3] and bacterial genome sequences to identify emerging resistance threats in real time. These predictive insights allow hospitals to take proactive measures— such as optimizing antibiotic prescriptions—before resistant strains spread. Beyond clinical settings [4], AI models assist public health agencies in forecasting outbreaks and strategically deploying resources. By integrating large-scale datasets, these systems enhance surveillance, enabling faster, datadriven decisions to curb AMR progression. This approach not only improves patient outcomes but also supports global efforts in combating antibiotic resistance through smarter, pre-emptive interventions [5].

Climate change affects livestock through heat stress, water scarcity, unstable feed supplies, altered disease patterns and extreme weather. This chapter examines how artificial intelligence supports climate-smart and sustainable animal husbandry. Machine learning, predictive analytics and IoT-enabled monitoring can optimize feeding, detect disease, assess welfare and improve the use of water, energy and other resources. Intelligent technologies can also assist in reducing greenhouse-gas emissions and developing adaptive production strategies. The chapter evaluates the contribution of AI to productivity, climate resilience and environmental responsibility while addressing problems such as insufficient data, limited infrastructure, implementation costs and ethical concerns. Future integration with sustainable agricultural frameworks is also discussed.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Antibiotics are produced by some true bacteria, actinomycetes and fungi that destroy or inhibit the growth of other microorganisms. Some antibiotics are synthesized or modified in the laboratory; however, their origins are always in living cells.

9.1 Assay of Antibiotics

Soil serves as the major repository of microorganisms some of which produces antibiotics. Four groups of soil microorganisms viz; Streptomyces, Bacillus, Penicillium and Cephalosporium represent three microbial types, namely, actinomycetes, true bacteria and molds respectively.

Following procedure may be applied to isolate antibiotic-producing microorganisms from soil:

This chapter examines immune-system failure arising from reactions against self-antigens or inadequate immune responses. It introduces immunological tolerance, central and peripheral tolerance, clonal deletion and clonal anergy. Important autoimmune diseases—including myasthenia gravis, Graves’ disease, systemic lupus erythematosus, multiple sclerosis, rheumatoid arthritis and Sjögren’s syndrome—are considered. The chapter also discusses molecular mimicry and the production of autoantibodies. Primary, secondary and severe combined immunodeficiency disorders are differentiated, with examples such as Chédiak–Higashi syndrome. Immune-privileged sites and immunological paralysis are also covered. These topics explain the mechanisms that normally prevent self-reactivity and the consequences that arise when immune regulation becomes defective.

A. Answer the following.

Q.1.     What is the definition of sequestered antigens ?
Ans.     Sequestered antigens are antigens which are protected from the immune system and no immune response is generated against them. Ex lens and uveal proteins of the eye, chondrocyte antigens in cartilage and antigens of spermatozoa.

Q.2.     What is autoimmune haemolytic anaemia ?
Ans.    It is an antibody mediated autoimmune disease and antibodies are generated against self RBCs. It occurs in unmatched erythrocyte antigen in transfusion or in erythroblastosis fetalis. The antibodies developed in autoimmune haemolytic anaemia are of two types (1) warm autoantibodies (2) cold agglutinins. The warm

One of the characteristics of the immune system is to recognize and discriminate self from the non-self antigens. But, sometimes, there is a failure to discriminate between self and non-self antigens in the body and an autoimmune response occurs. Body starts producing antibodies against its own cells/antigens. The self antigens against which the immune system responds are called autoantigens and the antibodies produced against self antigens are called autoantibodies.

One of the characteristics of the immune system is to recognize and discriminate self from the non-self antigens.  But, sometimes, there is a failure to discriminate between self and non-self antigens in the body and an autoimmune response occurs. Body starts producing antibodies against its own cells/antigens. These antibodies are called autoantibodies and this state is called autoimmunity.

Bovine babesiosis is a significant febrile disease, primarily affecting cattle and buffalo, transmitted by ticks. The disease is marked by the rapid proliferation of parasites within the bloodstream, leading to the extensive destruction of red blood cells. This results in a cascade of clinical symptoms, including anemia, jaundice, hemoglobinuria, and splenomegaly, with the potential for severe cases to culminate in death. The acute form of bovine babesiosis is particularly severe, often characterized by these pronounced symptoms, which can lead to rapid deterioration in the affected animals. Babesiosis is not merely an acute illness but can also persist as a chronic infection. Chronic bovine babesiosis is typified by ongoing anemia and varying degrees of weight loss, reflecting the enduring impact of the parasite within the host. Economically, this disease poses a formidable challenge, especially in regions where cattle are most susceptible. Since the first description of Babesia bovis by Victor Babes in Romania in 1888, numerous distinct species have been identified, each contributing to the global burden of this disease (Hunfeld et al., 2008). The impact of bovine babesiosis on livestock improvement efforts in endemic regions is profound. The introduction of susceptible, naive animals into these areas often leads to significant losses, as these animals are highly vulnerable to the disease.

B.anthracis causes anthrax in animals and Wool sorter’s disease, hide porter’s disease, Malignant Pustule in humans. B.cerus causes food poisoning in humans. Other bacillus in this group is non-pathogenic and they are called as anthracoids. B.licheniformis is an emerging pathogen and it is implicated in sporadic abortions in cattle and sheep.
Systematics
Domain : Bacteria
Phylum : Firmicutes
Class : Bacilli
Order : Bacillales
Family : Bacillaceae
Genus : Bacillus
Species : B. anthracis, B. cereus, B. subtilis, B. mycoides,
B. megaterium, B.mesentricus
Family Characters
They are gram +ve large rods, aerobic (facultative anaerobic), endospore forming, capsulated, mostly catalase positive and fermentative organisms. They are motile by peritrichous flagella.
Bacillus Anthracis
History
• Discovery of the anthrax bacillus is credited to Davaine and Rayer (1863 –1868).
• Considerable historic interest attached to anthrax bacilli;

Size – Shapes - Arrangements – Morphology

Bacteria are very small, most being approximately 0.5 to 1.0 mm diameter. Smallest bacteria are Mycoplasmas, as small as 0.2 micrometers (almost as small as largest poxviruses).

Accepted wisdom is that bacteria are smaller than eukaryotes. But certain cyanobacteria are quite large; Oscillatoria cells are 7 micrometers diameter, size of red blood cells. And certain eukaryotes (e.g. Nanochlorum eukaryotum) are very small, only 1 to 2 micrometers, but true eukaryotes (nucleus, chloroplast, mitochondrion are present). So size difference is, like many generalizations, only a useful yardstick, not an absolute truth.

Epulopiscium fishelsoni, discovered in 1985 in intestinal tract of sturgeonfish, is an enormous, cigar-shaped cell, as large as 80 x 600 micrometers (that’s 0.6 mm, large enough to be seen by the naked eye). Amazingly, this cell is prokaryotic! Initial evidence by EM was hard to believe, but confirmed rRNA comparisons with other organisms, a cousin of Gram-positive Clostridium genus.

The relative size of the bacteria are as follows :

The genetics is the study of inheritance and variability of organisms. Bacterial genetics is a sub-field which specifically studies transmission of genetic information from one bacterium to its progeny or between the interbreeding bacteria and the expression of genetic information.

Mutation

Hugo de Vries (1848-1935, Dutch botanist) coined the term ‘mutation’. It is originated from a Latin word which means ‘to change’. The heritable change in bacterial DNA sequences is known as bacterial mutation. The mutation will generate a new type of bacteria with altered genotypical and phenotypical characteristics, known as ‘mutant’. The parent bacterial cell from which the mutants are generated is known as ‘wild type’. In nature bacterial mutation is a rare incidence. The spontaneous mutation occurs at a rate of 1 in 10⁵ to 10⁸. The phenotypical expression of mutation occurs quickly as the bacteria are haploid with shorter generation time.

10.1 Transformation of E. coli by electroporation Principle

Preparation of electro-competent bacteria is considerably easier than preparing cells for transformation by chemical methods. Bacteria are simply grown to mid –log phase, chilled, centrifuged, washed extensively with ice cold buffer containing 10% glycerol. DNA may be introduced immediately into the bacteria by exposing them to a short high voltage electrical discharge. Alternatively, the cell suspension may be snap frozen and stored at-70^oC for 6 months before electroporation, without loss of transforming efficiency.

Since E.coli cells are small, they require very high strengths (12.5- 15KV cm^-1) for electroporation as compared to those used to introduce DNA into eukaryotic cells. Electroporation is temperature dependent and is best carried out at 0-4°C.

The highest efficiency of transformation is obtained when the concentration of input DNA is high (1-10 μg/ml) and when the length and intensity of the electrical pulse are such that only 30-50% of the cells survive in the procedure. Under these conditions, as many as 80% of the surviving cells may be transformed. Higher frequencies of transformation (colonies/ molecules input DNA) are obtained when the DNA concentration is low (?10pg/ml).

The method outlined in this protocol is followed with most strains of E.coli and with plasmids <15kb in size. However, substantial variation in the efficiency of transformation between the strains of E. coli. has been reported. As in case with chemical transformation, linear plasmid DNAs introduced into E.coli by a pulsed electrical discharge transform very efficiently from 10 to 1000 fold but less efficiently than the corresponding closed circular DNA – perhaps because the exposed terminal of linear DNA are susceptible to attack by intracellular nucleases.