\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Dr. Hamid was a member of Intelligent Drive Team of Vehicle System Engineering Research\r\nGroup (Universiti Teknologi Malaysia). The team leads the Advanced Driver\r\nAssistance Systems (ADAS) and AV-related research in Malaysia, where\r\nprevious collaborators include Proton Holdings Berhad and Smart Mobility\r\nResearch Center (Tokyo). Umar Zakir served as an AV Scientist with\r\nMoovita (Singapore & Malaysia) from 2017-2018, before embarking on a\r\nnew journey as an AV Senior Engineer in Espoo, Finland. 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Antimicrobials are indispensable in human medicine for treating and preventing infectious diseases. In addition, the same classes of antimicrobials are extensively used in livestock not only for the treatment and prevention of infections but also for the growth promotion [1]. The latter has, however, been banned in the European Union (EU) since 2006 [2].
\nThe amounts of antimicrobials utilised in livestock are vast and often exceed those in humans. Data suggest that in the EU approximately 70% of antimicrobials were sold for use in livestock in 2014 [3]. The consumption of antimicrobials in humans and animals has indeed been associated with the occurrence of antimicrobial resistance (AMR) in zoonotic bacteria [3], which are the causative agents of zoonoses and can be transmitted directly between animals and humans or via the food chain. AMR in zoonotic bacteria is a subject of major concern.
\nEven though AMR is an ancient and naturally occurring phenomenon in some bacteria [4], the excessive use of antimicrobials in humans and livestock, as well as poor hygiene conditions and practices in the food production chain, accelerates the emergence of resistance in zoonotic bacteria [5]. The alarming consequence of AMR coupled with the paucity of novel antimicrobials is the rise in the frequency of multidrug resistant (MDR) zoonotic bacteria that may lead to an impaired response to antimicrobial therapy or ultimately even treatment failure [6].
\nThe most current data regarding AMR in zoonotic bacteria are published annually by European Food Safety Authority (EFSA) and European Centre for Disease Control and Prevention (ECDC), but usually with a two-year delay in publishing. According to the recent report on AMR in zoonotic bacteria in 2016 [5], resistance in Salmonella and Campylobacter is considered of the highest concern. The scope of this review is, therefore, to discuss and emphasise the current trends of AMR in Salmonella and Campylobacter in the light of the recent EFSA/ECDC report, and the role of whole genome sequencing (WGS) in the surveillance of AMR in Salmonella and Campylobacter along the food production chain.
\nFor more than a decade, campylobacteriosis has been the most common zoonosis in Europe. Salmonellosis is the second most commonly reported enteric infection, and the leading cause of food-borne outbreaks. Campylobacter and Salmonella combined accounted for almost 95% of the reported and confirmed zoonoses cases in 2016 (Figure 1).
\nCommon zoonoses in Europe in 2016. STEC: Shiga toxin-producing Escherichia coli [7].
Salmonellosis is a food-borne gastrointestinal infection caused by zoonotic bacteria Salmonella spp. Several thousand serovars of Salmonella spp. enterica exist, yet only some are causing disease symptoms. Nontyphoidal serovars are transmitted via the food chain, whereas typhoidal serovars, the causative agents of typhoid fever, are restricted to humans [8]. Whilst the majority of nontyphoidal Salmonella infections are self-limiting and do not require any antibiotic treatment, some cases result in life-threatening systemic infections that must be treated with antimicrobials, primarily fluoroquinolones (FQ) or third-generation cephalosporins [5]. Resistance to these drugs may jeopardise the efficiency of the antimicrobial therapy.
\nCampylobacter spp. are common gut commensals of several animal species, especially birds [9], and the leading cause of gastroenteritis in humans, yet the infections often go unreported. The majority of campylobacterioses are caused by two species, namely Campylobacter jejuni and Campylobacter coli. Symptoms of campylobacteriosis are also usually mild and self-limiting, although some patients with acute infections that can trigger autoimmune inflammatory conditions need to be treated with antimicrobials, primarily macrolides and FQ [5]. Emergence of resistance in Campylobacter is common and thus of concern.
\nIn the Member States (MS) of the EU monitoring and reporting data on zoonoses and AMR for Salmonella and Campylobacter from animals, food, feed and humans are mandatory [10]. Comparing AMR data from different countries and assessing trends has long been challenging due to inadequate harmonisation of the methodology and reporting among the MS [11]. Recently, great progress has been made in terms of harmonisation of AMR surveillance programs, especially for food animals and foods with the new legislation [12]. In addition, a protocol for harmonised monitoring of AMR in humans has been developed [13], but it is not a legal document that would obligate the MS to its implementation.
\nAccording to the recent report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2016 published by EFSA/ECDC [7], the declining trend of salmonellosis in Europe has ended. In 2016, there were 94,530 confirmed cases of salmonellosis with the highest notification rate per 100,000 population in Eastern Europe (in average 46.9 per 100,000), mostly on the account of Czech Republic (110) and Slovakia (97.7), followed by Northern (20.6), Western (18.8) and Southern Europe (13.0). Additionally, Salmonella was most regularly detected in food-borne outbreaks (22.3%), which have resulted in the highest burden of hospitalisations (45.6% of the total number of hospitalised cases) and deaths (50% of the total number of deaths among outbreak cases) [7]. Outbreaks were linked to several sources, e.g., Polish eggs [14], infant formula [15] and sesame seeds [16].
\nSalmonella was the most prevalent in meat from turkeys (7.74% of the samples tested positive) and from broilers (6.39%), as well as dried seeds (8.0%) [7], which are an important source of infections, especially due to a long shelf life and low moisture [17]. Chicken, turkey and other avian species are commonly inhabited with Salmonella without noticeable symptoms [18], which is in addition to the practices in the food production chain [19], considered the highest risk for contamination of meat products. Even though Salmonella was significantly less frequently detected in eggs and their products, they remain the most important source of outbreaks [14, 20], most probably due to a large worldwide consumption coupled with low concentrations of Salmonella that cannot be detected [7]. Rapid methods with improved sensitivity are thus needed to address this shortfall, e.g., real-time recombinase polymerase amplification [21] or sequence-based methods.
\nThe most recent data (from 2016) on AMR in Salmonella from poultry, meat thereof, and humans are provided by EFSA/ECDC [5], whereas data on other livestock are presented in the last-year report [22]. Generally, as shown in Figure 2, the isolates of Salmonella spp. along the food production chain tend to be highly resistant to tetracyclines and ciprofloxacin (in average in the EU up to 65%), sulfonamides (56%) and ampicillin (45%) with the highest observed frequency in poultry, meat thereof and pigs, respectively (Figure 2). Resistance rates seemed to be higher in Southern or Eastern Europe than in Northern or Western Europe [5, 22]. Such extreme rates of resistance that could indeed reflect an extensive use of these three antimicrobials in livestock [23] are of concern and could be facilitating further dissemination of AMR.
\nPrevalence of resistance in Salmonella spp. AMP: ampicillin, SUL: sulfonamides, CIP: ciprofloxacin, TET: tetracyclines [5, 22].
Sulfonamides and ampicillin were the former first-line drugs against salmonellosis [24]. Nevertheless, ampicillin, a critically important antimicrobial [25], is used for treating community acquired pneumonia, complicated severe acute malnutrition and sepsis in neonates and children [26]. In contrast, sulfonamides and tetracyclines are classified as highly important antimicrobials [25]. Sulfonamides are the first-line drugs against urinary tract infections and tetracyclines against Chlamydia trachomatis and cholera [26].
\nFQ resistance to ciprofloxacin or nalidixic acid that is reflecting similar genetic mechanisms [11] was remarkably high in isolates from poultry meat (Figure 2), followed by poultry [5] and has steeply increased since 2004 [27]. That is of concern, because FQ are in addition to third-generation cephalosporins clinically important for the treatment of salmonellosis and several other infections, yet both may be used in livestock.
\nBroiler meat (64.7%), broilers (53.8%), turkey (50.5%) and turkey meat (43.7%) were the main sources of FQ resistance. In contrast, isolates from pigs, humans and laying hens had better susceptibility (Figure 3) [5, 22]. The ranges of FQ resistance, however, varied extremely among the countries and even regions. In Spain, for example, isolates from pigs in Catalonia (50%) [28] and humans in Extremadura (35%) [29] exhibited much higher levels of FQ resistance than reported by EFSA/ECDC for Spain (7 and 14.8%, respectively).
\nRates of FQ resistance in isolates of Salmonella spp. from various sources: broiler meat, broilers, turkeys, turkey meat, laying hens, humans and pigs, respectively [5, 22].
FQ resistance in isolates from broilers and meat thereof was in average most prevalent in Southern (62.2, 70.1%, respectively) and Eastern Europe (65.5, 71.5%, respectively) and exceeded 90% in Cyprus, Slovenia and Croatia (Figure 4). Of note, only a minority of the MS from Northern Europe reported these data [5]. A rapid increase in FQ resistance is evident in Europe, for instance, in isolates from broilers in Spain resistance increased from 0 [30] to 55.6% in just a few years.
\nPrevalence of FQ-resistant isolates of Salmonella spp. from broilers, meat thereof and humans in Europe. UK: United Kingdom [5].
In contrast, the proportions of FQ resistance in humans were significantly lower (11%) [5] and has remained at a relatively stable level since 2009 [31]. The biggest share of FQ resistance was detected in Northern Europe, mostly on the account of Estonia (36.0%), Finland (26.3%), Norway (24.7%) and Ireland (22.9%) (Figure 4). Human-associated serovars commonly detected in Europe [32, 33, 34, 35] that frequently exhibited FQ resistance were S. Infantis (23.4%) and S. Kentucky (85.8%) [5].
\nFQ resistance is a result of a complex mechanism, but it is still not fully understood [36]. Many point mutations in the genes encoding for gyrase and topoisomerase, the two enzymes that are inhibited by FQ, were identified as the causative agents [37]. In addition, plasmids may harbour genes for efflux pumps, target protection proteins or drug-modifying enzymes [38].
\nResistance to third-generation cephalosporins was rare in humans as well as in livestock [5], yet when combined with FQ resistance, it poses a serious risk to human health, in terms of reducing the efficiency of these drugs against salmonellosis and thus leaving only the reserve antimicrobials as a feasible therapy option [24]. Resistance to cephalosporins is conferred by genes encoding for AmpC β-lactamase as well as for various extended spectrum β-lactamase (ESBL) that can be located on plasmids. Such isolates were observed in Germany [39] and are assumed to have clonally spread from livestock to humans. Worryingly, in Portugal, more than a third of the isolates from broiler meat (39.4%) exhibited resistance to cefotaxime and ceftazidime and in Italy 12% from broilers [5]. In addition, combined resistance to FQ and cephalosporins was detected in poultry and humans in Spain, Belgium and France [40, 41].
\nSalmonella in the food production chain presents an important reservoir of genetic resistance determinants, which could be mobilised and transferred via the food chain to either other human pathogens or commensal bacteria [42]. Notably, importation of meat products [43] and travelling in endemic areas [44], where the rates of resistance to critically important antimicrobials are alarmingly high [45], were linked to the global spread of MDR strains.
\nIn general, 26.5% of the human isolates of Salmonella and 50.3% of broiler meat displayed MDR phenotype (defined as resistant to at least three antimicrobials of the nine antimicrobial classes tested). The highest prevalence of MDR isolates from humans was observed in Portugal (51%) and from broiler meat in Slovenia (100%) [5]. MDR strains isolated from pigs in Germany were associated with integrons, which might have an important role in dissemination of resistance [46].
\nThe majority of MDR isolates belonged to serovars S. Infantis and S. Kentucky. Among human isolates of S. Kentucky, which is the seventh most common serovar, MDR was recorded at extremely high levels (76.3%) [5]. S. Kentucky ST198 clone that is displaying high-level resistance to ciprofloxacin and frequently also to amoxicillin, streptomycin, spectinomycin, gentamicin, sulfamethoxazole and tetracycline has been imported from North Africa and has been widely spread across Europe in humans and food production chain [32]. In addition, acquisition of extended-spectrum β-lactamase, plasmid-encoded cephalosporinase or carbapenemase in this clone was detected in Mediterranean area [47] and in Poland [33]. Combined resistance was also detected in S. Kentucky from humans and livestock in Belgium, Luxembourg, Malta, the Netherlands and Germany [5].
\nS. Infantis was the most prevalent serovar in broilers and the fourth among human infections. Multi-drug resistance to FQ, sulfonamides and tetracyclines was observed frequently in the isolates from broilers (75.3%), broiler meat (72.6%), as well as in isolates from humans in two MS (Austria and Slovenia) that together with Hungary and Croatia accounted for a majority of the S. Infantis isolates from broilers. This indicates the presence of a specific MDR clone prevalent in this geographical region [5]. In addition, resistance to cephalosporins was recorded in the isolates from either humans, food or poultry in Great Britain [48], Switzerland [34], Italy [35], as well as in the USA [49], in some cases located on a plasmid and thus conferring a risk of transfer. Such strains can be transmitted from broilers and broiler meat to humans and may lead to human infections [35].
\nWhilst Campylobacter, with 246,307 confirmed infections in 2016 and 6.1% increase relative to 2015, accounted for the majority of zoonoses in Europe, the death toll was low (0.03%). The highest notification rate per 100,000 population was observed in Eastern Europe (71.4), followed by Western (65.7), Southern (56.3) and Northern Europe (55.0). Czech Republic (228.2) and Slovakia (140.5) were the countries with the highest prevalence [7].
\nCampylobacters were most frequently detected in turkeys (65.3%) and meat thereof (11%) as well as in broilers (27.3%) and meat thereof (36.7%) [7], making the poultry food production chain the main source of contamination. This is in concordance to data from several reports [50, 51]. The prevalence in retail poultry meat was, however, reported even up to almost 90% [50]. Campylobacter was also detected in cattle [52], pigs [53] and sheep [54]. Contaminated farm environment or equipment as well as the presence of Campylobacter in other animals and wildlife, were significantly associated with the prevalence of Campylobacter in poultry [55]. Furthermore, recent data suggest that human clinical C. jejuni isolates in Central Europe can be attributed to domesticated poultry, cattle livestock and environmental sources [56].
\nOutbreaks can be traced back to several sources (e.g., raw milk [57], water [58] and chicken liver pate [59]) and even associated with antimicrobial-resistant strains [57]. However, a limited number of highly contaminated products are most probably responsible for the majority of Campylobacter infections. Effective and harmonised surveillance systems, especially in the poultry food production chain, that are oriented towards categorising risks should thus be established [50].
\nCampylobacter spp. in 2016 displayed extremely high resistance levels to FQ, which is particularly worrisome, as FQ are used as the first-line drugs against campylobacteriosis. Consequently, in some EU countries, FQ therapy of campylobacteriosis is no longer feasible. In average, the highest share of resistant isolates was detected in poultry and meat thereof, especially in the Member States of Southern and Eastern Europe (Figure 5) [5]. Data suggest that the use of FQ in livestock, specifically pigs, selects for FQ-resistant strains and accelerates the dissemination of such strains [60].
\nPrevalence of resistance in Campylobacter spp. ERM: erythromycin, CIP: ciprofloxacin, TET: tetracyclines [5, 22].
In general, resistance to erythromycin, the second clinically important antimicrobial for treating campylobacteriosis, is generally uncommon; however, more resistant isolates were detected in pigs (21.6% in 2015), as seen in Figure 5 [22], which could reflect a wide use of macrolides for the treatment of common infections in pigs [61]. In contrast to C. jejuni from humans (2.1%), markedly higher erythromycin resistance level was observed in C. coli (11.0%), with the highest proportion in Estonia (63.2%) and Portugal (50%). Similar trends could be observed in isolates from livestock and food [5]. Similarly, high levels of resistance (62.4%) were recorded in C. coli from pigs in Spain [22] and even higher in Campylobacter isolates from poultry meat in Italy (72.1%) [62].
\nResistance to macrolides in Campylobacter most commonly occurs via chromosomal mutations in 23S rRNA [63] that reduces the binding affinity of macrolides to the binding site. These mutations were, however, demonstrated to have a fitness cost and to slow growth rates [64]. Recently, transferrable erythromycin resistance, conferred by the rRNA methylase erm(B) gene and located on either plasmids or associated with chromosomal multidrug resistance genomic islands, was detected in humans and livestock [65].
\nSouthern Europe in average recorded higher prevalence of resistance to tetracyclines, which may also be used for the treatment of campylobacteriosis in humans, and is, in addition to FQ resistance, a very common feature [5]. Marked variations in tetracycline resistance could be observed between C. coli and C. jejuni, countries and sources of isolation. Resistance rates varied from very low (<10%) in C. coli from pigs in Sweden [66], moderate in Campylobacter spp. from cattle in Poland (20.9%) [52], C. jejuni from broiler carcasses in Belgium (47%) [67] and C. jejuni from chicken meat in France (53.6%) [51] to extremely high in isolates of C. coli from pigs in France (93%) [66], as well as Campylobacter spp. from quails in Portugal (96.7%) [68]. In general, C. coli exhibited higher resistance levels [5].
\nFQ resistance first emerged in Southeast Asia early in the 1990s with a rapid increase from 0 to 84% over the period of 4 years [69] and has been widely spread to the other parts of the world, which might be due to an enhanced fitness of FQ-resistant isolates [70]. Significant portion of infections with FQ-resistant Campylobacter could be acquired through travel [71]. Extreme rates of FQ resistance in endemic areas [72] are therefore of concern.
\nIn Europe, FQ resistance in Campylobacter spp. was extremely high, but it varied among the sources of isolation, species and countries. In average, isolates of C. coli exhibited markedly higher resistance rates than isolates of C. jejuni. As seen in Figure 6, turkey (96.8% in C. coli and 76.2% in C. jejuni) and meat thereof (100% in C. coli, 74.5% in C. jejuni) presented the main sources of resistance, closely followed by broilers and meat thereof [5].
\nSources and prevalence of FQ-resistant isolates of C. jejuni (turkey, turkey meat, broilers, broiler meat and human, respectively) and C. coli (turkey meat, turkeys, broilers, broiler meat, humans and pigs, respectively) [5, 22].
A rapid increase in FQ resistance in Campylobacter is evident in the last 14 years [27]. In Slovenia, for instance, the resistance level to nalidixic acid rapidly increased in isolates from broiler meat from 49.1% in 2001–2003 [73] to 78.6% in 2006 [11]. In 2016, in average, 77.3% of Campylobacter spp. from broilers exhibited FQ resistance [5]. Recent data suggest the presence and clonal spread of FQ-resistant C. jejuni clonal complex ST-21 in central Europe (Slovenia, Germany, Austria) [74].
\nFQ resistance in C. jejuni (Figure 7) from broilers (in average 66.9%) varied from 8.4% in Finland to 97.9% in Latvia [5]. Furthermore, in 2014, Latvia reported on 100% resistant isolates of Campylobacter from chicken [75]. In humans, the highest rates of FQ resistance were reported for C. coli from Italy and Portugal (100%) and in for C. jejuni from Portugal and Estonia (>90%). Notably, 9 out of 19 EU MS recorded 80–100% resistance rates for C. coli (Figure 7) [5].
\nRates of FQ resistance in C. jejuni from broilers and humans, in C. coli from humans and combined resistance to FQ and erythromycin in C. coli from humans [5].
Overall, 9.2% of human C. coli exhibited combined resistance to ciprofloxacin, erythromycin and tetracycline with resistance rates ranging from 0 to 57.9% (Estonia), which is shown in Figure 7 [5]. Erythromycin resistance is often associated with MDR phenotype [63]. In Finland, for example, 94.7% of Campylobacter isolates from humans were, in addition to erythromycin, resistant to FQ, and 73.7% to tetracycline [76]. Combined resistance to the first-line drugs may be associated with adverse events such as delayed recovery, invasive illness and prolonged treatment with feasible alternative antimicrobials [77, 78].
\nFQ resistance in C. jejuni and C. coli can be mediated through specific point mutations in gyrA gene, encoding for DNA gyrase or through chromosomally encoded multidrug efflux pump. The two mechanisms work synergistically [79]. Efflux pumps in Campylobacter, primarily CmeABC, are involved in resistance to broad spectrum of antimicrobials, including macrolides and quinolones [80], as well as cross-resistance to other compounds such as bile salts [81]. Therapeutic application of efflux pump inhibitors (e.g., epigallocatechin gallate) that were shown to restore macrolide efficacy could be a feasible treatment option in combination with the macrolide therapy [80, 82, 83, 84].
\nThe EU harmonised system on the monitoring and reporting of antimicrobial resistance in zoonotic and commensal bacteria (EC Decision 2013/652/EU; EFSA, EDSA) [12] is based on the phenotypic assessment of AMR of selected bacterial species (Salmonella, Campylobacter, E. coli) in selected food-producing animal species (poultry, pigs, cattle) and food products (chicken, pork, beef meat), using dilution methods (ISO standard 20776-1:2006, ISO standard 20776-2:2007) and EUCAST epidemiological cut-off values (ECOFF-values) as interpretative criteria (EC Decision 2013/652/EU) [12]. In accordance with this legislation, 170 isolates are examined for antimicrobial susceptibility to a panel of 15 antimicrobial substances, for each combination of bacterial species and type of sample of animal population or food category each year by each member state. In 2016, ECDC also published EU protocol for harmonised monitoring of antimicrobial resistance in human Salmonella and Campylobacter isolates, aimed to increase the comparability of AMR data collected at the EU level from different Member States, and to improve the comparison of data among human isolates and isolates from animals and food products [13]. Beside dilution method as a preferred method, disk diffusion and gradient strip diffusion method are also allowed.
\nOn the isolate level, genotyping of human isolates is also recommended, in terms of the assessment of resistance mechanisms and detection of the epidemic spread of resistance, particularly multi-drug resistant Salmonella, but it is not required in reporting [13].
\nIn recent years, the development of high-throughput technologies and platforms for massive DNA sequencing, and genomics tools has opened new possibilities also in the surveillance of AMR in common zoonotic bacteria. WGS, together with appropriate databases, general (NCBI, ENA) or specialised for AMR (ARG-ANNOT, ResFinder, CARD, RED-DB, Bacmet), bioinformatic tools (BLAST) and platforms enable detection of antibiotic resistance genetic loci in the genomes of bacterial isolates or microbiomes and reveal the mechanisms leading to AMR. While WGS offers very rapid and efficient tool for detection of the antibiotic resistance genes (ARG) in genomes of individual bacterial isolates, the main issue remains how to predict from these data the actual antimicrobial susceptibility, and epidemiological or clinical cut-off values [85]. However, differentiation among isolates with acquired or intrinsic resistance on the basis of phenotypic MIC determinations only is also not totally accurate. Furthermore, it should be considered that also the strains that contain the genes associated with antimicrobial resistance but do not exhibit phenotypic resistance present certain risk for the horizontal spread when consumed.
\nThe usefulness of WGS for antimicrobial resistance surveillance was confirmed in several studies. Examination of 640 nontyphoidal Salmonella isolates from retail meat and human clinical samples identified known resistance genes and phenotypic resistance to 14 antimicrobials, where the correlation between resistance genotypes and phenotypes was close to 100% for most classes of antibiotics, and lower for aminoglycosides and beta-lactams [86]. In addition to known ARG, several unique resistance genes were found, more in the human isolates (n = 59) than in the retail meat isolates (n = 36). The authors concluded that the use of more appropriate MIC breakpoints and inclusion of new AGSs in the databases will further improve the correlations between phenotypic and genotypic observations. For Salmonella typhimurium isolates (n = 50) from Danish pigs, high concordance (99.74%) between phenotypic and predicted antimicrobial susceptibility was observed as well [87]. Phenotypic resistance to quinolones and fluoroquinolones due to chromosomal mutations, however, could not be detected by ResFinder platform.
\nGenomic approach is increasingly used also in the developing of control methods and identification of antimicrobial resistance markers for evidence-based decisions in epidemiology and surveillance of foodborne diseases. OMICS datasets have been found as a powerful tool to complement current studies that are starting to be used also in some risk assessment areas. In a current comprehensive study “Syst-OMICS,” 4500 Salmonella genomes will be sequenced and analysis pipeline built in order to study Salmonella genome evolution, antibiotic resistance and virulence genes [88]. The data of the first 3377 genomes already sequenced are stored in the newly established Salmonella Foodborne Syst-OMICS database (SalFoS,
Comparative genomics of the WGS was successfully used also in the examination of 589 Campylobacter isolates from retail chicken meat exhibiting phenotypic resistance to 9 antimicrobials [89]. For most antimicrobial agents (ciprofloxacin, nalidixic acid, gentamicin, azithromycin, erythromycin and clindamycin), the observed phenotypic resistance, determined on the basis of the comparison of measured MICs with established ECOFF cut-off values, was in accordance with the presence of the known resistance genes or mutations. In the case of telithromycin, however, the observed point mutations in the 23S rRNA, which is a well-known mechanism of resistance to these classes of antimicrobials, did not regularly cause phenotypic resistance. Another recent study on C. jejuni isolates from the poultry (n = 502) demonstrated successful use of genomics in the study of fluoroquinolone resistance [90]. The isolates were clustered according to the presence/absence of the gyrA mutations causing fluoroquinolone resistance. Beside the WGS of isolates from the mentioned study, previously published (ENA) Campylobacter genomes were included in the comparative analyses of the genomes. Although no significant associations were found between trade patterns, antimicrobial use in livestock and population of C. jejuni, this approach proved to be successful, especially when big datasets are available.
\nIn conclusion, comparative genomics of WGS is increasingly used in the prediction of phenotypic antimicrobial resistance and surveillance of antimicrobial resistance of common zoonotic bacteria. However, as it is based on the detection of already known ARG, the success is highly dependent on the quality of databases, which need to be regularly updated with newly discovered resistance mechanisms and well-curated.
\nAntimicrobial resistant zoonotic bacteria pose a serious risk to human health. High rates of FQ resistance in both Salmonella and Campylobacter are of concern due to their wide use for the treatment of human infections. In some regions of the EU, the level of FQ resistance is so high that Campylobacter infections cannot be treated with FQ anymore. In addition, the emergence of multi-drug resistant Salmonella and Campylobacter further limits the therapy options and is possibly associated with adverse treatment effects. Greater efforts are needed to limit the wide spread of AMR in zoonotic bacteria—implementation of antimicrobial stewardship, especially in the developing countries, development of novel antimicrobials, improvement of practices in the food production chain, reduction of the amounts of antimicrobials sold for use in livestock, improvement of AMR surveillance programs, in terms of greater harmonisation, and application of rapid sequence-based methods in the routine surveillance of antimicrobial resistance.
\nThis work was supported by the Slovenian Research Agency (ARRS) through the Research program P4-0116 (Microbiology and Biotechnology of Food and Environment) and P4-0097 (Nutrition and Microbial Ecology of Gastrointestinal Tract).
\nNo competing financial interests exist.
An optical fiber is an extended cylindrical optical waveguide. In its simplest form, it consists of a core having a certain refractive index nc and is surrounded by a clad (sometimes called skin) of refractive index ncl (or ns). An optical fiber is used to guide light through its core, from one end to another, based on the principle of total internal reflection which mandates that nc must be always higher than ncl. Basically, optical fibers are made of highly pure silica glass doped with some impurities in order to increase nc or decrease ncl [1, 2, 3]. Recently, polymeric optical fibers got more attention as alternatives of some glass based optical fibers [1, 4].
Optical fibers are involved in many technological applications such as telecommunications, sensing [4, 5]; fiber lasers and fiber amplifiers [6]; fiber gratings which can act as mirrors [7, 8]; mode converters [9]; modulators; and couplers and switches [10, 11]. Optical fibers are considered ideal optical transmission media since communication cables hundreds of kilometers in length can be obtained with low absorption and low loss due to the purity and cross-sectional uniformity of the manufactured optical fibers. Moreover, accurate tuning of the refractive indices of both core and clad guarantee extremely low scattering loss at the interfaces [1].
The commonly known optical fibers are step index and graded index (GR-IN) optical fibers. The former means that the core’s refractive index is homogeneous while it suffers an abrupt change at the boundary with the clad. For a GR-IN optical fiber, the core does not have a constant value of refractive index but it rather has a radial distribution of refractive index. These two types of optical fibers can be classified into either single-mode or multi-mode optical fibers. Single-mode optical fiber only sustains one mode of propagation while the multi-mode optical fiber can sustain up to hundreds of propagation modes [1, 3]. The number of the propagation modes is related to the numerical aperture of the fiber, which, in turn, depends on the refractive indices of both core and clad.
Accurate characterization of optical fibers is required in order to know about their functions and performances. There are many methods of optical fibers characterization such as optical microscopy, electron microscopy, X-ray spectrometry, infrared spectroscopy, light diffraction, light scattering, optical interferometry, and digital holography [1, 3, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. Optical interferometry is an effective accurate tool for studying and characterizing optical fibers. It depends on the determination of the phase difference between a ray of light transmitting the fiber’s cross-section and a reference ray reaching the interference plane directly without crossing the fiber. This phase shift can be transformed into a refractive index map representing the radial distribution of the refractive index across the fiber or, in other words, the refractive index profile (RIP). Interferometry can detect tiny changes in refractive index if an external effect is applied on the fiber. The change of refractive index can be in situ detected if the interferometer is developed to achieve this task. Interference patterns can be digitally processed and analyzed in order to increase the accuracy of the obtained results [1, 17, 22, 26, 27, 30, 31, 32, 33, 34, 35].
Interference techniques can be classified into either two-beam interferometers such as Michelson, Mach-Zehnder, Pluta polarizing microscope, Lioyd’s mirror, etc., or multiple-beam interferometers such as Fabry-Pérot and Fizeau interferometers [1, 3, 25, 36, 37, 38, 39]. A two-beam interferometer produces a pattern of alternate bright and dark fringes of equal thicknesses when two beams, usually, of equal intensities Io suffering a relative phase difference δ are superposed. The resultant intensity distribution I of the interference pattern is given as:
Multiple-beam interference takes place when light rays fall on two parallel optical plates enclosing a small distance between each other while their inner surfaces are highly reflecting and partially transparent. The intensities of both reflected, I(r), and transmitted, I(t), light distributions that are redistributed due to the multiple-beam interference are given as [40]
where, I(i) is the intensity of the incident light, T and R are the products of the transmission and reflection coefficients of the two surfaces, respectively, while δ is the phase difference between any two consecutive interfered rays.
On the other hand, holography was firstly presented by Gabor in 1947 as a lens-less process for image formation by reconstruction of wave-fronts [41, 42, 43]. It offers 3D characterization such as the depth of field from recording and reconstructing the whole optical wave field, intensity, and phase [41, 42]. Holographic interferometry is a non-destructive, contactless tool that can be used for measuring shapes, deformations and refractive index distributions [44, 45]. The modern digital holography was introduced in 1994 [46, 47, 48]. Moreover, the phase shifting interferometric (PSI) technique was introduced by Hariharan et al. as an accurate method for measuring interference fringes in the real time [49]. Recently, digital holographic phase shifting interferometry (DHPSI) was used to investigate some optical parameters of fibrous materials [17, 18, 21, 26, 27, 28, 29].
In DHPSI, frequently a set of four [20] or five [23, 33] phase shifted holograms with known mutual phase shifts starting with 00 and having 900 separations have to be recorded [21]. These recorded holograms can be represented by:
where a(ζ, η) and b(ζ, η) are the additive and the multiplicative distortions and
or,
In digital holography, the recorded wavefield is reconstructed, based on Fresnel diffraction integral, by multiplying the stored hologram by the complex conjugate of the reference wave r*(ζ, η) to calculate the diffraction field b’(x’, y’) in the image plane, see Figure 1. This can be calculated using the finite discrete form of the Fresnel approximation to the diffraction integral as:
Geometry of digital holographic axes and the planes systems.
The parameters used in this formula depend on the used CCD array of N × M pixels and the pixel pitches Δζ and Δη. The distance between the hologram and the image plane is denoted by d’. The pixel spacings in the reconstructed field of image are:
The convolution of h(ζ, η)r*(ζ, η) can be used as alternative of Fresnel approximation [37]. The resulting pixel spacing for this convolution approach is
In addition, the phase shifted holograms are used to overcome the problems of the d.c. term and twin image, in which the calculated complex wavefield is used instead of a real hologram in the convolution approach.
The intensity and phase distributions in the reconstruction plane are given by
So, the optical phase differences due to phase objects can be extracted.
Mach-Zehnder interference-like system is used as a digital holographic setup as shown in Figure 2 [20, 23, 29, 33]. The optical waveguide sample, such as optical fiber, is immersed in a liquid of refractive index nL near or matching the cladding refractive index nclad of the sample. The interference patterns are recorded using a charge-coupled device, that is, CCD camera.
Mach-Zehnder digital holographic interferometric set-up, S F: spatial filter, L: collimating lens, BS: beam splitter, M: mirror, and MO: microscopic objective.
In this chapter, we illustrate some featured work on interferometric characterization (sometimes, implying digital holographic interferometry) of different optical fibers done by our research group during the last three decades. In Section 2, interferometric characterization of conventional step-index and GR-IN optical fibers is presented. Section 3 illustrates characterization of the conventional optical fibers when they are suffering mechanical bending. In Section 4, interferometric characterization of a special type of optical fibers called polarization maintaining (PM) optical fibers is presented. In the last section, we elucidate thick optical fibers and their interferometric characterization with a special interferometric system, developed in our laboratory, called lens-fiber interferometry (LFI).
In 1994, Hamza et al. derived a mathematical expression to calculate the RIP of an optical fiber by considering the refraction of optical rays at the liquid-clad and clad-core interfaces, see Figure 3 [12]. It was the first time to consider the refraction of the transmitted rays to reconstruct the RIP of a fiber. The derived expressions for calculating the RIP in case of two-beam and multiple-beam interferences, based on Figure 3, are given by Eqs. (12) and (13), respectively.
An incident ray (object ray) is refracted due to a clad-core fiber causing a fringe shift Z when interferes with a reference ray.
where, R is the fiber’s radius and e is the skin’s thickness. nL, ns, and nc are the refractive indices of the immersion liquid, skin, and core, respectively. λ is the wavelength of the used illuminating source. Ls and Lc are the geometrical path lengths inside the skin and the core, respectively. Z is the fringe shift due to the presence of the fiber while h is the interfringe spacing and d is the distance measured from the center of the fiber to the position of the incident ray.
In that work, they used Fizeau interferometer to determine the refractive index profile of FOS Ge-doped step-index multi-mode optical fiber with a core radius 19.5 μm. The fiber was immersed in a liquid of refractive index nL = 1.4665, which was a little bit greater than ns while the wavelength of the used illuminating source was λ = 546.1 nm. The Fizeau interferogram of this fiber is shown in Figure 4a. The obtained RIP was compared with the profile calculated for the same fiber when the refraction of light through the fiber was neglected as was usually done by other authors before this work. There was a significant difference between the two profiles, see Figure 4b. Therefore, the refraction through the fiber was recommended to be considered for calculating RIPs particularly when the refractive index of the immersion liquid is not close to the fiber’s refractive index.
(a) An interference pattern of Fizeau fringes, in transmission, for a FOS step-index optical fiber. (b) RIPs of this fiber in case of considering and neglecting the retraction of the crossing rays inside the fiber.
In 2008, another mathematical model was derived in order to determine RIPs of fibers having regular and/or irregular cross-sections [38]. This method was based on immersing the investigated fiber in two liquids with different, but so closed, refractive indices. They applied this method on a single-mode optical fiber, having a small core of radius <5 μm while the fiber’s radius was 60.6 μm, as shown by Fizeau interferograms in Figure 5 when the fiber was immersed in two liquids with refractive indices (a) 1.4589 and (b) 1.4574. The obtained RIP of this fiber is illustrated in Figure 5c showing that this fiber has nc = 1.4630 and ncl = 1.4596. This method was simple and accurate enough to detect such a small core of a step-index optical fiber.
Fizeau interferograms, in transmission, for a single-mode optical fiber when it was immersed in two liquids of refractive indices (a) 1.4589, (b) 1.4574. (c) RIP of the single-mode optical fiber having the interferograms shown in (a) and (b).
A GR-IN optical fiber with a radial refractive index distribution was suggested to be divided into a finite number (M) of concentric layers where each layer has its own value of refractive index, see Figure 6a. The thickness (a) of each layer equals R/M, where R is the radius of the graded-index part. When the ray falls on the fiber at a distance dQ apart from the fiber’s center, the ray refracts through Q layers. The nearest layer to the fiber’s center has a refractive index nQ. The fiber’s RIP can be calculated using Eq. (14) in case of two-beam interference and Eq. (15) in case of multiple-beam interference [13]. Another model was presented in order to get RIP of a GR-IN optical fiber by considering the real path of the optical ray due to the refraction in the core region as well as adding a correction for the ray passing through the immersion liquid [50], see Figure 6b. In this case, the fringe shift was obtained by assuming values for both the profile shape parameter (α) and the difference between refractive indices of core and clad (Δn). A prepared software was programmed to iterate and get the best values of α and Δn and comapre the calculated fringe shift with the experimentally obtained one.
(a) A schematic diagram shows the path of an optical ray crossing Q layers in the core region. (b) A schematic diagram shows the path of an optical ray crossing a GR-IN core optical fiber.
According to Figure 6b, the optical pathlengths of the ray crossing the core
where, R is the core’s radius, k is the minimum distance between the fiber’s center and the bent ray, ε is the half of the angle determined by the two radii that are enclosing the bent ray inside the graded-index region, and γ is the half of the angle between the incident and the emerged rays. Figure 7 shows the interferograms of LDF GR-IN optical fiber when it was investigated by (a) Pluta and (b) Fizeau interferometers. Figure 8 shows the RIPs calculated by these last models for the LDF optical fiber. The last model, presented in 2001 [50], provided more accurate values of the RIP of a GR-IN optical fiber compared with its previous presented model in Ref. [13].
(a) Pluta duplicated image of LDF GR-IN optical fiber and (b) Fizeau interferogram of the same sample. Reference [50] with permission.
A comparison between RIPs of LDF GR-IN optical fiber using the model in Ref. [27] (dots) and model in Ref. [28] (solid curve) in case of (a) multiple-beam Fizeau interference and (b) two-beam Pluta interference. Ref. [50] with permission.
However, the former requires knowing the function describing the index profile while the aim is to find the parameters of this function.
Optical fibers, which are isotropic materials, can suffer a birefringence under external mechanical bending effects [1, 22, 33, 51]. The induced birefringence can be used in sensing applications [52, 53, 54]. However, bending has an unfavorable effect on the optical fibers used in telecommunications where it, sometimes, causes a mode disturbance and consequently a signal attenuation [55, 56]. An approach to calculate the refractive index profile of a bent optical fiber was proposed where the fiber was divided into layers and slabs simultaneously [22]. The refraction of the optical rays at the liquid-clad and clad-core interfaces was considered. Unfortunately, this approach did not consider the change of refractive index inside each slab. Also, the expected change of refractive index due to the release of stresses near the fiber’s free surface has not been considered. However, this approach succeeded to present good information about the variation of mode propagation due to bending.
In 2014, Ramadan et al. calculated the refractive index and the induced birefringence profiles of bent step-index optical fibers using digital holographic Mach-Zehnder interferometer [33]. In that work, they considered two different processes controlling the variations of the refractive index of the bent fiber: (1) the linear refractive index variation due to the applied stress along the bent radius and (2) the release of this stress on the fiber’s surface. The first one is dominant when approaching the center of the fiber while the second one is dominant near the fiber’s free surface and decays on moving toward the fiber’s center. Figure 9 shows the difference between the paths of optical rays through the bent fiber in the compressed and expanded parts. The stress release was supposed to have a radial dependence on the fiber’s radius, which enabled the construction of 2D RIP of the investigated bent homogeneous optical fiber. Based on the expected stress values due to the bending effect, a function describing the RIP was proposed and used to integrate the optical path of the ray traversing the fiber [50]. By adapting the appropriate parameters of this function, the optical phase differences were estimated and matched those phase differences that were experimentally obtained. By this assumption, a realistic induced stress profile due to bending was obtained [33]. DHPSI was used in that study where the recorded phase shifted holograms were combined and processed to extract the phase map of the fiber [18]. By considering both of the mentioned effects, the following function was chosen to describe the RIP of the bent optical fiber [33].
A schematic diagram shows the path of an optical ray crossing a bent homogeneous optical fiber.
where ρ is the strain-optic coefficient, nbf is the refractive index of the bent-free fiber, R is the radius of bending, ro is the radius of the fiber, ncl is the clad’s refractive index, rs is the proposed parameter to control the distance suffering stress release from the surface of the fiber, and x is the distance between the center of the fiber and the position of the incident ray.
The first term of Eq. (18) gives the bent-induced birefringence,
which is correlated to the generated stress S (r,x) inside the fiber
Eq. (20) evaluates the distribution of stress over the fiber’s cross-section for different bending radii where E is the Young’s modulus of the bent fiber. The signs of Δn are opposite to the signs of tensile and compressive stresses. The tensile stress was chosen to be positive.
Since bending such a step-index optical fiber converts it into a weekly graded-index fiber, Bouguer’s formula [40] was used to correlate the radius, incidence angles, and refractive index of the bent fiber as follows:
where n(x,r) is the refractive index at radius r. By applying this formula at the incidence point, one obtains
This equation was numerically solved to get K satisfying the lower integration limit of the optical path difference for a certain value of x. Based on the model described in Ref. [50], the infinitesimal change in the geometrical distance along the path of the optical ray with respect to the radius variation was given as:
By integration with respect to r, the total path length inside the fiber is:
The optical path length difference between this ray, passed through the fiber, and the reference ray passed through the liquid is:
The phase difference is given as:
Figure 10 shows a set of five shifted holograms of a bent step-index optical fiber with a bending radius R = 8 mm when the incident light was vibrating parallel to the fiber’s axis. They were recorded in order to apply the DHPSI technique and reconstruct the RIP of the bent fiber. The 2π shifted interferogram was analyzed and its reconstructed interference phase map, enhanced phase map, and interference phase distribution are shown in Figures 11a–c, respectively. The refractive index cross-section distribution of the bent optical fiber is shown in Figure 12 while the strain-optic coefficients in compression and expansion were 0.208 and 0.224, respectively.
A set of five shifted interferograms of a bent step-index optical fiber.
(a) The reconstructed interference phase map modulo 2π, (b) its enhanced phase map, and (c) the interference phase distribution.
The refractive index cross-section distribution of the bent optical fiber, R = 8.
In 2017, Ramadan et al. presented a theory to recover the RIP of a bent GR-IN optical fiber inside the core region using DHPSI [35]. They assumed the two different processes controlling the shape of the RIP: (1) the linear variation due to stresses in the direction of the bent radius and (2) the release of the stresses near the fiber’s surface.
The total optical path length of the optical ray crossing the bent GR-IN optical fiber is given by Eq. (27), see Figure 13. The calculated optical path length differences of the interfered rays can be transformed, afterward, into a phase difference map using Eq. (26).
schematic diagram shows the ray tracing in case of traversing bent GR-IN fiber.
with,
Figure 14a shows a set of five phase shifted interferograms for the bent GR-IN optical fiber with bending radius R = 8 mm when the incident light was vibrating parallel to the fier’s axis. The enhanced reconstructed phase modulo 2π and the interference phase distribution of the bent fiber are shown in Figure 14b. Due to the bending process, the GR-IN optical fiber exhibited a birefringence where the RIPs when the incident light vibrated parallel and perpendicular to the fiber’s axis were different, see Figure 15.
(a) A set of five phase shifted interferograms of a bent GR-IN optical fiber. (b) The enhanced reconstructed phase modulo 2π and the interference phase distribution. Ref. [35] with permission.
Refractive index cross-section distribution of the bent GR-IN optical fiber when the incident light vibrates (a) parallel and (b) perpendicular to the fiber’s axis. (c) The birefringence cross-section distribution, R = 8 mm. Ref. [35] with permission.
A PM fiber is any fiber that preserves and transmits the polarization state of the light launched into the fiber even if this fiber is subjected to environmental perturbations [57]. This advantage cannot be verified by conventional single-mode optical fibers outside the laboratory conditions. A PM fiber is tailored to oblige the two orthogonally polarized modes traveling with different velocities (i.e., different propagation constants). This difference in velocities prevents the optical energy from suffering a “cross-coupling” and preserves the polarization state of the transmitted light. Therefore, a PM fiber used in any application requires delivering a polarized light such as in telecommunications, medical applications, and sensing. In interferometric applications, it is used to affirm that the interfered rays have the same polarization states. To maintain such a difference of velocities, the core of the fiber has to be anisotropic either geometrically by making the core cross-section as an ellipse or by applying a uniaxial stress. The most known PM fibers used today are, PANDA, bow tie, and elliptical-jacket fibers. These types are designed by the same way where the cores are flanked by areas of high-expansion glass and shrunk-back more than the surrounding silica then the core is frozen under tension. The birefringence is induced due to this tension, which means creation of two different indices of refraction: a higher index in the direction parallel to the applied stress and a lower index perpendicular to the direction of the applied stress. In the next two subsections, we briefly illustrate both the manufacturing process and interferometric characterization of PANDA and bow tie PM optical fibers.
PANDA PM optical fiber is preferable in telecommunications [57, 58]. It is modified by insertion of stress rods to provide PM properties according to the procedure described in Figure 16. In this process, two holes are ultrasonically drilled along a single-mode optical fiber; then, the stress rods are inserted in these two holes and the fiber is finally drawn [57]. In 2014, Wahba used the off-axis DHPSI to reconstruct the 3D RIP of a PM PANDA optical fiber [23]. The multilayer model was used to calculate the RIP of this fiber in the directions of fast and slow axes. By rotating the PANDA fiber, different interferograms were recorded and analyzed in order to reconstrut the 3D RIP of this fiber, see Figure 17. The reconstructed 3D RIPs of PANDA fiber are shown in Figure 18 when the incident light was vibrating in the direction of (a) fast axis and (b) slow axis.
Manufacturing steps of a PANDA PM optical fiber.
The left column shows three orientations of PANDA PM optical fiber as it was rotated during the characterization process where the slow axis makes an angle (a-i) 0°, (b-i) 45° and (c-i) 90° with the horizontal axis. The middle column shows their reconstructed interference phase modulo 2π while the right column shows their phase difference maps. Ref. [23] with permission.
The 3D RIPs of PANDA PM optical fiber in the directions of (a) fast axis and (b) slow axes. Ref. [23] with permission.
A bow tie optical fiber is fabricated on a lathe using the inside vapor-phase oxidation (IVPO) via the process called gas-phase etching to create the required stress [57]. This process is summarized in Figure 19 where a ring of boron-doped silica is purely deposited of boron tribromide in combination with silicon tetrachloride. The rotation of the lathe stopped when a sufficiently thick layer was formed to allow two diametrically opposed sections to be etched away. The final shape of the bow tie and stress levels are controlled by varying the arc through which the etching burner is rotated. Recently, Ramadan et al. estimated the optical phase variations of optical rays traversing a PM optical fiber from its cross-section images [59]. They proposed an algorithm to recognize the different areas of the fiber’s cross-section, which was immersed in a matching liquid and investigated by Mach-Zehnder interferometer.
Manufacturing steps of a bow tie PM optical fiber.
These areas were scanned to calculate the optical paths for certain values of refractive indices and the optical phases across the PM optical fiber were recovered. The experimental interferograms of the bow tie PM optical fiber, shown in Figure 20, were analyzed to extract their optical phase distributions and compare them with the optimized estimated optical phase maps, see Figure 21. This was a direct and accurate method to get information about refractive index, birefringence, and the beat length of a PM optical fiber.
(a and c) Cross-sections of the bow tie optical fiber. (b and d) Experimentally obtained phase shifted interferograms when the incident light vibrates parallel and perpendicular to fiber’s axis, respectively. Ref. [59] with permission.
The calculated and the experimental phase differences of the bow tie optical fiber when the incident light vibrates (a) parallel and (b) perpendicular to the fiber’s axis. Ref. [59] with permission.
Optical fibers having diameters in the order of 100 μm, or less, are convenient to be investigated using interferometric methods when the samples are put in immersion liquids of refractive indices close to the refractive indices of the fibers as described in the previous sections [12, 13]. Optical fibers of diameters bigger than 150 μm cannot be investigated by normal interferometry where the planes of fringes in both liquid and fiber cannot be focused simultaneously. In 2000, Ramadan presented a novel interferometric method to recover such a problem for homogeneous thick optical fibers, commonly used in short-distance data transmission, without using immersion liquids [16]. This type of interference was called lens-fiber interferometry (LFI) since the interference fringes were produced by a combination of an aberrated cylindrical lens and a thick optical fiber. The aberrated cylindrical lens was used to focus a parallel beam on this fiber, which was located in the focal plane of the cylindrical lens [60], see Figure 22.
The ray tracing diagram of an optical ray crossing a homogeneous thick optical fiber.
Two-beam interference produced by the superposition of two optical rays emerging from the fiber was recorded and explained. Due to the aberration of the cylindrical lens, one of these two rays crossed the thick fiber before its center while the other ray crossed after the fiber?s center. Therefore, for each point in the image plane, two rays having two different initial incidence angles on the thick fiber are superposed, see Figure 23. The optical path length of each ray can be obtained by tracing this ray geometrically, as given by Eq. (30), which can be transformed into phase differences for the interfered rays using Eq. (31). The difference in the optical path lengths of each pair of interfered rays can be transformed into an intensity distribution describing the interference fringes using Eq. (32). On the other hand, the scattered rays from the outer surface of the fiber do not contribute in the interference because of the limited range of the incident rays on the fiber. This is in contrast with previous works done by Watkins [14, 15, 61]. By comparing the experimentally obtained interferograms with those reconstructed theoretically as shown in Figure 24, Ramadan was able to determine the refractive index of the investigated thick optical fiber. The advantage is that the used system requires no matching liquid where the experiment is performed when the thick fiber is just held in air. This enables monitoring the probable variation in radius or refractive index of the fiber particularly during the manufacturing process or under external effects.
The relation between the position of each two interfered rays on the screen and their incidence angles on the thick fiber.
(a) A selected and extended part of the obtained interferogram of a thick optical fiber, (b) the enhanced fringes of (a) and (c) the simulated fringes.
where Δ(z1) and Δ(z2) are the optical path lengths of the two interfered rays. In 2004, Hamza et al. developed LFI technique in order to determine the refractive index of the core of a skin-core thick optical fiber [60]. They derived a mathematical expression for the optical paths through the fiber in order to reconstruct the interfernce pattern due to the used fiber when it is used as a thick fiber in the LFI system. By comparing the experimentally obtained patterns with the theoretically reconstructed ones, they were able to estimate the core’s refractive index with an accuracy of 8 × 10−4. Due to its simplicity and applicability, LFI was used, afterward, to measure the refractive index of a liquid [62] and to monitor the thickness variations of a transparent sheet inserted between the cylindrical lens and the thick fiber [63].
This chapter is an attempt to highlight the interferometric techniques used for characterization of optical fibers. Application of two- and multiple-beam interference on different types of fibers is illustrated. Section 2 dealt with conventional optical fibers where we illustrated the theoretical models used to reconstruct the refractive index profiles of these fibers. In these models, the refraction of the light ray traversing the fiber has been considered. Digital holography was explained as an important candidate used for accurate retrieving of phase maps and consequently refractive index profiles of the fibers. In Section 3, we mentioned the problem of fiber bending. Recovering the refractive index profile and mode propagation of a bent fiber considering the refraction of the light rays traversing the fiber is a quite difficult task since bending-induced stresses are responsible for refractive index variations. Also, these stresses are released at the outer surface of the bent fiber. Therefore, we illustrated a successful model that was recently presented to recover the index profile in this case with experimental illustrative data. Another important type of optical fibers is the polarization maintaining optical fibers, which prevent cross-coupling by conserving the state of beam polarization during propagation. In Section 4, we presented interferometric techniques applied on two different polarization maintaining optical fibers, panda and bow tie, to reconstruct their refractive index profiles. Most interference techniques require immersing the fiber in a suitable liquid in order to minimize the phase difference between the fiber and its surrounding medium. In Section 5, an interference technique is presented and applied on a thick optical fiber to recover its refractive index without using an immersion liquid (i.e., in air), which makes the technique suitable for in-situ studying of thick fibers.
The authors would like to acknowledge Prof. A. Hamza, the leader of optics research groups in Mansoura and Damietta Universities, and Prof. T. Sokkar for their continuous support and useful discussions. Also, many thanks to the Optics Research Group members in Damietta University for their useful suggestions and comments.
The authors declare no conflict of interest.
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