Definitions of the main terms used in the literature on fish introduction (modified after [16, 18, 34].
\r\n\tThis book will be very useful for a wide variety of readers, such as Chemists, Pharmaceuticals, Biochemists, Biotechnologists, Industrialists, Engineers, Researchers, Teachers and Students.
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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59375",title:"Alien Fish Species in France with Emphasis on the Recent Invasion of Gobies",doi:"10.5772/intechopen.73408",slug:"alien-fish-species-in-france-with-emphasis-on-the-recent-invasion-of-gobies",body:'Introduction of alien species (also sometimes called non-native, transplanted, or exotic with a slightly different meaning, see Table 1) constitutes worldwide one of the major threats to biodiversity, with alteration or destruction of habitats, pollution, overexploitation, and climate change [1, 2]. In freshwater ecosystems, the introduction of fish is considered as a significant component of human-caused environmental changes [3]. The rate of introductions has strongly increased in the past century [4–7]. In France, for instance, the number of alien aquatic plant and animal species has increased exponentially in freshwater ecosystems from less than 10 prior to the beginning of the nineteenth century up to 148 today and shows no sign of decreasing [7].
Term | Definition |
---|---|
Non-native or foreign | Species not occurring naturally in a geographic area |
Exotic | Species introduced from other biogeographic realms |
Indigenous or native | A species occurring naturally in a specific geographical area without human intervention |
Introduced population | Population that arrives at locations not normally achievable by that species, with intentional or accidental human assistance |
Naturalized | Self-sustaining populations in the wild of a non-native species |
Invasive | Non-native species that spread and cause significant ecological changes or cause severe economic losses |
Translocated | Species that is transported from a region where it is native to another part within the same country |
For fish, the main causes of introduction of alien species are aquaculture [3, 6, 8], commercial and recreational fisheries [9, 10], aquarium fish market [11], and management of aquatic ecosystems [3, 12]. Introductions could also result from a spread following the human modifications of hydrosystems, such as the construction of canals [3, 13] or dams [14] that allow species to disperse by their own means or transported by ship ballasts [3, 15]. Today, more than 600 freshwater fish species have been introduced into areas outside their native range globally [16], which resulted in that more than half of the river basins across the world host at least one alien fish species [17]. Among these 600 species, Toussaint et al. [18] found that 14 are present into at least one of the 1054 river basins studied in the 6 biogeographic realms defined by [19]: Afrotropical, Australian, Nearctic, Neotropical, Oriental, and Paleartic. Three species are present in all six realms: rainbow trout (Oncorhynchus mykiss), common carp (Cyprinus carpio), mosquitofish (Gambusia affinis), and three more, goldfish (Carassius auratus), sea trout (Salmo trutta), and Eastern mosquitofish (Gambusia holbrooki) in five realms [18]. These few alien species contribute the most to the global homogenization pattern, whereas most introduced species have low impact on the global change in dissimilarity, i.e., beta-diversity [18]. The authors thus concluded that focusing conservation efforts in controlling the spread of these few species may be more relevant to counteract the global homogenization trend [18].
Most often, exotic species freshly introduced are not able to survive in their new environment and it is generally considered that only a low percentage succeeds to establish sustainable populations and become invasive [1, 20]. However, García-Berthou et al. [21] found that the average percentage established of the 123 alien aquatic species into six European countries (United Kingdom, France, Spain, Sweden, Germany, and Italy) is 63% (167 of 264 introductions), much higher than the 5–20% suggested by Willamson’s “tens” [21]. Once established, the eradication of a freshwater non-native species is almost impossible [17], which is a real problem because it is still today very difficult to prevent new introductions and to predict the success and effects of invading species [20]. There is indeed no consensus about what makes a successful invader, even though some biological attributes have been proposed, among which a high environmental tolerance (e.g., eurythermal and euryhaline), a high genetic variability, a short generation span, a rapid growth, an early sexual maturity, a high reproductive capacity, and a broad diet [22]. Based on the analysis of 10 life-history traits between 13 non-native and 46 native freshwater fish species inhabiting the Central European biogeographical region, Grabowska and Przybylski [23] found that the former were significantly different from the latter. Non-native species tend to be small or medium in size, have a short longevity and mature early, a rather low fecundity but with large eggs, spawn at least twice each year over an extended reproductive season, and exhibit some form of parental care. MacDougall et al. [24] assume that the divergence in at least one biological attribute (on the basis that it results in a “fitness difference”) can better explain invasion success than a particular suite of specific life-history attributes [23]. For instance, the gibel carp (Carassius gibelio) possess a unique reproductive attribute (the eggs can be activated by the sperm of other cyprinid species, allowing the production of progeny in the absence of conspecific males), which probably partly explain why it is one of the most successful invader in Poland and more generally in Central Europe [23]. More broadly, the theory of MacDougall et al. [24] belongs to a vein of interest related to the ecosystem naiveté, with at least two hypotheses that can be extended to freshwater fish even if the latter has been mostly tested for terrestrial plants. Ricciardi and Atkinson [25] proposed the phylogenetic distinctiveness hypothesis: larger impacts are caused by exotic species that add novel taxa to the community. The evolutionary naïveté hypothesis [26, 27] assumes that impact of exotic species depends on the recipient community’s evolutionary experience with functionally similar species. Both hypotheses have originally been produced to explain the ecological impact of exotic species but can also be used to investigate their success in terms of establishment.
The success or failure of an alien species relies probably partly on its biological attributes (or on one or a few specific attributes) but also depends on the recipient ecosystem characteristics, including both biotic and abiotic factors [20, 28, 29]. All aquatic ecosystems seem potentially colonizable even though some might be more susceptible to invasion: simple systems (i.e., with rather low native species richness) or complex systems (i.e., species-rich communities) [17], geographically and historically isolated environments (e.g., islands), disturbed or anthropogenic habitats, or regions where no co-adapted foes, including competitors, predators, parasites, or diseases are present [30]. At last, the human-mediated propagule pressure (the number of individuals introduced as well as the frequency of introductions in a given area) is positively correlated with the establishment of alien species [1, 17]. Commensalism with human activity has also been found as one of the most consistent attribute of the success of invasive species [22, 30].
Most alien fish species have generally low impacts on native species and ecosystems [1], but high-impact invaders comprise at least 10% of the total number of invaders [31]. The main consequences of alien species on native species and ecosystems are varying and non-exclusive: hybridization, predation, competition, extirpation, dissemination of diseases and parasites, habitat change, and food web alteration [3, 12, 15, 32, 33]. In metropolitan France, more than one-third of the freshwater fish species are alien [7, 34]. Even though none has yet been documented in France as the cause of native species extinction, several of them are well known as causing major ecosystem disturbances.
The aim of the present chapter is first to reassess the knowledge acquired on alien fish species in France in the past years, and second to focus on the recent invasions of gobies in the north-east of the country [35] to dissect possible factors enhancing successful invasion.
Several atlas and field guides have been written on the European fish fauna, among which the last and most completed was published 10 years ago [36]. It appears that this fauna is one of the poorest across the world as these authors only recognized 579 species in European freshwaters west of the Urals, particularly in comparison with the 13,000 freshwater fish species described in the world [19]. Among these 579 species, 33 have been introduced from regions outside Europe (North America and Asia in a large majority [37]), of which 28 are established [36]. Besides, much more species have been moved between European countries [34]. Consequently, Leprieur et al. [17] have highlighted that western and southern Europe regions are among the six areas where introduced species represent more than a quarter of all species. A database developed from the project DAISIE (Delivering Alien Invasive Species Inventories for Europe) was launched about a decade ago [38], and includes now more than 12,000 alien animals and plants in Europe (
The French fish fauna has also been extensively studied in the past decades (e.g., [40–43]. Traditionally about 80 species were recognized [34], yet with the advent of the DNA barcoding and integrative taxonomy [44] several taxonomic revisions have been done: some new species have been described [45–47], others have been invalidated [48]. For instance, it was thought that the Northern pike (Esox Lucius) was the only species present in Europe, but recent integrative analyses based on both morphological and molecular characters concluded that three species are actually present in France, the Northern pike (which is the most common), E. aquitanicus (from the Charente to the Adour drainages), and E. cisalpinus (mostly in the Lake Geneva). Besides, more than 40 alien species have been either voluntary or involuntary introduced in the past decades in France [42]. Nearly one-third are no longer present in France (or their presence is very doubtful), among which several salmonids (Oncorhynchus tschawytscha, O. kisutch, and Coregonus spp.) and centrarchids (Pomoxis spp., Lepomis spp.) [42]. Among those that are still present today, most have established self-sustaining populations (Table 2); even though some occupied a very restricted area, such as the rock bass (Ambloplites rupestris) [49]. Few other alien species are probably not established, such as the rainbow trout Oncorhynchus mykiss or the grass carp Ctenopharyngodon idella. In total, the fish species richness has increased in the past decades [49] and there are today more than 100 species inhabiting France, which belong to 26 families. The two most speciose families are Cyprinidae (n = 40) and Salmonidae (n = 9), whereas 12 are monotypic, among which Gadidae [50].
Latin name | Date of first observation in France | Vectors of introduction | Native range | French name | References |
---|---|---|---|---|---|
Ambloplites rupestris | 1904 | Release: recreational fishing | Crapet des roches | [5, 41] | |
Ameirus melas | 1871 | Escape: from the “Museum National d’Histoire Naturelle” | North America | Poisson chat | [5, 40, 42] |
Aspius aspius | 1976 | ? | Aspe | [5, 40, 42] | |
Carassius auratus | Around 1750 | Release: ornamental purposes | Carassin doré | [5, 40] | |
Carassius carassius | Around 1750 | Release: aquaculture | Carassin commun | [5, 40] | |
Carassius gibelio | Around 1850 | Release: aquaculture | Carassin argenté | [5] | |
Cobitis bilineata | Around 1995 | Unintentionally introduced | Loche transalpine | [42] | |
Coregonus albula | 1860 | Release: recreational fishing | Petite marène | [5, 40] | |
Cyprinus carpio | Around 1250 | Release: aquaculture | Carpe commune | [5, 42, 40] | |
Gambusia affinis | 1924 | Release: anti-mosquito biological control | Gambusie | [5, 42, 40] | |
Gambusia holbrooki | 1924 | Release: anti-mosquito biological control | Gambusie | [5, 42, 40] | |
Lepomis gibbosus | 1977 | Release: recreational fishing | North America | Perche soleil | [5, 42, 40] |
Leuciscus idus | Around 1950 | Dispersal: unintentionally introduced during stock enhancement | Ide mélanote | [5, 42, 40] | |
Micropterus salmoides | 1890 | Release: recreational fishing | Black-bass à grande bouche | [5, 42, 40] | |
Neogobius fluviatilis | 2014 | Dispersal: shipping | Ponto-caspian | Gobie fluviatile | Unpublished data |
Ponticola kessleri | 2011 | Dispersal: shipping | Ponto-caspian | Gobie de Kessler | [35, 42] |
Neogobius melanostomus | 2011 | Dispersal: shipping | Ponto-caspian | Gobie à taches noires | [35, 42] |
Pachychilon pictum | Around 1980 | ? | Epirine lippue | [5, 42] | |
Proterorhinus semilunaris | 2007 | Dispersal: shipping | Ponto-caspian | Gobie demi-lune | [35, 42, 90] |
Pseudorasbora parva | Around 1978 | Escape: from aquaculture production unit | Pseudorasbora | [5, 42, 91] | |
Salvelinus fontinalis | 1876 | Release: recreational fishing | Omble de fontaine | [5, 42, 40] | |
Salvelinus namaycush | 1886 | Release: recreational fishing | Cristivomer | [5, 42, 40] | |
Sander lucioperca | Around 1880 | Release: recreational fishing | Sandre | [40–42] | |
Silurus glanis | 1857 | ? | Silure glane | [5, 40, 42, 92] | |
Umbra pygmea | 1910 | Release: aquaculture | Umbre pygmée | [5, 42, 93] | |
Vimba vimba | 1989 | Dispersal | Vimbe | [5, 42] |
List of alien fish considered as naturalized in French inland waters.
Silurus glanis is included in this table but its alien status remains questionable. Vectors of introductions have been classified into: Dispersal (range expansion by active or passive means from populations of neighboring countries. It includes accidental transport by human means), Escape (escaped from captivity) or Release (deliberately released into the wild)
The timing and reasons of introductions of alien species in France are, in general terms, similar to other European countries [3, 16, 34], such as in Belgium [51], Germany/Austria [52, 53], Bulgaria [55], Poland [56], or Norway [57]. The first species that was introduced in France is the common carp Cyprinus carpio in roman times, followed by the goldfish Carassius auratus [41, 42]. Nevertheless, this is only during the second half of the nineteenth century that more frequent introductions occurred under the auspices of the Imperial Society of zoological acclimatization (“Société impériale zoologique d’acclimatation”) [41, 42], which was established in 1855 [34]. Introductions were first motivated by research curiosity and to improve fish stocks for fishery. Introductions concerned exclusively European and North American fish, among which various salmonids and centrarchids [41, 42]. Then, new species were deliberately introduced to improve the fish market economy by diversifying the market of native species, for sport fishing, to act as biological control agents of algal blooms in eutrophic ecosystems, or to control mosquitoes [3]. More recently, because of stricter legislation and change in fisheries management practices away from stocking with non-natives [34], the main pathways for alien fish introduction are via either the ornamental trade and subsequent unintentional introduction [6, 11] or angling practices, such as for the asp (Aspius aspius), which is one of the three alien species that showed the most spectacular colonization in France during the past decades [49]. Besides, several introductions were accidental (e.g., during stocking events), which is probably the case for species not favored for fishing, such as Pseudorasbora parva, Pachychilon pictum, and pumpkinseed Lepomis gibbosus [49]. Other introductions result from natural species range expansion accelerated by several human activities and infrastructures, such as fluvial transport and artificial canals [56]. In conclusion, the attitude to the introduction of non-native fish have changed over time from efforts made to seek out and introduce new species actively to the protection of hydrosystems face from all new species [33, 34].
The negative impacts of alien species on native species and ecosystems, as for other countries [16], such as Belgium [51], Norway [57], Bulgaria [55], or Poland [56], are most often unknown in France and needs further studies [5, 41, 42] to develop a functional policy on alien species introductions and the protection of aquatic ecosystems integrity [51]. The recent European Union legislation addressing the problem of invasive alien species or IAS (EU Regulation No. 1143/2014) identifies different types of intervention including prevention, early warning, and rapid response. It required member states to develop a list of invasive alien species of concern in addition to a list of Union concern (see EU 2016/1141 and 2017/1263). These lists are dynamic at the Member State and EU levels and need scientific evidences to identify and prioritize IAS of regional and indeed global concern. We still need a quantitative methodology to assess potential impacts of invasive fish even if some recent proposals have been done [58, 59].
The rainbow trout (Oncorhynchus mykiss), which is a salmonid originating from the west coast of North America, was one of the first fish species to be domesticated and introduced globally: today it is present in more than 90 countries [60, 61]. In France, it was first introduced in the beginning of 1880 for angling [41]. Thereafter, with the control of artificial production, it has become one of the leading species in inland European aquaculture [62] and accounts for more than three-quarters of the French fish production [61]. Yet, rainbow trout is still considered non-established in France [49], and in most European countries, except in few Norwegian drainage basins where only six self-reproducing populations are confirmed by the mid-1990s [57], as well as in few Alpine rivers in Austria, Slovenia, Switzerland, and Italy [36]. The failure of rainbow trout to establish in most parts of Europe may to a large extent be caused by its susceptibility to whirling disease, a myxozoan parasite Myxobolus cerebralis [57]. Nevertheless, even though it is not established, it is still very common in France, because of escapees from aquaculture and intentional releases in lakes, rivers, and particularly private ponds for sport fishing [16, 51]. The impacts of rainbow trout on native species and ecosystems is poorly documented in France [5], but it is generally considered that this species do not show severe environmental impacts across Europe [3].
The grass carp (Ctenopharyngodon idella) is native from East Asia and was first introduced in France in the end of 1950s [41], similar to other European countries [34]. As rainbow trout and two Asian carps (silver carp Hypophthalmichthys molitrix and bighead carp H. nobilis), the grass carp does not breed in natural conditions in France because it requires very specific conditions that are not met in this country: large rivers with a strong current (1 m/s), important and rapid water level variations (1–2 m), water temperatures comprised between 20 and 25°C during several weeks in summer, and long unregulated water courses in which the pelagic eggs can incubate [63]. Yet, easy artificial reproduction has allowed this species to be spread in numerous countries [55], even though it is only very occasionally found in open waters in France [63] or Belgium [51]. However, it is present in numerous ponds across France [63]. The impact of grass carp is poorly document in France [41, 63]. Yet in other countries, it is considered that several parasites were transferred, which infested the common carp in Bulgaria [55] and Poland [56]. More generally, it is considered that grass carp can significantly influence native ecosystems because of their prevalence in some water bodies [56]. For instance, they are reported to destroy the spawning grounds of native phythophilous fish species through foraging on macrophytes, which could led to the decreased of fishing of some species in several lakes or depletion of wild fowl fauna, particularly those feeding on soft aquatic vegetation, e.g., coot, Fulica atra, and swan, Cygnus sp. [56]. Nevertheless, it is important to mention that the use of grass carp has allowed in certain cases to effectively control macrophyte development while avoiding the use of more costly and environmentally unacceptable alternatives such as insecticides or herbicides [16].
These two examples illustrated what has occurred in the past in France (and more generally in Europe) to improve angling, aquaculture production, and management of ecosystems. Today, it would be a futile and potentially a controversial exercise to try to eradicate these two species and more generally already established alien fish species because of high expense, difficulty of success, and the likelihood of imposing substantial collateral damage [56, 64]. However, as the possible outcomes of introductions are still very poorly documented, the precautionary approach (“guilty until proven innocent”) is most appropriate for dealing with new alien species introductions [3, 51]. Because, nowadays, aquaculture is the main pathway of initial introduction of new fish species in Europe [6], one possible way to decrease risks while increasing production [16] would be therefore to rely more on the production of local species with valuable qualities such as pikeperch (Sander lucioperca) or European perch (Perca fluviatilis) ([54], Teletchea et al. 2009).
The topmouth gudgeon is a small cyprinid originating from East Asia, including Japan, the Korean section of the Amur River Basin, China (basins of the rivers Yangtze and Hoanghe), and Taiwan [65]. It was accidentally released in Europe in early 1960s with stocking material of Asian herbivorous cyprinids [51, 56, 65–67]. Then, because of both stocking and natural range expansion, it has rapidly spread across Europe [51, 56, 67] and more generally in numerous countries in the world, being now classified as a worldwide pest [66]. It still continues today to expand its range, and represent one of the most common alien species in France [49]. Similarly, following its introduction into lakes in the UK in 1996, populations appear to establish rapidly and become dominant in the fish community (often >97% by number) [64]. The reason for its success is its very high reproductive rate, which gives rise to dense populations of fish that compete with fry of other species [65]. Besides, this species is opportunist and has a wider ecological and physiological tolerance than many European fish species and can survive to a moderate degree of pollution, elevated temperatures, and low water levels. The ability to spawn on any smooth-surfaced object, such as branches, leaves, and artificial substrata, is another important factor likely to have contributed to the rapid dispersal of this species [65]. The impact of the topmouth gudgeon is poorly documented in France [42]. Yet, in other countries, it has been shown that it can compete for food with other species such as Aphanius anatolie and Orthrias sp. [65]. It was also described that populations of Leucapius delineatus decreased when topmouth gudgeon increased, the latter being a vector of a lethal pathogen for the former [49, 64, 65]. More generally, their high abundance provokes concerns of detrimental ecological impacts through, for example, high competition for resources such as food and spawning habitat, and they become a pest species to anglers [64]. The topmouth gudgeon is included in the list of exotic species of concern in the framework of European Union legislation addressing the problem of invasive alien species (EU Regulation No. 1143/2014).
The recent, spectacular invasion of French hydrosystems by gobies is a good medium to discuss about what makes the success of an invasion. Since 2007, four freshwater Gobiidae species have been introduced in French hydrosystems: the tubenose goby (Proterorhinus semilunaris) in 2007, the bighead goby (Ponticola kessleri) in 2010, the round goby (Neogobius melanostomus) in 2011, and the monkey goby (Neogobius fluviatilis) in 2015. All that Ponto-Caspian species have moved in Europe with a contiguous East to West range expansion, with a spread from the Black Sea to the Rhine Delta observed as early as in the 1960s for the tubenose goby. Here, we focused on the round goby that reach locally high densities in many locations of the Upper Rhine and the Moselle River [35, 68]. This species has begun to spread in the 1990s [69]. It was observed for the first time in a downstream section of the Upper Rhine in Germany (between Düsseldorf and Cologne) in 2008, upstream of the confluence with the Neckar in 2010, in the French Upper Rhine (the Gambsheim fishway) in 2011 and in Basel harbor, 143 km upstream the Gambsheim fishway, in 2012 [35, 70]. Five years after its first observation, the round goby represented in several locations along the Upper Rhine more than 80% of the total catch by electrofishing (100 fishing points). The relative density of the round goby never fall below 25% of the total catch 1 year after its first observation, with a maximum value reaching 90% in a location dominated by rip-rap embankment [68]. This population dynamic is an amazing success that we dissected considering first the species bio/ecological traits and secondly the characteristics of its recipient ecosystems.
Potential reasons for the proliferation of the round goby include (1) its reproductive success, (2) its singular behavior by comparison with native species, and (3) its tolerance to a wide range of physicochemical conditions. The fecundity per round goby female during a reproductive season ranges between some hundreds and a maximum of 5200 eggs that are divided in up to six spawns per year (unpublished results and values reported in [71, 72]). This number of eggs is not important by comparison with native species but the round goby exhibits two characteristics that make them prolific: multiple spawning combined to a protracted reproductive season and some forms of parental cares [73, 74]. The male occupies and defends a nest—an enclosed cavity—to which females are attracted to spawn adhesive eggs on the undersize of rocks [75–77]. In laboratory experiments conducted in Canada, up to three females were selected by a male and spawned sequentially in a nest [78], but field observations reported that up to 15 different females could enter a nest to spawn [76]. Inside the nest, eggs are regularly inspected by males and constantly ventilated using pectoral and caudal fins. In Europe, gobies are the most typical species of guarders—nest spawners according to the typology of parental investment recently used by [23] in a comparative study between exotic and native fish.
Gobies lack a swimbladder, which makes their positioning in the water column predominantly benthic. They stand at the bottom and are considered bad swimmers in that they cannot fight against an important current or make jumps. A consequence is that at any stage of their biological cycle the gobies need numerous shelter and hiding places in their environment. Mineral structures (pebble, stones, and blocks) or macrophytes are useful habitats but in a given environment they would be more frequent in hard substrates as typically rip-rap embankments [79–81]. Few other native species, such as the European bullhead (Cottus gobio), the freshwater blenny (Salaria fluviatilis) or the ruffe (Gymnocephalus cernua), have these characteristics [43, 82]. Neogobius melanostomus inhabits a wide range of temperate freshwater and brackish-water ecosystems [72]. It has also demonstrated its capacity to adapt to local conditions in terms of prey availability [72]. This species exhibits a wide thermal tolerance, ranging from −1 to 30°C, but its energetic optimum temperature is estimated to be 26°C [72]. They would also be fairly little sensitive to pollutions. The distinguishing ecological features of the round goby by comparison with native ones make them singular in the range of bio/ecological profiles of species in place, a distinctiveness that could promote its success [25].
There are multiple potential and non-exclusive hypotheses to explain the gobie’s success from the hydrosystem point of view. Among these, we emphasize the ideas (1) that the environments are not saturated in species and (2) that the rivers were man-modified in a way favoring the installation of exotic species.
The environments invaded by exotic species are not saturated in species for two main reasons. First, they correspond to hydrosystems that were largely defaunated during the Würm glaciation (80,000–10,000 BP). At the end of this period, the Rhine basin was recolonized by fish species from refuge areas that were outside ice range extension [83]. This recolonization by natural process takes a long time in the Rhine River considering the isolation of this basin and its geographical orientation with the downstream part to the North. The process was artificially accelerated these last centuries by human-aided introduction and the opening of the hydrographic basin with canals. Nowadays, the Upper Rhine is the main navigating way in Europe with two-third of goods transported on that fluvial road (330 millions of tons per year). Man activities allowed species from refuge area during the last glaciation, in particular the Ponto-Caspian area, to reach this unsaturated ecosystem. The Rhine has hence become the main entrance point for the dispersal of many invasive aquatic animal species in France over recent decades [7]. Another reason why ecosystems are not saturated in species is that pollution and human activities have profoundly modified natural communities, leaving vacant ecological niches within the hydrosystem. The decline of the Atlantic salmon (Salmo salar) or the European eel (Anguilla anguilla) in French inland waters are for example well documented. In conclusion, the ecosystems are not saturated because the post-glaciation process of recolonization is not achieved and several native species in place have already declined.
Second, the river stretches that served as entrance point in French hydrosystems are highly modified in terms of structure, quality, and functioning. The alteration of their habitats has placed the native species in a situation of anachronism: they are un-adapted to their own natural environment. The changes of habitats were too fast since the nineteenth century to allow a real adaptive response from species in place. Most of them disappeared, and the others can have a declining level of competition. The resulting consequence is that the remained native species in place is not efficient to compete with some euryecious and prolific species. Furthermore, the rule of biotic factors in the success of gobies can be explained from a theoretical point of view by the invasional meltdown [84] and the enemy release hypotheses [85, 86, 87]. To be explained, the invasional meltdown can be drawn schematically. The propagule pressure received by a navigated and highly modified hydrosystem, such as the Rhine, is so important that a first exotic species always finished successfully. This one became a factor favoring a second exotic species, for example, because it will decrease the pressure of a potential predator. The two exotic species can then pave the way for a third exotic species and so on. This concept could probably be applied to the round goby in that it was preceded by the invasion of crustaceans and molluscs fed massively by this fish [88]. The enemy release hypothesis assumes the advantage of the loss of the original parasite burden of an invader. A recent study [89] revealed that 3 years after its first observation, the round goby hosted only one macroparasite in the French Upper Rhine, whereas in all other locations along its invasive pathway or its native range a minimum of three macroparasites were reported. This is typically an example of the enemy release an introduced species can benefit at least at the beginning of the invasion process.
The main goal of the present chapter is to give an update picture of alien fish species in France and their fate in the past decades. We dissected how the fish introduction history in France switched from voluntary introduction in the nineteenth century to unintentional but human-aided introductions (aquarium trade and global ship transport). The 28 alien fish established represent one-third of the fish species in France and >25% of the European exotic fish. Four species of our list are included among the 100 worst invasive species of Europe (DAISIE) and three others among the 100 worst invasive species of the world (IUCN). The information gathered will allow discussing the possible reasons explaining whether an alien species is able or not to establish sustainable populations in France and thereafter became invasive, such as gobies. Now and in the near future, natural resource managers have no other choice than to deal with them because no invasive fish have spontaneously collapsed up to a local extinction in France.
Smartphone-based analysis has recently emerged as a useful tool, and it has been found to be promising in several fields including point-of-care analyses [1], chemical and biological sensing [2], microscopy and healthcare diagnostics [3], water quality sensing for environment [4, 5, 6], leaf color analysis for agriculture [7], pH [8, 9] and glucose [10] sensing, fluorescent imaging [11], imaging cytometry [12], electrochemical sensing [13], and immunoassays [14].
With recent advances on camera and sensor technologies, current smartphones are equipped with a low-power high-performance processor with up to 2.5 GHz operating frequencies; built-in high-resolution digital camera, generally above 5 Mpixels and up to 40 Mpixels; and built-in single or dual LEDs, which allow capturing an image even in low-light conditions [15]. Moreover, they are provided with advanced onboard sensors such as moisture sensors, proximity sensors, electromagnetic compasses, accelerometers, and gyroscopes [16]. These sensors generally provide fast response, being portable and of low cost, and the ability to be used in the field without extensive training [17]. Therefore, smartphones have become a tool as powerful as low-cost computers, which lead them to be valuable instruments in the analysis.
Over the last decade, smartphones have been increasingly used in a variety of scientific fields as spectrometers [18, 19, 20, 21, 22] and colorimeters [23, 24, 25]. Smartphone spectrometers use the wavelength components, which give spectral information, of the collimated light from the optical source which is dispersed after interaction with samples [18]. The color spectrum image is transformed into various color spaces for the extraction of quantitative data. The wavelength of the spectrum generally changes between 400 and 700 nm because of the optical filters set in front of the camera in the manufacturing process. Spectral information has been used in many applications including water monitoring [6], gas detection [26], and food quality control [27]. Smartphone colorimeters are commonly used instruments that quantify the concentration of the samples based on color changes due to concentration (like peroxide amount [28]) or time (methylene blue degradation [6]). In the colorimetric analysis, features of the referenced images need to be extracted for training the system, mostly created with machine learning or neural networks, which perform the quantitative analysis for the test images. Smartphone colorimeters have applications in both solid samples like paper-based test and liquid samples like colored solutions [29]. Both smartphone spectrometers and colorimeters are powerful tools for rapid qualitative and quantitative analyses due to the fact that they can be used in conditions where sophisticated tools or time-consuming steps cannot be used. They are rapid and low-cost tools that require less sample consumption and provide portability to perform the analysis in remote locations or locations with poor infrastructure [17].
While smartphones offer an attractive alternative to sophisticated tools for imaging and analysis in the field, there are some concerns about their suitability for quantitative analysis [1]. First, unlike scientific cameras, smartphone cameras have mostly limited control of camera parameters like exposure time, shutter speed, ISO, and color balance, and no access to raw image data which has a linear relationship with scene radiation. In addition, image processing algorithms, such as demosaicing, noise reduction, edge sharpening, white balance, and image compression, are applied automatically and vary significantly across smartphones. These methods corrupt the linearity of the pixel intensity values which causes loss of information that can be used in quantitative analysis. So, it is difficult to set and maintain imaging parameters to get accurate and repeatable information for analysis [30]. Second, ambient light conditions are hard to control during imaging in uncontrolled environments. Last, small color changes cannot be detected in an analysis as the red, green, and blue (RGB) intensity values may not be sufficient. These aforementioned concerns make smartphones questionable for quantitative analysis. Early studies in smartphone-based colorimetric analysis for medical and scientific applications pointed out these concerns and concluded that smartphones are incapable of pathology [31] or limited due to their image quality [32].
However, subsequent researchers have proposed a smartphone camera-based microscope which captures qualitatively relevant features of malaria and tuberculosis [33]. These counter conclusions and also advancements in camera and sensors together with the increasing capability in computer processing prove that smartphones are more portable, cost-efficient, and user-friendly platforms which make them alternatives to sophisticated and high-cost devices in quantitative analysis.
For instance, it was shown that a smartphone by itself is capable of quantifying colorimetric test strips without any external attachments [1, 34]. In [35], water monitoring was implemented with a simple holding attachment. With the help of 3D printing, housing and optical components were integrated into a colorimetric plate reader for enzyme-linked immunosorbent assay (ELISA) [36]. A 3D printed custom cradle including various optical components and an external broadband source, was demonstrated in [37] for biomarker absorption analysis. To detect miRNA sequences in a liquid-based assay, a smartphone-based fluorimeter system was designed using an external laser as the source [21]. Due to precise light confinement and flexible nature, optical fibers were integrated to smartphone spectrometer and applied for food quality monitoring [18]. To have more portable and cheap spectrometers, new designs were proposed without external electrical and optical components. A fiber optical bench was assembled on top of the smartphone without external LEDs to design a surface plasmon resonance-based refractive index sensor [38]. A spectrometer system was reported in [10] to detect glucose and troponin-I using built-in flash as a light source and a compact disk for the reflection grating.
In the colorimetric analysis, color information could be obtained with paper-based sensors to quantify the color variation in different color spaces such as RGB, HSV, and L*a*b* [24, 39, 40, 41]. In [25], alcohol concentration in saliva was detected using paper-based test converting images from RGB to HSV color space. This was the conventional approach which needs only a smartphone camera to capture and process an image. Besides the conventional approach, non-conventional approaches were applied on liquid samples in vials for detection of chlorine in water [42], and ripeness estimation of fruits [7]. Here, quantification was calculated using analytical formulas extracted from color space parameters. However, it is prone to deviation due to the disadvantages of JPEG images such as low bit depth and heavy post-processing (white balance, contrast, and brightness adjustment) [43, 44]. On the other hand, it was shown in [28, 41] that JPEG images could be used in the colorimetric analysis when advanced algorithms like machine learning were used to process the images. Unfortunately, advanced algorithms need more computational power as they need much larger datasets for training and testing the images. To address this issue, it was reported to use local database referenced with a single image for the quantification of the concentration level of solutions [45]. The proposed design was applied on nitrite, phosphate, chromium, and phenol solutions to quantify the concentration value using a single reference image which was captured and processed initially.
In literature, there are many lab-on-a-chip designs proposed for various applications [17, 46]. Bisphenol-A (BPA) detection in distilled and commercial water samples was demonstrated in [29] where a plastic fiber-based smartphone spectrometer with a custom-designed immersion probe and a cradle were used. The smartphone spectrometer was converted to a reflection probe spectrometer working within the visible spectrum. Explosive types were detected in [17] with the paper-based test using hierarchical clustering analysis and principal component analysis (PCA) regarding the color discrimination of the explosives. A closed chamber was used to eliminate ambient light conditions during imaging. Linear correlation and PCA methods were employed, respectively, for univariate and multivariate analyses [46]. It was reported that univariate analysis did not give statistically significant results due to ambient light conditions as imaging was not performed in a controlled environment. However, multivariate analysis gave promising results running PCA methods on eight different color spaces and clustering methods including red, green, blue, hue, saturation, value, lightness, and intensity parameters. A smartphone-based colorimetric reader for ELISA was proposed in [47] where imaging was performed in a controlled environment illuminated from the bottom. Ozcan and his research group [12, 14, 48, 49, 50, 51] had many designs for specific applications such as food allergen testing [48], urine [14] and blood [12] analysis, immunoassays [49], microscopy [50], and cytometry [51].
The rest of this chapter is organized as follows. The next section introduces the hardware designs for smartphone-based spectrometer and colorimetry. Section 3 presents the mobile apps and image processing algorithms to be used in smartphones. Section 4 describes assay preparation to be used in testing the performance of both spectrometer and colorimeter. Section 5 details the metric to evaluate the performance of the proposed designs. Closing remarks are given in Section 6.
In this section, a brief review of hardware designs of smartphone spectrometer and colorimetry is presented. Because of space constraints, only two examples of spectrometer and colorimetry are demonstrated in Section 2.1 and 2.2, respectively.
Recently, there has been a growing variety of spectrometer designs for specific applications. In [19], a compact imaging spectrometer was reported equipped with motorized selfie stick for remote sensing. Another spectrometer was designed to calculate spectra and quantify analytes by the assembly of medium-density-fiberboard, a DVD slice for diffraction grid, and mini incandescent lamps [20]. A flexible fiber bundle probe was integrated with a custom-designed cradle to convey spectra data to smartphone camera for food quality monitoring [18].
The next design [6] is illustrated in Figure 1a, which is a low-cost, portable, plastic fiber-based spectrometric smartphone to analyze dye adsorption for field-deployable environmental and wastewater management. In the design, the rear camera of LG G4 (1/2.6″ sensor size with 5312 × 2988 resolution, 1.12 μm pixel size) was used to collect the spectral data of the assays.
(a) The smartphone spectrometer with the inset (top-left) of a plastic fiber assembled into the built-in flash. (b) The spectral images from different-sized plastic fibers are shown.
The smartphone was fitted into a custom-designed cradle assembled with hot-plug apparatus toting a diffraction grating, and the whole part was connected with smartphone case and cuvette holder manufactured from Acrylonitrile Butadiene Styrene polymer using a 3D printer (Zortrax M200) with a 150-g polymer. Two pieces of plastic fiber cables were used in the design. The first 1.5-mm-diameter fiber carried the light from the smartphone flash to the cuvette while the second fiber cable with a diameter of 0.25 mm transmitted the light from cuvette to the camera which passed through the assay. The diameter of the second cable was critical as the light for spectral data was carried with this cable. Therefore, the effect of diameter on spectral data was analyzed using 0.25-, 0.5-, and 1.0-mm cables as given in Figure 1b, and 0.25-mm diameter was found to be adequate based on this experiment. A custom cradle was specially designed to align plastic optical fibers with smartphone optical components. As the cradle was solid, the solution could be placed into the cuvette slot. In order to simplify the spectrometer system design, no collector lens or mirrored components were placed in the light path. Besides cost, the most important factor in choosing plastic optical fibers instead of glass-based fiber optics was the ability to use plastic optical fibers without special tools for stripping and cutting.
To test the performance of the system, methylene blue (MB) solutions were prepared with different amounts and their respective spectral views are given in Figure 2. At the top row, the concentration of the solution varies from 0 ppm (most left), which corresponds to distilled water (DW), to 10 ppm (most right). The spectrum views are given at the bottom row where the reduction in red intensity with the concentration is quite visible.
MB solutions with a spectral view from 0.1 to 10 ppm.
This design was further improved to make it compatible with immersion probe, which made it more practical and user-friendly as illustrated in Figure 3 [29]. Schematic diagram of the 3D printed cradle is described in Figure 3a. The immersion probe was attached to the fiber-coupled smartphone flashlight and the reflection caused by radiation is carried to the camera via the grating. Plastic (PMMA)-based bifurcated fiber bundle was used to manufacture the probe with the diameter of 0.5 mm (also known as Y-cable) as shown in Figure 3b. The overall design is illustrated in Figure 3c. It was reported that no additional optical components were used in the reflection-based smartphone spectrometer system. The spectrum views of blank solution (left) and 5-ppm BPA solution (right) are given in Figure 3d to demonstrate color variation can be detected with the spectrometer system.
(a) The custom-designed cradle for smartphone-based spectrometer. Immersion probe for absorbance measurement is given in (b) and overall design is illustrated in (c). (d) The spectral images obtained from smartphone spectrometer.
As an alternative to spectrometric analysis, colorimetry is also widely used in many applications including food allergen testing [48], albumin testing in urine analysis [14], blood analysis [12], pH quantification [41], and water monitoring [45].
A digital tube reader designed in the 3D printer was equipped with two interchangeable LEDs to illuminate the test and control tubes so that the absorption spectrum of the colorimetric assay could be analyzed [48]. An albumin tester platform was proposed in [14] using an optomechanical attachment aligned with a smartphone camera. The 3D printed cradle was integrated to a compact laser diode, two AA batteries, a plastic lens, and an emission interference filter. An albumin-based fluorescent signal was obtained from the test tube by a digital fluorescent tube reader to calculate the albumin concentration values after comparison with a control tube. In [12], blood analysis was implemented with an integration of red blood cell counting, white blood cell counting, and hemoglobin measurement devices to smartphone cradle.
Smartphone-based colorimetric detection of pH, which varies between 0 and 14.0, was investigated with paper-based test [41]. The performance of the system was tested under two conditions: controlled and ambient illumination environments. To create controlled illumination settings, 3D printed cradle was equipped with apparatus which eliminates the interference of the present light as shown in Figure 4a. Four strips of same pH level were located side by side for imaging with an apparatus, then color calibration and white balancing were performed for those strips with the X-Rite ColorChecker Passport. The imaging was continued with replacing the strips in six different orientations as in Figure 4b.
The overall smartphone-based colorimetry with apparatus and X-rite ColorChecker passport for color correction are shown in (a). The pH strips with various orientations used in imaging are given in (b), and (c) shows random orientations and positions of the test strips inside the smartphone field of view for dual-illumination tests.
Later, random orientations as shown in Figure 4c were used to mimic scenarios that could happen when untrained users take a picture. The reason for using a group of four strips is to see the effect of luminance variation due to their positioning with respect to the camera flash.
For the ambient illumination environments, no apparatus was used, and instead of using the smartphone flash as a light source, sunlight and fluorescent and halogen sources were used. To test the system under challenging conditions, the light sources were used in solo and in dual and triple combinations. The images captured in both controlled and ambient illumination environments with a different replacement of the strips were used to train the machine learning algorithm which was designed to quantify the pH values accurately. Since the training set was enriched with the images captured in various and complicated scenarios, it was reported that pH values were detected with 100% accuracy.
One possible drawback of the colorimetry method in [41] is its computational cost due to its large training dataset. To address this issue, single-image referenced colorimeter was proposed in [45]. The system was simplified in the sense of both hardware and software design. Instead of using machine learning algorithm which needs a large dataset, it used local dataset created by a user with a single reference image. In addition, images from the local dataset were compared with test images using color-matching algorithms computationally cheaper than machine learning algorithms. The hardware design was also simplified into a cardboard box as shown in Figure 5. It was painted white, and white light-emitting diodes were mounted to the box ceiling to minimize the ambient illumination effects. A holder platform with the same height as the camera was placed for the assays to maintain the same distance for imaging. The system was tested on four different (nitrite, phosphate, chromium, and phenol) assays, and it was reported that the performance accuracy was between 76 and 100% depending on the assay types.
Experimental setup proposed in [45].
The previous section described the hardware designs for smartphone-based spectrometry and colorimetry. This section presents mobile applications and algorithms proposed for these designs.
Software applications are necessary tools due to complementary characteristics for the hardware designs of spectrometer and colorimetry. Mobile apps were therefore developed to make the overall system user-friendly [14, 15, 25, 28, 36, 45, 46, 52].
Albumin Tester [14] application was developed for Android phones to let the user determine the albumin concentration in the urine sample. To test alcohol in saliva, SPAQ [15, 25] application was developed which estimated the alcohol level based on the histogram distribution. Colorimetric Plate Reader app [36] was proposed for qualitative and quantitative ELISA test. PhotoMetrix [46] application was introduced, which runs the univariate and multivariate analyses to quantify the analytes in the samples. Colorimetric Test Reader app was presented in [52], which determines pH, protein, and glucose values in the assay.
Solmaz et al. [28] developed ChemTrainer app which quantifies the peroxide content running machine learning algorithm on the remote server as shown in Figure 6a. After capturing a photograph of colorimetric test strips, the app sends mean RGB values for a region of interest to the remoter server as machine learning algorithm needs only mean values for the classification. A message queue service was employed to enable multiple users to reach the server simultaneously.
The communication between the smartphone and a remote server is illustrated in (a) and the developed ChemTrainer app is presented in (b).
Screenshots of the ChemTrainer are given in Figure 6b. In the opening page, there are two options for the user: either capturing a new image with “Experiment” button or using existing image from the phone with the “Load From Gallery” button. After an image is captured or loaded from the gallery, the user may proceed or retake a new image. Next, a region of interest needs to be cropped with adjustable crop box. The app calculates the average red, green, and blue values of the cropped image and sends to the server, which runs a classification algorithm that decides the class of image. In the meantime, the app displays a progress animation until the result comes back from the server.
The ChemTrainer app was further improved to be able to work with single-image reference (SIR) and named as ChemTrainerSIR [45]. In addition, it gained additional features like saving location and time data for the previous experiments, which help the user to analyze the past results when needed. The ChemTrainerSIR app is described in Figure 7 with screenshots. There are “train” and “experiment” options on the home page as shown in Figure 7a. Training steps are introduced in Figure 7 from b to g (top row), while testing steps are described at the bottom row from h to m. If the user selects the train option, some initial information needs to be entered such as the name of the chemical compound as the name of the model (e.g., phosphate), the units of measurement (e.g., ppm), and the number of samples with known concentration levels. The user either captures an image of samples or loads from the gallery. Then, the user enters the reference values for each sample which will be used later in the testing phase. All these information are stored in a designated folder in internal storage. In the testing phase, the user first selects the model, which was used in training. Then, a new image is taken to quantify the concentration value based on comparison with the reference model.
Opening page of the ChemTrainerSIR app is given in (a). The top row (b–g) shows training steps, while the bottom row (h–m) presents the implementation of the app on the sample.
A digital image can be acquired either as monochrome (black and white) or color image using electronic equipment utilizing charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS) sensors.
These sensors use a two-dimensional array of millions of tiny light pixels to capture an image. These pixels collect photons and store them as an electrical signal after the shutter button of the camera is pressed which leads to the beginning of the exposure. The pixels are closed after the exposure finishes, and intensity value in the pixel is quantified as digital values by measuring the strength of the electrical signal, which is directly related with the number of photons stored in the pixel. However, this approach would only create a monochrome or gray scale image as the pixels are unable to distinguish photons in terms of color. A color filter needs to be placed over the pixel to capture a color image. This filter allows only one of the primary colors, that is red (R), green (G), or blue (B), to pass into the pixel, so that it stores only filtered photons for the respective color. In other words, the intensity of each pixel gives single color information which leads to a RAW image. Here, each pixel has only one of R, G, or B information while all R, G, and B values need to be known for each pixel. Therefore, demosaicing is applied to determine other two missing color values by interpolating from nearby pixels where those colors are known. After demosaicing, other methods such as white-balance, gamma correction, color space correction, and compression are applied to convert the image from RAW to a common format like JPEG.
JPEG images have a small size and they can be displayable instantly. However, there are some concerns because of the methods that are applied to convert an image from RAW to JPEG. In the conversion process, the image is compressed resulting in providing a non-linear RGB color space with only 8-bit color depth [43, 53]. However, RAW images contain original image data with 10–14 bits of color information. The conversion process corrupts the linearity of the image. A linear image conserves the relation between the intensity value and the number of photons which maintains the linearity with scene radiance. This linearity is required for quantitative scientific data acquisition in many applications [6, 30, 36, 37, 54]. As a linear image, the RAW format is therefore generally chosen. The main issue is how to reach a RAW image in smartphones. Although most semi-professional and professional cameras have access to reach the images in a RAW format, it is unconventional for smartphones. With recent developments, the latest smartphones offer access to images in the RAW format [55]. The RAW images could be found in three extensions such as “.NEF” (Nikon), “CR2” (Canon), and “.DNG.” The most used format is “.DNG” as it has a common open format. Currently, no app is available to process the “.DNG” images in a smartphone yet. Therefore, free DCRAW software [18] can be used to convert “.DNG” image to tagged image file format (TIFF) for easier extraction of the R, G, and B values of the image.
RGB is the most commonly used color model in image processing. However, it can be converted to other models such as hue, saturation, and value (HSV); hue, saturation, and lightness (HSL); hue, saturation, and intensity (HSI); and lightness, green-red, and blue-yellow (L*a*b*). Hue is defined with the color portion of the color model and described with a number from 0 to 360°. Saturation is defined with the amount of gray in the color, from 0 to 100%. Value, lightness, or intensity is the brightness or intensity of the color, from 0 to 100% where black is represented with 0 while 100 is the brightest.
After the image acquisition, numerous image processing methods can be employed to improve the image visualization so that better features can be extracted from the image. The feature extraction plays a critical role in some methods like PCA, convolutional neural network, and machine learning, which interpret multiple types of information contained in an image using these features [46].
The performance of these methods was investigated with RAW and JPEG image formats with different color spaces such as RGB, HSV, and L*a*b*. RAW and JPEG image formats were studied in [6, 29] after converting images from RGB to HSV. Absorbance experiments were employed based on V components of HSV and it was reported that RAW format outperforms the JPEG formats in absorbance measurements. On the other hand, [41] showed that JPEG images gave a similar performance with RAM image if least-squares support-vector machine (LS-SVM) was employed in creating the learning model. Based on this conclusion, RGB, HSV, and L*a*b* color spaces were investigated using JPEG formats for quantifying peroxide content based on machine learning classifiers [28]. JPEG images were also used in [45] where the images were converted from RGB to L*a*b* color space. Instead of machine learning algorithms, color matching algorithms such as deltaE and color correlation methods were employed due to their simplicity. It was reported that deltaE showed superior performance with L*a*b color space for colorimetric water quality detection.
In previous sections, various hardware and software designs were introduced for smartphone-based spectrometer and colorimetry. These designs need to be tested under the conditions that users may encounter in real life. In this section, strip and assay preparations are introduced, which are commonly used for water quality and field tests.
Colorimetric detection of pH values was studied in [41], which used pH strips to test their proposed system. First, solutions were prepared by mixing deionized water with sodium hydroxide (NaOH) and nitric acid (HNO3) to ensure the pH values in the range of 0–14.0. During the preparations, pH values were checked with a pH meter (HI 2223, Hanna Instruments, RI, USA) calibrated with standard buffers, pH 4.0 (HI 7004) and 7.0 (HI 7007) prior to using pH indicator strips (Merck, Germany). In addition, dual-illumination tests were performed with buffer solutions (4.0–9.0, Sigma-Aldrich, USA). Before imaging pH strips, they were immersed into the pH solutions for 5 s and wiped gently with tissue paper, so that light refraction caused by the liquid drops could be minimized.
Peroxide quantification with colorimetric tests was investigated in [28] and hydrogen peroxide (H2O2) solutions were prepared for the peroxide test strips (Quantofix Peroxide 100). First, a stock solution with 500 ppm concentration of H2O2 (Sigma-Aldrich) was prepared in distilled water. The stock solution was later diluted to prepare the initial concentrations such as 1, 3, 10, 30, and 100 ppm. The peroxide test strips were dipped into these solutions for 1 s, and images were taken by smartphone after they were dried on tissue paper for 5 s.
Bisphenol-A (BPA) detection with smartphone spectrometer was demonstrated in [29]. The BPA concentration was determined with absorbance measurements using an immersion probe. The phenolic compound was put into reaction with 4-Aminoantipyrine (4-AAP) (Sigma-Aldrich, >98%) and potassium ferricyanide (Carlo Erba) for colorimetric quantification. Around 200 ppm of BPA stock solution was prepared in ethanol and then test solutions ranging from 0.1 to 10.0 ppm were prepared by serial dilution from the stock solution. The pH of all solutions was set to 8.0 using 0.25 M sodium bicarbonate (NaHCO3) (Sigma-Aldrich, ≤99.7%) and distilled water. To finalize the solutions, 1.5 mL of 20.8 mM 4-AAP and 1.5 mL of 83.4 mM potassium ferricyanide solutions were mixed into 12 mL of BPA solutions. The solutions were ready for the absorbance measurement after 10 min of incubation.
Single-image-referenced colorimetric water quality detection in [45] was performed using four different analyte solutions. The first solution is nitrite
Quantitative performance evaluations of smartphone-based spectrometer and colorimeter are an important factor in the development of new algorithms and designs. Standard metrics for regression and classification problems can be used to assess smartphone-based system performance. The importance of metrics varies for each sensing scheme.
In a spectrometer, the absorbance spectrum needs to be calculated using multicolored images. RGB images are mostly converted to HSV images and value (V of HSV) is used to calculate the absorbance (A) using the Beer-Lambert law [56],
where I0 is the transmitted light intensity of reference solution (mostly distilled water), and I is the transmitted light intensity of the other solutions. After the absorbance graph is plotted with respect to wavelength, the reference wavelength point which gives the maximum absorbance of the reference solution is selected. Then, the calibration curve, which is basically the linear regression line, is plotted with respect to the reference wavelength point to calculate R2 (the coefficient of determination). R2 is the first metric to evaluate to assess the performance of the model. R2 values greater than 0.9 are acceptable values, although a larger coefficient is accepted as a more successful result. Next, evaluation term is the limit of detection (LOD) defined as the lowest quantity or concentration of an analyte that can be reliably detected with a given analytical method. It is calculated as three standard deviations above the reference solution. The slope of the calibration curve is the sensitivity of the spectrometer.
In classification-based colorimetry, the following metrics are available: classification accuracy, sensitivity (recall), specificity, precision, and f1-score. These metrics are the same in traditional machine learning classification tasks and can be extracted from the confusion matrix. Classification accuracy is detection accuracy in the case of analytical detection. For binary classification problems with only two classes, the receiver operation characteristic (ROC) curve and area under curve (AUC) are additional metrics. In a confusion matrix, rows represent the instances in an actual (true) class while columns represent the instances in a predicted class. To calculate the detection accuracy, diagonal elements of the confusion matrix are summed and divided by the total number of data points. Precision is calculated by the ratio of true positive events to the sum of true and false positive events as given below:
The sensitivity (recall) is the ratio of true positive to the sum of true positive and false negative:
Lastly, f1 score is the harmonic average of precision and the recall and is equal to 1 for perfect precision and recall:
Regression and classification metrics should be chosen based on the colorimetric detection scheme. Spectrometric detection requires the use of regression metrics while the detection of discrete color change should be assessed with classification metrics.
In this chapter, a review of smartphone-based colorimetric determination of chromogenic assays has been provided on color spaces, existing color matching and detection techniques, hardware and software designs, and performance metrics that have been developed over the past few decades.
After a broad survey of the smartphone spectrometers, a technical background of the methods for image acquisition system, image analysis, and measurement procedure, which are commonly used as baseline methods in the literature, was introduced with their basic mathematical, statistical concepts and definitions, which are required for understanding the mathematics and techniques behind the proposed colorimetric detection methods.
In addition, portable hardware designs compatible with smartphones and their Android applications were introduced with fundamental differences including physical setup, interfaces, and challenges.
Moreover, performance metrics were analyzed in order to see which aspects are considered more in the evaluation and impacts of these perspectives on the evaluation results.
The authors thank Dr. Gazihan Alankuş for mobile apps and the “Ekosfer Laboratory and Research Services” for their assistance with the colorimetric assays. This research was supported by the scientific research projects coordination unit of Izmir Katip Celebi University (project no. 2018-ÖDL-MÜMF-0021).
The authors declare no conflict of interest.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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