Carotenoid in Disease Management

#### **Chapter 7**

## Role of Carotenoids in Parkinson's Diseases

*Fengjuan Jiao*

#### **Abstract**

Parkinson's disease (PD) is the second most common neurodegenerative disease, which is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) of the middle brain. Excessive reactive oxygen species (ROS) accumulation due to mitochondrial dysfunction or inflammation is the main factor contributing to the degeneration of dopaminergic neurons. In the preclinical and clinical studies, carotenoids and their major components including vitamin and astaxanthin were found to have antioxidant, anti-inflammatory, autophagy-promoting, and mitochondrial dysfunction improving functions. This chapter focuses on the current status of research on carotenoids and their major components in PD, which can provide help for the prevention and treatment of PD.

**Keywords:** carotenoids, Parkinson's disease, vitamins, astaxanthin, antioxidant, anti-inflammatory

#### **1. Introduction**

Parkinson's disease (PD) is the second most common neurodegenerative disease characterized primarily by movement disorders, which affects about 1% of the world's people over the age of 60. It is predicted that more than 14 million people will be affected by PD by 2040 [1]. Clinically, PD is mainly characterized by motor symptoms including resting tremor, bradykinesia, muscle rigidity, and gait abnormalities, and some nonmotor neurological symptoms including olfactory disorders, constipation, depression, and cognitive decline. The major pathological hallmarks of PD are the selective loss of the dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the appearance of Lewis bodies (LBs) in the cytoplasm of neurons. The LBs are eosinophilic inclusions composed of α-synuclein, ubiquitin, synphilin-1, neurofilament proteins, Parkin and other proteins that can lead to degeneration of dopaminergic neurons, with a consequent decrease in dopamine levels and increases of oxidative stress [2, 3].

Currently, drugs are also the main treatment strategies for PD, which can improve some symptoms of PD patients and delay the progression of PD through dopaminergic stimulation or cholinergic and glutamatergic inhibition. However, the improvement of symptoms may disappear and/or motor abnormalities may appear over time, with the disease progression or the treatment discontinuation. In recent years, increased attention is given to the patient's diet, and the use of food supplements and functional food rich in antioxidants plays a very important role in the treatment

and prevention of PD. Recently, carotenoids have received increased attention for their anti-oxidative and anti-apoptotic activities in PD. In this chapter, we discuss the pharmacological profiles of carotenoids and the protective roles of carotenoids in PD.

#### **2. Overview of carotenoids**

Carotenoids are one of the most widespread and prevalent lipid-soluble pigments in nature, which are synthesized by various plants, microalgae, bacteria, and fungi. However, animals cannot synthesize carotenoids and they must obtain them through diet to meet their nutritional requirements.

To date, more than 750 different chemical structures of carotenoids have been determined [4]. Carotenoids usually contain a hydrocarbon backbone of 40 carbon atoms, an acyclic C40H56 structure consisting of eight isoprene units. The C40H56 structure has a long central light-absorbing conjugated polyene compound constituting the chromophoric system that absorbs light from the visible region. The central structure can be modified by desaturation, cyclization of one or both ends, and addition of oxygen-containing functional groups such as hydroxy, epoxy, and/ or oxo groups [5]. All of these modifications contribute to the enormous diversity of carotenoids in nature. According to the modifications of oxygen-containing functional groups, carotenoids are classified as carotenes and xanthophylls, with only the latter containing the functional groups [6]. Polar xanthophylls, such as astaxanthin, β-cryptoxanthin, lutein, and zeaxanthin, are usually located in the lipid bilayer membranes, whereas nonpolar carotenoids, such as α-carotene, β-carotene, and lycopene, are usually located in the inner part of a cell membrane [7, 8].

#### **3. Biological functions of carotenoids**

In higher plants, algae, fungi, and bacteria, carotenoids (mainly xanthophylls) are located in specific pigment-protein complexes and act as accessory pigments to harvest light for photosynthesis [9]. Moreover, carotenoids also act as photoprotectors by quenching energy excesses under high light stress and preventing the formation of highly reactive, destructive, and potentially lethal singlet oxygen species. Additionally, carotenoids in plants also play a vital ecological role by contributing to the color of fruit and flower, which is an important signal to attract insects and animals for pollination and seed dispersal [10]. Besides these functions in plant and microorganisms, carotenoids are key compounds for human health. Vitamin A deficiency is still a cause of blindness in children and may be associated with reduced immune function, gastrointestinal disease, and measles [11]. α-, β-, and γ-carotene, as well as β-cryptoxanthin, were found to have provitamin A activity and are precursors for the synthesis of retinoids, retinol (vitamin A), retinal, and retinoic acid, with β-carotene being the main precursor of vitamin A [12, 13]. In addition, zeaxanthin and lutein are the pigment components of the macula that can prevent age-related macular degeneration [14]. Additionally, the Age-related Eye Disease Study (AREDS) showed that a formulation containing β-carotene, zinc, vitamin C, and vitamin E had grateful benefits in the prevention of age-related macular degeneration (AMD) [15]. Etminan et al. found that serum lycopene levels are inversely correlated with the

incidence of prostate cancer [16]. Recently, growing evidence has shown that carotenoids can reduce oxidative damage by neutralizing ROS, exerting anti-inflammatory and anti-apoptotic effects *in vitr*o and *in vivo* in many diseases [17].

#### **3.1 Antioxidant activity**

Several forms of reactive oxygen species (ROS), such as superoxide (O2 ~ -), hydrogen peroxide (H2O2), and hydroxyl radicals (HO.), are produced during various metabolic processes in cells or under exogenous stress. Besides, the ROS generation can also trigger mitochondrial dysfunction and protein misfolding in the endoplasmic reticulum (ER), which further augments ROS production [18]. Together, these free radicals can cause the cleavage of DNA, peroxidize lipids, alter enzyme activity, and kill cells [19]. The antioxidant effect of carotenoids has been well demonstrated in a number of *in vitro* studies, including lipids in homogeneous solution, liposomes, isolated membranes, nuclear, and intact cells. This antioxidant activity is related to the chemical structures of the carotenoids [20]. For example, β-carotene can reduce the nuclear damage induced by xanthine oxidase/hypoxanthine, as well as inhibit the lipid peroxidation induced either by enzymatic sources or by nonenzymatic sources of oxy radicals [21, 22]. In addition, carotenoids can protect cells from certain oxidative stresses by activating endogenous antioxidant enzyme activities and reducing DNA damage. In a mouse model of streptozotocin-induced Alzheimer's disease (AD), β-carotene at a dose of 2.05 mg/kg was found to attenuate streptozotocin-induced cognitive deficit via increase of glutathione peroxidase (GSH), superoxide dismutase (SOD), and catalase (CAT) activities and inhibition of acetylcholinesterase (AChE) [23]. Crocetin, a carotenoid isolated from the Chinese herbal medicine *Crocus sativus* L. (saffron), exerts cardioprotective effects by increasing SOD and GSH activities in norepinephrine-induced cardiac hypertrophy in rat models [24]. Crocin, another carotenoid component of saffron, has also been shown to prevent rat pheochromocytoma (PC12) cell death induced by serum/glucose deprivation via enhancement of SOD activity [25]. At low concentrations, β-cryptoxanthin (1 and 4 μM) can protect HeLa and Caco-2 cells from damage induced by H2O2 or by visible light in the presence of a photosensitizer through enhancement of DNA repair [26]. However, a number of *in vitro* and *in vivo* studies have found that carotenoids may lose their effectiveness as antioxidants at high concentrations or high partial pressures of oxygen [27]. Lycopene and β-carotene are protective against xanthine/xanthine oxidaseinduced DNA damage only at relatively low concentrations (1–3 μM), and these levels are comparable with those in the plasma of individuals consuming a carotenoid-rich diet. However, at higher concentrations (4–10 μM), lycopene and β-carotene contribute to increased DNA damage [28]. However, another *in vitro* study showed that both lycopene and β-carotene at concentrations ranging from 0.25 to 10 μM significantly inhibit strand breakage induced by 4-hydroxyestradiol (4-OHE2)/copper sulfate (catechol estrogens) by up to approximately 90% in plasmid DNA [29]. The antioxidant activity of aspartate aminotransferase (AST) is associated with the activation of mitogen-activated protein kinase (MAPK), nuclear factor erythroid 2-related factor 2 (NRF2)/antioxidant response element (ARE), and phosphatidylinositol 3-kinase (PI3K/AKT) pathways [30–32]. The Keap1-Nrf2 [Kelch-like ECH-associated protein 1-nuclear factor-like 2] pathway plays an important role in protecting cells against oxidative and xenobiotic stresses. Furthermore, NRF2 is a transcription factor that regulates the expression of a wide array of cytoprotective antioxidant genes that have been implicated in several diseases [33]. Recently, Nan et al. discovered that AST firmly binds to the active pocket of KEAP1 to form the KEAP1-NRF2-AST complex via computer molecular simulation. This result suggested

that AST may competitively inhibit the binding of NRF2 to the Kelch domain of KEAP, which could be the possible reason for the astaxanthin-mediated mechanism of antioxidant action of NRF2 [34]. The antioxidant properties of astaxanthin (ASTX) are related to the two oxygenated groups on each of its two rings [35]. It has been shown that ASTX not only quenches ROS, but also promotes the nuclear translocation of NRF2, thereby increasing the expression of endogenous antioxidants [36, 37].

#### **3.2 Anti-inflammatory activity**

Inflammation is a normal and crucial response to tissue injury caused by foreign organisms such as pathogens, dust particles, and viruses, which is initiated by the synthesis and secretion of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-12, and interferon γ (IFN-γ) in macrophages [38]. The binding of pro-inflammatory cytokines to the receptors triggers the MAPK signaling pathway, which ultimately results in the activation of nuclear factorkappa B (NF-κB) and the c-Jun [39]. Subsequently, these transcription factors activate the expression of genes including cytokines (TNF-α and IL-1β), chemokines, adhesion molecules, and inducible effector enzymes such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), thereby generating a feedforward loop of the inflammatory response [40]. Growing evidence has highlighted the importance of inflammation in the development of chronic diseases such as neurodegeneration, cancer, diabetes, cardiovascular disease, and inflammatory bowel disease [41]. In the last decade, some carotenoids have been shown to have anti-inflammatory effects *in vivo* and*in vitro*. The ability of lutein to inhibit inflammation has been demonstrated in mice and cellular models of retinopathy and in a clinical trial studying retinal health [42–44]. Additionally, it has been shown that lutein reduces pro-inflammatory cytokine release by suppressing the activation of the NF-κB pathway and Nrf2 in the presence of many oxidative stressors [45, 46]. Crocin and crocetin were shown to effectively reduce lipopolysaccharide (LPS)-induced nitric oxide (NO) release, production of TNF-α, IL-1β, and intracellular ROS, and NF-κB activation in cultured rat brain microglial cells [47]. Astaxanthin, a member of the xanthophyll family, has an anti-inflammatory effect similar to other carotenoids. Astaxanthin (AST) can improve the sensory and motor function of spinal cord injury (SCI) rats by reducing the expression of N-methyl-D-aspartate receptor subunit 2B (NR2B) and phosphop38 (p-p38) MAPK as inflammatory signaling mediators and tumor necrosis factor-α (TNF-α) as an inflammatory cytokine [48]. In another rat model, AST could decrease the tissue damage and mechanical pain after SCI through its anti-inflammatory and antioxidant effects [49]. Furthermore, AST also exhibits its anti-inflammatory actions by inhibiting cyclooxygenase 1 (COX-1) and NO in LPS-stimulated BV2 microglial cells [50]. In streptozotocin-induced diabetic animal models, it has also been found that astaxanthin reduced hippocampal and retinal inflammation by reducing activity of NF-κB and/or decreased the expression of pro-inflammatory factors, ameliorating cognitive impairment, retinal oxidative stress, and depression [51–53]. Crocin and crocetin effectively inhibited LPS-induced nitric oxide (NO) release, TNF-α, IL-1β, and intracellular ROS production, and NF-κB activation in cultured rat brain microglia. These results suggest that crocin and crocetin may provide neuroprotection by reducing the production of various pro-inflammatory factors and ROS from activated microglia [47]. Fucoxanthin (Fx), another member of the marine xanthophylls, exerts anti-inflammatory effects against LPS stimulation.

In LPS-activated BV-2 microglia, fucoxanthin dose-dependently inhibited the secretion of pro-inflammatory mediators including IL-6, TNF-α, ROS, prostaglandin (PG) E2, and NO productions, and also blocked Akt/NF-κB and MAPKs/AP-1 pathways [54]. Lycopene, one of the more abundant carotenoids in tomatoes, has been demonstrated to ameliorate Aβ-induced learning and memory deficits, LPS-induced depression-like behaviors, and D-galactose-induced cognitive impairments by downregulating the expression of inflammatory cytokines and mediating Nrf2/NF-κB transcriptional pathway in animal models [55–57].

#### **3.3 Anti-apoptotic activity**

Apoptosis, also known as programmed death, usually occurs during development and aging as an important mechanism for maintaining cell populations in tissues. In addition, apoptosis also occurs as a defense mechanism when the individuals are in immune reactions or when cells are damaged by noxious agents. Excessive apoptosis is associated with many human diseases including neurodegenerative diseases, cancer, ischemic injury, and autoimmune diseases [58, 59]. Many key apoptotic proteins are involved in two major apoptotic pathways, which are the intrinsic mitochondrial pathway and the extrinsic death receptor pathway [59]. An increasing number of researchers have highlighted either anti-apoptotic or pro-apoptotic effects of some carotenoids depending on the pathological condition. It has been found that lycopene exhibits mainly anti-apoptotic effects in neurological diseases. For example, lycopene can inhibit apoptosis via downregulation of pro-apoptotic proteins including Bcl-2-associated X protein (Bax) and upregulation of anti-apoptotic proteins including B-cell lymphoma 2 (Bcl-2) and B-cell lymphoma-extra large (Bcl-xL), thereby protecting against neuronal damage [60–62]. Additionally, lycopene inhibits mitochondrial damage-induced neuronal apoptosis through blocking the opening of mitochondrial permeability transition pore (mtPTP), thereby reducing mitochondrial depolarization and the release of cytochrome c [63–65]. However, in most tumor studies, lycopene may exhibit pro-apoptotic effects. It has been shown that lycopene promotes apoptosis in oral cancer via PI3K/AKT/m-TOR (mammalian target of rapamycin) signaling pathway [66]. Furthermore, lycopene and β-carotene inhibit cell proliferation, arrest the cell cycle in different phases, and increase apoptosis in human breast cancer cell lines [67]. Lycopene increases intrinsic apoptosis via increase of active caspase-3 and the Bax-to-Bcl-2 ratio in pancreatic cancer PANC-1 cells [68]. Similarly, the pro-apoptotic activity of fucoxanthin is one of the main factors responsible for its anticancer properties. In various tumor cells and animal models, fucoxanthin can induce G1 cell-cycle arrest and attribute to the molecular regulation facilitating apoptosis [69]. AST has been reported to enhance BAD phosphorylation through activation of the PI3K/AKT survival pathway and further downregulated the expression of cytochrome c and caspases-3/9 [70]. However, it has also been reported that AST can induce the intrinsic apoptotic pathway in a hamster model of oral cancer via inactivation of extracellular signalregulated kinase (Erk)/MAPK and PI3K/Akt cascades, which ultimately inhibit the NF-κB and Wnt/β-catenin signaling pathways [71]. Recent studies have shown that the anti-apoptotic and pro-apoptotic activities of other carotenoids, such as crocin, lutein, and crocetin, play critical roles in normal cell survival and tumor cell clearance [72–78].

#### **3.4 Regulation of autophagy**

Autophagy is a catabolic process necessary for intracellular clearance of damaged organelles, protein complexes, and the recycling of nutritional building blocks. Dysfunction of autophagy has been shown to be associated with the pathogenesis of various diseases such as aging, cancer, and neurodegenerative diseases [79]. It has been shown that some carotenoids are able to regulate autophagy in cellular and animal models. Astaxanthin has been found to downregulate the level of autophagy in carbon tetrachloride (CCL4)-induced liver fibrosis mice and hepatic ischemiareperfusion (IR) injury mice [80, 81]. However, a recent study shows that astaxanthin prevents acetaminophen (APAP)-induced liver injury (AILI) via improvement of oxidative stress and enhancement of autophagy [82]. It has been recently demonstrated that lutein attenuated autophagosome formation by regulating mTOR-mediated pathway, which in turn decreases cobalt chloride-induced autophagy in rat Müller cells, while it induced autophagy via upregulation of autophagy-related genes including autophagy-related 4A cysteine peptidase (ATG4A), autophagy-related 5 (ATG5), autophagy-related 7 (ATG7), autophagy-related 12 (ATG12), and beclin-1 (BENC1) in rat intestinal epithelial (IEC-6) cells [83, 84]. In an animal model of myocardial ischemia/reperfusion injury, crocin administration during ischemia significantly promoted autophagy and was accompanied by the activation of AMPK. In contrast, crocin inhibited autophagy during reperfusion and was accompanied by activation of Akt [85]. In addition, in a model of murine traumatic brain injury, fucoxanthin protects neuronal cells from death through the activation of the Nrf2-autophagy pathway [86]. The regulation of autophagy by carotenoids is still controversial, and the precise molecular mechanism of this regulation is still unclear.

#### **4. Roles of carotenoids in Parkinson's diseases**

Inflammation and oxidative stress are two important factors that contribute to neuronal degeneration in PD [87]. Dietary antioxidants of carotenoids have been suggested as neuroprotective agents for PD based on their property of reducing oxidative damage [88]. In a prospective cohort study that included 38,937 women and 45,837 men, the dietary intake of β-carotene was associated with a lower risk of PD in a 14.9-year follow-up [89]. Another clinical study showed that total carotenoids, β-carotene, and lutein-zeaxanthin, are shown to slow down the rate of parkinsonian sign progression in older adults [90]. Epidemiological studies have shown that serum α-carotene, β-carotene, and lycopene levels are significantly decreased in PD patients, and decreased serum carotenoid levels are also associated with motor dysfunction, suggesting that carotenoids may have a neuroprotective effect in PD [89, 91].

#### **4.1 Astaxanthin**

Astaxanthin is an important natural carotenoid that is widely distributed in marine organisms, such as microalgae, krill, shrimp, crab, and salmon [92]. Currently, accumulating evidence has revealed that astaxanthin plays a protective role in PD through its antioxidant, anti-apoptotic, and anti-inflammatory activities. In docosahexenoic acid hydroperoxide (DHA-OOH) or 6-hydroxydopamine (6-OHDA)-treated dopaminergic SH-SY5Y cells, astaxanthin treatment significantly attenuates intracellular ROS production and mitochondrial dysfunction. In addition,

#### *Role of Carotenoids in Parkinson's Diseases DOI: http://dx.doi.org/10.5772/intechopen.112311*

astaxanthin also inhibits oxidative stress-induced neuronal cell damage mainly via attenuating activation of p38 MAPK and expression of cleaved caspase-9 and caspase-3 [93–95]. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is a neurotoxin that targets specifically dopaminergic neurons and has been widely used to develop animal models of PD. MPTP-induced Parkinsonism exhibits key pathological features, such as oxidative stress, mitochondrial dysfunction, and loss of dopaminergic neurons, in the substantia nigra [96]. In MPP+ (1-methyl-4-phenylpyridinium) induced PC12 cells, astaxanthin pretreatment inhibited the increase of ROS and NADPH oxidase 2 (NOX2) and increased the expression of NRF2 and heme oxygenase 1 (HO-1), suggesting that astaxanthin treatment could inhibit MPP + -induced oxidative stress through the activation of HO-1/NOX2 axis [97]. Subsequently, it was found that astaxanthin could inhibit MPP + -induced oxidative damage by inhibition of the Sp1 (activated transcription factor 1)/NR1 (N-methyl-D-aspartate (NMDA) receptor subunit 1) pathway [98]. Astaxanthin attenuates MPTP/MPP + -induced oxidative damage by inhibiting ROS production and preventing mitochondrial Δψm collapse, which in turn protects against MPTP/MPP + -induced neuronal damage [99]. Both in young and aged mice, astaxanthin (30 mg/kg b.w.) can protect against MPTP-induced damage to nigrostriatal dopaminergic neurons. However, astaxanthin was less efficacious in protection against the MPTP-induced loss of tyrosine hydroxylase (TH) in neurons in the aged mice compared to the young mice, which suggested that aging may be a critical factor to consider during the development of novel therapeutics for neurodegenerative disorders like PD [100]. A recent study has reported that AST inhibits endoplasmic reticulum (ER) stress and protects against MPP+/ MPTP-induced neuronal apoptosis by targeting the microRNA-7 (miR-7)/α-synuclein axis [101]. Additionally, AST reduces the abnormal mitochondrial membrane depolarization induced by excitatory glutamate injury in primary cortical neurons and prevents neuronal death by regulating ionotropic glutamate subtype receptors such as NMDA, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), kainic acid (KA), cytosolic secondary calcium rise, and mitochondrial calcium buffering [102]. AST is distributed in nature not only as nonesterified AST but also as fatty acid acylated AST esters [103]. It was shown that nonesterified astaxanthin significantly attenuated neuronal damage in mice with PD by enhancing its antioxidant and antiinflammatory capacities [99, 100, 104]. Administration of AST can inhibit apoptosis of dopaminergic neurons in the brain of MPTP-induced mice via mitochondriamediated pathway and JNK and P38 MAPK pathways, and in particular, DHA-AST had a better performance compared with nonesterified AST [105].

Inflammation is one of the pathogenic mechanisms of pathogenesis in PD. Peroxynitrite produced by activated microglia was found to be cytotoxic to dopaminergic neurons, while astaxanthin can protect the mitochondrial respiratory chain from peroxynitrite toxicity [106]. Overall, these results suggested that astaxanthin might act as a potent therapeutic to prevent oxidative stress, apoptosis, and inflammation, subsequently preventing the loss of dopaminergic neurons in the SNpc.

#### **4.2 Lycopene**

Lycopene, an aliphatic hydrocarbon carotenoid found mainly in the ripened tomatoes, has strong antioxidant property [107]. Because lycopene can cross the blood-brain barrier (BBB), it is considered a potentially useful agent in the management of neurodegenerative diseases [108]. In an *in vivo* model, the administration of lycopene protected against MPTP-induced depletion of striatal dopamine (DA)

and its metabolites in a dose-dependent manner. In addition, lycopene also reversed MPTP-induced oxidative stress, motor defects, and apoptosis in PD mice [109]. Lycopene reduced ROS production by improving the abnormal opening of the mitochondrial permeability transition pore and the impaired mitochondrial membrane potential, which in turn ameliorated MPP + -induced apoptosis in SH-SY5Y cells [110]. Moreover, it was found that lycopene not only exerts its antioxidant activity by reducing the malondialdehyde (MDA) content in the substantia nigra and striatum and increasing the activity of SOD, glutathione peroxidase (GSH-Px), and CAT enzymes, but also reduces the autophagy activity in dopaminergic neurons and has a protective effect on rotenone-induced neuronal death [111, 112].

#### **4.3 β-Carotene**

β-Carotene is a carotenoid found in yellow-orange fruits and vegetables. A higher intake of β-carotene-containing foods contributes to a lower risk of PD in men and women [113, 114]. The relationship between serum β -carotene levels and the risk of PD is currently controversial in clinical studies. Some studies have reported that serum β-carotene levels are significantly lower in PD patients, while other studies suggest that there was no significant difference in serum β -carotene levels between PD patients and control groups [91, 115, 116]. Pretreatment of mice with large doses of β -carotene prevented the loss of GSH caused by MPTP and partially protected dopaminergic nigrostriatal neurons from damage in mice killed 30 days after injection with MPTP [117]. However, another group of investigators showed that large oral doses of β -carotene failed to prevent the neurotoxicity of MPTP in marmosets [118]. A recent study showed that β-carotene-loaded nanoparticles improved motor function, memory, and survival in *Drosophila melanogaster*, and also restored oxidative stress indicators including catalase (CAT), superoxide dismutase (SOD), ROS, and thiobarbituric acid reactive substances (TBARS), dopamine levels, and AChE activity after exposure to rotenone [119].

#### **4.4 Lutein**

Lutein, a carotenoid of the xanthophyll family, is found in some vegetables such as spinach, kale, and broccoli, as well as in marigold flowers, and is often used as a source of supplemental micronutrients [120]. In a rotenone-induced PD model of *Drosophila melanogaster*, administration of luteolin-loaded nanoparticles ameliorated rotenone-induced motor impairment and decreased survival. It also inhibited rotenone-induced reductions in dopamine levels and acetylcholinesterase activity and increases in oxidative stress indicators [121]. Additionally, lutein significantly reversed MPTP-induced neuronal apoptosis by inhibiting the activation of pro-apoptotic markers such as Bax, caspases-3, −8, and − 9 and enhancing the expression of the anti-apoptotic marker Bcl-2, as well as increasing dopamine levels in the striatum of mice. In addition, luteolin ameliorated MPTP-induced oxidative stress, mitochondrial dysfunction, and motor impairment, suggesting a neuroprotective effect of luteolin on dopaminergic neurons [122].

#### **4.5 Fucoxanthin**

Fucoxanthin is a natural carotenoid produced mainly by brown macroalgae and microdiatoms, which have many bioactive functions in neuroprotective agents [123].

#### *Role of Carotenoids in Parkinson's Diseases DOI: http://dx.doi.org/10.5772/intechopen.112311*

Both *in vivo* and *in vitro* toxicity assays reveal that fucoxanthin lacks any relevant toxic effects, and a number of increasing studies demonstrate the potential benefits of fucoxanthin in neuroprotection [124, 125]. It has been showed that fucoxanthin exhibits concentration-dependent agonistic effects on dopamine D3 and D4 receptors with half maximal effective concentrations (EC50) of 16.87 and 81.87 μM, respectively. Mechanistically, fucoxanthin binds with the Ser196 and Thr115 of D3 receptors and Ser196 and Asp115 of D4 receptors with a low binding energy and H-bond interactions to exert its agonistic effects. These results suggest that fucoxanthin may be a potential D3/D4 agonist for the treatment of PD [126]. In addition, fucoxanthin reversed MPTP-induced activation of microglia and increase in pro-inflammatory cytokines in the SNpc region, and ameliorated α synuclein abnormal accumulation and motor impairment in PD mice [127]. Keap1/Nrf2 is the major pathway regulating antioxidant responses, which can protect against oxidative stress-induced cellular damage [86, 128]. Fucoxanthin blocks the interaction between Keap1 and Nrf2 by binding to the hydrophobic region of Keap1 pocket, which suppressed 6-hydroxydopamine (6-OHDA)-induced accumulation of ROS, the disruption of mitochondrial membrane potential, and apoptosis. Additionally, in 6-OHDA-induced PC12 cells, fucoxanthin also upregulated the expression of Keap1 downstream antioxidant enzymes in a dose-dependent manner, including nicotinamide heme oxygenase-1 (HO-1), glutamate-cysteine ligase modification subunit (GCLM), and glutamatecysteine ligase catalytic subunit (GCLC) [129]. Furthermore, fucoxanthin can prevent neurotoxicity and suppress the high concentration of levodopa (L-DOPA) induced cell apoptosis in the 6-OHDA-induced PC12 cells by improving the reduction in mitochondrial membrane potential, suppressing ROS expression, and inhibiting activation of ERK/JNK-c-Jun pathway and expression of cleaved caspase-3 [130].

#### **4.6 Crocin and crocetin**

Crocin and crocetin are two main carotenoids commonly found in *Crocus sativus* L. (saffron), which have been shown to have antioxidant and neuroprotective properties in various animal models [131, 132]. Similarly, crocin was also found to significantly alleviate rotenone-induced behavioral alterations, reduce oxidative stress, and enhance the levels of antioxidant enzymes and mitochondrial enzymes in the striatal region in a rotenone-induced PD mouse model. Furthermore, crocin differentially restores cholinergic function, dopamine and α-synuclein levels in the striatal region of mice [133]. In a rotenone-induced *Drosophila* model, saffron methanolic extract and crocin significantly reduced rotenone-induced oxidative stress, mitochondrial dysfunction, and cell death. Furthermore, saffron methanolic extract and crocin can also attenuate acetylcholinesterase (AChE) activity, restore dopamine levels, and delay the onset of locomotor deficits of flies, suggesting their effectiveness to mitigate cholinergic function [134]. Crocin administration attenuates ROT-induced neurotoxicity by increasing tyrosine hydroxylase (TH) and dopamine levels as well as decreasing α-synuclein expression. Mechanistically, crocin showed neuroprotective effects in rotenone-induced PD via activation of PI3K/Akt/mTOR signaling pathway and enhanced the expression of microRNA-7 (miRNA-7) and microRNA-221 (miRNA 221) [135]. Crocetin protects against 6-OHDA-induced neuronal damage and motor injury via increase of antioxidant enzyme activity and dopamine content in the striatum and decreasing TBARS content in the substantia nigra in a model of rat Parkinsonism [136]. Crocin improves MPTP-induced motor impairment and decreases cell death in the substantia nigra [137]. Additionally, crocin has

been shown to reduce the nitrite levels in the hippocampus and improve the aversive memory of 6-OHDA rat model of PD [138]. Moreover, crocin inhibits endoplasmic reticulum(ER) stress by inverting the C/EBP homologous protein (CHOP)-Wnt pathway, which significantly attenuated MPP + -induced apoptosis and mitochondrial damage [139]. Depression is a common nonmotor symptom in patients with PD and difficult to treat [140]. Interestingly, crocin can inhibit the spontaneous discharge in dopaminergic neurons in the ventral tegmental area (VTA) by activating mTOR, which subsequently improves the synaptic plasticity in the medial prefrontal cortex (mPFC), and ameliorates depression-like behavior in PD mice [141]. According to evidence, environmental factors, such as pesticides, herbicides, and heavy metals, play a part in induction and progression of PD [142]. Malathion, an organophosphate insecticide that binds to cholinesterase enzymes, can cause Parkinson-like symptoms by activating the apoptotic pathway and increasing α-synuclein expression in the striatum [72]. Malathion induced apoptosis by increasing Bax/bcl2 ratio, expression of cleaved caspase-3 and cleaved caspase-9, and α-synuclein protein and RNA level in rat striatal tissues, wheat administration of crocin significantly reduced the apoptotic pathway, suggesting that crocin ameliorates the neurotoxic effects of malathion through its anti-apoptotic activity [72].

Abnormal aggregation and accumulation of α-synuclein in neural tissues is an important factor in the pathogenesis of PD [143]. It was found that saffron and its main components, crocin-1, crocin-2, and crocetin, dose-dependently inhibited α-synuclein aggregation and dissociated α-synuclein fibrils, with crocetin being the most potent. This effect was observed under a transmission electron microscope, which showed mainly a reduction and shortening of α-synuclein fibrils [144]. Mutations, A53T, A30p, and E46K in the α-synuclein gene, have been found to be associated with rare familial Parkinson. Among them, the E46K mutant protein more effectively promotes α-synuclein aggregation and increases amyloid fiber formation than wild-type and other mutations [145, 146]. Both results from molecular dynamics and docking studies indicate that crocin as a molecular chaperone may prevent the formation of α-synuclein aggregates by binding to the C-terminal and mainly NAC (NAM, ATAF1/2, and CUC2) domain of the E46K α-synuclein, and stabilize the protein by masking the aggregation hotspot, and consequently inhibit amyloid fibril formation in E46K α-synuclein [147]. Additionally, administering dietary saffron and crocetin to the PD transgenic fly models (A30P or G51Dmutated α-synuclein) had a positive effect on α-synuclein aggregation, climbing ability, survival rate, and eye morphological defects [148].

#### **5. Conclusions**

In PD cells and animal models, a growing number of studies have demonstrated that carotenoid administration protects dopaminergic neurons through various mechanisms such as quenching ROS, ameliorating mitochondrial damage, upregulating antioxidant enzyme systems, and causing anti-neuroinflammatory effects. Although the protective effects of certain carotenoids on neurons have been extensively studied, their specific mechanisms *in vivo* need to be further clarified. For example, the mechanisms by which carotenoids inhibit neuroinflammation and activate autophagy have not been thoroughly investigated. Additionally, clinical application studies in PD patients are needed. For example, the correlation between carotenoid intake and disease occurrence may be inferred from comparative studies of races that eat different

*Role of Carotenoids in Parkinson's Diseases DOI: http://dx.doi.org/10.5772/intechopen.112311*

foods. In terms of the complexity in the pathogenic mechanisms underlying PD, the protection of dopaminergic neurons by dietary carotenoids alone is limited. Thereby, other neuroprotective reagents or therapies that confer synergistic effects in combination with carotenoids in PD will be essential in searching for effective treatments.

#### **Conflict of interest**

The authors declare no conflict of interest.

#### **Notes/thanks/other declarations**

N/A.

### **Author details**

Fengjuan Jiao School of Mental Health, Jining Medical University, Jining, Shandong, PR China

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© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### *Edited by Akkinapally Venketeshwer Rao and Leticia Rao*

In recent years scientists have identified several biologically active compounds from plant and animal foods having beneficial health effects. Technically, they are not "nutrients," despite having beneficial health properties. Instead, they are referred to as phytonutrients, nutraceuticals, and functional ingredients. Taking advantage of these compounds' benefits, food, pharmaceutical, and cosmetic manufacturers now include them in their commercial products. However, unlike vitamins and minerals, they do not have recommended levels of daily intake. Consumers are simply advised to include foods and supplements that are good sources of these phytonutrients as part of a healthy diet. In general, biologically active compounds are classified based on their solubility in water or lipids. The focus of this book is on a lipid-soluble group of compounds referred to as carotenoids. Written by international researchers, this book addresses topics such as the properties of various carotenoids, their dietary sources, mechanisms of action, and their role in the management and prevention of chronic human diseases. This book provides valuable information for a wide range of readers, including consumers, researchers, nutritionists, other health professionals, and regulatory agency personnel.

### *Tomasz Brzozowski, Physiology Series Editor*

Published in London, UK © 2024 IntechOpen © 123dartist / iStock

Dietary Carotenoids - Sources, Properties, and Role in Human Health

IntechOpen Series

Physiology, Volume 25

Dietary Carotenoids

Sources, Properties, and Role in Human Health

*Edited by Akkinapally Venketeshwer Rao*

*and Leticia Rao*


*2.5. Trolox Equivalent Antioxidant Capacity*

The Trolox equivalent antioxidant capacity (TEAC) was determined according to the assay reported by Re et al. [28]. ABTS•+ was generated and diluted as in the original description. Next, solutions of ABTS•+ (2 mL) and amaranth extracts (20 μL with a concentration of 2.5 mg/mL) were vortexed and heated at 30 ◦C (TH-24 block heater, Meditherm, Warsaw, Poland) within 6 min. After incubation, absorbance was measured at 734 nm (DU-7500 spectrophotometer). The results were expressed as μmol Trolox equivalents (TE) per g of extract or per g of FM of plant.


**4. Applications to seismo-volcanic data**

Eruptions are usually preceded by some kind of change in seismicity, making seismic data one of the key dataset in any attempt to forecast volcanic activity [4]. As we mentioned before, manual detection and classification of discrete events can be very time consuming, up to becoming unfeasible during a volcanic crisis.

**113**

generalization accuracy.

*Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

An automatic classification procedure becomes therefore highly valuable, also as a first step towards forecasting techniques such as material Failure Forecast Method (FFM) [87, 88]. Feature vectors should be built in order to provide most information about the source, minimizing e.g., path and site effects. In many cases features can be independent from a specific physical model describing a phenomenon. This allows ML to work well even when there is no scientific agreement on the generation of a given seismic signal. A good example in volcano seismology is given by the LP events. Standardizing data, making them independent from unwanted variables is also in general a convenient approach [31]. Time-domain and spectral-based amplitudes, spectral phases, auto- and cross-correlations, statistical and dynamical parameters have been considered as the output of data reduction procedures that can be included into feature vectors [14]. In the literature, these have included linear predictor coding for spectrograms [66], wavelet transforms [89], spectral autocorrelation functions [90], statistical and cepstral coefficients [91]. Extracted feature

CA is probably the most used class of unsupervised techniques and the applications to volcano seismology follow this general rule. Spectral clustering was applied e.g., to seismic data of Piton de la Fournaise [60]. The fact that e.g., LP seismic signals can be clustered into families indicates that the family members are very similar to each other. The existence of similar events implies similar location and similar source process, i.e., it means the presence of a source that repeats over time in an almost identical way. Clustering data after some kind of normalization forces CA algorithms to look for similar shapes, independently of size. If significant variations in amplitude are then seen within families, this can indicate that the source processes of these events are not only repeatable but also scalable in size, as observed e.g., at Soufrière Hills Volcano, Montserrat [92] or at Irazú, Costa Rica [93]. The similarity of events in the different classes can then be used to detect other events, e.g., for the purpose of stacking them and obtain more accurate phase arrivals; this was done e.g., at Kanlaon, Philippines [94]. For this purpose, an efficient open-source package

is available, called Repeating Earthquake Detector in Python (REDPy) [95].

In volcano-seismology SOM were applied e.g., to Raoul Island, New Zealand [61]. A hierarchical clustering was applied to results of SOM tremor analysis at Ruapehu [62] and Tongariro [96] in New Zealand, using the Scilab environment. A similar combined approach was applied in Matlab to Etna volcanic tremor [97]. Several geometries of SOM were used, with rectangular or hexagonal nearest neighbors cells, planar, toroidal or spherical maps, etc. [61]. The classic ANN/MLP approach was applied e.g., to seismic data recorded at Vesuvius [66], Stromboli [98], Etna [99], while DNN architectures were applied e.g., to Volcán de Fuego, Colima [100]. The use of genetic algorithms for the optimization of the MLP configuration was proposed for the analysis of seismic data of Villarrica, Chile [101]. CNN were applied e.g., to Llaima Volcano (Chile) seismic data, comparing the results to other methods of classification [102]. RNNs were applied, together with other methods, to classify signals of Deception Island Volcano, Antarctica [68]. The architectures were trained with data recorded in 1995–2002 and models were tested on data recorded in 2016–2017, showing good

Supervised LR models have been applied in the estimation of landslide susceptibility [103] and to volcano seismic data to estimate the ending date of an eruption at Telica (Nicaragua) and Nevado del Ruiz (Colombia) [104]. SVM were applied many times to volcano seismology e.g., to classify volcanic signals recorded at Llaima, Chile [105] and Ubinas, Peru [106]. Multinomial Logistic Regression was used, together with other methods, to evaluate the feasibility of earthquake prediction

using 30 years of historical data in Indonesia, also at volcanoes [107].

vectors become then the input to one or another ML method.

### *Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

*Updates in Volcanology – Transdisciplinary Nature of Volcano Science*

Sparse Multinomial Logistic Regression (SMLR) is a class of supervised methods

for learning sparse classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero [81]. The sparsity concept is similar to the one at the base of Non-negative Matrix Factorization (NMF) [82]. The sparsity-promoting priors result in an automatic feature selection, enabling to somehow avoid the so-called "curse of dimensionality". So, sparsity in the kernel basis functions and automatic feature selection can be achieved at the same time [83]. SMLR methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. There are fast algorithms for SMLR that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional

A Decision Tree (DT) is an acyclic graph. At each branching node, a specific feature *xi* is examined. The left or right branch is followed depending on the value of *xi* in relation to a given threshold. A class is assigned to each datum when a leaf node is reached. As usual, a DT can be learned from labeled data, using different strategies. In the DT class we can mention Best First Decision Tree (BFT), Functional Tree (FT), J48 Decision Tree (J48DT), Naïve Bayes Tree (NBT) and Reduced Error Pruning Trees (REPT). Ensemble learning techniques such as Random SubSpace (RSS) can be used to combine the results of the different

The Boosting concept, a kind of ensemble meta-algorithm mostly (but not only) associated to supervised learning, uses original training data to create iteratively multiple models by using a weak learner. Each model would be different from the previous one as the weak learners try to "fix" the errors made by previous models. An ensemble model will then combine the results of the different weak models. On the other side, Bootstrap aggregating, also called by the contracted name Bagging, consists of creating many "almost-copies" of the training data (each copy is slightly different from the others) and then apply a weak learner to each copy and finally combine the results. A popular and effective algorithm based on bagging is Random Forest (RF). Random Forest (**Figure 3d**) is different from the standard bagging in just one way. At each learning step, a random subset of the features is chosen; this helps to minimize correlation of the trees, as correlated predictors are not efficient in improving classification accuracy. Particular attention has to be taken in order to

best choose the number of trees and the size of the random feature subsets.

A Hidden Markov Model (HMM) (**Figure 2e**) is a statistical model in which the system being modeled is assumed to be a Markov process. It describes a sequence of possible events for which the probability of each event depends only on the state occupied in the previous event. The states are unobservable ("hidden") but at each state the Model emits a "message" which depends probabilistically on the current state. Applications are wide in scope, from reinforcement learning to temporal pattern recognition, and the approach works well when time is important; speech [85], handwriting and gesture recognition are then typical fields of applications, but also

Eruptions are usually preceded by some kind of change in seismicity, making seismic data one of the key dataset in any attempt to forecast volcanic activity [4]. As we mentioned before, manual detection and classification of discrete events can be very time consuming, up to becoming unfeasible during a volcanic crisis.

**112**

volcano seismology [69, 86].

**4. Applications to seismo-volcanic data**

feature spaces.

trees [84].

An automatic classification procedure becomes therefore highly valuable, also as a first step towards forecasting techniques such as material Failure Forecast Method (FFM) [87, 88]. Feature vectors should be built in order to provide most information about the source, minimizing e.g., path and site effects. In many cases features can be independent from a specific physical model describing a phenomenon. This allows ML to work well even when there is no scientific agreement on the generation of a given seismic signal. A good example in volcano seismology is given by the LP events. Standardizing data, making them independent from unwanted variables is also in general a convenient approach [31]. Time-domain and spectral-based amplitudes, spectral phases, auto- and cross-correlations, statistical and dynamical parameters have been considered as the output of data reduction procedures that can be included into feature vectors [14]. In the literature, these have included linear predictor coding for spectrograms [66], wavelet transforms [89], spectral autocorrelation functions [90], statistical and cepstral coefficients [91]. Extracted feature vectors become then the input to one or another ML method.

CA is probably the most used class of unsupervised techniques and the applications to volcano seismology follow this general rule. Spectral clustering was applied e.g., to seismic data of Piton de la Fournaise [60]. The fact that e.g., LP seismic signals can be clustered into families indicates that the family members are very similar to each other. The existence of similar events implies similar location and similar source process, i.e., it means the presence of a source that repeats over time in an almost identical way. Clustering data after some kind of normalization forces CA algorithms to look for similar shapes, independently of size. If significant variations in amplitude are then seen within families, this can indicate that the source processes of these events are not only repeatable but also scalable in size, as observed e.g., at Soufrière Hills Volcano, Montserrat [92] or at Irazú, Costa Rica [93]. The similarity of events in the different classes can then be used to detect other events, e.g., for the purpose of stacking them and obtain more accurate phase arrivals; this was done e.g., at Kanlaon, Philippines [94]. For this purpose, an efficient open-source package is available, called Repeating Earthquake Detector in Python (REDPy) [95].

In volcano-seismology SOM were applied e.g., to Raoul Island, New Zealand [61]. A hierarchical clustering was applied to results of SOM tremor analysis at Ruapehu [62] and Tongariro [96] in New Zealand, using the Scilab environment. A similar combined approach was applied in Matlab to Etna volcanic tremor [97]. Several geometries of SOM were used, with rectangular or hexagonal nearest neighbors cells, planar, toroidal or spherical maps, etc. [61]. The classic ANN/MLP approach was applied e.g., to seismic data recorded at Vesuvius [66], Stromboli [98], Etna [99], while DNN architectures were applied e.g., to Volcán de Fuego, Colima [100]. The use of genetic algorithms for the optimization of the MLP configuration was proposed for the analysis of seismic data of Villarrica, Chile [101]. CNN were applied e.g., to Llaima Volcano (Chile) seismic data, comparing the results to other methods of classification [102]. RNNs were applied, together with other methods, to classify signals of Deception Island Volcano, Antarctica [68]. The architectures were trained with data recorded in 1995–2002 and models were tested on data recorded in 2016–2017, showing good generalization accuracy.

Supervised LR models have been applied in the estimation of landslide susceptibility [103] and to volcano seismic data to estimate the ending date of an eruption at Telica (Nicaragua) and Nevado del Ruiz (Colombia) [104]. SVM were applied many times to volcano seismology e.g., to classify volcanic signals recorded at Llaima, Chile [105] and Ubinas, Peru [106]. Multinomial Logistic Regression was used, together with other methods, to evaluate the feasibility of earthquake prediction using 30 years of historical data in Indonesia, also at volcanoes [107].

RF was applied to the discrimination of rockfalls and VT recorded at Piton de la Fournaise in 2009–2011 and 2014–2015. 60 features were used, and excellent results were obtained. However, a RF trained with 2009–2011 data did not perform well on data recorded in 2014–2015, demonstrating how difficult it is to generalize models even at the same volcano [108]. RF, together with other methods, was recently used on volcano seismic data with the specific purpose to determine when an eruption has ended [104], a problem which is far from being trivial. RF was also used to derive ensemble mean decision tree predictions of sudden steam-driven eruptions at Whakaari (New Zealand) [109].

Most of the methods described so far try to classify discrete seismic events that were already extracted from the continuous stream, i.e., already characterized by a given start and end. There are therefore in general two separated phases: detection and classification [106]. Continuous HMM on the other side are able to process continuous data and can therefore extract and classify in a single, potential realtime, step. HMM are finite-state machines and model sequential patterns where time direction is an essential information. This is typical of (volcano) seismic data. For instance, P waves always arrive before S waves. HMM-based volcanic seismic data classifiers have therefore been used by many authors [87, 110–113]. HMM are also used routinely in some volcano observatories e.g., at Colima and Popocatepetl in Mexico [71]. Etna seismic data was processed by HMM applied to characters generated by the Symbolic Aggregate approXimation (SAX) which maps seismic data into symbols of a given alphabet [114]. HMM can be also combined with standardization procedures such as Empirical Mode Decomposition (EMD) when classifying volcano seismic data [31].

Another characteristic common to many of the applications published in the literature is the fact that feature vectors are extracted from data recorded at a single station. There are relatively few attempts to build multi-station classification schemes. At Piton de la Fournaise a system based on RF was implemented [115]. At the same volcano, a multi-station approach was used to classify tremor measurements and identify fundamental frequencies of the tremor associated to different eruptive behavior [60]. A scalable multi-station, multi-channel classifier, using also the empirical mode decomposition (EMD) first proposed by [31] was applied to Ubinas volcano (Peru). The principal component analysis is used to reduce the dimensionality of the feature vector and a supervised classification is carried out using various methods, with SVM obtaining the best performance [116]. Of course, with a multi-station approach particular care has to be taken in order to build a system which is robust with respect to the loss of one or more seismic stations due to volcanic activity or technical failures.

Open source software and open access papers are luckily becoming more and more common. If we consider the processing and classification of volcano seismic data, several tools are now available for free download and use, especially within the Python environment. Among the most popular, we can cite ObsPy [117] and Msnoise [118], with which researchers and observatories can easily process big quantities of continuous seismic data. Once these tools have produced suitable feature vectors, we can look for open source software to implement the different ML approaches described in this contribution. Many generic ML libraries are available e.g., on GitHub [59] but very few are dedicated specifically to the classification of volcano seismic data. Among these, we can cite the recent package Python Interface for the Classification of Seismic Signals (PICOSS) [119]. It is a graphical, modular open source software for detection, segmentation and classification of seismic data. Modules are independent and adaptable. The classification is currently based on two modules that use Frequency Index analysis [120] or a multi-volcano pre-trained neural network, in a transfer learning fashion [52]. The concept of a multi-volcano

**115**

*Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

models [125].

recognizer is also at the core of the EU-funded VULCAN.ears project [31, 121]. The aim is to build an automatic Volcano Seismic Recognition (VSR) system, conceptually supervised (as it is based on HMM) but practically unsupervised, because once it is trained on a number of volcanoes with labeled sample data, it can be used on volcanoes without any previous data in an unsupervised fashion. The idea is in fact to build robust models trained on many datasets recorded by different teams on different volcanoes, and to integrate these models on the routinely used monitoring system of any volcano observatory. Also in this case, the open source software is made freely available; this includes a command interface called PyVERSO [122] based on HTK, a speech recognition HMM toolkit [123], a graphical interface called geoStudio and a script called liveVSR, able to process real-time data downloaded from any online seismic data server [124], together with some pre-trained ML

As we mentioned before, in order to train supervised models for classifying seismic events, few events with reliable labels are better than many unreliably labeled examples. Just to give a rough idea, 20 labeled events per class is a good starting point, but a minimum of 50 labeled events per class is recommended. Labelling discrete events is enough for many methods, but for approaches like HMM, where the concept is to run the classification on continuous data, it is essential to have a sufficient number of continuously labeled time periods, in order to "show" the classifier enough examples of transition from tremor to a discrete event, and then back to tremor. It is important to have many examples also of "garbage" events, i.e., events we are not interested in, so that the classifier can recognize and discard them. Finally, it is advisable to have a wide variability of events within each given class rather than having many very similar events. There is not yet an agreement on a single file format to store these labels. As speech recognition is much older and more developed than seismic recognition, it is suggested to adopt standard labelling formats of that domain, i.e., the transcription MLF files, which are normal text files that include for each event the start time, the end time and of course the label. These files can be created manually with a simple text editor, or by using a program with a GUI, such as geoStudio [124] or Seismo\_volcanalysis [126]. Other graphical software packages like SWARM [127] use other formats to store the labels, such as CSV, but it is always possible to build scripts that convert the resulting label files into MLF format, which remains the recommended one.

**5. Applications of machine learning to geochemical data**

combining both unsupervised and supervised approaches.

ML applications to geochemical data of volcanoes are increasing in the last years, although most of them are limited to the use of cluster analysis. CA has been used for example to identify and quantify mixing processes using the chemistry of minerals [128], also for the study of volcanic aquifers [129, 130] or to differentiate magmatic systems e.g., [131]. Platforms used to carry out these analyses include the Statistical Toolbox in Matlab [132], or the R platform [54]; some geochemical software made in this last platform include the CA as the GCDkit [33]. In most ML analyses on geochemical samples it is common to use whole rock major elements and selected trace elements; some applications also include isotopic ratios. Many ML applications to geochemical data use more than one technique, frequently

A combination of SVM, RF and SMLR approaches were used by [37] to account for variations of geochemical composition of rocks from eight different tectonic settings. The authors note that SVM used to discriminate tectonic settings as used by [34] is a powerful tool. The RF approach is shown to have the advantage, with

### *Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

*Updates in Volcanology – Transdisciplinary Nature of Volcano Science*

at Whakaari (New Zealand) [109].

classifying volcano seismic data [31].

volcanic activity or technical failures.

RF was applied to the discrimination of rockfalls and VT recorded at Piton de la Fournaise in 2009–2011 and 2014–2015. 60 features were used, and excellent results were obtained. However, a RF trained with 2009–2011 data did not perform well on data recorded in 2014–2015, demonstrating how difficult it is to generalize models even at the same volcano [108]. RF, together with other methods, was recently used on volcano seismic data with the specific purpose to determine when an eruption has ended [104], a problem which is far from being trivial. RF was also used to derive ensemble mean decision tree predictions of sudden steam-driven eruptions

Most of the methods described so far try to classify discrete seismic events that were already extracted from the continuous stream, i.e., already characterized by a given start and end. There are therefore in general two separated phases: detection and classification [106]. Continuous HMM on the other side are able to process continuous data and can therefore extract and classify in a single, potential realtime, step. HMM are finite-state machines and model sequential patterns where time direction is an essential information. This is typical of (volcano) seismic data. For instance, P waves always arrive before S waves. HMM-based volcanic seismic data classifiers have therefore been used by many authors [87, 110–113]. HMM are also used routinely in some volcano observatories e.g., at Colima and Popocatepetl in Mexico [71]. Etna seismic data was processed by HMM applied to characters generated by the Symbolic Aggregate approXimation (SAX) which maps seismic data into symbols of a given alphabet [114]. HMM can be also combined with standardization procedures such as Empirical Mode Decomposition (EMD) when

Another characteristic common to many of the applications published in the literature is the fact that feature vectors are extracted from data recorded at a single station. There are relatively few attempts to build multi-station classification schemes. At Piton de la Fournaise a system based on RF was implemented [115]. At the same volcano, a multi-station approach was used to classify tremor measurements and identify fundamental frequencies of the tremor associated to different eruptive behavior [60]. A scalable multi-station, multi-channel classifier, using also the empirical mode decomposition (EMD) first proposed by [31] was applied to Ubinas volcano (Peru). The principal component analysis is used to reduce the dimensionality of the feature vector and a supervised classification is carried out using various methods, with SVM obtaining the best performance [116]. Of course, with a multi-station approach particular care has to be taken in order to build a system which is robust with respect to the loss of one or more seismic stations due to

Open source software and open access papers are luckily becoming more and more common. If we consider the processing and classification of volcano seismic data, several tools are now available for free download and use, especially within the Python environment. Among the most popular, we can cite ObsPy [117] and Msnoise [118], with which researchers and observatories can easily process big quantities of continuous seismic data. Once these tools have produced suitable feature vectors, we can look for open source software to implement the different ML approaches described in this contribution. Many generic ML libraries are available e.g., on GitHub [59] but very few are dedicated specifically to the classification of volcano seismic data. Among these, we can cite the recent package Python Interface for the Classification of Seismic Signals (PICOSS) [119]. It is a graphical, modular open source software for detection, segmentation and classification of seismic data. Modules are independent and adaptable. The classification is currently based on two modules that use Frequency Index analysis [120] or a multi-volcano pre-trained neural network, in a transfer learning fashion [52]. The concept of a multi-volcano

**114**

recognizer is also at the core of the EU-funded VULCAN.ears project [31, 121]. The aim is to build an automatic Volcano Seismic Recognition (VSR) system, conceptually supervised (as it is based on HMM) but practically unsupervised, because once it is trained on a number of volcanoes with labeled sample data, it can be used on volcanoes without any previous data in an unsupervised fashion. The idea is in fact to build robust models trained on many datasets recorded by different teams on different volcanoes, and to integrate these models on the routinely used monitoring system of any volcano observatory. Also in this case, the open source software is made freely available; this includes a command interface called PyVERSO [122] based on HTK, a speech recognition HMM toolkit [123], a graphical interface called geoStudio and a script called liveVSR, able to process real-time data downloaded from any online seismic data server [124], together with some pre-trained ML models [125].

As we mentioned before, in order to train supervised models for classifying seismic events, few events with reliable labels are better than many unreliably labeled examples. Just to give a rough idea, 20 labeled events per class is a good starting point, but a minimum of 50 labeled events per class is recommended. Labelling discrete events is enough for many methods, but for approaches like HMM, where the concept is to run the classification on continuous data, it is essential to have a sufficient number of continuously labeled time periods, in order to "show" the classifier enough examples of transition from tremor to a discrete event, and then back to tremor. It is important to have many examples also of "garbage" events, i.e., events we are not interested in, so that the classifier can recognize and discard them. Finally, it is advisable to have a wide variability of events within each given class rather than having many very similar events. There is not yet an agreement on a single file format to store these labels. As speech recognition is much older and more developed than seismic recognition, it is suggested to adopt standard labelling formats of that domain, i.e., the transcription MLF files, which are normal text files that include for each event the start time, the end time and of course the label. These files can be created manually with a simple text editor, or by using a program with a GUI, such as geoStudio [124] or Seismo\_volcanalysis [126]. Other graphical software packages like SWARM [127] use other formats to store the labels, such as CSV, but it is always possible to build scripts that convert the resulting label files into MLF format, which remains the recommended one.