Ions monitored within the frame of LC-MS/MS analyses targeted at T-2 and HT-2 toxin.
\r\n\tIn conclusion, this book is intended for Engineers for research in the domains of speech signals and ECG denoising and also in the domain of image denoising. Many mathematical tools can be used for speech enhancement, ECG Denoising, and Image Denoising. Among those tools, we can mention wavelets, Empirical Mode Decomposition, Total Variation Denoising, Non-Local Means (NLMS), Kalman Filtering, Wiener Filtering, Deep Learning, etc.
",isbn:"978-1-83768-030-6",printIsbn:"978-1-83768-029-0",pdfIsbn:"978-1-83768-031-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"9885534183ae520bcc63a91d4d083390",bookSignature:"Dr. Mourad Talbi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11943.jpg",keywords:"Speech Enhancement, Thresholding, Signal to Noise Ratio, Wavelets, ECG Denoising, Empirical Mode Decomposition, Total Variation Denoising, Image Denoising, SNR, Non-local Means (NLMS), Kalman Filtering, Wiener Filtering",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 11th 2022",dateEndSecondStepPublish:"July 12th 2022",dateEndThirdStepPublish:"September 10th 2022",dateEndFourthStepPublish:"November 29th 2022",dateEndFifthStepPublish:"January 28th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Assistant Professor Mourad Talbi obtained his Ph.D. in Electronics at the Faculty of Sciences of Tunis, Tunisia. He has 25 years of experience in teaching mathematics and signal processing and is a member of the Laboratory of Nano-Materials and Systems for Renewable Energies (LaNSER).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"104874",title:"Dr.",name:"Mourad",middleName:null,surname:"Talbi",slug:"mourad-talbi",fullName:"Mourad Talbi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRypeQAC/Profile_Picture_2022-05-12T08:35:36.png",biography:"Mourad Talbi is an Assistant Professor in Electrical Engineering at the Center of Researches and Technologies of Energy of Borj Cedria (CRTEn), Tunis, Tunisia. He is a member of Laboratory of Nano-Materials and Systems for Renewable Energies (LaNSER). He has an experiance of 25 years in teaching mathematics and signal processing. He has obtained his Master degree in automatics and signal processing at National Engineering School of Tunis, in 2004. He has obtained his PhD Thesis in Electronics at Faculty of Sciences of Tunis, in 2010, and his HDR in Electronics at Faculty of sciences of Tunis, in 2015.",institutionString:"Tunis El Manar University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Tunis El Manar University",institutionURL:null,country:{name:"Tunisia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453624",firstName:"Martina",lastName:"Scerbe",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/453624/images/20399_n.jpg",email:"martina.s@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"10198",title:"Response Surface Methodology in Engineering Science",subtitle:null,isOpenForSubmission:!1,hash:"1942bec30d40572f519327ca7a6d7aae",slug:"response-surface-methodology-in-engineering-science",bookSignature:"Palanikumar Kayaroganam",coverURL:"https://cdn.intechopen.com/books/images_new/10198.jpg",editedByType:"Edited by",editors:[{id:"321730",title:"Prof.",name:"Palanikumar",surname:"Kayaroganam",slug:"palanikumar-kayaroganam",fullName:"Palanikumar Kayaroganam"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Type A trichothecenes, T-2 and HT-2 toxins included, are generally more toxic than type B trichothecenes (e.g. deoxynivalenol and nivalenol). Structure/activity-relationship studies revealed that 12,13-epoxide group and C9-C10-double bond are essential for their toxicity [7]. Toxicological studies show that T-2 toxin is a very potent cytotoxic and immunosuppressive agent, which can cause acute intoxication and chronic diseases in both humans and animals [6]. Given that T-2 toxin is metabolised into HT-2 toxin after ingestion, they are considered to be equally toxic [8]. The symptoms of acute T-2 intoxication of different mammalian species include skin necrosis, asthenia, lack of appetite, panting, vomiting, diarrhoea, anorexia, myocardial damage, lethargy, as well as haemorrhages and necrosis of the epithelium of the stomach and intestines, bone marrow, spleen, testis and ovary [1, 8, 9, 10]. The International Agency for Research on Cancer (IARC) classified T-2 toxin into Group 3 carcinogens because of the lack of data on its carcinogenicity in humans and only limited evidence on its carcinogenicity in experimental animals [11].
Data collected in a number of European countries have shown substantial variations in
Literature data on the impact of cereal processing on the levels of T-2 and HT-2 contamination are also very scarce. Data gathered insofar have suggested that toxins in reference, when milled, get to be relocated into various milling fractions, but are not eliminated [19]; in addition, they have been proven resistant to processing. Even more so, due to their hull binding, subsequent processing of cereals leads to significant rise in levels of these toxins in finished products [16]. Scudamore [20] concluded that final processing, such as boiling, fermentation, baking, frying, and extrusion, has no impact on the level of contamination with these mycotoxins. Some studies evidenced that processing of raw cereals greatly reduces T-2 and HT-2 levels in food products, but makes these toxins concentrate in high levels in by-products [6]. In industrial food processing settings, the processing time and temperature combination has been shown to be crucial for the reduction of mycotoxin content in a finished food product. While conventional food preparation at temperatures up to 100°C has a negligible effect on most mycotoxins, higher temperatures used with frying, roasting, toasting and extrusion have been shown to be capable of decreasing mycotoxin contamination [21].
The studies quoted under this chapter bring data on the incidence of T-2 and HT-2 toxin in different unprocessed cereals during a two-year period. They also demonstrate the influence of certain food processing methods, such as cooking, roasting and extrusion, performed under predefined conditions, on the levels of T-2 and HT-2 toxin in contaminated cereals. For this purpose, after application of the quantitative screening method termed the enzyme-linked immunosorbent assay (ELISA), which enabled the determination of summary T-2 and HT-2 toxin concentrations, the concentrations of each mycotoxin in positive samples were determined using a confirmatory method in terms of liquid chromatography tandem-mass spectrometry (LC-MS/MS), also used in the investigations devoted to the possibilities of reduction of concentrations of these mycotoxins.
During the period spanning from May 2015 to April 2017, a total of 285 samples of unprocessed cereals, in specific maize (n = 84), wheat (n = 56), oat (n = 72), barley (n = 44) and triticale (n = 29), were sampled from households situated in the Northern, Central and Eastern part of Croatia. All cereals were grown in the crop season 2015 and 2016 and had not undergone any physical or thermal treatment other than drying, cleaning and sorting prior to sampling. Sampling and sample preparation of unprocessed cereals were performed in full line with the Commission Regulation 401/2006 [22], laying down the methods of sampling to be exercised within the frame of the official control of mycotoxin levels in foodstuffs. The aggregate cereal samples were combined of three, five or ten incremental samples, depending on the lot weight, each lot thereby weighing at least 1 kg.
The samples were stored in a cool and dry place and transported to the laboratory within 48 h. The prepared test portions (500 g per sample) were ground into a fine powder having a particle size of 1.0 mm using an analytical mill (Cylotec 1093, Tecator, Sweden), and then stored at 4°C pending analyses.
T-2 toxin (Art. No. 34071, 100 μg/mL in acetonitrile) and HT-2 toxin (Art. No. 34136, 100 μg/mL in acetonitrile) standards were provided by Sigma-Aldrich Chemie GmbH (Steinheim, Germany). A RIDASCREEN® T-2/HT-2 toxin kit (Art. No. R3805) was provided by R-Biopharm (Darmstadt, Germany). PuriTox Total Myco-MS solid phase clean-up columns (Art. No. TC-MT3000) were produced by R-BiopharmRône LTD (Glasgow, Scotland). The Certified Reference oat flour Material (CRM) (Art. No. TET039RM) having the reference values of 85.3 ± 13.7 μg/kg for T-2 toxin and 86.9 ± 11.9 μg/kg for HT-2 toxin, was purchased from Fapas, Fera Science Ltd. (York, UK).
All chemicals used for ELISA and LC-MS/MS analyses were of an analytical grade (acetic acid, Art. No. 33209, Sigma-Aldrich Chemie GmbH, Steinheim, Germany) or a HPLC grade (acetonitrile, Art. No. 34851, and methanol, Art. No. 34885 Sigma-Aldrich Chemie GmbH, Steinheim, Germany). Ultrapure water was supplied by the Merck system Direct-Q 3 UV (New Jersey, USA).
All samples were first analysed using the validated ELISA method. After grinding, to 5 g of a homogenised sample, 25 mL of methanol/distilled water solution (70/30; v/v) were added, except for oat samples, to which 25 mL of the appropriate extraction buffer provided with the ELISA kit were added. The extraction of T-2/HT-2 toxin was performed using a head-over-head shaker for 10 min (100 rpm, room temperature) followed by an additional 10-min centrifugation (4000 rpm, room temperature). When it comes to oat samples, the obtained supernatant was diluted in methanol/distilled water (70/30; v/v) in 1:2 ratio, while the supernatant obtained with all other biological materials under study was diluted in distilled water in 1:2 ratio. The obtained solutions were then transferred into the wells of the ELISA micro-titration plate. The ELISA tests were performed using a ChemWell autoanalyser (Awareness Technology Inc. 2910, Palm City, USA), observing thereby the instructions given by the kit provider. Once the stop solution had been injected, the absorbances were determined at 450 nm. In order to calculate the summary T-2/HT-2 concentration in an individual sample, the results provided by the calibration curve were multiplied by the corresponding sample dilution factor. The calculation of the summary toxin concentrations was guided by the average recovery values ascertained by method validation.
Samples, in which T-2/HT-2 concentrations higher than the ELISA’s Limit of Detection were established, were further analysed using the LC-MS/MS method. To that effect, to 2.5 g of a test sample, 10 mL of 80%-acetonitrile were added and vortexed for 30 s; afterwards, the samples were put on a head-over-head shaker for 10 min (100 rpm, room temperature). The samples were then centrifuged (10 min, 4000 rpm, room temperature). Two millilitres of the obtained supernatant were acidified with 20 μL of (glacial) acetic acid; after that, 1.4 mL of the obtained solution was passed through the PuriTox Total Myco-MS columns (R-Biopharm, Glasgow, Scotland). Five hundred microliters were then evaporated under a nitrogen stream at 40°C and reconstituted in 250 μL of 1%-acetic acid in 20% acetonitrile. Fifty microliters were injected onto the LC-MS/MS system. The LC-MS/MS system consisted of a degasser, a binary pump, an auto-sampler and a column compartment (Infinity 1260, Agilent Technologies, Santa Clara, USA) coupled with a triple quadrupole mass detector (6410 QQQ, Agilent Technologies, Santa Clara, USA). The chromatography separation was performed on an XBridge BEH C18 column (particle size 2.5 μm, dimensions 4.6 × 150 mm) (Waters, Milford, Massachusetts, USA). The mobile phase consisted of 0.1%-acetic acid (constituent A) and acetonitrile (constituent B). The flow rate was 0.8 mL/min and the temperature was set at 40°C. A gradient elution program was employed: 0–3 min 90% A, 18 min 10% A, 18.1 min 90% A with a 3-minute post-run time. Mass spectrometry conditions were as follows: electrospray ionisation (ESI), positive polarity, source temperature 350°C, gas flow 9 L/min, nebulizer 45 psi, and capillary voltage 6 kV. The mass spectrometer was operated in the multiple reaction monitoring (MRM) mode. Table 1 shows the ions monitored within the frame of LC-MS/MS analyses targeted at T-2 and HT-2 toxin. The final concentrations of T-2 and HT-2 toxin were calculated based on the dilution factor and the average recovery values obtained during the validation process.
Mycotoxin | Precursor ion | Fragmentor voltage (V) | Product ions | Collision energy (eV) | Cell accelerator voltage (V) |
---|---|---|---|---|---|
T-2 toxin | 489.2 | 200 | 387.1b | 20 | 1 |
245.1a | 27 | ||||
HT-2 toxin | 447.2 | 100 | 345.1a | 18 | 1 |
285.1b | 20 |
Ions monitored within the frame of LC-MS/MS analyses targeted at T-2 and HT-2 toxin.
A more intense ion—used as a quantifier.
A less intense ion—used as a qualifier.
For the ELISA method, the limit of detection (LOD) and the limit of quantification (LOQ) were calculated from the mean value of 10 control samples of a given cereal (maize, wheat, oat, barley or triticale) plus 3- and 10-fold standard deviation. For each cereal, the recoveries were determined in three replicates per concentration level per day. To that goal, the control samples were supplemented with 50% T-2 and 50% HT-2 toxin standard working solutions (either 500 μg/L or 1000 μg/L).
As for the LC-MS/MS method, the LOD and LOQ values were estimated according to the Guidance document on the estimation of LOD and LOQ for measurements in the field of contaminants in feed and food [23] via paired observations. In brief, 10 pseudo-blank samples of different cereals, i.e. maize, oat, wheat, barley and triticale, were chosen based on the results obtained using the ELISA technique (<LOD) and spiked with the T-2 and HT-2 standard at the level of 25 μg/kg. Then, both spiked and pseudo-blank samples were analysed, and the difference in signal abundances and the standard deviation was calculated. The obtained data were used for the calculation of the LOD and the LOQ. Linearity was tested in the range of 5–100 ng/mL, using the standard solution of each mycotoxin. The recovery was determined by virtue of analysing six blank maize samples spiked with T-2 and HT-2 toxin at 50 μg/kg, while the trueness was tested using six replicates, making use of oat flour as the CRM, and later compared with the values assigned for each mycotoxin by the manufacturer.
Three samples, which were found to be the most contaminated with T-2 and HT-2 toxins, underwent thermal processing in terms of cooking, roasting and extrusion. These samples and the concentrations of T-2/HT-2 toxins in them were as follows: a maize sample (summary toxin concentration 384 μg/kg that of T-2128 μg/kg and that of HT-2256 μg/kg), an oat sample (summary toxin concentration 267 μg/kg that of T-2107 μg/kg and that of HT-2160 μg/kg) and a triticale sample (summary toxin concentration 151 μg/kg, that of T-2 47.0 μg/kg, and that of HT-2104 μg/kg). From each sample, three parallels were used during processing, and after that three replicates of each were used for the analyses.
The above described contaminated maize, oat and triticale samples were cooked in boiling water (96°C) for 10, 20 and 30 min. After that, cereals were filtered and the samples were left to dry overnight. As for roasting, the contaminated cereals were roasted in an oven (LV9/11/P 320, Nabertherm, Germany) at three different temperatures (180°C, 200°C and 220°C) for 30 min (at each temperature). Once cooked and roasted, the cereals were milled into a fine powder having a particle size of 1.0 mm using an analytical mill (Cyclotec 1093, Tecator, Sweden), intended to be analysed for the levels of T-2 and HT-2 toxin using the LC-MS/MS method.
Before the extrusion cooking, all samples were milled using an IKA MF10 laboratory mill (IKA Werke GmbH, Staufen, Germany) having a 2-mm sieve. The blend preparation was performed based on 1 kg d. m. The samples were conditioned at 25% moisture by spraying an adequate amount of distilled water, while continuously mixed using a laboratory mixer (Kenwood KMM020, JVC Kenwood, Uithoorn, The Netherlands). The prepared mixtures were then put into plastic bags (one bag per sample) and stored overnight in the refrigerator at 4°C in order to equilibrate the moisture. Before the extrusion, the samples were brought to room temperature. The prepared samples were extruded in a single-screw laboratory extruder (Brabender GmbH, Model 19/20DN, Duisburg, Germany) at three different temperature profiles: 135/150/150°C; 135/170/170°C and 135/190/190°C (extruder’s dosing/compression/ejection zone). Other constant extrusion parameters were as follows: screw: 4:1; die: 4 mm; screw speed: 100 rpm; and dosing speed: 40 rpm. After the extrusion, the samples were air-dried overnight.
In all cereal samples, the moisture content was measured before and after thermal processing by taking a 5-g sample and heating it in an oven at 105 ± 2°C. Moisture was determined according to the 712:2009 ISO standard [24].
Concentrations (μg/kg) obtained by the ELISA assay are expressed as mean summary values (T-2/HT-2) ± standard deviation (SD), while in toxin-positive samples, the concentrations are given as individual concentrations of each mycotoxin (T-2 or HT-2) obtained by the LC-MS/MS. Statistical analysis was performed using the Statistica ver. 10.0 software (StatSoft Inc. 1984-2011, Tulsa, OK, USA) and made use of the analysis of variance (ANOVA), the statistical significance thereby being set at 95% (p = 0.05).
The ELISA assay, used as a quantitative screening method for the determination of summary concentrations of T-2/HT-2 toxins, was first validated and then applied for the analyses of the sampled cereals. Its cross-reactivity declared by the kit manufacturer is approximately 85% for T-2 toxin and 100% for the HT-2 toxin. Validation of the ELISA method resulted in LODs ranging from 20.6 to 30.1 μg/kg and LOQs ranging from 26.7 to 37.4 μg/kg, depending on the type of cereal under consideration. The mean recovery value equalled to 90.1%, with the mean coefficient of variation (CV) of 7.8% (Table 2).
Cereals | LOD (μg/kg) | LOQ (μg/kg) | Level of fortification (μg/kg) | Mean recoverya (%) | CV (%) |
---|---|---|---|---|---|
Maize | 20.6 | 26.7 | 50 | 94.3 | 6.3 |
100 | 97.8 | 5.8 | |||
200 | 100.5 | 9.2 | |||
Wheat | 27.3 | 33.6 | 50 | 90.7 | 7.2 |
100 | 89.3 | 8.3 | |||
200 | 95.8 | 6.4 | |||
Oat | 25.8 | 31.9 | 50 | 72.2 | 6.1 |
100 | 75.3 | 7.6 | |||
200 | 78.5 | 7.9 | |||
Barley | 30.1 | 37.4 | 50 | 88.3 | 7.4 |
100 | 92.6 | 8.9 | |||
200 | 94.8 | 10.3 | |||
Triticale | 26.2 | 34.7 | 50 | 90.7 | 8.6 |
100 | 94.1 | 7.7 | |||
200 | 96.2 | 9.3 |
Validation of the ELISA method used for the determination of summary concentrations of T-2/HT-2 toxins in cereals.
Three replicates per concentration level per day; analyses were performed by spiking the negative material (before validation, confirmed by the LC-MS/MS).
LOD: limit of detection; LOQ: limit of quantification; CV: coefficient of variation.
Validation of the LC-MS/MS method resulted in LODs spanning from 5.5 to 8.3 μg/kg, and LOQs ranging from 18.2 to 27.5 μg/kg for T-2 and HT-2 toxin, respectively. The mean recovery values were 89.6 and 77.0%, with the mean CV of 5.1% and 8.9%, respectively. The analyses of oat flour sample (CRM) used for the determination of trueness, resulted in concentrations of 77.9 μg/kg for T-2 (91.3% of the mean certified value) and 75.6 μg/kg for HT-2 (86.9% of the mean certified value). Figure 1 presents a typical LC-MS/MS-MRM chromatogram of the analysed CRM. The determination of linearity resulted in correlation coefficients higher than 0.99 for both analytes (Table 3).
A typical LC-MS/MS-MRM chromatogram of the certified reference material (CRM, oat flour) with values of 77.9 μg/kg determined for T-2 and 75.6 μg/kg determined for HT-2; TIC; HT-2 MRM 447.2 → 285.1; HT-2 MRM 447.2 → 345.1; T-2 MRM 489.2 → 245.1; and T-2 MRM 489.2 → 387.1.
Analyte | LOD (μg/kg) | LOQ (μg/kg) | Correlation coefficient | Mean recoveryb (%) | CV (%) | Truenessc (%) |
---|---|---|---|---|---|---|
T-2 toxin | 5.5 | 18.2 | 0.998 | 89.6 | 5.1 | 91.3 |
HT-2 toxin | 8.3 | 27.5 | 0.995 | 77.0 | 8.9 | 86.9 |
Validation of the LC-MS/MS method used for the determination of T-2 and HT-2 toxin in positive cereal samplesa.
Samples in which T-2/HT-2 summary concentrations were higher than the LOD of the ELISA method.
Six blank maize samples spiked at 50 μg/kg per day.
Six oat flour CRM replicates with the assigned reference values of 85.3 ± 13.7 μg/kg for T-2 and 86.9 ± 11.9 μg/kg for HT-2 toxin.
LOD: limit of detection; LOQ: limit of quantification; CV: coefficient of variation.
Given the obtained validation results, both analytical methods were recognised as suitable for analyses of different cereals, the ELISA assay thereby being employed as a screening method used for the determination of summary concentrations of T-2/HT-2 toxins, and the LC-MS/MS thereby being used as a confirmatory method exploited to the effect of determination of individual concentrations of mycotoxins under study.
Data published insofar have revealed that the exposure to T-2 and HT-2 toxins primarily comes as a result of the consumption of cereal grains and cereal by-products, wherein the levels of these toxins found in forages and oilseed meals are generally low. It has been established that T-2 and HT-2 toxins occur together, HT-2 thereby representing approximately two-thirds of the summary T-2/HT-2 concentration. The highest mean T-2/HT-2 concentrations were determined in grains and milled grain products, in particular in oat and oat-based products, the aforementioned applying equally for food, feed and raw cereals. Higher toxin concentrations established in unprocessed vs. processed grains indicate that grain processing succeeds in lowering, at least to some point, both T-2 and HT-2 concentrations [16].
In this study, the levels of T-2 and HT-2 were analysed in different unprocessed cereals (maize, wheat, oat, barley and triticale) sampled from Croatian households during a two-year period. In the first study step, summary concentrations of these toxins were established using the ELISA method. The results obtained in unprocessed cereal samples are shown in Table 4.
Cereals | Indicative levela (μg/kg) | Positive (%) | Meanb (μg/kg) | SD (μg/kg) | Min (μg/kg) | Max (μg/kg) |
---|---|---|---|---|---|---|
Maize (n = 84) | 200 | 32.1 | 94.8 | 63.7 | 26.9 | 420 |
Wheat (n = 56) | 100 | 21.4 | 65.6 | 25.2 | 29.4 | 50.6 |
Oat (n = 72) | 1000 | 56.9 | 136 | 55.6 | 32.4 | 273 |
Barley (n = 44) | 200 | 22.7 | 61.3 | 20.6 | 31.6 | 70.2 |
Triticale (n = 29) | 100 | 34.5 | 76.6 | 30.4 | 36.0 | 169 |
Summary concentrations of T-2/HT-2 toxins determined in unprocessed cereal samples using the ELISA method.
Indicative levels of the summary T-2/HT-2 concentrations from which onwards/above investigations should be performed in case of repetitive findings according to the Commission Recommendation 2013/165/EU [18].
Mean concentration found in positive samples (>LOD).
The study results show a significantly higher (p < 0.05) incidence of T-2/HT-2 toxins in oats (56.9%) in comparison to other unprocessed cereals. However, the maximal summary concentration was determined in maize sample (420 μg/kg), but the maximal mean value was observed in oat (136 ± 55.6 μg/kg). The results also show that in two samples, one maize (mentioned above) and one triticale (169 μg/kg), summary concentrations of T-2/HT-2 were higher than stipulated indicative levels from which onwards/above investigations should be performed in case of repetitive findings according to the Commission Recommendation [18]. Such a finding imposes the need for further sampling and investigations of the causes of such a high level of mycotoxin contamination. The lowest percentage of positive samples was established in the wheat (21.4%) and barley sample pool (22.7%), in which the lowest mean T-2/HT-2 concentrations were determined, as well.
Literature data mainly address individual T-2 toxin concentrations rather than the summary T-2/HT-2 concentrations. Of note, the level of contamination greatly varies depending on the geographical region (country), the period of investigation and the type of cereal and/or final product investigated [25, 26, 27]. It is known that T-2/HT-2 toxins are produced by moulds of the
T-2 toxin levels in different food components retrieved from different parts of the world were found to range from 6 to 2406 μg/kg [28, 29, 30, 31, 32, 33]. Investigations performed in eight European countries on over 3000 samples showed 20% of T-2-positive and 14% of HT-2-positive samples. In the United Kingdom, T-2 was found in 16% of wheat and 12% of barley samples, the LOD thereby being 10 μg/kg. When it comes to oat, T-2 was identified in 84% of samples, with the mean and the maximal concentration of 84 and 2406 μg/kg, respectively [31, 32, 33]. In a study performed in Germany, toxin concentrations found in oats varied from 14 to 214 μg/kg [34]. Within the 2005–2008 timeframe, T-2 toxin was detected in 50% of different barley cultivars coming from the Czech Republic, in the mean concentration of 30 μg/kg [35]. In Serbia, the toxin concentrations determined in 54 analysed samples were lower than the LOD of 0.3 μg/kg [36]. The most frequently
Data published earlier in Croatia showed the T-2 toxin presence in 57% of maize, 25% of wheat, 32% of barley and 18% of oat samples [15]. The highest detected concentration of T-2 toxin in maize was 210 μg/kg, with the pertaining mean value of 110 μg/kg pointing towards a
In this study, after the implementation of the ELISA method, which was used for the determination of summary values of T-2/HT-2 toxins in all samples under investigation, the LC-MS/MS method was implemented for the determination and confirmation of each mycotoxin in positive samples (>LOD). Table 5 shows individual levels of T-2 and HT-2 toxin determined using the LC-MS/MS method.
Cereals | T-2 toxin (μg/kg) | HT-2 toxin (μg/kg) | Share T-2:HT-2a | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | SD | Min | Max | Mean | SD | Min | Max | ||
Maize (n = 25) | 27.6 | 10.7 | 23.1 | 128 | 66.3 | 21.9 | 30.9 | 256 | 1:2.4 |
Wheat (n = 12) | 23.1 | 6.8 | 18.2 | 42.4 | 39.0 | 16.1 | 28.4 | 89.4 | 1:1.7 |
Oat (n = 44) | 45.2 | 21.1 | 32.4 | 107 | 89.1 | 38.3 | 31.6 | 160 | 1:2.0 |
Barley (n = 10) | 22.4 | 8.7 | 18.5 | 50.3 | 37.6 | 15.9 | 28.0 | 80.4 | 1:1.7 |
Triticale (n = 11) | 26.4 | 6.8 | 21.0 | 47.0 | 47.6 | 24.9 | 27.7 | 104 | 1:1.8 |
Levels of T-2 and HT-2 toxin determined in positive unprocessed cereal samples using the LC-MS/MS method.
the share of mean values of T-2 and HT-2.
When the individual concentrations of T-2 and HT-2 toxin obtained by the LC-MS/MS method were summed up, it was established that the latter sum slightly differs from, and is mainly lower than, the summary concentrations of these mycotoxins determined using the ELISA method. This can be explained by the cross-reactivity and a lower specificity of the ELISA method. It is known that the ELISA represents an easy-to-use purification technique with a lesser need for an extensive clean-up, but it may suffer from undesired cross-reactivity with other trichothecenes that give rise to metric uncertainty [16].
Figure 2 presents the LC-MS/MS-MRM chromatogram of the most contaminated sample (maize), in which the concentration of T-2 toxin of 128 μg/kg and the concentration of HT-2 toxin of 256 μg/kg was determined (summary T-2/HT-2 toxins concentration, 384 μg/kg). Together with the most contaminated oat and the most contaminated triticale sample, this sample was further subjected to thermal processing.
A typical LC-MS/MS-MRM chromatogram of the contaminated maize sample subsequently subjected to thermal reduction processing (128 μg/kg of T-2 and 256 μg/kg of HT-2); TIC; HT-2 MRM 447.2 → 285.1; HT-2 MRM 447.2 → 345.1; T-2 MRM 489.2 → 245.1; and T-2 MRM 489.2 → 387.1.
Based on the comparison of the mean T-2 and HT-2 toxin concentrations, the shares of T-2:HT-2 were established to be in the range from 1:1.7 in wheat and barley to 1:2.4 in maize, with the mean share value of 1:1.9 in all investigated cereal samples. The determined share values are comparable to those stated by other literature sources, which show that HT-2 is present in cereals and their products in levels higher than those of T-2 toxin, representing approximately two-thirds (1:2) of the summary T-2/HT-2 concentrations [16].
In general, different cereal treatments implemented by the food industry are known to decrease mycotoxin concentrations, but mostly do not eliminate these toxins completely. These food processing operations include sorting, trimming, cleaning, cooking, baking, frying, roasting, flaking and extrusion, and have variable effects on the level of contamination. In their recently published study, Schmidt et al. [39] stated that in comparison to other mycotoxins, thermal degradation of T-2 and HT-2 has not been the subject of many studies. In the last decades, some research on the effects of thermal degradation has mainly been performed on oats, known to be the cereal most contaminated with these mycotoxins [34, 39, 40, 41]. Scudamore [20] concluded that final processing, such as boiling, fermentation, baking, frying, and extrusion, has no impact on T-2 and HT-2 contamination. A greater extent of thermal degradation of T-2 as compared to HT-2 has been established, as well [34, 39, 41].
Nevertheless, the efficiency of T-2 and HT-2 toxin reduction using thermal processing techniques is still under-established, mostly because of the fairly small data pool on the subject-matter provided insofar, obtained under various, mutually different thermal degradation conditions, which, in turn, yields inconsistent study outcomes and study conclusions. In light of the foregoing, in order to establish the degree of thermal degradation and reduction of T-2 and HT-2 toxin in naturally contaminated cereals, this study made use of three thermal processing methods, that is to say, cooking, roasting and extrusion, each of them running at three different temperatures for different lengths of time.
Cooking is the preparation of a meal using heat [42]. Several studies have reported about the effect of cooking on the reduction of
Cerealsa | T-2/HT-2 after cooking | Cooking time (96°C) | ||
---|---|---|---|---|
10 min | 20 min | 30 min | ||
Maize | T-2/HT-2 (μg/kg) | 375 | 366 | 372 |
T-2/HT-2 R (%) | 2.3 | 4.7 | 3.1 | |
T-2 (μg/kg) | 117 | 123 | 110 | |
T-2 R (%) | 8.6 | 3.9 | 14.1 | |
HT-2 (μg/kg) | 258 | 243 | 270 | |
HT-2 R (%) | NR | 5.1 | NR | |
Triticale | T-2/HT-2 (μg/kg) | 252 | 271 | 262 |
T-2/HT-2 R (%) | 5.6 | NR | 1.9 | |
T-2 (μg/kg) | 104 | 99.5 | 94.8 | |
T-2 R (%) | 2.8 | 7.0 | 11.4 | |
HT-2 (μg/kg) | 172 | 151 | 167 | |
HT-2 R (%) | NR | 5.6 | NR | |
Oat | T-2/HT-2 (μg/kg) | 152 | 137 | 144 |
T-2/HT-2 R (%) | NR | 9.3 | 4.6 | |
T-2 (μg/kg) | 43.2 | 40.8 | 40.1 | |
T-2 R (%) | 8.1 | 13.2 | 14.7 | |
HT-2 (μg/kg) | 108 | 96.2 | 104 | |
HT-2 R (%) | NR | 7.5 | NR |
Reduction of T-2/HT-2 toxins achieved by various cooking times.
Concentrations in cereals before cooking: 384 μg/kg (128 μg/kg of T-2 and 256 μg/kg of HT-2) in maize, 267 μg/kg (107 μg/kg of T-2 and 160 μg/kg of HT-2) in oat and 151 μg/kg (47.0 μg/kg of T-2 and 104 μg/kg of HT-2) in triticale.
T-2 and HT-2 toxin concentrations are presented as the mean value of three replicates; R: reduction; NR: not reduced.
The maximal reduction of T-2 toxin was observed in oat (14.7%) cooked for 30 min, whereas the greatest HT-2 (7.5%) and summary T-2/HT-2 concentration reduction (9.3%) were obtained in oat cooked for 20 min. A slightly greater reduction was observed for T-2 toxin (9.3%) in comparison to the HT-2 toxin, which was mostly unreduced in all three types of cereals despite cooking. A prolonged cooking time (30 min) achieved no significantly greater reduction of summary T-2/HT-2 concentrations or individual toxin levels. The results show that the reduction of T-2/HT-2 summary concentrations achieved by cooking can be considered negligible (R < 10%), suggesting that cooking as a thermal processing method does not represent a valuable tool when it comes to the decontamination of cereals contaminated with T-2 and HT-2. After cooking of noodles, Kamimura et al. [43] determined the residual rate of T-2 toxin in fortified samples of up to 76%. Schwake-Anduschus et al. [34] stated that T-2/HT-2 toxin levels are relatively stable during short-time cooking. This was also confirmed by this study, as it resulted in a very small reduction of both mycotoxins despite the prolonged cooking time (30 min).
Roasting is a cooking method that uses dry heat in form of an open flame, oven, or other heat sources. Roasting can enhance flavour through caramelisation and Maillard browning of the food surface [42]. The results of T-2 and HT-2 reduction via roasting, obtained in this study at three different temperatures (180–220°C), are presented in Table 7. Figure 3 presents a typical LC-MS/MS-MRM chromatogram of a contaminated maize sample obtained after roasting at the temperature of 220°C during 30 min, in which a significant reduction of both T-2 toxin (60.7%) and HT-2 toxin (46.1%) can be seen.
Cerealsa | T-2/HT-2 after roasting | Temperature of roastingb | ||
---|---|---|---|---|
180°C | 200°C | 220°C | ||
Maize | T-2/HT-2 (μg/kg) | 270 | 225 | 188 |
T-2/HT-2 R (%) | 29.7 | 41.4 | 51.0 | |
T-2 (μg/kg) | 80.1 | 65.3 | 50.3 | |
T-2 R (%) | 37.4 | 49.0 | 60.7 | |
HT-2 (μg/kg) | 190 | 160 | 138 | |
HT-2 R (%) | 25.8 | 37.5 | 46.1 | |
Triticale | T-2/HT-2 (μg/kg) | 190 | 173 | 151 |
T-2/HT-2 R (%) | 28.8 | 35.2 | 43.4 | |
T-2 (μg/kg) | 56.2 | 43.3 | 38.2 | |
T-2 R (%) | 47.5 | 59.5 | 64.3 | |
HT-2 (μg/kg) | 138 | 130 | 113 | |
HT-2 R (%) | 13.8 | 18.8 | 29.4 | |
Oat | T-2/HT-2 (μg/kg) | 101 | 84.3 | 68.9 |
T-2/HT-2 R (%) | 33.1 | 44.2 | 54.4 | |
T-2 (μg/kg) | 28.1 | 22.7 | ND | |
T-2 R (%) | 40.2 | 51.7 | CR | |
HT-2 (μg/kg) | 72.9 | 61.6 | 59.3 | |
HT-2 R (%) | 29.9 | 40.8 | 43.0 |
Reduction of T-2/HT-2 toxins achieved by roasting of contaminated cereals at different temperatures.
Concentrations in cereals before reduction: 384 μg/kg (128 μg/kg of T-2 and 256 μg/kg of HT-2) in maize, 267 μg/kg (107 μg/kg of T-2 and 160 μg/kg of HT-2) in oat and 151 μg/kg (47.0 μg/kg of T-2 and 104 μg/kg of HT-2) in triticale.
Roasting was carried out for 30 minutes at the default temperatures.
T-2 and HT-2 toxin concentrations are presented as the mean value of three replicates; R: reduction; ND: not detected; CR: completely reduced.
A typical LC-MS/MS-MRM chromatogram of a contaminated maize sample after roasting at the temperature of 220°C for 30 min (50.3 μg/kg of T-2 and 138 μg/kg of HT-2); TIC; HT-2 MRM 447.2 → 285.1; HT-2 MRM 447.2 → 345.1; T-2 MRM 489.2 → 245.1; and T-2 MRM 489.2 → 387.1.
Published data on the influence of roasting on the reduction of mycotoxins are mostly linked to aflatoxins, ochratoxins or some
Extrusion is used in the production of cereal products such as breakfast foods, snacks and animal feedstuffs, and represents one of the fastest-growing food-processing operations in the recent years due to its many advantages. Extrusion cooking of cereals combines pumping, kneading, mixing, shearing, cooking and forming, all in one processing session. As several operations are carried out simultaneously, they interact with each other [39]. Cereals are passed through an extruder under pressure, undergo mechanical shearing stresses at elevated temperature, and rapidly expand when forced through the outlet die [50]. Apart from its main goal in terms of improving the product quality, extrusion may also significantly improve the product safety because of its potential to reduce mycotoxin levels in cereals [51]. The effectiveness of mycotoxin reduction also depends on the presence of minor ingredients or additives. Scudamore et al. [20] explained that during extrusion, contaminants are subjected to both high temperatures and chemical reactions mediated by free radical mechanisms so that they might be susceptible to some degree of breakdown, the effects on mycotoxins thereby generally being variable.
The reduction of T-2/HT-2 toxins achieved by extrusion cooking of contaminated cereals in this study using three different regimes of extrusion is shown in Table 8. Figure 4 presents a typical LC-MS/MS-MRM chromatogram obtained after extrusion cooking of a contaminated maize sample at the defined temperatures of 135-190-190°C.
Cerealsa | T-2/HT-2 after extrusion | Regime of extrusionb | ||
---|---|---|---|---|
135-150-150°C | 135-170-170°C | 135-190-190°C | ||
Maize | T-2/HT-2 (μg/kg) | 28.9 | 49.7 | 32.2 |
T-2/HT-2 R (%) | 92.5 | 87.1 | 91.6 | |
T-2 (μg/kg) | ND | ND | ND | |
T-2 R (%) | CR | CR | CR | |
HT-2 (μg/kg) | ND | ND | ND | |
HT-2 R (%) | CR | CR | CR | |
Triticale | T-2/HT-2 (μg/kg) | 37.7 | 45.9 | 41.3 |
T-2/HT-2 R (%) | 85.9 | 82.8 | 84.5 | |
T-2 (μg/kg) | ND | ND | ND | |
T-2 R (%) | CR | CR | CR | |
HT-2 (μg/kg) | ND | ND | ND | |
HT-2 R (%) | CR | CR | CR | |
Oat | T-2/HT-2 (μg/kg) | 40.7 | 38.1 | ND |
T-2/HT-2 R (%) | 73.0 | 74.8 | CR | |
T-2 (μg/kg) | ND | ND | ND | |
T-2 R (%) | CR | CR | CR | |
HT-2 (μg/kg) | ND | ND | ND | |
HT-2 R (%) | CR | CR | CR |
Reduction of T-2/HT-2 toxins achieved by extrusion cooking of contaminated cereals at different temperatures.
Concentrations in cereals before reduction: 384 μg/kg (128 μg/kg of T-2 and 256 μg/kg of HT-2) in maize, 267 μg/kg (107 μg/kg of T-2 and 160 μg/kg of HT-2) in oat and 151 μg/kg (47 μg/kg of T-2 and 104 μg/kg of HT-2) in triticale.
Moisture in extruded samples ranges from 11.0 to 12.0%.
T-2 and HT-2 toxin concentrations are presented as the mean value of three replicates; R: reduction; ND: not detected; CR: completely reduced.
A typical LC-MS/MS-MRM chromatogram of a contaminated maize sample after extrusion at 135-190-190°C (T-2 and HT-2 toxin not detected); TIC; HT-2 MRM 447.2 → 285.1; HT-2 MRM 447.2 → 345.1; T-2 MRM 489.2 → 245.1; and T-2 MRM 489.2 → 387.1.
By virtue of extrusion cooking under three temperature regimes and with the same moisture content (25%), an almost complete reduction of T-2 and HT-2 toxin was achieved. The results show a similar effect of extrusion independent of the type of cereal and the applied temperature regime, based on which this method can be considered as the most effective and most valuable when it comes to the reduction of mycotoxins. As the LC-MS/MS method failed to determine any of the two mycotoxins in any of the extruded samples, the summary T-2/HT-2 concentrations were analysed using the ELISA method. The results showed T-2/HT-2 concentrations slightly higher than the method’s LOQs, except for the oat subjected to the extrusion temperature regime of 135-190-190°C, in which the presence of the above toxins was not detected at all. The reduction of T-2/HT-2 achieved under the 135-150-150°C extrusion regime ranged from 73.0% in oats to 92.5% in maize. However, it should be taken into account that the presence of individual mycotoxins was not confirmed by the LC-MS/MS method and that the ELISA method showed higher T-2/HT-2 summary concentrations.
When comparing the degradation rates of T-2 against those of HT-2 toxin, it was revealed that T-2 shows a higher mitigation in the extrusion cooking process [39, 41]. Some investigations showed that T-2 and HT-2 degradation during extrusion are not influenced by the heating temperature to the same extent and that other variables present during processing are responsible for a more complex degradation pattern. This observation can be linked to the results of this study, in which a significant influence of the extrusion temperature regime was not determined. Schmidt et al. [39] stated that due to the strong interference of various parameters during extrusion, it is not possible to attribute toxin degradation to just one of them. Among the factors of influence, the water content plays an important role in extrusion cooking, because it is essential for starch gelatinization and strongly affects fluid viscosity and the expansion ratio. Extrusion cooking was shown to decrease the mycotoxin content at rates depending on the moisture level, screw centrifugation, extruder geometry, die temperature, die size, screw speed and additives [51], while the extrusion temperature was found to be a minor factor of influence. As opposed to that, high moisture levels and high shear rates substantially contribute to the toxin degradation [39, 52]. The authors elaborated that since the fate of T-2 and HT-2 and the formation of so far unknown degradation products or bound forms remains unclear, it cannot be concluded that extrusion cooking of contaminated oats is accompanied by a detoxification process. Scudamore et al. [52] pointed out that the inconsistency of the results presented in the literature may be a consequence of failure to control or report all conditions under which the extrusion process was taking place. For example, chemical breakdown taking place during an extrusion process is related to the duration of the process, so that the loss of mycotoxin will depend on the residence time of the material in the extruder. Differences in parameters implemented during extrusion cooking carried out in this study may also explain an almost complete reduction of T-2 and HT-2 toxin achieved, as opposed to other studies quoted above, in which only partial or smaller toxin reduction has been witnessed when using this thermal processing method.
Among the analysed cereals, the highest percentage of T-2- and HT-2-positive samples were determined in oats, followed by maize, triticale and barley, whereas the highest mean and maximal concentration of the toxins was determined in maize. The summary T-2/HT-2 concentrations found in one maize and one triticale sample were higher than the indicative levels stipulated by the European Commission, suggesting that further sampling should be performed and that the production conditions should be investigated more thoroughly. As for the thermal methods of toxin reduction, roasting appears to efficiently reduce T-2 and HT-2 concentrations, whereas cooking does not significantly reduces these mycotoxins. Extrusion cooking seems to be far more efficient since it resulted in an almost complete T-2/HT-2 elimination in all cereals, independent of the temperature regime applied during the extrusion process.
Maintenance of railway tracks is essential for the safe operation of trains. Railway operators conduct track inspections using track geometry cars and track maintenance crews. However, regional railway operators, who carry fewer passengers, often lack the personnel and funds to conduct adequate track inspections. The monitoring of railway track geometry from an in-service vehicle has become increasingly attractive over the past decade [1].
To address this problem, a system that can monitor the track condition inexpensively and frequently using a device incorporating sensors and a global navigation satellite system (GNSS) unit, which is installed on in-service trains, has been developed [2, 3]. The system calculates root mean square (RMS) values from the vertical acceleration, lateral acceleration, and roll angular velocity of the car body. To select sites for repair, we adopt the method of prioritizing sites with the highest numerical values.
The acceleration RMS is closely related to the general health of the track [4]. In Ref. [5], RMS values are used to identify track irregularities for longitudinal level, alignment, cross-level. However, monitoring based on RMS values alone is not sufficient. Without frequency information, it is difficult to identify the type of track fault. Furthermore, since the amount of data generated by constant measurement is enormous, it is necessary to automate the analysis in order to monitor and predict the track condition efficiently.
In this study, we propose a method to classify the types of track faults automatically by means of machine learning, using a CNN trained on images created via a CWT from the vibration acceleration on the time-frequency plane. A continuous wavelet transform (CWT) is a transformation technique that emphasizes certain portions of the waveform by suppressing other portions as it proceeds by multiplying a target waveform using a mother wavelet [6]. A convolutional neural network (CNN) is a class of deep neural networks. It is widely used for image recognition.
To verify the effectiveness of the algorithm we developed, we first describe the results of simulating the vibration of a car body when passing over a faulty track. Next, we describe the results of diagnosing track faults from the vertical vibration acceleration data of a car body measured by a regional railway.
It should be necessary for railway operators to control track irregularity, such as vertical rail profiles, lateral alignment, gauge, cross-level, twist (depicted in Figure 1) properly. Track irregularities cause vehicle vibrations that degrade the rider’s comfort and increase the risk of derailments. Track irregularities are strongly correlated with vehicle vibrations. Thus, it can be possible to estimate general trends of the track condition by analyzing vehicle vibrations.
Track structure and irregularities.
Although track geometry measurement systems using in-service vehicles are becoming increasingly attractive around the world [2, 7, 8, 9], the repeated checking of the same track provides the information regarding track geometry degradation, which can be fed back to the track maintenance section for taking essential actions. The use of vehicle responses in the track geometry assessment process allows identifying of critical defects, which could not have been identified from geometry parameters, and thus, improve the maintenance operations.
Tsunashima et al. proposed techniques of condition monitoring of railway tracks based on time-frequency analysis [10]. They compared the performance of Hilbert-Huang transforms (HHT) and CWT for identifying track faults from car body vibration. It is shown that the feature of track fault can be identified in time-frequency plane.
Tsunashima proposed a classifier based on a machine learning technique for identifying track faults automatically from measured car body vibration [5]. It is shown that the degradation of track can be classified in the feature space consisting of car body vibration RMS.
Faghih-Roohi et al. proposed a deep convolutional neural network for the analysis of image data for the detection of rail surface defects [11]. They explored the efficiency of the proposed deep convolutional neural network for the detection and classification of rail surface defects.
Zheng et al. proposed a multi-object detection method based on a deep convolutional neural network that can achieve non-destructive detection of rail surface and fastener defects [12]. A defect detection model based on Mask R-CNN and ResNet framework was utilized to detect the surface defects.
Jin et al. proposed a machine learning framework based on wavelet scattering networks and neural networks for identifying railhead defects [13].
Alvarenga
When a train runs on a track, vibrations that correspond to the track geometry are generated [15, 16]. Therefore, in this study, to verify the relationship between the type of track fault and the car body vibration acceleration, and to evaluate the effectiveness of time-frequency analysis in detecting track faults, we simulated the occurrence of track faults, calculated the vertical vibration acceleration of the car body, and then applied a CWT, a method of time-frequency analysis, to the results.
A CWT is a method that simultaneously detects the frequency and time characteristics of an unsteady signal, by comparing the original signal with dilated and translated versions of a small wavelike function called the mother wavelet. Using this method, it is possible to view the amplitude and frequency information of the vibration acceleration as an image. In this study, we used the
This technique is well suited for analyzing unsteady signals, such as
where, variables
The vehicle model used in the simulation is shown in Figure 2 [10]. The vehicle model consists of a total of seven rigid bodies: one car body, two bogies, and four wheelsets. The car body and bogie were assigned two degrees of freedom (DOF) for bounce and pitch, and the wheelset was assigned one DOF for the bounce. The vehicle’s parameters were obtained from measurement data from a regional railway vehicle equipped with an onboard sensing device.
Vehicle model [
In the simulation, the vehicle model was run at 60 [km/h] for 500 [m], and the results were output for the section between 100 [m] and 350 [m]. We set rail joint faults (joint depressions) at 4 points; otherwise, the track was assumed to be straight. To set the rail joint faults, we used the function model shown in Figure 3 [18].
Track fault model.
The geometry of the modeled track are represented by
and
The track geometry used in the simulation is shown in Figure 4. At the 150 [m] and 200 [m] points, we set depths of
Track geometry with different faults.
In both cases, we set the depression length
The simulated vertical vibration acceleration of the car body is shown in Figure 5a. The figure shows that characteristic vibrations corresponding to the track geometry are generated at the points where the track faults were set. Figure 5b shows the result of the CWT of the simulated vertical vibration acceleration. The color bar indicates the magnitude of the amplitude in the time-frequency plane.
Simulated car body vertical acceleration and its CWT image.
At 150 [m] and 200 [m], the points where the joint depressions were simulated, vibrations in the frequency band of 15–30 [Hz] were detected due to the impulse-like track geometry, and variations depending on depth
Figure 6 shows the track condition monitoring system developed and applied for regional railway lines in Japan [2].
Track condition monitoring system [
Accelerometers and rate gyros in the onboard sensing device measure the car body vibration. A GNSS receiver detects the location and speed of the train. Collected data are transmitted to the data server in the monitoring center continuously via a mobile phone network.
The diagnostic software analyses the collected data and results are fed back to the railway operators through online channels via tablet computers. The diagnostic results are used to facilitate the maintenance work of railway operators.
Convolutional neural networks are a method used in the field of machine learning called deep learning and are particularly suitable for image recognition. In this study, we examined the effectiveness of classifying longitudinal level irregularities and joint depressions automatically, using a diagnostic algorithm, we constructed based on a convolutional neural network trained on CWT images generated from vertical vibration acceleration data from a car body. The diagnostic procedure is shown in Figure 7.
Diagnostic procedure.
The car body’s vertical acceleration with track faults was collected in a regional railway line using the track condition monitoring system. The input data for the classifier consists of vertical vibration acceleration measurements from an onboard sensing device in a car body, which are then converted into images using a CWT. Figure 8 shows an example of converting the measurements into a CWT image.
Measured car-body vertical acceleration and its CWT image.
The vibration characteristics of the joint depression at the distance of 25.82 [km] appear in the 10–30 [Hz] frequency range. The vibration characteristics of the longitudinal level irregularities around 25.95 [km] appear in the 0–5 [Hz] frequency range.
In this study, we investigated the following three types of diagnoses:
Classification of images into three types: longitudinal level irregularity, joint depression, and normal.
Classification of the degradation level of longitudinal level irregularity into normal, medium, and large.
Classification of the degradation level of joint depression into normal, medium, and large.
Examples of images used for each task are shown in Figures 9–11. The images were created with an aspect ratio of 1:1 (150 × 150 pixels), which is optimal for training.
CWT images of faulty track.
CWT images of the different levels of a degraded track (track irregularity).
CWT images of the different levels of a degraded track (joint depression).
For diagnosing the level of degradation of longitudinal level irregularities, in cases where the one-side amplitude of the vibration acceleration was normal, images of car body acceleration of
For diagnosing the level of degradation of joint depressions, in cases where the one-side amplitude of the vibration acceleration was normal, images of body acceleration of 0 to were used. To diagnose medium degradation, images of
We prepared a total of 300 images: 100 normal images, 100 images with a longitudinal level irregularity, and 100 images with a joint depression. We set aside 80% of the images for training and 20% for evaluation as shown in Figure 9.
Figure 12 shows the configuration of the trained convolutional neural network (see Appendix B). In the figure, the name of the process and the size (vertical × horizontal × channels) before processing are indicated above each layer, and the size after processing is indicated below the layer.
Network configuration.
The Convolution layer applies the convolution operation to the image, representing it in matrix form; the Max pooling layer performs information compression; the Affine layer combines information from different layers, and the Output layer outputs a set of probabilities indicating how well the image matches the three types of training image data. The number of training sessions was set to 50.
Figure 13 shows the results of using images for evaluation to discriminate longitudinal level irregularity track faults versus joint depression track faults versus normal track. The overall accuracy rate was 98.3%, demonstrating that convolutional neural networks are effective for the classification of track faults.
Detection accuracy for the type of track fault.
In order to classify the degradation level of longitudinal level irregularities into three types: normal, medium, and large, we prepared a total of 300 images: 100 normal, 100 medium, and 100 large. We set aside 80% of the images for training and 20% for evaluation. The network configuration and the number of training sessions were the same as in Section 5.3.
Detection results using the trained model are shown in Figure 14. The overall accuracy rate was 98.3%, demonstrating that the level of longitudinal level irregularity can be classified with high accuracy into normal, medium, and large.
Detection accuracy for the different levels of a degraded track (track irregularity).
In order to classify the degradation level of joint depression into three types: normal, medium, and large, we prepared a total of 300 images: 100 normal, 100 medium, and 100 large. We set aside 80% of the images for training and 20% for evaluation. The network configuration and the number of training sessions were the same as in Section 5.3.
Detection results using the trained model are shown in Figure 15. Some incorrect diagnoses were made in the images of normal and medium joint depression. However, the overall accuracy was 96.7%, which was sufficient to classify the level of joint depression, demonstrating that the diagnostic algorithm we developed is effective for the diagnosis of joint depression.
Detection accuracy for the different levels of a degraded track (joint depression).
Figure 16 shows an example of an image that was diagnosed incorrectly. The right side of Figure 16a was diagnosed as normal, even though it shows joint depression. Conversely, the left side of Figure 16a shows an image that was diagnosed correctly as a joint depression. Comparing those, the feature representing the joint depression is extremely small in the incorrectly diagnosed image. This reveals that an incorrect diagnosis can occur when the features are extremely small.
CWT images that were diagnosed incorrectly.
The right side of Figure 16b was diagnosed as normal, even though it shows a large track irregularity. Conversely, the left side of Figure 16b shows an image that was diagnosed correctly as a large track irregularity. The reason for the incorrect diagnosis was that the large amplitude of the vertical acceleration, shown in red color, was appeared at the bottom of the CWT image.
In this study, we proposed a method to classify the type and level of track faults automatically using a convolutional neural network trained on car body vibration acceleration measurements converted into images using a CWT, a well-known method of time-frequency analysis. The algorithm we developed was used to perform the diagnosis of track conditions on actual measurements.
The results demonstrated that it is possible to diagnose the type and level of degradation of track faults with high accuracy.
In future work, we plan to improve the algorithm to estimate the locations of track faults accurately in actual measurements and monitor the condition of railway tracks in more detail.
This research was funded by Nihon University Research Grant for Social Implementation (19-006) (2019). We would like to thank Editage (www.editage.jp) for English language editing.
The authors declare no conflict of interest.
A CWT is a method that simultaneously detects the frequency and time characteristics of an unsteady signal, by comparing the original signal with dilated and translated versions of a small wavelike function called the mother wavelet. The CWT computes the inner products of a continuous signal with a set of continuous wavelets according to the following equation
where, variables
In this study, we used the real-valued
Real-valued Morlet wavelet.
A Convolutional Neural Network (CNN) is a well-known deep learning architecture. There are numerous variants of CNN architectures. The basic components of CNN consist of convolutional layer, pooling layer, and fully-connected layers [19].
The objective of the convolution operation is to extract the significant features from the input image. The convolution layer is composed of several convolution kernels which are used to compute different feature maps. The feature maps are generated by the convolution operation with the filter that acts as the feature extractor as follows.
where
The Pooling layer is responsible for reducing the spatial size of the feature maps. This is to decrease the computational power required to process the data through size reduction. It is useful for extracting dominant features. There are two types of Pooling: Max Pooling and Average Pooling. Max Pooling returns the maximum value from the portion of the image. On the other hand, Average Pooling returns the average value. In this study, Max Pooling were used. Figure 18 shows the example of the Max Pooling operation.
Max pooling.
Rectified linear unit (ReLU) is one of the most famous activation functions. In this study, the following function is used to adjust the output of the Pooling Layer.
where
Softmax function defined by
was used in output layer. Where
In fully connected layers, the neuron applies a linear transformation to the input vector through a weights matrix. In this study, an Affine transformation was used in fully connected layer.
The loss function is the function that computes the distance between the current output of the algorithm and the expected output. In this study, we employed the categorical cross-entropy, which is well suited to classification tasks.
CWT | continuous wavelet transform |
RMS | root mean square |
CNN | convolutional neural network |
HHT | Hilbert–Huang transform |
GNSS | global navigation satellite system |
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. 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His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. 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He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. 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A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",institutionString:null,institution:{name:"Universidade Paulista",institutionURL:null,country:{name:"Brazil"}}},{id:"191123",title:"Dr.",name:"Juan 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