Chemistry

Standardization of advanced biologically derived spectrochemical datasets


1.

Baker, M. J. et al. Medical purposes of infrared and Raman spectroscopy: state of play and future challenges. Analyst 143, 1735–1757 (2018).

2.

Melin, A. M., Perromat, A. & Déléris, G. Pharmacologic utility of Fourier rework IR spectroscopy: in vivo toxicity of carbon tetrachloride on rat liver. Biopolymers 57, 160–168 (2000).

three.

Eliasson, C. & Matousek, P. Noninvasive authentication of pharmaceutical merchandise via packaging utilizing spatially offset Raman spectroscopy. Anal. Chem. 79, 1696–1701 (2007).

four.

Baker, M. J. et al. Utilizing Fourier rework IR spectroscopy to investigate organic supplies. Nat. Protoc. 9, 1771–1791 (2014).

5.

Llabjani, V. et al. Polybrominated diphenyl ether-associated alterations in cell biochemistry as decided by attenuated complete reflection Fourier-transform infrared spectroscopy: a comparability with DNA-reactive and/or endocrine-disrupting brokers. Environ. Sci. Technol. 43, 3356–3364 (2009).

6.

Hofmann-Wellenhof, B., Lichtenegger, H. & Collins, J. International Positioning System: Idea and Apply (Springer Science & Enterprise Media, Vienna, 2012).

7.

Morris, P. & Perkins, A. Diagnostic imaging. Lancet 379, 1525–1533 (2012).

eight.

Lee, S. S. et al. Crohn illness of the small bowel: comparability of CT enterography, MR enterography, and small-bowel follow-through as diagnostic methods. Radiology 251, 751–761 (2009).

9.

Lagleyre, S. et al. Reliability of high-resolution CT scan in prognosis of otosclerosis. Otol. Neurotol. 30, 1152–1159 (2009).

10.

Kalita, J. & Misra, U. Comparability of CT scan and MRI findings within the prognosis of Japanese encephalitis. J. Neurol. Sci. 174, three–eight (2000).

11.

Schrevens, L., Lorent, N., Dooms, C. & Vansteenkiste, J. The position of PET scan in prognosis, staging, and administration of non-small cell lung most cancers. Oncologist 9, 633–643 (2004).

12.

Jagust, W., Reed, B., Mungas, D., Ellis, W. & Decarli, C. What does fluorodeoxyglucose PET imaging add to a medical prognosis of dementia? Neurology 69, 871–877 (2007).

13.

Zhou, M. et al. Medical utility of breast-specific gamma imaging for evaluating illness extent within the newly identified breast most cancers affected person. Am. J. Surg. 197, 159–163 (2009).

14.

Wallace, B. A. et al. Biomedical purposes of synchrotron radiation round dichroism spectroscopy: identification of mutant proteins related to illness and improvement of a reference database for fold motifs. Faraday Focus on. 126, 237–243 (2004).

15.

Greenfield, N. J. Utilizing round dichroism spectra to estimate protein secondary construction. Nat. Protoc. 1, 2876–2890 (2006).

16.

Micsonai, A. et al. Correct secondary construction prediction and fold recognition for round dichroism spectroscopy. Proc. Natl. Acad. Sci. USA 112, E3095–E3103 (2015).

17.

Miles, A. J. & Wallace, B. A. Round dichroism spectroscopy of membrane proteins. Chem. Soc. Rev. 45, 4859–4872 (2016).

18.

Brown, J. Q., Vishwanath, Okay., Palmer, G. M. & Ramanujam, N. Advances in quantitative UV–seen spectroscopy for medical and pre-clinical utility in most cancers. Curr. Opin. Biotechnol. 20, 119–131 (2009).

19.

Yang, P.-W. et al. Seen-absorption spectroscopy as a biomarker to foretell therapy response and prognosis of surgically resected esophageal most cancers. Sci. Rep. 6, 33414 (2016).

20.

World Well being Group. Fluorescence microscopy for illness prognosis and environmental monitoring. https://apps.who.int/iris/deal with/10665/119734 (2005).

21.

Shahzad, A. et al. Diagnostic utility of fluorescence spectroscopy in oncology discipline: hopes and challenges. Appl. Spectrosc. Rev. 45, 92–99 (2010).

22.

Sieroń, A. et al. The position of fluorescence prognosis in medical apply. Onco Targets Ther. 6, 977 (2013).

23.

Shin, D., Vigneswaran, N., Gillenwater, A. & Richards-Kortum, R. Advances in fluorescence imaging methods to detect oral most cancers and its precursors. Future Oncol. 6, 1143–1154 (2010).

24.

Shahzad, A. et al. Rising purposes of fluorescence spectroscopy in medical microbiology discipline. J. Transl. Med. 7, 99 (2009).

25.

Möller-Hartmann, W. et al. Medical utility of proton magnetic resonance spectroscopy within the prognosis of intracranial mass lesions. Neuroradiology 44, 371–381 (2002).

26.

Gowda, G. N. et al. Metabolomics-based strategies for early illness diagnostics. Knowledgeable Rev. Mol. Diagn. eight, 617–633 (2008).

27.

Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The medical use of structural MRI in Alzheimer illness. Nat. Rev. Neurol. 6, 67–77 (2010).

28.

Chan, A. W. et al. 1 H-NMR urinary metabolomic profiling for prognosis of gastric most cancers. Br. J. Most cancers 114, 59–62 (2016).

29.

Palmnas, M. S. & Vogel, H. J. The way forward for NMR metabolomics in most cancers remedy: in the direction of personalizing therapy and creating focused medicine? Metabolites three, 373–396 (2013).

30.

Patil, P. & Dasgupta, B. Position of diagnostic ultrasound within the evaluation of musculoskeletal illnesses. Ther. Adv. Musculoskelet. Dis. four, 341–355 (2012).

31.

Navani, N. et al. Lung most cancers prognosis and staging with endobronchial ultrasound-guided transbronchial needle aspiration in contrast with standard approaches: an open-label, pragmatic, randomised managed trial. Lancet Respir. Med. three, 282–289 (2015).

32.

Menon, U. et al. Sensitivity and specificity of multimodal and ultrasound screening for ovarian most cancers, and stage distribution of detected cancers: outcomes of the prevalence display screen of the UK Collaborative Trial of Ovarian Most cancers Screening (UKCTOCS). Lancet Oncol. 10, 327–340 (2009).

33.

Smith-Bindman, R. et al. Endovaginal ultrasound to exclude endometrial most cancers and different endometrial abnormalities. JAMA 280, 1510–1517 (1998).

34.

Gajjar, Okay. et al. Diagnostic segregation of human mind tumours utilizing Fourier-transform infrared and/or Raman spectroscopy coupled with discriminant evaluation. Anal. Strategies 5, 89–102 (2013).

35.

Bury, D. et al. Phenotyping metastatic mind tumors making use of spectrochemical analyses: segregation of various most cancers varieties. Anal. Lett. 52, 575–587 (2019).

36.

Arms, J. R. et al. Attenuated Whole Reflection Fourier Remodel Infrared (ATR-FTIR) spectral discrimination of mind tumour severity from serum samples. J. Biophotonics 7, 189–199 (2014).

37.

Arms, J. R. et al. Mind tumour differentiation: fast stratified serum diagnostics by way of attenuated complete reflection Fourier-transform infrared spectroscopy. J. Neurooncol. 127, 463–472 (2016).

38.

Walsh, M. J., Kajdacsy-Balla, A., Holton, S. E. & Bhargava, R. Attenuated complete reflectance Fourier-transform infrared spectroscopic imaging for breast histopathology. Vib. Spectrosc. 60, 23–28 (2012).

39.

Lane, R. & Website positioning, S. S. Attenuated complete reflectance fourier rework infrared spectroscopy methodology to distinguish between regular and cancerous breast cells. J. Nanosci. Nanotechnol. 12, 7395–7400 (2012).

40.

Backhaus, J. et al. Prognosis of breast most cancers with infrared spectroscopy from serum samples. Vib. Spectrosc. 52, 173–177 (2010).

41.

Wang, J.-S. et al. FT-IR spectroscopic evaluation of regular and cancerous tissues of esophagus. World J. Gastroenterol. 9, 1897–1899 (2003).

42.

Maziak, D. E. et al. Fourier-transform infrared spectroscopic examine of attribute molecular construction in most cancers cells of esophagus: an exploratory examine. Most cancers Detect. Prev. 31, 244–253 (2007).

43.

McIntosh, L. M. et al. Infrared spectra of basal cell carcinomas are distinct from non-tumor-bearing pores and skin parts. J. Make investments. Dermatol. 112, 951–956 (1999).

44.

McIntosh, L. M. et al. In direction of non-invasive screening of pores and skin lesions by near-infrared spectroscopy. J. Make investments. Dermatol. 116, 175–181 (2001).

45.

Mostaço-Guidolin, L. B., Murakami, L. S., Nomizo, A. & Bachmann, L. Fourier rework infrared spectroscopy of pores and skin most cancers cells and tissues. Appl. Spectrosc. Rev. 44, 438–455 (2009).

46.

Mordechai, S. et al. Attainable frequent biomarkers from FTIR microspectroscopy of cervical most cancers and melanoma. J. Microsc. 215, 86–91 (2004).

47.

Hammody, Z., Sahu, R. Okay., Mordechai, S., Cagnano, E. & Argov, S. Characterization of malignant melanoma utilizing vibrational spectroscopy. Sci. World J. 5, 173–182 (2005).

48.

Kondepati, V. R., Keese, M., Mueller, R., Manegold, B. C. & Backhaus, J. Software of near-infrared spectroscopy for the prognosis of colorectal most cancers in resected human tissue specimens. Vib. Spectrosc. 44, 236–242 (2007).

49.

Rigas, B., Morgello, S., Goldman, I. S. & Wong, P. Human colorectal cancers show irregular Fourier-transform infrared spectra. Proc. Natl. Acad. Sci. USA 87, 8140–8144 (1990).

50.

Yao, H., Shi, X. & Zhang, Y. The usage of FTIR-ATR spectrometry for analysis of surgical resection margin in colorectal most cancers: a pilot examine of 56 samples. J. Spectrosc. 2014, four (2014).

51.

Lewis, P. D. et al. Analysis of FTIR Spectroscopy as a diagnostic instrument for lung most cancers utilizing sputum. BMC Most cancers 10, 640 (2010).

52.

Akalin, A. et al. Classification of malignant and benign tumors of the lung by infrared spectral histopathology (SHP). Lab. Make investments. 95, 406–421 (2015).

53.

Großerueschkamp, F. et al. Marker-free automated histopathological annotation of lung tumour subtypes by FTIR imaging. Analyst 140, 2114–2120 (2015).

54.

Owens, G. L. et al. Vibrational biospectroscopy coupled with multivariate evaluation extracts doubtlessly diagnostic options in blood plasma/serum of ovarian most cancers sufferers. J. Biophotonics 7, 200–209 (2014).

55.

Gajjar, Okay. et al. Fourier-transform infrared spectroscopy coupled with a classification machine for the evaluation of blood plasma or serum: a novel diagnostic method for ovarian most cancers. Analyst 138, 3917–3926 (2013).

56.

Theophilou, G., Lima, Okay. M. G., Martin-Hirsch, P. L., Stringfellow, H. F. & Martin, F. L. ATR-FTIR spectroscopy coupled with chemometric evaluation discriminates regular, borderline and malignant ovarian tissue: classifying subtypes of human most cancers. Analyst 141, 585–594 (2016).

57.

Mehrotra, R., Tyagi, G., Jangir, D. Okay., Dawar, R. & Gupta, N. Evaluation of ovarian tumor pathology by Fourier Remodel Infrared Spectroscopy. J. Ovarian Res. three, 27 (2010).

58.

Paraskevaidi, M. et al. Potential of mid-infrared spectroscopy as a non-invasive diagnostic take a look at in urine for endometrial or ovarian most cancers. Analyst 143, 3156–3163 (2018).

59.

Taylor, S. E. et al. Infrared spectroscopy with multivariate evaluation to interrogate endometrial tissue: a novel and goal diagnostic method. Br. J. Most cancers 104, 790–797 (2011).

60.

Paraskevaidi, M. et al. Aluminium foil instead substrate for the spectroscopic interrogation of endometrial most cancers. J. Biophotonics 11, e201700372 (2018).

61.

Gajjar, Okay. et al. Histology verification demonstrates that biospectroscopy evaluation of cervical cytology identifies underlying illness extra precisely than standard screening: eradicating the confounder of discordance. PLoS ONE 9, e82416 (2014).

62.

Walsh, M. J. et al. IR microspectroscopy: potential purposes in cervical most cancers screening. Most cancers Lett. 246, 1–11 (2007).

63.

Wooden, B. R., Quinn, M. A., Burden, F. R. & McNaughton, D. An investigation into FTIR spectroscopy as a biodiagnostic instrument for cervical most cancers. Biospectroscopy 2, 143–153 (1996).

64.

Podshyvalov, A. et al. Distinction of cervical most cancers biopsies by use of infrared microspectroscopy and probabilistic neural networks. Appl. Choose. 44, 3725–3734 (2005).

65.

Theophilou, G. et al. A biospectroscopic evaluation of human prostate tissue obtained from totally different time durations factors to a trans-generational alteration in spectral phenotype. Sci. Rep. 5, 13465 (2015).

66.

Baker, M. J. et al. Investigating FTIR based mostly histopathology for the prognosis of prostate most cancers. J. Biophotonics 2, 104–113 (2009).

67.

Derenne, A., Gasper, R. & Goormaghtigh, E. The FTIR spectrum of prostate most cancers cells permits the classification of anticancer medicine in line with their mode of motion. Analyst 136, 1134–1141 (2011).

68.

Gazi, E. et al. A correlation of FTIR spectra derived from prostate most cancers biopsies with Gleason grade and tumour stage. Eur. Urol. 50, 750–761 (2006).

69.

Paraskevaidi, M. et al. Differential prognosis of Alzheimer’s illness utilizing spectrochemical evaluation of blood. Proc. Natl. Acad. Sci. USA 114, E7929–E7938 (2017).

70.

Carmona, P. et al. Discrimination evaluation of blood plasma related to Alzheimer’s illness utilizing vibrational spectroscopy. J. Alzheimers Dis. 34, 911–920 (2013).

71.

Carmona, P., Molina, M., López-Tobar, E. & Toledano, A. Vibrational spectroscopic evaluation of peripheral blood plasma of sufferers with Alzheimer’s illness. Anal. Bioanal. Chem. 407, 7747–7756 (2015).

72.

Paraskevaidi, M. et al. Blood-based near-infrared spectroscopy for the fast low-cost detection of Alzheimer’s illness. Analyst 143, 5959–5964 (2018).

73.

Sitole, L., Steffens, F., Krüger, T. P. J. & Meyer, D. Mid-ATR-FTIR spectroscopic profiling of HIV/AIDS sera for novel programs diagnostics in world well being. OMICS 18, 513–523 (2014).

74.

Coopman, R. et al. Glycation in human fingernail clippings utilizing ATR-FTIR spectrometry, a brand new marker for the prognosis and monitoring of diabetes mellitus. Clin. Biochem. 50, 62–67 (2017).

75.

Scott, D. A. et al. Diabetes-related molecular signatures in infrared spectra of human saliva. Diabetol. Metab. Syndr. 2, 48 (2010).

76.

Varma, V. Okay., Kajdacsy-Balla, A., Akkina, S. Okay., Setty, S. & Walsh, M. J. A label-free method by infrared spectroscopic imaging for interrogating the biochemistry of diabetic nephropathy development. Kidney Int. 89, 1153–1159 (2016).

77.

Lechowicz, L., Chrapek, M., Gaweda, J., Urbaniak, M. & Konieczna, I. Use of Fourier-transform infrared spectroscopy within the prognosis of rheumatoid arthritis: a pilot examine. Mol. Biol. Rep. 43, 1321–1326 (2016).

78.

Canvin, J. et al. Infrared spectroscopy: shedding gentle on synovitis in sufferers with rheumatoid arthritis. Rheumatology 42, 76–82 (2003).

79.

Oemrawsingh, R. M. et al. Close to-infrared spectroscopy predicts cardiovascular end result in sufferers with coronary artery illness. J. Am. Coll. Cardiol. 64, 2510–2518 (2014).

80.

Wang, J. et al. Close to-infrared spectroscopic characterization of human superior atherosclerotic plaques. J. Am. Coll. Cardiol. 39, 1305–1313 (2002).

81.

Martin, M. et al. The impact of frequent anticoagulants in detection and quantification of malaria parasitemia in human pink blood cells by ATR-FTIR spectroscopy. Analyst 142, 1192–1199 (2017).

82.

Khoshmanesh, A. et al. Detection and quantification of early-stage malaria parasites in laboratory contaminated erythrocytes by attenuated complete reflectance infrared spectroscopy and multivariate evaluation. Anal. Chem. 86, 4379–4386 (2014).

83.

Roy, S. et al. Simultaneous ATR-FTIR based mostly dedication of malaria parasitemia, glucose and urea in complete blood dried onto a glass slide. Anal. Chem. 89, 5238–5245 (2017).

84.

Markus, A. P. J. et al. New method for prognosis and monitoring of alcaptonuria: quantification of homogentisic acid in urine with mid-infrared spectrometry. Anal. Chim. Acta 429, 287–292 (2001).

85.

Grimard, V. et al. Phosphorylation-induced conformational adjustments of cystic fibrosis transmembrane conductance regulator monitored by Attenuated Whole Reflection-Fourier Remodel IR Spectroscopy and Fluorescence Spectroscopy. J. Biol. Chem. 279, 5528–5536 (2004).

86.

Aksoy, C., Guliyev, A., Kilic, E., Uckan, D. & Severcan, F. Bone marrow mesenchymal stem cells in sufferers with beta thalassemia main: molecular evaluation with attenuated complete reflection-Fourier rework infrared spectroscopy examine as a novel methodology. Stem Cells Dev. 21, 2000–2011 (2012).

87.

Graça, G. et al. Mid-infrared (MIR) metabolic fingerprinting of amniotic fluid: a potential avenue for early prognosis of prenatal problems? Anal. Chim. Acta 764, 24–31 (2013).

88.

Hasegawa, J. et al. Analysis of placental operate utilizing close to infrared spectroscopy throughout fetal development restriction. J. Perinat. Med. 38, 29–32 (2010).

89.

Theelen, T., Berendschot, T. T., Hoyng, C. B., Boon, C. J. & Klevering, B. J. Close to-infrared reflectance imaging of neovascular age-related macular degeneration. Graefe’s Arch. Clin. Exp. Ophthalmol. 247, 1625–1633 (2009).

90.

Semoun, O. et al. Infrared options of basic choroidal neovascularisation in exudative age-related macular degeneration. Br. J. Ophthalmol. 93, 182–185 (2009).

91.

Peters, A. S. et al. Serum-infrared spectroscopy is appropriate for prognosis of atherosclerosis and its medical manifestations. Vib. Spectrosc. 92, 20–26 (2017).

92.

Afara, I. O., Prasadam, I., Arabshahi, Z., Xiao, Y. & Oloyede, A. Monitoring osteoarthritis development utilizing close to infrared (NIR) spectroscopy. Sci. Rep. 7, 11463 (2017).

93.

Bi, X. et al. Fourier rework infrared imaging and MR microscopy research detect compositional and structural adjustments in cartilage in a rabbit mannequin of osteoarthritis. Anal. Bioanal. Chem. 387, 1601–1612 (2007).

94.

David-Vaudey, E. et al. Fourier Remodel Infrared Imaging of focal lesions in human osteoarthritic cartilage. Eur. Cell. Mater. 10, 51–60 (2005).

95.

Trevisan, J., Angelov, P. P., Carmichael, P. L., Scott, A. D. & Martin, F. L. Extracting organic data with computational evaluation of Fourier-transform infrared (FTIR) biospectroscopy datasets: present practices to future views. Analyst 137, 3202–3215 (2012).

96.

Andrew Chan, Okay. L. & Kazarian, S. G. Attenuated complete reflection Fourier-transform infrared (ATR-FTIR) imaging of tissues and dwell cells. Chem. Soc. Rev. 45, 1850–1864 (2016).

97.

Pilling, M. & Gardner, P. Elementary developments in infrared spectroscopic imaging for biomedical purposes. Chem. Soc. Rev. 45, 1935–1957 (2016).

98.

Martin, F. L. et al. Distinguishing cell varieties or populations based mostly on the computational evaluation of their infrared spectra. Nat. Protoc. 5, 1748–1760 (2010).

99.

Butler, H. J. et al. Utilizing Raman spectroscopy to characterize organic supplies. Nat. Protoc. 11, 664–687 (2016).

100.

Kong, L. et al. Characterization of bacterial spore germination utilizing phase-contrast and fluorescence microscopy, Raman spectroscopy and optical tweezers. Nat. Protoc. 6, 625–639 (2011).

101.

Harmsen, S., Wall, M. A., Huang, R. & Kircher, M. F. Most cancers imaging utilizing surface-enhanced resonance Raman scattering nanoparticles. Nat. Protoc. 12, 1400–1414 (2017).

102.

Beckonert, O. et al. Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat. Protoc. 2, 2692–2703 (2007).

103.

Felten, J. et al. Vibrational spectroscopic picture evaluation of organic materials utilizing multivariate curve decision–alternating least squares (MCR-ALS). Nat. Protoc. 10, 217–240 (2015).

104.

Yang, H., Yang, S., Kong, J., Dong, A. & Yu, S. Acquiring details about protein secondary buildings in aqueous answer utilizing Fourier rework IR spectroscopy. Nat. Protoc. 10, 382–396 (2015).

105.

Sreedhar, H. et al. Excessive-definition Fourier rework infrared (FT-IR) spectroscopic imaging of human tissue sections in the direction of bettering pathology. J. Vis. Exp. 2015, 52332 (2015).

106.

Varriale, A. et al. Fluorescence correlation spectroscopy assay for gliadin in meals. Anal. Chem. 79, 4687–4689 (2007).

107.

Music, X., Li, H., Al-Qadiri, H. M. & Lin, M. Detection of herbicides in ingesting water by surface-enhanced Raman spectroscopy coupled with gold nanostructures. J. Meals Meas. Charact. 7, 107–113 (2013).

108.

Osborne, B. G. & Fearn, T. Close to-infrared spectroscopy in meals evaluation. Encyclopedia Anal. Chem. 5, 4069–4082 (2000).

109.

Qu, J.-H. et al. Purposes of near-infrared spectroscopy in meals security analysis and management: a assessment of latest analysis advances. Crit. Rev. Meals Sci. Nutr. 55, 1939–1954 (2015).

110.

Penido, C. A. F., Pacheco, M. T. T., Lednev, I. Okay. & Silveira, L. Raman spectroscopy in forensic evaluation: identification of cocaine and different unlawful medicine of abuse. J. Raman Spectrosc. 47, 28–38 (2016).

111.

Ryder, A. G. Classification of narcotics in strong mixtures utilizing principal element evaluation and Raman spectroscopy. J. Forensic Sci. 47, 275–284 (2002).

112.

Harrigan, G. G. et al. Software of high-throughput Fourier-transform infrared spectroscopy in toxicology research: contribution to a examine on the event of an animal mannequin for idiosyncratic toxicity. Toxicol. Lett. 146, 197–205 (2004).

113.

Choo-Smith, L.-P. et al. Investigating microbial (micro) colony heterogeneity by vibrational spectroscopy. Appl. Environ. Microbiol. 67, 1461–1469 (2001).

114.

Helm, D., Labischinski, H., Schallehn, G. & Naumann, D. Classification and identification of micro organism by Fourier-transform infrared spectroscopy. Microbiology 137, 69–79 (1991).

115.

Carmona, P., Monzon, M., Monleon, E., Badiola, J. J. & Monreal, J. In vivo detection of scrapie circumstances from blood by infrared spectroscopy. J. Gen. Virol. 86, 3425–3431 (2005).

116.

Cui, L. et al. Practical single-cell method to probing nitrogen-fixing micro organism in soil communities by resonance Raman spectroscopy with 15N2 labeling. Anal. Chem. 90, 5082–5089 (2018).

117.

Lasch, P. & Naumann, D. Infrared spectroscopy in microbiology. in Encyclopedia of Analytical Chemistry (eds Brown, J. & Pawlu, T.) (Arcler Press, Oakville, ON, Canada, 2015).

118.

Maquelin, Okay. et al. Identification of medically related microorganisms by vibrational spectroscopy. J. Microbiol. Strategies 51, 255–271 (2002).

119.

Day, J. S., Edwards, H. G., Dobrowski, S. A. & Voice, A. M. The detection of medication of abuse in fingerprints utilizing Raman spectroscopy I: latent fingerprints. Spectrochim. Acta A 60, 563–568 (2004).

120.

Macleod, N. A. & Matousek, P. Rising non-invasive raman strategies in course of management and forensic purposes. Pharm. Res. 25, 2205–2215 (2008).

121.

Lewis, I., Daniel, N. Jr, Chaffin, N., Griffiths, P. & Tungol, M. Raman spectroscopic research of explosive supplies: in the direction of a fieldable explosives detector. Spectrochim. Acta A 51, 1985–2000 (1995).

122.

Hargreaves, M. D. & Matousek, P. Risk detection of liquid explosive precursor mixtures by Spatially Offset Raman Spectroscopy (SORS). in Optics and Photonics for Counterterrorism and Crime Preventing V (ed. Lewis, C.) Proceedings of SPIE, Vol. 7486, 74860B (Worldwide Society for Optics and Photonics, Bellingham, WA, 2009).

123.

Ali, E. M., Edwards, H. G., Hargreaves, M. D. & Scowen, I. J. Raman spectroscopic investigation of cocaine hydrochloride on human nail in a forensic context. Anal. Bioanal. Chem. 390, 1159–1166 (2008).

124.

Vergote, G. J., Vervaet, C., Remon, J. P., Haemers, T. & Verpoort, F. Close to-infrared FT-Raman spectroscopy as a fast analytical instrument for the dedication of diltiazem hydrochloride in tablets. Eur. J. Pharm. Sci. 16, 63–67 (2002).

125.

Lohr, D. et al. Non-destructive dedication of carbohydrate reserves in leaves of decorative cuttings by near-infrared spectroscopy (NIRS) as a key indicator for high quality assessments. Biosyst. Eng. 158, 51–63 (2017).

126.

Heys, Okay. A., Shore, R. F., Pereira, M. G. & Martin, F. L. Ranges of organochlorine pesticides are related to amyloid aggregation in apex avian brains. Environ. Sci. Technol. 51, 8672–8681 (2017).

127.

Comino, F., Aranda, V., García-Ruiz, R. & Domínguez-Vidal, A. Infrared spectroscopy as a instrument for the evaluation of soil organic high quality in agricultural soils underneath contrasting administration practices. Ecol. Indic. 87, 117–126 (2018).

128.

Eliasson, C., Macleod, N. & Matousek, P. Noninvasive detection of hid liquid explosives utilizing Raman spectroscopy. Anal. Chem. 79, 8185–8189 (2007).

129.

Liu, H.-B., Zhong, H., Karpowicz, N., Chen, Y. & Zhang, X.-C. Terahertz spectroscopy and imaging for protection and safety purposes. Proc. IEEE 95, 1514–1527 (2007).

130.

Golightly, R. S., Doering, W. E. & Natan, M. J. Floor-enhanced Raman spectroscopy and homeland safety: an ideal match? ACS Nano three, 2859–2869 (2009).

131.

Wang, Y., Veltkamp, D. J. & Kowalski, B. R. Multivariate instrument standardization. Anal. Chem. 63, 2750–2756 (1991).

132.

Brouckaert, D., Uyttersprot, J.-S., Broeckx, W. & De Beer, T. Calibration switch of a Raman spectroscopic quantification methodology for the evaluation of liquid detergent compositions from at-line laboratory to in-line industrial scale. Talanta 179, 386–392 (2018).

133.

Vasconcelos de Andrade, E. W., Medeiros de Morais, C. L., Lopes da Costa, F. S. & Gomes de Lima, Okay. M. A multivariate management chart method for calibration switch between NIR spectrometers for simultaneous dedication of rifampicin and isoniazid in pharmaceutical formulation. Curr. Anal. Chem. 14, 488–494 (2018).

134.

Zamora-Rojas, E., Pérez-Marín, D., De Pedro-Sanz, E., Guerrero-Ginel, J. & Garrido-Varo, A. Handheld NIRS evaluation for routine meat high quality management: database switch from at-line devices. Chemom. Intellig. Lab. Syst. 114, 30–35 (2012).

135.

Panchuk, V., Kirsanov, D., Oleneva, E., Semenov, V. & Legin, A. Calibration switch between totally different analytical strategies. Talanta 170, 457–463 (2017).

136.

de Morais, Cd. L. M. & de Lima, Okay. M. G. Dedication and analytical validation of creatinine content material in serum utilizing picture evaluation by multivariate switch calibration procedures. Anal. Strategies 7, 6904–6910 (2015).

137.

Khaydukova, M. et al. Multivariate calibration switch between two several types of multisensor programs. Sens. Actuators B Chem. 246, 994–1000 (2017).

138.

Barreiro, P., Herrero, D., Hernández, N., Gracia, A. & León, L. Calibration switch between moveable and laboratory NIR spectrophotometers. Acta Hortic. 802, 373–378 (2008).

139.

Sulub, Y., LoBrutto, R., Vivilecchia, R. & Wabuyele, B. W. Content material uniformity dedication of pharmaceutical tablets utilizing 5 near-infrared reflectance spectrometers: a course of analytical expertise (PAT) method utilizing strong multivariate calibration switch algorithms. Anal. Chim. Acta 611, 143–150 (2008).

140.

Zhang, L., Small, G. W. & Arnold, M. A. Multivariate calibration standardization throughout devices for the dedication of glucose by Fourier rework near-infrared spectrometry. Anal. Chem. 75, 5905–5915 (2003).

141.

Koehler, F. W. IV, Small, G. W., Combs, R. J., Knapp, R. B. & Kroutil, R. T. Calibration switch algorithm for automated qualitative evaluation by passive Fourier rework infrared spectrometry. Anal. Chem. 72, 1690–1698 (2000).

142.

Martens, H., Høy, M., Smart, B. M., Bro, R. & Brockhoff, P. B. Pre-whitening of knowledge by covariance-weighted pre-processing. J. Chemom. 17, 153–165 (2003).

143.

Feudale, R. N. et al. Switch of multivariate calibration fashions: a assessment. Chemom. Intellig. Lab. Syst. 64, 181–192 (2002).

144.

Woody, N. A., Feudale, R. N., Myles, A. J. & Brown, S. D. Switch of multivariate calibrations between 4 near-infrared spectrometers utilizing orthogonal sign correction. Anal. Chem. 76, 2595–2600 (2004).

145.

Greensill, C., Wolfs, P., Spiegelman, C. & Walsh, Okay. Calibration switch between PDA-based NIR spectrometers within the NIR evaluation of melon soluble solids content material. Appl. Spectrosc. 55, 647–653 (2001).

146.

Sjöblom, J., Svensson, O., Josefson, M., Kullberg, H. & Wold, S. An analysis of orthogonal sign correction utilized to calibration switch of close to infrared spectra. Chemom. Intellig. Lab. Syst. 44, 229–244 (1998).

147.

Rodrigues, R. R. et al. Analysis of calibration switch strategies utilizing the ATR-FTIR method to foretell density of crude oil. Chemom. Intellig. Lab. Syst. 166, 7–13 (2017).

148.

Andrews, D. T. & Wentzell, P. D. Purposes of most chance principal element evaluation: incomplete knowledge units and calibration switch. Anal. Chim. Acta 350, 341–352 (1997).

149.

Bouveresse, E., Massart, D. & Dardenne, P. Calibration switch throughout near-infrared spectrometric devices utilizing Shenk’s algorithm: results of various standardisation samples. Anal. Chim. Acta 297, 405–416 (1994).

150.

Shenk, J. S. & Westerhaus, M. O. Populations structuring of close to infrared spectra and modified partial least squares regression. Crop Sci. 31, 1548–1555 (1991).

151.

Paatero, P. & Tapper, U. Optimistic matrix factorization: A non-negative issue mannequin with optimum utilization of error estimates of knowledge values. Environmetrics 5, 111–126 (1994).

152.

Xie, Y. & Hopke, P. Okay. Calibration switch as a knowledge reconstruction downside. Anal. Chim. Acta 384, 193–205 (1999).

153.

Goodacre, R. et al. On mass spectrometer instrument standardization and interlaboratory calibration switch utilizing neural networks. Anal. Chim. Acta 348, 511–532 (1997).

154.

Chen, W.-R., Bin, J., Lu, H.-M., Zhang, Z.-M. & Liang, Y.-Z. Calibration switch by way of an excessive studying machine auto-encoder. Analyst 141, 1973–1980 (2016).

155.

Hu, Y., Peng, S., Bi, Y. & Tang, L. Calibration switch based mostly on most margin criterion for qualitative evaluation utilizing Fourier rework infrared spectroscopy. Analyst 137, 5913–5918 (2012).

156.

Fan, W., Liang, Y., Yuan, D. & Wang, J. Calibration mannequin switch for near-infrared spectra based mostly on canonical correlation evaluation. Anal. Chim. Acta 623, 22–29 (2008).

157.

Isabelle, M. et al. Multi-centre Raman spectral mapping of oesophageal most cancers tissues: a examine to evaluate system transferability. Faraday Focus on. 187, 87–103 (2016).

158.

Wang, Z., Dean, T. & Kowalski, B. R. Additive background correction in multivariate instrument standardization. Anal. Chem. 67, 2379–2385 (1995).

159.

Kennard, R. W. & Stone, L. A. Laptop aided design of experiments. Technometrics 11, 137–148 (1969).

160.

Palonpon, A. F. et al. Raman and SERS microscopy for molecular imaging of dwell cells. Nat. Protoc. eight, 677–692 (2013).

161.

Witze, E. S., Previous, W. M., Resing, Okay. A. & Ahn, N. G. Mapping protein post-translational modifications with mass spectrometry. Nat. Strategies four, 798–806 (2007).

162.

Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

163.

Pence, I. & Mahadevan-Jansen, A. Medical instrumentation and purposes of Raman spectroscopy. Chem. Soc. Rev. 45, 1958–1979 (2016).

164.

Ibrahim, O. et al. Improved protocols for pre-processing Raman spectra of formalin fastened paraffin preserved tissue sections. Anal. Strategies 9, 4709–4717 (2017).

165.

Tfayli, A. et al. Digital dewaxing of Raman alerts: discrimination between nevi and melanoma spectra obtained from paraffin-embedded pores and skin biopsies. Appl. Spectrosc. 63, 564–570 (2009).

166.

Byrne, H. J., Knief, P., Keating, M. E. & Bonnier, F. Spectral pre and submit processing for infrared and Raman spectroscopy of organic tissues and cells. Chem. Soc. Rev. 45, 1865–1878 (2016).

167.

Meade, A. D. et al. Research of chemical fixation results in human cell strains utilizing Raman microspectroscopy. Anal. Bioanal. Chem. 396, 1781–1791 (2010).

168.

Baker, M. J. et al. Growing and understanding biofluid vibrational spectroscopy: a crucial assessment. Chem. Soc. Rev. 45, 1803–1818 (2016).

169.

Bonifacio, A., Cervo, S. & Sergo, V. Label-free surface-enhanced Raman spectroscopy of biofluids: elementary points and diagnostic purposes. Anal. Bioanal. Chem. 407, 8265–8277 (2015).

170.

Mitchell, A. L., Gajjar, Okay. B., Theophilou, G., Martin, F. L. & Martin-Hirsch, P. L. Vibrational spectroscopy of biofluids for illness screening or prognosis: translation from the laboratory to a medical setting. J. Biophotonics 7, 153–165 (2014).

171.

Lovergne, L. et al. Biofluid infrared spectro-diagnostics: pre-analytical issues for medical purposes. Faraday Focus on. 187, 521–537 (2016).

172.

Bonifacio, A. et al. Floor-enhanced Raman spectroscopy of blood plasma and serum utilizing Ag and Au nanoparticles: a scientific examine. Anal. Bioanal. Chem. 406, 2355–2365 (2014).

173.

Paraskevaidi, M., Martin-Hirsch, P. L. & Martin, F. L. ATR-FTIR spectroscopy instruments for medical prognosis and illness investigation. in Nanotechnology Characterization Instruments for Biosensing and Medical Prognosis (ed. Kumar, C. S. S. R.) 163–211 (Springer, Berlin, 2017).

174.

Mitchell, B. L., Yasui, Y., Li, C. I., Fitzpatrick, A. L. & Lampe, P. D. Affect of freeze–thaw cycles and storage time on plasma samples utilized in mass spectrometry based mostly biomarker discovery initiatives. Most cancers Inform. 1, 98–104 (2005).

175.

Glassford, S. E., Byrne, B. & Kazarian, S. G. Latest purposes of ATR FTIR spectroscopy and imaging to proteins. Biochim. Biophys. Acta 1834, 2849–2858 (2013).

176.

Kundu, J., Le, F., Nordlander, P. & Halas, N. J. Floor enhanced infrared absorption (SEIRA) spectroscopy on nanoshell mixture substrates. Chem. Phys. Lett. 452, 115–119 (2008).

177.

Jones, S., Carley, S. & Harrison, M. An introduction to energy and pattern dimension estimation. Emerg. Med. J. 20, 453–458 (2003).

178.

Beebe, Okay. R., Pell, R. J. & Seasholtz, M. B. Chemometrics: A Sensible Information Vol. four (Wiley, New York,1998).

179.

Pavia, D. L., Lampman, G. M., Kriz, G. S. & Vyvyan, J. A. Introduction to Spectroscopy (Cengage Studying, Belmont, CA, 2008).

180.

Hastie, T., Tibshirani, R. & Friedman, J. The Components of Statistical Studying: Information Mining, Inference, and Prediction 2nd edn (Springer, New York, 2009).

181.

Bro, R. & Smilde, A. Okay. Principal element evaluation. Anal. Strategies 6, 2812–2831 (2014).

182.

Martin, F. L. et al. Figuring out variables accountable for clustering in discriminant evaluation of knowledge from infrared microspectroscopy of a organic pattern. J. Comput. Biol. 14, 1176–1184 (2007).

183.

Martens, H. & Martens, M. Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Meals Qual. Desire. 11, 5–16 (2000).

184.

Rousseeuw, P. J. & Hubert, M. Strong statistics for outlier detection. Wiley Interdiscip. Rev. Information Min. Knowl. Discov. 1, 73–79 (2011).

185.

Jiang, F., Liu, G., Du, J. & Sui, Y. Initialization of Okay-modes clustering utilizing outlier detection methods. Inf. Sci. 332, 167–183 (2016).

186.

Domingues, R., Filippone, M., Michiardi, P. & Zouaoui, J. A comparative analysis of outlier detection algorithms: experiments and analyses. Sample Recognit. 74, 406–421 (2018).

187.

Bakeev, Okay. A. Course of Analytical Know-how: Spectroscopic Instruments and Implementation Methods for the Chemical and Pharmaceutical Industries 2nd edn (John Wiley & Sons, Chichester, UK, 2010).

188.

Kuligowski, J., Quintás, G., Herwig, C. & Lendl, B. A fast methodology for the differentiation of yeast cells grown underneath carbon and nitrogen-limited situations by way of partial least squares discriminant evaluation using infrared micro-spectroscopic knowledge of complete yeast cells. Talanta 99, 566–573 (2012).

189.

Morais, C. L. & Lima, Okay. M. Evaluating unfolded and two-dimensional discriminant evaluation and help vector machines for classification of EEM knowledge. Chemom. Intell. Lab. Syst. 170, 1–2 (2017).

190.

Seasholtz, M. B. & Kowalski, B. The parsimony precept utilized to multivariate calibration. Anal. Chim. Acta 277, 165–177 (1993).

191.

Morais, C. L. & Lima, Okay. M. Principal element evaluation with linear and quadratic discriminant evaluation for identification of most cancers samples based mostly on mass spectrometry. J. Braz. Chem. Soc. 29, 472–481 (2017).

192.

Brereton, R. G. & Lloyd, G. R. Partial least squares discriminant evaluation: taking the magic away. J. Chemom. 28, 213–225 (2014).

193.

Hibbert, D. B. Vocabulary of ideas and phrases in chemometrics (IUPAC Suggestions 2016). Pure Appl. Chem. 88, 407–443 (2016).

194.

McCall, J. Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184, 205–222 (2005).

195.

Soares, S. F. C., Gomes, A. A., Araujo, M. C. U., Galvão Filho, A. R. & Galvão, R. Okay. H. The successive projections algorithm. Developments Anal. Chem. 42, 84–98 (2013).

196.

Kamandar, M. & Ghassemian, H. Most relevance, minimal redundancy characteristic extraction for hyperspectral pictures. in 2010 18th Iranian Convention on Electrical Engineering: Proceedings 254–259 (IEEE, Isfahan, Iran, 2010).

197.

Sattlecker, M., Stone, N., Smith, J. & Bessant, C. Evaluation of robustness and transferability of classification fashions constructed for most cancers diagnostics utilizing Raman spectroscopy. J. Raman Spectrosc. 42, 897–903 (2011).

198.

Guo, S. et al. In direction of an enchancment of mannequin transferability for Raman spectroscopy in organic purposes. Vib. Spectrosc. 91, 111–118 (2017).

199.

Luo, X. et al. Calibration switch throughout close to infrared spectrometers for measuring hematocrit within the blood of grazing cattle. J. Close to Infrared Spectrosc. 25, 15–25 (2017).

200.

Vaughan, A. A. et al. Liquid chromatography–mass spectrometry calibration switch and metabolomics knowledge fusion. Anal. Chem. 84, 9848–9857 (2012).

201.

Rodriguez, J. D., Westenberger, B. J., Buhse, L. F. & Kauffman, J. F. Standardization of Raman spectra for switch of spectral libraries throughout totally different devices. Analyst 136, 4232–4240 (2011).

202.

Yu, B., Ji, H. & Kang, Y. Standardization of close to infrared spectra based mostly on multi-task studying. Spectrosc. Lett. 49, 23–29 (2016).

203.

Ni, L., Han, M., Luan, S. & Zhang, L. Screening wavelengths with constant and secure alerts to comprehend calibration mannequin switch of close to infrared spectra. Spectrochim. Acta A 206, 350–358 (2019).

204.

Hu, R. & Xia, J. Calibration switch of close to infrared spectroscopy based mostly on DS algorithm. in 2011 Worldwide Convention on Electrical Data and Management Engineering (ICEICE) 3062–3065 (IEEE, Wuhan, China).

205.

Forina, M. et al. Switch of calibration operate in near-infrared spectroscopy. Chemom. Intellig. Lab. Syst. 27, 189–203 (1995).

206.

Xiao, H. et al. Comparability of benchtop Fourier-transform (FT) and moveable grating scanning spectrometers for dedication of complete soluble strong contents in single grape berry (Vitis vinifera L.) and calibration switch. Sensors 17, 2693 (2017).

207.

Yahaya, O., MatJafri, M., Aziz, A. & Omar, A. Seen spectroscopy calibration switch mannequin in figuring out pH of Sala mangoes. J. Instrum. 10, T05002 (2015).

208.

Bin, J., Li, X., Fan, W., Zhou, J.-h & Wang, C.-w Calibration switch of near-infrared spectroscopy by canonical correlation evaluation coupled with wavelet rework. Analyst 142, 2229–2238 (2017).

209.

Monakhova, Y. B. & Diehl, B. W. Switch of multivariate regression fashions between high-resolution NMR devices: utility to authenticity management of sunflower lecithin. Magn. Reson. Chem. 54, 712–717 (2016).

210.

Zuo, Q., Xiong, S., Chen, Z.-P., Chen, Y. & Yu, R.-Q. A novel calibration technique based mostly on background correction for quantitative round dichroism spectroscopy. Talanta 174, 320–324 (2017).

211.

Savitzky, A. & Golay, M. J. Smoothing and differentiation of knowledge by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).

212.

Geladi, P., MacDougall, D. & Martens, H. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Appl. Spectrosc. 39, 491–500 (1985).

213.

Barnes, R., Dhanoa, M. S. & Lister, S. J. Normal regular variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43, 772–777 (1989).

214.

Brereton, R. G. Chemometrics: Information Evaluation for the Laboratory and Chemical Plant (John Wiley & Sons, Chichester, UK, 2003).

215.

Dixon, S. J. & Brereton, R. G. Comparability of efficiency of 5 frequent classifiers represented as boundary strategies: Euclidean Distance to Centroids, Linear Discriminant Evaluation, Quadratic Discriminant Evaluation, Studying Vector Quantization and Assist Vector Machines, as depending on knowledge construction. Chemom. Intell. Lab. Syst. 95, 1–17 (2009).

216.

Cowl, T. & Hart, P. Nearest neighbor sample classification. IEEE Trans. Inf. Idea 13, 21–27 (1967).

217.

Cortes, C. & Vapnik, V. Assist-vector networks. Mach. Study. 20, 273–297 (1995).

218.

Abraham, A. Synthetic neural networks. in Handbook of Measuring System Design (eds Sydenham, P. H. & Thorn, R.) (John Wiley & Sons, Chichester, UK, 2005).

219.

Fawagreh, Okay., Gaber, M. M. & Elyan, E. Random forests: from early developments to latest developments. Syst. Sci. Management Eng. 2, 602–609 (2014).

220.

LeCun, Y., Bengio, Y. & Hinton, G. Deep studying. Nature 521, 436–444 (2015).


Supply hyperlink
asubhan

wordpress autoblog

amazon autoblog

affiliate autoblog

wordpress web site

web site improvement

Show More

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Close