1. Brereton, R. G., Jansen, J. , Lopes, J., Marini, F., Pomerantsev, A., Rodionova, O., Roger, J., Walczak, B. & Tauler, R. Chemometrics in analytical chemistry—part II: modeling, validation, and applications. Anal. Bioanal. Chem. 410, 6691-6704 (2018).
2. Szabadváry, F. in History of analytical chemistry (Pergamon Press, Oxford [u.a.], 1966).
3. Brereton, R. G., Jansen, J. , Lopes, J., Marini, F., Pomerantsev, A., Rodionova, O., Roger, J., Walczak, B. & Tauler, R. Chemometrics in analytical chemistry—part I: history, experimental design and data analysis tools. Anal. Bioanal. Chem. 409, 5891-5899 (2017).
4. Student. Errors of Routine Analysis. Biometrika 19, 151-164 (1927).
5. Wernimont, G. Statistics applied to analysis. Anal. Chem. 21, 115-120 (1949).
6. Wallace, R. M. ANALYSIS OF ABSORPTION SPECTRA OF MULTICOMPONENT SYSTEMS1. J. Phys. Chem. 64, 899-901 (1960).
7. Weber, G. Enumeration of components in complex systems by fluorescence spectrophotometry. Nature 190, 27-29 (1961).
8. Fisher, R. A. in Statistical methods for research workers (Oliver & Boyd, Edinburgh [usw.], 1925).
9. Peason, K. On lines and planes of closest fit to systems of point in space. Philosophical Magazine 2, 559-572 (1901).
10. Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417 (1933).
11. Spearman, C. The Proof and Measurement of Association between Two Things. The American Journal of Psychology 15, 72-101 (1904).
12. Spearman, C. General Intelligence, Objectively Determined and Measured. American Journal of Psychology 15, 201-293 (1904).
13. Vincent, D. F. The origin and development of factor analysis. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2, 107-117 (1953).
14. Fisher, R. A. The use of multiple measurements in taxonomic problems. Annals of eugenics 7, 179-188 (1936).
15. Malinowski, E. R., Weiner, P. H. & Levinstone, A. R. Factor analysis of solvent shifts in proton magnetic resonance. J. Phys. Chem. 74, 4537-4542 (1970).
16. Jurs, P. C., Kowalski, B. R., Isnehour, T. L. & Reilley, C. N. Investigation of combined patterns from diverse analytical data using computerized learning machines. Anal. Chem. 41, 1949-1953 (1969).
17. Jurs, P. C. & Isenhour, T. L. in Chemical applications of pattern recognition (Wiley, 1975).
18. Massart, D. L., Janssens, C., Kaufman, L. & Smits, R. Application of the theory of graphs to the optimization of chromatographic separation schemes for multicomponent samples. Anal. Chem. 44, 2390-2393 (1972).
19. Brereton, R. G. A short history of chemometrics: a personal view. J. Chemometrics 28, 749-760 (2014).
20. Wold, S. Spline functions, a new tool in data-analysis. Kemisk Tidskrift 84, 34-37 (1972).
21. Kowalski, B., Brown, S. & Vandeginste, B. Editorial. J. Chemometrics 1, 1-2 (1987).
22. Muro, C. K., Doty, K. C., Bueno, J., Halámková, L. & Lednev, I. K. Vibrational Spectroscopy: Recent Developments to Revolutionize Forensic Science. Anal. Chem. 87, 306-327 (2015).
23. Kumar, R. & Sharma, V. Chemometrics in forensic science. TrAC Trends in Analytical Chemistry 105, 191-201 (2018).
24. National Research Council. in Strengthening forensic science in the United States: a path forward (National Academies Press, 2009).
25. Bovens, M., Ahrens, B., Alberink, I., Nordgaard, A., Salonen, T. & Huhtala, S. Chemometrics in forensic chemistry—Part I: Implications to the forensic workflow. Forensic Sci. Int. 301, 82-90 (2019).
26. Li, R., Norman, S. & Schober, J. in Forensic biology (CRC Press, 2015).
27. Virkler, K. & Lednev, I. K. Analysis of body fluids for forensic purposes: from laboratory testing to non-destructive rapid confirmatory identification at a crime scene. Forensic Sci. Int. 188, 1-17 (2009).
28. Muro, C. K., Doty, K. C., de Souza Fernandes, L. & Lednev, I. K. Forensic body fluid identification and differentiation by Raman spectroscopy. Forensic Chemistry 1, 31-38 (2016).
29. Barni, F., Lewis, S. W., Berti, A., Miskelly, G. M. & Lago, G. Forensic application of the luminol reaction as a presumptive test for latent blood detection. 72, 896-913 (2007).
30. Hochmeister, M. N., Budowle, B., Sparkes, R., Rudin, O., Gehrig, C., Thali, M., Schmidt, L., Cordier, A. & Dirnhofer, R. Validation studies of an immunochromatographic 1-step test for the forensic identification of human blood. J. Forensic Sci. 44, 597-602 (1999).
31. Turrina, S., Filippini, G., Atzei, R., Zaglia, E. & de Leo, D. Validation studies of rapid stain identification-blood (RSID-blood) kit in forensic caseworks. Forensic Science International: Genetics Supplement Series 1, 74-75 (2008).
32. Raju, P. S. & Iyengar, N. K. Acid phosphatase reaction as a specific test for the identification of seminal stains. J.Crim.L.Criminology & Police Sci. 55, 522 (1964).
33. Allery, J., Telmon, N., Mieusset, R., Blanc, A. & Rougé, D. Cytological detection of spermatozoa: comparison of three staining methods. J. Forensic Sci. 46, 349-351 (2001).
34. Miller, K. W., Old, J., Fischer, B. R., Schweers, B., Stipinaite, S. & Reich, K. Developmental Validation of the SPERM HY‐LITERTM Kit for the Identification of Human Spermatozoa in Forensic Samples. J. Forensic Sci. 56, 853-865 (2011).
35. Hochmeister, M. N., Budowle, B., Rudin, O., Gehrig, C., Borer, U., Thali, M. & Dirnhofer, R. Evaluation of prostate-specific antigen (PSA) membrane test assays for the forensic identification of seminal fluid. J. Forensic Sci. 44, 1057-1060 (1999).
36. Old, J., Schweers, B. A., Boonlayangoor, P. W., Fischer, B., Miller, K. W. P. & Reich, K. Developmental Validation of RSID™-Semen: A Lateral Flow Immunochromatographic Strip Test for the Forensic Detection of Human Semen. J. Forensic Sci. 57, 489-499 (2012).
37. Ceska, M., Hultman, E. & Ingelman, B. A new method for determination of α-amylase. Experientia 25, 555-556 (1969).
38. Willott, G. M. An Improved Test for the Detection of Salivary Amylase in Stains. J. Forensic Sci. Soc. 14, 341-344 (1974).
39. Old, J. B., Schweers, B. A., Boonlayangoor, P. W. & Reich, K. A. Developmental Validation of RSID™‐Saliva: A Lateral Flow Immunochromatographic Strip Test for the Forensic Detection of Saliva. J. Forensic Sci. 54, 866-873 (2009).
40. Quarino, L., Dang, Q., Hartmann, J. & Moynihan, N. An ELISA method for the identification of salivary amylase. J. Forensic Sci. 50, JFS2004417-4 (2005).
41. Zapata, F., Fernández de la Ossa, M & García-Ruiz, C. Emerging spectrometric techniques for the forensic analysis of body fluids. Trends in Analytical Chemistry 64, 53-63 (2015).
42. Virkler, K. & Lednev, I. K. Raman spectroscopy offers great potential for the nondestructive confirmatory identification of body fluids. Forensic Sci. Int. 181, e1-e5 (2008).
43. Elkins, K. M. Rapid Presumptive “Fingerprinting” of Body Fluids and Materials by ATR FT‐IR Spectroscopy. J. Forensic Sci. 56, 1580-1587 (2011).
44. Sikirzhytski, V., Virkler, K. & Lednev, I. K. Discriminant analysis of Raman spectra for body fluid identification for forensic purposes. Sensors 10, 2869-2884 (2010).
45. Orphanou, C. The detection and discrimination of human body fluids using ATR FT-IR spectroscopy. Forensic Sci. Int. 252, e10-e16 (2015).
46. Virkler, K. & Lednev, I. K. Raman spectroscopic signature of blood and its potential application to forensic body fluid identification. Anal. Bioanal. Chem. 396, 525-534 (2010).
47. Virkler, K. & Lednev, I. K. Forensic body fluid identification: the Raman spectroscopic signature of saliva. Analyst 135, 512-517 (2010).
48. Virkler, K. & Lednev, I. K. Raman spectroscopic signature of semen and its potential application to forensic body fluid identification. Forensic Sci. Int. 193, 56-62 (2009).
49. Sikirzhytski, V., Sikirzhytskaya, A. & Lednev, I. K. Multidimensional Raman spectroscopic signature of sweat and its potential application to forensic body fluid identification. Anal. Chim. Acta 718, 78-83 (2012).
50. Sikirzhytskaya, A., Sikirzhytski, V. & Lednev, I. K. Raman spectroscopic signature of vaginal fluid and its potential application in forensic body fluid identification. Forensic Sci. Int. 216, 44- 48 (2012).
51. Sikirzhytskaya, A., Sikirzhytski, V. & Lednev, I. K. Raman spectroscopy coupled with advanced statistics for differentiating menstrual and peripheral blood. Journal of biophotonics 7, 59-67 (2014).
52. McLaughlin, G., Doty, K. C. & Lednev, I. K. Raman spectroscopy of blood for species identification. Anal. Chem. 86, 11628-11633 (2014).
53. Mistek, E., Halámková, L., Doty, K. C., Muro, C. K. & Lednev, I. K. Race differentiation by Raman spectroscopy of a bloodstain for forensic purposes. Anal. Chem. 88, 7453-7456 (2016).
54. Sikirzhytskaya, A., Sikirzhytski, V. & Lednev, I. K. Determining gender by Raman spectroscopy of a bloodstain. Anal. Chem. 89, 1486-1492 (2017).
55. Muro, C. K. & Lednev, I. K. Race differentiation based on Raman spectroscopy of semen traces for forensic purposes. Anal. Chem. 89, 4344-4348 (2017).
56. Muro, C. K., de Souza Fernandes, L. & Lednev, I. K. Sex determination based on Raman spectroscopy of saliva traces for forensic purposes. Anal. Chem. 88, 12489-12493 (2016).
57. Mistek, E. & Lednev, I. K. Identification of species' blood by attenuated total reflection spectroscopy. Anal. Bioanal. Chem. 407, 7435 (2015).
58. Zapata, F., de la Ossa, Ma Ángeles Fernández & García-Ruiz, C. Differentiation of body fluid stains on fabrics using external reflection Fourier transform infrared spectroscopy (FT-IR) and chemometrics. Appl. Spectrosc. 70, 654-665 (2016).
59. Edelman, G., Manti, V., van Ruth, S. M., van Leeuwen, T. & Aalders, M. Identification and age estimation of blood stains on colored backgrounds by near infrared spectroscopy. Forensic Sci. Int. 220, 239-244 (2012).
60. Edelman, G. J., Roos, M., Bolck, A. & Aalders, M. C. Practical implementation of blood stain age estimation using spectroscopy. IEEE Journal of Selected Topics in Quantum Electronics 22, 415-421 (2016).
61. Kourti, T. & MacGregor, J. F. Process analysis, monitoring and diagnosis, using multivariate projection methods. Chemometrics Intellig. Lab. Syst. 28, 3-21 (1995).
62. Joe Qin, S. Statistical process monitoring: basics and beyond. J. Chemometrics 17, 480-502 (2003).
63. Brereton, R. G. in Chemometrics for pattern recognition (John Wiley & Sons, 2009).
64. Prinz, M., Grellner, W. & Schmitt, C. DNA typing of urine samples following several years of storage. Int. J. Legal Med. 106, 75-79 (1993).
65. Linfert, D. R., Wu, A. H. & Tsongalis, G. J. The effect of pathologic substances and adulterants on the DNA typing of urine. J. Forensic Sci. 43, 1041-1045 (1998).
66. Castella, V., Dimo-Simonin, N., Brandt-Casadevall, C., Robinson, N., Saugy, M., Taroni, F. & Mangin, P. Forensic identification of urine samples: a comparison between nuclear and mitochondrial DNA markers. Int. J. Legal Med. 120, 67-72 (2006).
67. Niazi, A. & Leardi, R. Genetic algorithms in chemometrics. J. Chemometrics 26, 345-351 (2012).
68. Bremmer, R. H., de Bruin, K. G., van Gemert, Martin J. C., van Leeuwen, T. G. & Aalders, M. C. G. Forensic quest for age determination of bloodstains. Forensic Sci. Int. 216, 1-11 (2012).
69. Horecker, B. L. The absorption spectra of hemoglobin and its derivatives in the visible and near infra-red regions. J. Biol. Chem. 148, 173-183 (1943).
70. Wood, B. R., Tait, B. & McNaughton, D. Micro-Raman characterisation of the R to T state transition of haemoglobin within a single living erythrocyte. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research 1539, 58-70 (2001).
71. Sugawara, Y., Kadono, E., Suzuki, A., Yukuta, Y., Shibasaki, Y., Nishimura, N., Kameyama, Y., Hirota, M., Ishida, C. & Higuchi, N. Hemichrome formation observed in human haemoglobin A under various buffer conditions. Acta Physiol. Scand. 179, 49-59 (2003).
72. Hu, S., Smith, K. M. & Spiro, T. G. Assignment of protoheme resonance Raman spectrum by heme labeling in myoglobin. J. Am. Chem. Soc. 118, 12638-12646 (1996).
73. Asghari‐Khiavi, M., Mechler, A., Bambery, K. R., McNaughton, D. & Wood, B. R. A resonance Raman spectroscopic investigation into the effects of fixation and dehydration on heme environment of hemoglobin. J. Raman Spectrosc. 40, 1668-1674 (2009).
74. Lin, H., Zhang, Y., Wang, Q., Li, B., Huang, P. & Wang, Z. Estimation of the age of human bloodstains under the simulated indoor and outdoor crime scene conditions by ATR-FTIR spectroscopy. Scientific reports 7, 13254 (2017).
75. Li, B., Beveridge, P., O'hare, W. T. & Islam, M. The age estimation of blood stains up to 30 days old using visible wavelength hyperspectral image analysis and linear discriminant analysis. Science & Justice 53, 270-277 (2013).
76. Doty, K. C., McLaughlin, G. & Lednev, I. K. A Raman “spectroscopic clock” for bloodstain age determination: the first week after deposition. Anal. Bioanal. Chem. 408, 3993-4001 (2016).
77. Doty, K. C., Muro, C. K. & Lednev, I. K. Predicting the time of the crime: Bloodstain aging estimation for up to two years. Forensic Chemistry 5, 1-7 (2017).
78. McLaughlin, G., Sikirzhytski, V. & Lednev, I. K. Circumventing substrate interference in the Raman spectroscopic identification of blood stains. Forensic Sci. Int. 231, 157-166 (2013).
79. McLaughlin, G. & Lednev, I. K. In situ identification of semen stains on common substrates via Raman spectroscopy. J. Forensic Sci. 60, 595-604 (2015).
80. Long, D. A. in The Raman Effect: A Unified Treatment of the Theory of Raman Scattering by Molecules (Wiley, 2002).
81. 濵口 宏夫, 岩田 耕 一. in ラマン分光法 (講談社, 2015).
82. Heij, C., de Boer, P., Franses, P. H., Kloek, T. & van Dijk, H. K. in Econometric methods with applications in business and economics (Oxford University Press, 2004).
83. Rice, J. R. Experiments on gram-schmidt orthogonalization. Mathematics of Computation 20, 325-328 (1966).
84. 長谷川 健. in スペクトル定量分析 (講談社, 2005).
85. Jacobi, C. G. J. Ueber ein leichtes Verfahren, die in der Theorie der Sacularstorungen vorkommenden Gleichungen numerisch aufzulosen. J.reine angew.Math., 51-94 (1846).
86. Wold, H. Estimation of principal components and related models by iterative least squares. Multivariate analysis, 391-420 (1966).
87. Wold, H. in Quantitative sociology 307-357 (Elsevier, 1975).
88. Golub, G. & Kahan, W. Calculating the singular values and pseudo-inverse of a matrix. Journal of the Society for Industrial and Applied Mathematics, Series B: Numerical Analysis 2, 205-224 (1965).
89. Golub, G. H. & Reinsch, C. in Linear Algebra 134-151 (Springer, 1971).
90. Wold, S. in 40 Years of Chemometrics – From Bruce Kowalski to the Future 1-13 (American Chemical Society, 2015).
91. Johnson, R. A. & Wichern, D. W. in Applied multivariate statistical analysis (Prentice-Hall, Englewood Cliffs, NJ, 1998).
92. Dixon, S. J. & Brereton, R. G. Comparison of performance of five common classifiers represented as boundary methods: Euclidean Distance to Centroids, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization and Support Vector Machines, as dependent on data structure. 95, 1-17 (2009).
93. Frank, I. E. & Friedman, J. H. Classification: oldtimers and newcomers. J. Chemometrics 3, 463- 475 (1989).
94. Vapnik, V. N. in The nature of statistical learning theory (Springer, New York [u.a.], 2000).
95. Verhulst, P. Mathematical researches into the law of population growth increase. Nouveaux Mémoires de l’Académie Royale des Sciences et Belles-Lettres de Bruxelles 18, 1-42 (1845).
96. Jackson, J. E. in A user's guide to principal components (John Wiley & Sons, 2005).
97. Wold, S., Martens, H. & Wold, H. in Matrix pencils 286-293 (Springer, 1983).
98. Wold, S., Ruhe, A., Wold, H. & Dunn, I., WJ. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing 5, 735-743 (1984).
99. Barker, M. & Rayens, W. Partial least squares for discrimination. J. Chemometrics 17, 166-173 (2003).
100. Brereton, R. G. & Lloyd, G. R. Partial least squares discriminant analysis: taking the magic away. J. Chemometrics 28, 213-225 (2014).
101. Bishop, C. M. in Pattern recognition and machine learning (springer, 2006).
102. Vapnik, V. N. in Statistical learning theory (Wiley, 1998).
103. Osuna, E., Girosi, F. & Freund, R. Support Vector Machines: Training and Applications. Massachusetts Institute of Technology (1996).
104. Platt, J. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods—Support Vector Learning (pp. 185–208). AJ, MIT Press, Cambridge, MA (1999).
105. Karush, W. Minima of functions of several variables with inequalities as side constraints. M.Sc.Dissertation.Dept.of Mathematics, Univ.of Chicago (1939).
106. Kuhn, H. W. & Tucker, A. W. in Nonlinear Programming 481-492 (University of California Press, Berkeley, Calif., 1951).
107. Weston, J. &Watkins, C. Multi-class Support Vector Machines (D-Facto Publications, 1998).
108. Platt, J. C., Cristianini, N. & Shawe-Taylor, J. Large margin DAGs for multiclass classification (Advances in neural information processing systems, 2000).
109. Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J. & Williamson, R. C. Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443-1471 (2001).
110. Tax, D. M. &Duin, R. P. Data domain description using support vectors. (ESANN Ser. 99, 1999).
111. Vapnik, V. in The nature of statistical learning theory (Springer science & business media, 2013).
112. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection (Ijcai Ser. 14, Montreal, Canada, 1995).
113. Paraskevaidi, M., Morais, C. L. M., Lima, K. M. G., Snowden, J. S., Saxon, J. A., Richardson, A. M. T., Jones, M., Mann, D. M. A., Allsop, D., Martin-Hirsch P. L. & Martin, F. L. Differential diagnosis of Alzheimer’s disease using spectrochemical analysis of blood. Proceedings of the National Academy of Sciences 114, E7929-E7938 (2017).
114. Mordechai, S., Shufan, E., Katz, B. P. & Salman, A. Early diagnosis of Alzheimer's disease using infrared spectroscopy of isolated blood samples followed by multivariate analyses. Analyst 142, 1276-1284 (2017).
115. Khaustova, S., Shkurnikov, M., Tonevitsky, E., Artyushenko, V. & Tonevitsky, A. Noninvasive biochemical monitoring of physiological stress by Fourier transform infrared saliva spectroscopy. Analyst 135, 3183-3192 (2010).
116. Scott, D. A., Renaud, D. E., Krishnasamy, S., Meriç, P., Buduneli, N., Çetinkalp, S. & Liu, K. Diabetes-related molecular signatures in infrared spectra of human saliva. Diabetology & Metabolic Syndrome 2, 48 (2010).
117. Caetano Júnior, P. C., Strixino, J. F. & Raniero, L. Analysis of saliva by Fourier transform infrared spectroscopy for diagnosis of physiological stress in athletes. Research on Biomedical Engineering 31, 116-124 (2015).
118. Ward Jr, J. H. Hierarchical grouping to optimize an objective function. Journal of the American statistical association 58, 236-244 (1963).
119. Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of classification 31, 274-295 (2014).
120. Guidi, M. C., Mirri, C., Fratini, E., Licursi, V., Negri, R., Marcelli, A. & Amendola, R. In vivo skin leptin modulation after 14 MeV neutron irradiation: a molecular and FT-IR spectroscopic study. Anal. Bioanal. Chem. 404, 1317-1326 (2012).
121. Talari, A. C. S., Martinez, M. A. G., Movasaghi, Z., Rehman, S. & Rehman, I. U. Advances in Fourier transform infrared (FTIR) spectroscopy of biological tissues. Applied Spectroscopy Reviews 52, 456-506 (2017).
122. Zou, Y., Xia, P., Yang, F., Cao, F., Ma, K., Mi, Z., Huang, X., Cai, N., Jiang, B. & Zhao, X. Whole blood and semen identification using mid-infrared and Raman spectrum analysis for forensic applications. Analytical Methods 8, 3763-3767 (2016).
123. Oliver, K. V., Marechal, A. & Rich, P. R. Effects of the hydration state on the mid-infrared spectra of urea and creatinine in relation to urine analyses. Appl. Spectrosc. 70, 983-994 (2016).
124. Grdadolnik, J. & Maréchal, Y. Urea and urea–water solutions—an infrared study. J. Mol. Struct. 615, 177-189 (2002).
125. SDBSWeb. http://sdbs.db.aist.go.jp. (accessed December 2019)
126. Rousseau, D. L. " Polywater" and Sweat: Similarities between the Infrared Spectra. Science 171, 170-172 (1971).
127. Takamura, A., Watanabe, K., Akutsu, T. & Ozawa, T. Soft and robust identification of body fluid using Fourier transform infrared spectroscopy and chemometric strategies for forensic analysis. Scientific reports 8, 8459 (2018).
128. Doty, K. C. & Lednev, I. K. Differentiating donor age groups based on Raman spectroscopy of bloodstains for forensic purposes. ACS Central Science 4, 862-867 (2018).
129. Rose, C., Parker, A., Jefferson, B. & Cartmell, E. The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit. Rev. Environ. Sci. Technol. 45, 1827-1879 (2015).
130. Putnam, D. F. Composition and concentrative properties of human urine. CR-1802, NASA, Washington, DC (1971).
131. Altman, P. L. & Dittmer, D. S. Blood and Other Body Fluids, Federation of American Societies for Experimental Biology. Washington, DC (1961).
132. Shamim, W., Yousufuddin, M., Bakhai, A., Coats, A. J. & Honour, J. W. Gender differences in the urinary excretion rates of cortisol and androgen metabolites. Ann. Clin. Biochem. 37, 770-774 (2000).
133. Kim, Y. H., Kim, C. S., Park, S., Han, S. Y., Pyo, M. Y. & Yang, M. Gender differences in the levels of bisphenol A metabolites in urine. Biochem. Biophys. Res. Commun. 312, 441-448 (2003).
134. Guo, Z., Zhang, Y., Zou, L., Wang, D., Shao, C., Wang, Y., Sun, W. & Zhang, L. A proteomic analysis of individual and gender variations in normal human urine and cerebrospinal fluid using iTRAQ quantification. PLoS One 10, e0133270 (2015).
135. Merib, J., Spudeit, D. A., Corazza, G., Carasek, E. & Anderson, J. L. Magnetic ionic liquids as versatile extraction phases for the rapid determination of estrogens in human urine by dispersive liquid-liquid microextraction coupled with high-performance liquid chromatography-diode array detection. Anal. Bioanal. Chem. 410, 4689-4699 (2018).
136. Cho, H. W., Kim, S. B., Jeong, M. K., Park, Y., Ziegler, T. R. & Jones, D. P. Genetic algorithm- based feature selection in high-resolution NMR spectra. Expert Syst. Appl. 35, 967-975 (2008).
137. Bangalore, A. S., Shaffer, R. E., Small, G. W. & Arnold, M. A. Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. Anal. Chem. 68, 4200-4212 (1996).
138. Ullah, S., Groen, T. A., Schlerf, M., Skidmore, A. K., Nieuwenhuis, W. & Vaiphasa, C. Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5–14 µm) to discriminate vegetation species. Sensors 12, 8755-8769 (2012).
139. Duraipandian, S., Zheng, W., Ng, J., Low, J. J. H., Ilancheran, A. & Huang, Z. In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques. Analyst 136, 4328-4336 (2011).
140. Hajian-Tilaki, K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine 4, 627-635 (2013).
141. Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36 (1982).
142. Savitzky, A. & Golay, M. J. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627-1639 (1964).
143. Steiner, G., Bartels, T., Stelling, A., Krautwald-Junghanns, M. E., Fuhrmann, H., Sablinskas, V. & Koch, E. Gender determination of fertilized unincubated chicken eggs by infrared spectroscopic imaging. Anal. Bioanal. Chem. 400, 2775-2782 (2011).
144. Barr, D. B., Wilder, L. C., Caudill, S. P., Gonzalez, A. J., Needham, L. L. & Pirkle, J. L. Urinary Creatinine Concentrations in the U.S. Population: Implications for Urinary Biologic Monitoring Measurements. Environmental Health Perspectives 113, 192-200 (2005).
145. Takamura, A., Halamkova, L., Ozawa, T. & Lednev, I. K. Phenotype Profiling for Forensic Purposes: Determining Donor Sex based on Fourier Transform Infrared Spectroscopy of Urine Traces. Anal. Chem. 9, 6288-6295 (2019).
146. Miyaishi S, Moriya F, Yamamoto Y, Kitao T, Ishizu H. Discrimination between postmortem and antemortem blood by dot-ELISA for human myoglobin. Japanese Journal of Legal Medicine 48, 433-438 (1994).
147. Sugie, H., Nishikawa, T. & Funao, T. Quantitation of nucleotides, nucleosides and bases in antemortem and postmortem bloodstains by high-performance liquid chromatography. Forensic Sci. Int. 71, 123-130 (1995).
148. Sakurada, K., Sakai, I., Sekiguchi, K., Shiraishi, T., Ikegaya, H. & Yoshida, K. Usefulness of a latex agglutination assay for FDP D-dimer to demonstrate the presence of postmortem blood. Int. J. Legal Med. 119, 167-171 (2005).
149. Belsey, S. L. & Flanagan, R. J. Postmortem biochemistry: current applications. Journal of forensic and legal medicine 41, 49-57 (2016).
150. Petibois, C., Melin, A., Perromat, A., Cazorla, G. & Déléris, G. Glucose and lactate concentration determination on single microsamples by Fourier-transform infrared spectroscopy. J. Lab. Clin. Med. 135, 210-215 (2000).
151. Petibois, C., Gionnet, K., Gonçalves, M., Perromat, A., Moenner, M. & Déléris, G. Analytical performances of FT-IR spectrometry and imaging for concentration measurements within biological fluids, cells, and tissues. Analyst 131, 640-647 (2006).
152. Donaldson, A. E. & Lamont, I. L. Biochemistry changes that occur after death: potential markers for determining post-mortem interval. PloS one 8, e82011 (2013).
153. Keltanen, T., Nenonen, T., Ketola, R. A., Ojanperä, I., Sajantila, A. & Lindroos, K. Post-mortem analysis of lactate concentration in diabetics and metformin poisonings. Int. J. Legal Med. 129, 1225-1231 (2015).
154. Zilg, B., Alkass, K., Berg, S. & Druid, H. Postmortem identification of hyperglycemia. Forensic Sci. Int. 185, 89-95 (2009).
155. Petibois, C., Rigalleau, V., Melin, A. M., Perromat, A., Cazorla, G., Gin, H. & Déléris, G. Determination of glucose in dried serum samples by Fourier-transform infrared spectroscopy. Clin. Chem. 45, 1530-1535 (1999).
156. Takamura, A., Watanabe, K., Akutsu, T., Ikegaya, H. & Ozawa, T. Spectral Mining for Discriminating Blood Origins in the Presence of Substrate Interference via Attenuated Total\ Reflection Fourier Transform Infrared Spectroscopy: Postmortem or Antemortem Blood? Anal. Chem. 89, 9797-9804 (2017).
157. Rachmilewitz, E. A., Peisach, J. & Blumberg, W. E. Studies on the stability of oxyhemoglobin A and its constituent chains and their derivatives. J. Biol. Chem. 246, 3356-3366 (1971).
158. Spiro, T. G. & Strekas, T. C. Resonance Raman spectra of heme proteins. Effects of oxidation and spin state. J. Am. Chem. Soc. 96, 338-345 (1974).
159. Miki, T., Kai, A. & Ikeya, M. Electron spin resonance of bloodstains and its application to the estimation of time after bleeding. Forensic Sci. Int. 35, 149-158 (1987).
160. Wood, B. R., Caspers, P., Puppels, G. J., Pandiancherri, S. & McNaughton, D. Resonance Raman spectroscopy of red blood cells using near-infrared laser excitation. Anal. Bioanal. Chem. 387, 1691-1703 (2007).
161. Wesełucha-Birczyńska, A., Kozicki, M., Czepiel, J., Łabanowska, M., Nowak, P., Kowalczyk, G., Kurdziel, M., Birczyńska, M., Biesiada, G. & Mach, T. Human erythrocytes analyzed by generalized 2D Raman correlation spectroscopy. J. Mol. Struct. 1069, 305-312 (2014).
162. de Juan, A. & Tauler, R. Multivariate curve resolution (MCR) from 2000: progress in concepts and applications. Crit. Rev. Anal. Chem. 36, 163-176 (2006).
163. Malik, A., de Juan, A. & Tauler, R. in 40 Years of Chemometrics – From Bruce Kowalski to the Future 95-128 (American Chemical Society, 2015).
164. Lawson, C. L. & Hanson, R. J. in Solving least squares problems (Prentice-Hall, Englewood Cliffs, NJ [u.a.], 1974).
165. Lawson, C. L. & Hanson, R. J. in Solving least squares problems (Soc. for Industrial and Applied Math, Philadelphia, 1995).
166. Jaumot, J., Gargallo, R., de Juan, A. & Tauler, R. A graphical user-friendly interface for MCR- ALS: a new tool for multivariate curve resolution in MATLAB. Chemometrics Intellig. Lab. Syst. 76, 101-110 (2005).
167. Maeder, M. & Zuberbuehler, A. D. Nonlinear least-squares fitting of multivariate absorption data. Anal. Chem. 62, 2220-2224 (1990).
168. van Benthem, M. H., Keenan, M. R. & Haaland, D. M. Application of equality constraints on variables during alternating least squares procedures. J. Chemometrics 16, 613-622 (2002).
169. Gemperline, P. J. & Cash, E. Advantages of soft versus hard constraints in self-modeling curve resolution problems. Alternating least squares with penalty functions. Anal. Chem. 75, 4236-4243 (2003).
170. Richards, S., Miller, R. & Gemperline, P. Advantages of Soft versus Hard Constraints in Self- Modeling Curve Resolution Problems. Penalty Alternating Least Squares (P-ALS) Extension to Multi-way Problems. Appl. Spectrosc. 62, 197-206 (2008).
171. Abe, M., Kitagawa, T. & Kyogoku, Y. Resonance Raman spectra of octaethylporphyrinato‐Ni (II) and meso‐deuterated and 15N substituted derivatives. II. A normal coordinate analysis. J. Chem. Phys. 69, 4526-4534 (1978).
172. Lemler, P., Premasiri, W. R., DelMonaco, A. & Ziegler, L. D. NIR Raman spectra of whole human blood: effects of laser-induced and in vitro hemoglobin denaturation. Anal. Bioanal. Chem. 406, 193-200 (2014).
173. Premasiri, W. R., Lee, J. C. & Ziegler, L. D. Surface-enhanced Raman scattering of whole human blood, blood plasma, and red blood cells: cellular processes and bioanalytical sensing. The Journal of Physical Chemistry B 116, 9376-9386 (2012).
174. Wood, B. R., Hammer, L., Davis, L. & McNaughton, D. Raman microspectroscopy and imaging provides insights into heme aggregation and denaturation within human erythrocytes. J. Biomed. Opt. 10, 014005 (2005).
175. Rifkind, J. M., Abugo, O., Levy, A. & Heim, J. in Methods in enzymology 449-480 (Elsevier, 1994).
176. Macdonald, V. W. in Methods in enzymology 480-490 (Elsevier, 1994).
177. Riccio, A., Vitagliano, L., di Prisco, G., Zagari, A. & Mazzarella, L. The crystal structure of a tetrameric hemoglobin in a partial hemichrome state. Proceedings of the National Academy of Sciences 99, 9801-9806 (2002).
178. Wever, R., Oudega, B. & van Gelder, B. F. Generation of superoxide radicals during the autoxidation of mammalian oxyhemoglobin. Biochimica et Biophysica Acta (BBA)-Enzymology 302, 475-478 (1973).
179. Tsuruga, M., Matsuoka, A., Hachimori, A., Sugawara, Y. & Shikama, K. The Molecular Mechanism of Autoxidation for Human Oxyhemoglobin Tilting of the Distal Histidine Causes Nonequivalent Oxidation in the Β Chain. J. Biol. Chem. 273, 8607-8615 (1998).
180. Gotoh, T. & Shikama, K. Generation of the superoxide radical during autoxidation of oxymyoglobin. The Journal of Biochemistry 80, 397-399 (1976).
181. Wallace, W. J., Maxwell, J. C. & Caughey, W. S. A role for chloride in the autoxidation of hemoglobin under conditions similar to those in erythrocytes. FEBS Lett. 43, 33-36 (1974).
182. Atef, M. & El-Hefnawy, A. Conformational stability against auto-oxidation for mice and human oxyhemoglobins. Romanian J.Biophys 19, 187-198 (2009).
183. de Luca, M., Tauler, R., Ioele, G. & Ragno, G. Study of photodegradation kinetics of melatonin by multivariate curve resolution (MCR) with estimation of feasible band boundaries. Drug testing and analysis 5, 96-102 (2013).
184. Díaz-Cruz, J. M., Agulló, J., Díaz-Cruz, M. S., Ariño, C., Esteban, M. & Tauler, R. Implementation of a chemical equilibrium constraint in the multivariate curve resolution of voltammograms from systems with successive metal complexes. Analyst 126, 371-377 (2001).
185. de Juan, A., Maeder, M., Martınez, M. & Tauler, R. Combining hard-and soft-modelling to solve kinetic problems. Chemometrics Intellig. Lab. Syst. 54, 123-141 (2000).
186. de Luca, M., Mas, S., Ioele, G., Oliverio, F., Ragno, G & Tauler, R. Kinetic studies of nitrofurazone photodegradation by multivariate curve resolution applied to UV-spectral data. Int. J. Pharm. 386, 99-107 (2010).
187. Mas, S., de Juan, A., Lacorte, S. & Tauler, R. Photodegradation study of decabromodiphenyl ether by UV spectrophotometry and a hybrid hard-and soft-modelling approach. Anal. Chim. Acta 618, 18-28 (2008).
188. de Oliveira, R. R., de Lima, Kássio Michell Gomes, Tauler, R. & de Juan, A. Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UV–visible spectroscopic data. Talanta 125, 233-241 (2014).
189. de Oliveira Neves, Ana Carolina, Tauler, R. & de Lima, Kássio Michell Gomes. Area correlation constraint for the MCR− ALS quantification of cholesterol using EEM fluorescence data: A new approach. Anal. Chim. Acta 937, 21-28 (2016).
190. Winterbourn, C. C. & Carrell, R. W. Studies of hemoglobin denaturation and Heinz body formation in the unstable hemoglobins. J. Clin. Invest. 54, 678-689 (1974).
191. Bremmer, R. H., de Bruin, D. M., de Joode, M., Buma, W. J., van Leeuwen, T. G. & Aalders, M. C. G. Biphasic oxidation of oxy-hemoglobin in bloodstains. PloS one 6, e21845 (2011).
192. Takamura, A., Watanabe, D., Shimada, R. & Ozawa, T. Comprehensive modeling of bloodstain aging by multivariate Raman spectral resolution with kinetics. Communications Chemistry 2, 115 (2019).
193. 安藤 正浩, 濵口 宏夫. 装置と技術 仮想添加多変量解析による複雑系の定量分光分析. 分光研究 64, 280-284 (2015).
194. Ando, M., Lednev, I. K. & Hamaguchi, H. in Frontiers and Advances in Molecular Spectroscopy 369-378 (Elsevier, 2018).
195. Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 267-288 (1996).
196. Edwards, H., Farwell, D. W. & Williams, A. C. FT-Raman spectrum of cotton: a polymeric biomolecular analysis. Spectrochim. Acta, Pt. A: Mol. Spectrosc. 50, 807-811 (1994).
197. Cohen, J. A coefficient of agreement for nominal scales. Educational and psychological measurement, 20, 37-46 (1960).
198. Shibayama, S., Kaneko, H. & Funatsu, K. A novel calibration-minimum method for prediction of mole fraction in non-ideal mixture. AAPS PharmSciTech 18, 595-604 (2017).
199. Shibayama, S., Kaneko, H. & Funatsu, K. Formulation of the excess absorption in infrared spectra by numerical decomposition for effective process monitoring. Comput. Chem. Eng. 113, 86-97 (2018).
200. McLaughlin, G., Fikiet, M. A., Ando, M., Hamaguchi, H. & Lednev, I. K. Universal detection of body fluid traces in situ with Raman hyperspectroscopy for forensic purposes: Evaluation of a new detection algorithm (HAMAND) using semen samples. J. Raman Spectrosc. 50, 1147-1153 (2019).
201. Harold Hotelling. The Generalization of Student's Ratio. The Annals of Mathematical Statistics 2, 360-378 (1931).
202. Sikirzhytski, V., Sikirzhytskaya, A. & Lednev, I. K. Advanced statistical analysis of Raman spectroscopic data for the identification of body fluid traces: semen and blood mixtures. Forensic Sci. Int. 222, 259-265 (2012).
203. Smith, S. W. The scientist and engineer's guide to digital signal processing. (1997).
204. Buades, A., Coll, B. & Morel, J. A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4, 490-530 (2005).
205. Al‐Hetlani, E., Halamkova, L., Amin, M. O. & Lednev, I. K. Differentiating Smokers and Nonsmokers Based on Raman Spectroscopy of Oral Fluid and Advanced Statistics for Forensic Applications. Journal of biophotonics, e201960123 (2019).