This article is the second in a series that lists four key explanatory or tutorial references for each of the 29 chemometric methods previously described. The references selected are particularly helpful to explain the use of each method for spectroscopic data. Also included are common computer software platforms used for chemometrics.
In the August 2020 and June 2021 “Chemometrics in Spectroscopy” columns, we described 29 key common chemometric methods used by spectroscopists, and selected corresponding literature reference numbers using a set of tables. The first article introduced the chemometric methods. The second article introduced a two-part series with expanded references, included the following: Part I includes Tables I and II with references, and Part II includes Tables III through V with corresponding selected literature references. The five tables mentioned and the chemometric methods they cover are listed below:
In this article, a set of four references are given in Tables III and IV for each of 15 chemometric methods (numbers 15–29), and five references are given for each computer software platform introduced in Table V (method number 30). This article lists the corresponding references from 69 to 148. The method numbers and references for the two-part series are sequential from methods 1–30, which correspond to references 1–148, respectively. Thus, the two-part reference article series can be viewed as a single body of work.
For Part II of the series, Table III shows the variety of references for quantitative (calibration) methods used to take raw or preprocessed data and compute predictive calibration models for quantitative determination of physical or chemical parameters in a data set. Table IV demonstrates the references for the qualitative (classification) methods used to take raw or preprocessed data and compute predictive calibration models for classification of different groups or types of samples or of physical or chemical parameters in a data set. Table V includes references for using the most common programming languages or platforms for general data interpretation using chemometrics or other statistical analysis methods.
69. T. Næs and T. Isaksson, Appl. Spectrosc. 46(1), 34–43 (1992).
70. B. Igne, J.B. Reeves, G. McCarty, W.D. Hively, E. Lund, and C.R. Hurburgh, J. Near Infrared Spectrosc. 18(3), 167–176 (2010).
71. T. Naes, T. Isaksson, and B. Kowalski, Anal. Chem. 62(7), 664–673 (1990).
72. H. Nakagawa, T. Tajima, M. Kano, S. Kim, S. Hasebe, T. Suzuki, and H. Nakagami, Anal. Chem. 84(8), 3820–3826 (2012).
73. D. Broadhurst, R. Goodacre, A. Jones, J.J. Rowland, and D.B. Kell, Anal. Chim. Acta 348(1–3), 71–86 (1997).
74. M.J. Lerma-García, E.F. Simó-Alfonso, A. Bendini, and L. Cerretani, Food Chem. 124(2), 679–684 (2011).
75. A. O’Neil, R. Jee, and A. Moffat, Analyst 123(11), 2297–2302 (1998).
76. C. Schierle and M. Otto, J. Anal. Chem. 344(4–5), 190–194 (1992).
77. D. Svozil, V. Kvasnicka, and J. Pospichal, Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997).
78. P. Bhandare, Y. Mendelson, R.A. Peura, G. Janatsch, J.D. Kruse-Jarres, R. Marbach, and H.M. Heise, Appl. Spectrosc. 47(8), 1214–1221 (1993).
79. R. Goodacre, E.M. Timmins, P.J. Rooney, J.J. Rowland, and D.B. Kell, FEMS Microbiol. Lett. 140(2– 3), 233–239 (1996).
80. P.J. Gemperline, J.R. Long, and V.G. Gregoriou, Anal. Chem. 63(20), 2313–2323 (1991).
81. P. Geladi and B.R. Kowalski, Anal. Chim. Acta 185, 1–17 (1986).
82. M.P. Fuller, G.L. Ritter, and C.S. Draper, Appl. Spectrosc. 42(2), 217–227 (1988).
83. E. Vigneau, D. Bertrand, and E.M. Qannari, Chemometr. Intell. Lab. Syst. 35(2), 231–238 (1996).
84. F. Lindgren, P. Geladi, and S. Wold, J. Chemom. 8(6), 377–389 (1994).
85. C.W. Chang, D.A. Laird, M.J. Mausbach, and C.R. Hurburgh, Soil Sci. Soc. Am. J. 65(2), 480–490 (2001).
86. O. Faix and J.H. Böttcher, Holzforschung (HF) 47(1), 45–49 (1993).
87. S.K. Schreyer, M. Bidinosti, and P.D. Wentzell, Appl. Spectrosc. 56(6), 789–796 (2002).
88. P.J. Gemperline and A. Salt, J. Chemom. 3(2), 343–357 (1989).
89. A. Natekin and A. Knoll, Neurorobot. 7, 21 (2013).
90. S. Nawar and A.M. Mouazen, Sensors 17(10), 2428 (2017).
91. D.J. Brown, K.D. Shepherd, M.G. Walsh, M.D. Mays, and T.G. Reinsch, Geoderma 132(3–4), 273–290 (2006).
92. K.D. Shepherd, C.A. Palm, C.N. Gachengo, and B. Vanlauwe, Agron. J. 95(5), 1314–1322 (2003).
93. J.R. Quinlan, Int. J. Man-Mach. Stud. 27(3), 221–234 (1987).
94. A.J. Myles, R.N. Feudale, Y. Liu, N.A. Woody, and S.D. Brown, J. Chemom. 18(6), 275–285 (2004).
95. S. Kucheryavskiy, J. Anal. Test. 2(3), 274–289 (2018).
96. D. Fernandes Andrade, E.R. Pereira-Filho, and D. Amarasiriwardena, Appl. Spectrosc. Rev. 56(2), 1–17 (2021).
97. I. Rish, “An empirical study of the naive Bayes classifier,” in IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence 3(22), 41–46 (2001).
98. J.D. Rennie, L. Shih, J. Teevan, and D.R. Karger, “Tackling the poor assumptions of naive bayes text classifiers,” in Proceedings of the 20th International Conference on Machine Learning (ICML-03) 3, 616–623 (2003).
99. E. Tsolaki, P. Svolos, E. Kousi, E. Kapsalaki, K. Fountas, K. Theodorou, and I. Tsougos, Int. J. Comput. Assist. Radiol. Surg. 8(5), 751–761 (2013).
100. S. Sankaran and R. Ehsani, Trans. ASABE 55(1), 313–320 (2012).
101. J. Merlo, B. Chaix, H. Ohlsson, A. Beckman, K. Johnell, P. Hjerpe, L. Råstam, and K. Larsen, J. Epidemiol. Community Health 60(4), 290–297 (2006).
102. Y. So, “A tutorial on logistic regression,” SAS White Papers (1995). https://support.sas.com/rnd/app/stat/papers/logistic.pdf
103. T.C.B. Schut, P.J. Caspers, G.J. Puppels, A. Nijssen, F. Heule, M.H. Neumann, and D.P. Hayes, J. Invest. Dermatol. 119(1), 64–69 (2002).
104. M.G. Sowa, L. Leonardi, J.R. Payette, K.M. Cross, M. Gomez, and J. Fish, J. Biomed. Opt. 11(5), 054002 (2006).
105. H.L. Mark and D. Tunnell, Anal. Chem. 57(7), 1449–1456 (1985).
106. R.G. Whitfield, M.E. Gerger, and R.L. Sharp, Appl. Spectrosc. 41(7), 1204–1213 (1987).
107. R. De Maesschalck, D. Jouan-Rimbaud, and D.L. Massart, Chemometr. Intell. Lab. Syst. 50(1), 1–18 (2000).
108. G. Downey, Analyst 119(11), 2367–2375 (1994).
109. U. Von Luxburg, Stat Comput. 17(4), 395–416 (2007).
110. B. Li, Y. Wei, H. Duan, L. Xi, and X. Wu, Vib. Spectrosc. 62, 17–22 (2012).
111. R.M. Balabin, R.Z. Safieva, and E.I. Lomakina, Anal. Chim. Acta 671(1–2), 27–35 (2010).
112. V. Allen, J.H. Kalivas, and R.G. Rodriguez, Appl. Spectrosc. 53(6), 672–681 (1999).
113. G.P. Zhang, IEEE Trans. Syst. Man Cybern., Part C Appl. Rev. 30(4), 451–462 (2000).
114. W. Wu, B. Walczak, D.L. Massart, S. Heuerding, F. Erni, I.R. Last, and K.A. Prebble, Chemometr. Intell. Lab. Syst. 33(1), 35–46 (1996).
115. J.B. Sirven, B. Bousquet, L. Canioni, L. Sarger, S. Tellier, M. Potin-Gautier, and I. Le Hecho, Anal. Bioanal. Chem. 385(2), 256–262 (2006).
116. J. Acquarelli, T. van Laarhoven, J. Gerretzen, T.N. Tran, L.M. Buydens, and E. Marchiori, Anal. Chim. Acta 954, 22–31 (2017).
117. N.L. Afanador, A. Smolinska, T.N. Tran, and L. Blanchet, J. Chemom. 30(5), 232–241 (2016).
118. L. Sheng, T. Zhang, G. Niu, K. Wang, H. Tang, Y. Duan, and H. Li, J. Anal. At. Spectrom. 30(2), 453–458 (2015).
119. H. Chen, Z. Lin, H. Wu, L. Wang, T. Wu, and C. Tan, Spectrochim. Acta A 135, 185–191 (2015).
120. J. Bin, F.F. Ai, W. Fan, J.H. Zhou, Y.H. Yun, and Y.Z. Liang, RSC Advances 6(36), 30353–30361 (2016).
121. O.G. Meza-Márquez, T. Gallardo-Velázquez, and G. Osorio-Revilla, Meat Sci. 86(2), 511–519 (2010).
122. T. Gallardo-Velázquez, G. Osorio-Revilla, M. Zuñiga-de Loa, and Y. Rivera-Espinoza, Food Res. Int. 42(3), 313–318 (2009).
123. A.L. Pomerantsev and O.Y. Rodionova, J. Chemom. 28(6), 518–522 (2014).
124. E. Smidt, K. Meissl, M. Schwanninger, and P. Lechner, Waste Manage 28(10), 1699–1710 (2008).
125. J. Luts, F. Ojeda, R. Van de Plas, B. De Moor, S. Van Huffel, and J.A. Suykens, Anal. Chim. Acta 665(2), 129–145 (2010).
126. O. Devos, C. Ruckebusch, A. Durand, L. Duponchel, and J.P. Huvenne, Chemometr. Intell. Lab. Syst. 96(1), 27–33 (2009).
127. A. Borin, M.F. Ferrao, C. Mello, D.A. Maretto, and R.J. Poppi, Anal. Chim. Acta 579(1), 25–32 (2006).
128. R.M. Balabin, R.Z. Safieva, and E.I. Lomakina, Microchem. J. 98(1), 121–128 (2011).
129. A.L. Pomerantsev, Chemometrics in Excel (John Wiley & Sons, Hoboken, New Jersey, 2014).
130. J. Miller and J.C. Miller, Statistics and Chemometrics for Analytical Chemistry (Pearson Education, London, England, 2018).
131. H.J. Brightman, Data Analysis in Plain English: With Microsoft Excel (International Thomson Publishing, Boston, Massachusetts, 1998)
132. N.H. Spencer, Essentials of Multivariate Data Analysis (CRC Press, Boca Raton, Florida, 2013).
133. D. Grapov and J.W. Newman, Bioinformatics 28(17), 2288–2290 (2012).
134. T.C. O’Haver, Chemometr. Intell. Lab. Syst. 6(2), 95–103 (1989).
135. S. Mitra and T. Bose, Chemometr. Intell. Lab. Syst. 22(1), 3–16 (1994).
136. P. Geladi and H. Martens, J. Near Infrared Spectrosc. 4(1), 225–242 (1996).
137. P. Geladi, H. Martens, L. Hadjiiski, and P. Hopke, J. Near Infrared Spectrosc. 4(1), 243–255 (1996).
138. C.A. Andersson and R. Bro, Chemometr. Intell. Lab. Syst. 52(1), 1–4 (2000).
139. R.M. Jarvis, D. Broadhurst, H. Johnson, N.M. O’Boyle, and R. Goodacre, Bioinformatics 22(20), 2565–2566 (2006).
140. W. McKinney, Pandas (Python Data Analysis Library, 2015). http://pandas.pydata.org.
141. A. Hughes, Z. Liu, and M. Reeves, J. Open Res. Softw. 3(1) (2015).
142. B. Thompson, K. Sunden, D. Morrow, D. Kohler, and J. Wright, J. Open Source Softw. 4(33), 1141 (2019).
143. Y. Koush, J. Ashburner, E. Prilepin, R. Sladky, P. Zeidman, S. Bibikov, F. Scharnowski, A. Nikonorov, and D. Van De Ville, NeuroImage 156, 489–503 (2017).
144. U. Grömping, J. Stat. Softw. 17(1), 1–27 (2006).
145. S. Lê, J. Josse, and F. Husson, J. Stat. Softw. 25(1), 1–18 (2008).
146. R. Wehrens, Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences (Springer Science & Business Media, New York, New York, 2011).
147. K.M. Mullen and I.H. Van Stokkum, J. Stat. Softw. 18(3), 1–46 (2007).
148. A. Stevens and L. Ramirez-Lopez, An introduction to the prospectr package. R Package Vignette, Report No.: R Package Version 0.1, 3. (2014). https://cran.r- project.org/web/packages/ prospectr/vignettes/prospectr.html and https://cran.r-project.org/web/packages/prospectr/prospectr.pdf
Jerome Workman, Jr. serves on the Editorial Advisory Board of Spectroscopy and is the Senior Technical Editor for LCGC and Spectroscopy. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.
Howard Mark serves on the Editorial Advisory Board of Spectroscopy, and runs a consulting service, Mark Electronics, in Suffern, New York. Direct correspondence to: SpectroscopyEdit@mmhgroup.com ●
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