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Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study

Affiliation
Faculty of Science and Technology, Seikei University, Musashino Tokyo, Japan
Aoyagi, Satoka;
Affiliation
National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba Ibaraki, Japan
Fujiwara, Yukio;
Affiliation
Toyama Co., Ltd., 3816-1 Kishi, Yamakita-machi Ashigarakami-gun Kanagawa, Japan
Takano, Akio;
Affiliation
National Physical Laboratory, Hampton Road, Teddington Middlesex, United Kingdom
Vorng, Jean-Luc;
Affiliation
National Physical Laboratory, Hampton Road, Teddington Middlesex, United Kingdom
Gilmore, Ian S.;
Affiliation
Medtronic, Corporate Science and Technology, Mailstop LT240, 710 Medtronic Parkway, Minneapolis, United States
Wang, Yung-Chen;
Affiliation
Tascon GmbH, Mendelstr. 17, Münster, Germany
Tallarek, Elke;
Affiliation
Tascon GmbH, Mendelstr. 17, Münster, Germany
Hagenhoff, Birgit;
Affiliation
ULVAC-PHI, Inc., 2500 Hagisono, Chigasaki Kanagawa, Japan
Iida, Shin-Ichi;
ORCID
0000-0002-5866-901X
Affiliation
Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, Germany
Luch, Andreas;
ORCID
0000-0002-1854-5354
Affiliation
Department of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, Berlin, Germany
Jungnickel, Harald;
Affiliation
Analytical Science Team, Common Base Technology Division, Innovative Technology Laboratories, AGC Inc., 1150 Hazawa-cho, Kanagawa-ku Yokohama-shi Kanagawa, Japan
Lang, Yusheng;
Affiliation
Bio-imaging Team, Korea Research Institute of Standards and Science (KRISS), Daejeon, South Korea
Shon, Hyun Kyong;
Affiliation
Bio-imaging Team, Korea Research Institute of Standards and Science (KRISS), Daejeon, South Korea
Lee, Tae Geol;
Affiliation
Department of Chemistry, Tsinghua University, No. 30, Shuangqing Road, Haidian District, Beijing, China
Li, Zhanping;
Affiliation
Faculty of Science and Technology, Seikei University, Musashino Tokyo, Japan
Matsuda, Kazuhiro;
Affiliation
Analytical Technology and Solutions Laboratory, Kurashiki Research Center, KURARAY CO., LTD, 2045-1, Sakazu, Kurashiki Okayama, Japan
Mihara, Ichiro;
Affiliation
KOBELCO RESEARCH INSTITUTE, INC., 1-5-5, Takatsukadai, Nishi-ku Kobe Hyogo, Japan
Miisho, Ako;
Affiliation
Specialty Chemicals Development Center, Peripheral Products Operations, Canon Inc., Fukara Susono Shizuoka, Japan
Murayama, Yohei;
Affiliation
Platform Laboratory for Science and Technology, Asahi Kasei Corporation, 2-1 Samejima, Fuji Shizuoka, Japan
Nagatomi, Takaharu;
Affiliation
Analytical Science Research Laboratory, Kao Corp., Minato 1334, Wakayama-shi Wakayama, Japan
Ikeda, Reiko;
Affiliation
Analytical Science Research Laboratory, Kao Corp., Minato 1334, Wakayama-shi Wakayama, Japan
Okamoto, Masayuki;
Affiliation
Mitsui Chemical Analysis and Consulting Service Inc., 580-32 Nagaura, Sodegaura Chiba, Japan
Saiga, Kunio;
Affiliation
Mitsui Chemical Analysis and Consulting Service Inc., 580-32 Nagaura, Sodegaura Chiba, Japan
Tsuchiya, Toshihiko;
Affiliation
Sumitomo Electric Industries, Ltd., 1-1-1, Koyakita, Itami Hyogo, Japan
Uemura, Shigeaki

We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide.

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