Abstract

Developments in analytical chemistry technology, especially the combination between the partial least squares and spectroscopy, have contributed significantly to predicting the chemical concentrations and discriminating similar chemical analytes. However, spectral shift is an unwanted but inevitable factor for the spectroscopic analyzer, especially in practical application, which decreases the method’s accuracy and stability. To remove the term of spectral shift completely and increase the robustness of spectroscopic analysis method, Fourier transform based partial least squares method was proposed. The approach used Fourier transform first to transform the spectral shift in the “time domain” to the phase term in the “frequency domain.” The module of the Fourier transformed spectra was then calculated. As a result, the phase term was removed (the module of the phase term is 1), which means the spectral shift term was removed completely. Finally, the spectra modules were used to build the model and validate. The approach’s advantages are: (i) that the approach provides a new insight to treat the spectral shift in spectroscopic analyzer; (ii) that the model is insensitive to spectral shift; (iii) that the approach makes partial least squares combined with spectroscopy more suitable for practical application, rather than lab experiment, because spectral shift is permitted, which means the decreased requirements of measure environment. As an example, blood species discrimination, using Raman spectroscopy, was used in order to demonstrate this approach’s effectiveness.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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    [Crossref] [PubMed]
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    [Crossref]
  4. F. Allegrini and A. C. Olivieri, “IUPAC-consistent approach to the limit of detection in partial least-squares calibration,” Anal. Chem. 86(15), 7858–7866 (2014).
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  5. X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
    [Crossref] [PubMed]
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    [Crossref]
  8. L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
  26. F. Westad and H. Martens, “Shift and intensity modeling in spectroscopy—general concept and applications,” Chemometr. Intel. Lab. 45(1–2), 361–370 (1999).
    [Crossref]
  27. H. Bian and J. Gao, “Error analysis of the spectral shift for partial least squares models in Raman spectroscopy,” Opt. Express 26(7), 8016–8027 (2018).
    [Crossref] [PubMed]
  28. H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
    [Crossref] [PubMed]
  29. P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
    [Crossref]
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    [Crossref] [PubMed]
  31. C. Botella, J. Ferré, and R. Boqué, “Classification from microarray data using probabilistic discriminant partial least squares with reject option,” Talanta 80(1), 321–328 (2009).
    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref] [PubMed]

2018 (4)

K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using Raman spectroscopy: a survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
[Crossref] [PubMed]

C. J. Meunier, E. C. Mitchell, J. G. Roberts, J. V. Toups, G. S. McCarty, and L. A. Sombers, “Electrochemical selectivity achieved using a double voltammetric waveform and partial least squares regression: differentiating endogenous hydrogen peroxide fluctuations from shifts in pH,” Anal. Chem. 90(3), 1767–1776 (2018).
[Crossref] [PubMed]

H. Bian and J. Gao, “Error analysis of the spectral shift for partial least squares models in Raman spectroscopy,” Opt. Express 26(7), 8016–8027 (2018).
[Crossref] [PubMed]

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref] [PubMed]

2017 (5)

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

S. Kasemsumran, N. Suttiwijitpukdee, and V. Keeratinijakal, “Rapid classification of turmeric based on DNA fingerprint by near-infrared spectroscopy combined with moving window partial least squares-discrimination analysis,” Anal. Sci. 33(1), 111–115 (2017).
[Crossref] [PubMed]

2016 (3)

I. Marquetti, J. V. Link, A. L. G. Lemes, M. B. S. Scholz, P. Valderrama, and E. Bona, “Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee,” Comput. Electron. Agric. 121, 313–319 (2016).
[Crossref]

G. Aliakbarzadeh, H. Parastar, and H. Sereshti, “Classification of gas chromatographic fingerprints of saffron using partial least squares discriminant analysis together with different variable selection methods,” Chemometr. Intell. Lab. 158, 165–173 (2016).
[Crossref]

J. Lim, G. Kim, C. Mo, M. S. Kim, K. Chao, J. Qin, X. Fu, I. Baek, and B. K. Cho, “Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model,” Talanta 151, 183–191 (2016).
[Crossref] [PubMed]

2015 (6)

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
[Crossref]

X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
[Crossref] [PubMed]

K. H. Wong, V. Razmovski‐Naumovski, K. M. Li, G. Q. Li, and K. Chan, “The quality control of two Pueraria species using Raman spectroscopy coupled with partial least squares analysis,” J. Raman Spectrosc. 46(4), 361–368 (2015).
[Crossref]

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

J. B. Sleiman, B. Bousquet, N. Palka, and P. Mounaix, “Quantitative Analysis of Hexahydro-1,3,5-trinitro-1,3,5, Triazine/Pentaerythritol Tetranitrate (RDX-PETN) Mixtures by Terahertz Time Domain Spectroscopy,” Appl. Spectrosc. 69(12), 1464–1471 (2015).
[Crossref] [PubMed]

2014 (1)

F. Allegrini and A. C. Olivieri, “IUPAC-consistent approach to the limit of detection in partial least-squares calibration,” Anal. Chem. 86(15), 7858–7866 (2014).
[Crossref] [PubMed]

2010 (2)

L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
[Crossref] [PubMed]

V. Sikirzhytski, K. Virkler, and I. K. Lednev, “Discriminant analysis of Raman spectra for body fluid identification for forensic purposes,” Sensors (Basel) 10(4), 2869–2884 (2010).
[Crossref] [PubMed]

2009 (1)

C. Botella, J. Ferré, and R. Boqué, “Classification from microarray data using probabilistic discriminant partial least squares with reject option,” Talanta 80(1), 321–328 (2009).
[Crossref] [PubMed]

2005 (2)

Å. Eriksson, K. Persson Waller, K. Svennersten-Sjaunja, J.-E. Haugen, F. Lundby, and O. Lind, “Detection of mastitic milk using a gas-sensor array system (electronic nose),” Int. Dairy J. 15(12), 1193–1201 (2005).
[Crossref]

D. Cozzolino, A. Chree, J. R. Scaife, and I. Murray, “Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species,” J. Agric. Food Chem. 53(11), 4459–4463 (2005).
[Crossref] [PubMed]

2004 (1)

1999 (3)

F. Westad and H. Martens, “Shift and intensity modeling in spectroscopy—general concept and applications,” Chemometr. Intel. Lab. 45(1–2), 361–370 (1999).
[Crossref]

C. Y. Wang, C. T. Chen, C. P. Chiang, S. T. Young, S. N. Chow, and H. K. Chiang, “A probability-based multivariate statistical algorithm for autofluorescence spectroscopic identification of oral carcinogenesis,” Photochem. Photobiol. 69(4), 471–477 (1999).
[Crossref] [PubMed]

A. G. Ryder, G. M. O’Connor, and T. J. Glynn, “Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis,” J. Forensic Sci. 44(5), 12031J (1999).
[Crossref]

1996 (2)

T. R. Brown and R. Stoyanova, “NMR spectral quantitation by principal-component analysis. II. Determination of frequency and phase shifts,” J. Magn. Reson. B. 112(1), 32–43 (1996).
[Crossref] [PubMed]

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

1995 (1)

J. Gottfries, K. Blennow, A. Wallin, and C. G. Gottfries, “Diagnosis of dementias using partial least squares discriminant analysis,” Dementia 6(2), 83–88 (1995).
[PubMed]

1994 (1)

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

1989 (1)

T. Brekke, O. M. Kvalheim, and E. Sletten, “Prediction of physical properties of hydrocarbon mixtures by partial-least-squares calibration of carbon-13 nuclear magnetic resonance data,” Anal. Chim. Acta 223, 123–134 (1989).
[Crossref]

1988 (1)

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60(11), 1193–1202 (1988).
[Crossref]

Ahmed, M.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Aliakbarzadeh, G.

G. Aliakbarzadeh, H. Parastar, and H. Sereshti, “Classification of gas chromatographic fingerprints of saffron using partial least squares discriminant analysis together with different variable selection methods,” Chemometr. Intell. Lab. 158, 165–173 (2016).
[Crossref]

Allegrini, F.

F. Allegrini and A. C. Olivieri, “IUPAC-consistent approach to the limit of detection in partial least-squares calibration,” Anal. Chem. 86(15), 7858–7866 (2014).
[Crossref] [PubMed]

Amin, I.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Asif Hanif, M.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Baek, I.

J. Lim, G. Kim, C. Mo, M. S. Kim, K. Chao, J. Qin, X. Fu, I. Baek, and B. K. Cho, “Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model,” Talanta 151, 183–191 (2016).
[Crossref] [PubMed]

Bai, P.

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref] [PubMed]

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Baig, S. M.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Beddell, C. R.

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

Bian, H.

Blennow, K.

J. Gottfries, K. Blennow, A. Wallin, and C. G. Gottfries, “Diagnosis of dementias using partial least squares discriminant analysis,” Dementia 6(2), 83–88 (1995).
[PubMed]

Bona, E.

I. Marquetti, J. V. Link, A. L. G. Lemes, M. B. S. Scholz, P. Valderrama, and E. Bona, “Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee,” Comput. Electron. Agric. 121, 313–319 (2016).
[Crossref]

Bonnier, F.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Booksh, K.

Boqué, R.

C. Botella, J. Ferré, and R. Boqué, “Classification from microarray data using probabilistic discriminant partial least squares with reject option,” Talanta 80(1), 321–328 (2009).
[Crossref] [PubMed]

Botella, C.

C. Botella, J. Ferré, and R. Boqué, “Classification from microarray data using probabilistic discriminant partial least squares with reject option,” Talanta 80(1), 321–328 (2009).
[Crossref] [PubMed]

Bousquet, B.

Brekke, T.

T. Brekke, O. M. Kvalheim, and E. Sletten, “Prediction of physical properties of hydrocarbon mixtures by partial-least-squares calibration of carbon-13 nuclear magnetic resonance data,” Anal. Chim. Acta 223, 123–134 (1989).
[Crossref]

Brown, T. R.

T. R. Brown and R. Stoyanova, “NMR spectral quantitation by principal-component analysis. II. Determination of frequency and phase shifts,” J. Magn. Reson. B. 112(1), 32–43 (1996).
[Crossref] [PubMed]

Byrne, H. J.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Chan, K.

K. H. Wong, V. Razmovski‐Naumovski, K. M. Li, G. Q. Li, and K. Chan, “The quality control of two Pueraria species using Raman spectroscopy coupled with partial least squares analysis,” J. Raman Spectrosc. 46(4), 361–368 (2015).
[Crossref]

Chao, K.

J. Lim, G. Kim, C. Mo, M. S. Kim, K. Chao, J. Qin, X. Fu, I. Baek, and B. K. Cho, “Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model,” Talanta 151, 183–191 (2016).
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T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
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L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
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S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
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T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
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Å. Eriksson, K. Persson Waller, K. Svennersten-Sjaunja, J.-E. Haugen, F. Lundby, and O. Lind, “Detection of mastitic milk using a gas-sensor array system (electronic nose),” Int. Dairy J. 15(12), 1193–1201 (2005).
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M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
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Murray, I.

D. Cozzolino, A. Chree, J. R. Scaife, and I. Murray, “Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species,” J. Agric. Food Chem. 53(11), 4459–4463 (2005).
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H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
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M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
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M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
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O’Connor, G. M.

A. G. Ryder, G. M. O’Connor, and T. J. Glynn, “Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis,” J. Forensic Sci. 44(5), 12031J (1999).
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Parastar, H.

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[Crossref]

Pourbasheer, E.

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

Pu, X.

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

Qin, J.

J. Lim, G. Kim, C. Mo, M. S. Kim, K. Chao, J. Qin, X. Fu, I. Baek, and B. K. Cho, “Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model,” Talanta 151, 183–191 (2016).
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Rahman, M.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
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M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
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H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
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K. H. Wong, V. Razmovski‐Naumovski, K. M. Li, G. Q. Li, and K. Chan, “The quality control of two Pueraria species using Raman spectroscopy coupled with partial least squares analysis,” J. Raman Spectrosc. 46(4), 361–368 (2015).
[Crossref]

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C. J. Meunier, E. C. Mitchell, J. G. Roberts, J. V. Toups, G. S. McCarty, and L. A. Sombers, “Electrochemical selectivity achieved using a double voltammetric waveform and partial least squares regression: differentiating endogenous hydrogen peroxide fluctuations from shifts in pH,” Anal. Chem. 90(3), 1767–1776 (2018).
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M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
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A. G. Ryder, G. M. O’Connor, and T. J. Glynn, “Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis,” J. Forensic Sci. 44(5), 12031J (1999).
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Sadeghi, M.

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

Saleem, M.

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

Salman, S. R.

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

Sarkar, A.

M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
[Crossref]

Sarvi, S.

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

Scaife, J. R.

D. Cozzolino, A. Chree, J. R. Scaife, and I. Murray, “Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species,” J. Agric. Food Chem. 53(11), 4459–4463 (2005).
[Crossref] [PubMed]

Scholz, M. B. S.

I. Marquetti, J. V. Link, A. L. G. Lemes, M. B. S. Scholz, P. Valderrama, and E. Bona, “Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee,” Comput. Electron. Agric. 121, 313–319 (2016).
[Crossref]

Sereshti, H.

G. Aliakbarzadeh, H. Parastar, and H. Sereshti, “Classification of gas chromatographic fingerprints of saffron using partial least squares discriminant analysis together with different variable selection methods,” Chemometr. Intell. Lab. 158, 165–173 (2016).
[Crossref]

Sikirzhytski, V.

V. Sikirzhytski, K. Virkler, and I. K. Lednev, “Discriminant analysis of Raman spectra for body fluid identification for forensic purposes,” Sensors (Basel) 10(4), 2869–2884 (2010).
[Crossref] [PubMed]

Singh, M.

M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
[Crossref]

Sleiman, J. B.

Sletten, E.

T. Brekke, O. M. Kvalheim, and E. Sletten, “Prediction of physical properties of hydrocarbon mixtures by partial-least-squares calibration of carbon-13 nuclear magnetic resonance data,” Anal. Chim. Acta 223, 123–134 (1989).
[Crossref]

Sombers, L. A.

C. J. Meunier, E. C. Mitchell, J. G. Roberts, J. V. Toups, G. S. McCarty, and L. A. Sombers, “Electrochemical selectivity achieved using a double voltammetric waveform and partial least squares regression: differentiating endogenous hydrogen peroxide fluctuations from shifts in pH,” Anal. Chem. 90(3), 1767–1776 (2018).
[Crossref] [PubMed]

Spraul, M.

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

Stoyanova, R.

T. R. Brown and R. Stoyanova, “NMR spectral quantitation by principal-component analysis. II. Determination of frequency and phase shifts,” J. Magn. Reson. B. 112(1), 32–43 (1996).
[Crossref] [PubMed]

Suttiwijitpukdee, N.

S. Kasemsumran, N. Suttiwijitpukdee, and V. Keeratinijakal, “Rapid classification of turmeric based on DNA fingerprint by near-infrared spectroscopy combined with moving window partial least squares-discrimination analysis,” Anal. Sci. 33(1), 111–115 (2017).
[Crossref] [PubMed]

Svennersten-Sjaunja, K.

Å. Eriksson, K. Persson Waller, K. Svennersten-Sjaunja, J.-E. Haugen, F. Lundby, and O. Lind, “Detection of mastitic milk using a gas-sensor array system (electronic nose),” Int. Dairy J. 15(12), 1193–1201 (2005).
[Crossref]

Sweatman, B. C.

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

Tang, L. J.

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

Tao, W.

L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
[Crossref] [PubMed]

Tas, A. C.

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

Tavallali, H.

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

Thomas, E. V.

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60(11), 1193–1202 (1988).
[Crossref]

Tian, Y.

H. Bian, P. Wang, N. Wang, Y. Tian, P. Bai, H. Jiang, and J. Gao, “Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy,” Biomed. Opt. Express 9(8), 3512–3522 (2018).
[Crossref] [PubMed]

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Toups, J. V.

C. J. Meunier, E. C. Mitchell, J. G. Roberts, J. V. Toups, G. S. McCarty, and L. A. Sombers, “Electrochemical selectivity achieved using a double voltammetric waveform and partial least squares regression: differentiating endogenous hydrogen peroxide fluctuations from shifts in pH,” Anal. Chem. 90(3), 1767–1776 (2018).
[Crossref] [PubMed]

Valderrama, P.

I. Marquetti, J. V. Link, A. L. G. Lemes, M. B. S. Scholz, P. Valderrama, and E. Bona, “Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee,” Comput. Electron. Agric. 121, 313–319 (2016).
[Crossref]

Van Der Greef, J.

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

Venekamp, J.

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

Virkler, K.

V. Sikirzhytski, K. Virkler, and I. K. Lednev, “Discriminant analysis of Raman spectra for body fluid identification for forensic purposes,” Sensors (Basel) 10(4), 2869–2884 (2010).
[Crossref] [PubMed]

Vogels, J.

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

Vogt, F.

Wallin, A.

J. Gottfries, K. Blennow, A. Wallin, and C. G. Gottfries, “Diagnosis of dementias using partial least squares discriminant analysis,” Dementia 6(2), 83–88 (1995).
[PubMed]

Wang, C. Y.

C. Y. Wang, C. T. Chen, C. P. Chiang, S. T. Young, S. N. Chow, and H. K. Chiang, “A probability-based multivariate statistical algorithm for autofluorescence spectroscopic identification of oral carcinogenesis,” Photochem. Photobiol. 69(4), 471–477 (1999).
[Crossref] [PubMed]

Wang, J.

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Wang, N.

Wang, P.

Wang, Y.

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

Wen, Z.

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

Westad, F.

F. Westad and H. Martens, “Shift and intensity modeling in spectroscopy—general concept and applications,” Chemometr. Intel. Lab. 45(1–2), 361–370 (1999).
[Crossref]

Wong, K. H.

K. H. Wong, V. Razmovski‐Naumovski, K. M. Li, G. Q. Li, and K. Chan, “The quality control of two Pueraria species using Raman spectroscopy coupled with partial least squares analysis,” J. Raman Spectrosc. 46(4), 361–368 (2015).
[Crossref]

Xia, Y.

X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
[Crossref] [PubMed]

Xie, W.

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

Xu, T.

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

Yang, J. T.

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

Yang, Q.

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

Yao, W.

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Yin, H.

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Yin, J. H.

X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
[Crossref] [PubMed]

Young, S. T.

C. Y. Wang, C. T. Chen, C. P. Chiang, S. T. Young, S. N. Chow, and H. K. Chiang, “A probability-based multivariate statistical algorithm for autofluorescence spectroscopic identification of oral carcinogenesis,” Photochem. Photobiol. 69(4), 471–477 (1999).
[Crossref] [PubMed]

Yu, B.

L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
[Crossref] [PubMed]

Yu, R. Q.

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

Yuan, Y.

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

Zhang, L.

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
[Crossref] [PubMed]

Zhang, W.

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

Zhang, X. X.

X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
[Crossref] [PubMed]

Anal. Bioanal. Chem. (1)

L. Zhang, Q. Li, W. Tao, B. Yu, and Y. Du, “Quantitative analysis of thymine with surface-enhanced Raman spectroscopy and partial least squares (PLS) regression,” Anal. Bioanal. Chem. 398(4), 1827–1832 (2010).
[Crossref] [PubMed]

Anal. Chem. (3)

D. M. Haaland and E. V. Thomas, “Partial least-squares methods for spectral analyses. 1. Relation to other quantitative calibration methods and the extraction of qualitative information,” Anal. Chem. 60(11), 1193–1202 (1988).
[Crossref]

F. Allegrini and A. C. Olivieri, “IUPAC-consistent approach to the limit of detection in partial least-squares calibration,” Anal. Chem. 86(15), 7858–7866 (2014).
[Crossref] [PubMed]

C. J. Meunier, E. C. Mitchell, J. G. Roberts, J. V. Toups, G. S. McCarty, and L. A. Sombers, “Electrochemical selectivity achieved using a double voltammetric waveform and partial least squares regression: differentiating endogenous hydrogen peroxide fluctuations from shifts in pH,” Anal. Chem. 90(3), 1767–1776 (2018).
[Crossref] [PubMed]

Anal. Chim. Acta (1)

T. Brekke, O. M. Kvalheim, and E. Sletten, “Prediction of physical properties of hydrocarbon mixtures by partial-least-squares calibration of carbon-13 nuclear magnetic resonance data,” Anal. Chim. Acta 223, 123–134 (1989).
[Crossref]

Anal. Lett. (1)

P. Bai, J. Wang, H. Yin, Y. Tian, W. Yao, and J. Gao, “Discrimination of human and nonhuman blood by Raman spectroscopy and partial least squares discriminant analysis,” Anal. Lett. 50(2), 379–388 (2017).
[Crossref]

Anal. Sci. (1)

S. Kasemsumran, N. Suttiwijitpukdee, and V. Keeratinijakal, “Rapid classification of turmeric based on DNA fingerprint by near-infrared spectroscopy combined with moving window partial least squares-discrimination analysis,” Anal. Sci. 33(1), 111–115 (2017).
[Crossref] [PubMed]

Appl. Spectrosc. (2)

Arab. J. Chem. (1)

H. Khajehsharifi, E. Pourbasheer, H. Tavallali, S. Sarvi, and M. Sadeghi, “The comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samples,” Arab. J. Chem. 10, S3451–S3458 (2017).
[Crossref]

Biomed. Opt. Express (1)

Chemometr. Intel. Lab. (1)

F. Westad and H. Martens, “Shift and intensity modeling in spectroscopy—general concept and applications,” Chemometr. Intel. Lab. 45(1–2), 361–370 (1999).
[Crossref]

Chemometr. Intell. Lab. (1)

G. Aliakbarzadeh, H. Parastar, and H. Sereshti, “Classification of gas chromatographic fingerprints of saffron using partial least squares discriminant analysis together with different variable selection methods,” Chemometr. Intell. Lab. 158, 165–173 (2016).
[Crossref]

Comput. Electron. Agric. (1)

I. Marquetti, J. V. Link, A. L. G. Lemes, M. B. S. Scholz, P. Valderrama, and E. Bona, “Partial least square with discriminant analysis and near infrared spectroscopy for evaluation of geographic and genotypic origin of arabica coffee,” Comput. Electron. Agric. 121, 313–319 (2016).
[Crossref]

Dementia (1)

J. Gottfries, K. Blennow, A. Wallin, and C. G. Gottfries, “Diagnosis of dementias using partial least squares discriminant analysis,” Dementia 6(2), 83–88 (1995).
[PubMed]

Forensic Sci. Int. (1)

K. C. Doty and I. K. Lednev, “Differentiation of human blood from animal blood using Raman spectroscopy: a survey of forensically relevant species,” Forensic Sci. Int. 282, 204–210 (2018).
[Crossref] [PubMed]

Int. Dairy J. (1)

Å. Eriksson, K. Persson Waller, K. Svennersten-Sjaunja, J.-E. Haugen, F. Lundby, and O. Lind, “Detection of mastitic milk using a gas-sensor array system (electronic nose),” Int. Dairy J. 15(12), 1193–1201 (2005).
[Crossref]

J. Agric. Food Chem. (1)

D. Cozzolino, A. Chree, J. R. Scaife, and I. Murray, “Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species,” J. Agric. Food Chem. 53(11), 4459–4463 (2005).
[Crossref] [PubMed]

J. Anal. At. Spectrom. (1)

M. Singh, V. Karki, R. K. Mishra, A. Kumar, C. P. Kaushik, X. Mao, R. E. Russo, and A. Sarkar, “Analytical spectral dependent partial least squares regression: a study of nuclear waste glass from thorium based fuel using LIBS,” J. Anal. At. Spectrom. 30(12), 2507–2515 (2015).
[Crossref]

J. Biomed. Opt. (1)

X. X. Zhang, J. H. Yin, Z. H. Mao, and Y. Xia, “Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis,” J. Biomed. Opt. 20(6), 060501 (2015).
[Crossref] [PubMed]

J. Chemometr. (1)

J. Vogels, A. C. Tas, J. Venekamp, and J. Van Der Greef, “Partial linear fit: a new NMR spectroscopy preprocessing tool for pattern recognition applications,” J. Chemometr. 10(5–6), 425–438 (1996).
[Crossref]

J. Forensic Sci. (1)

A. G. Ryder, G. M. O’Connor, and T. J. Glynn, “Identifications and quantitative measurements of narcotics in solid mixtures using near-IR Raman spectroscopy and multivariate analysis,” J. Forensic Sci. 44(5), 12031J (1999).
[Crossref]

J. Magn. Reson. B. (1)

T. R. Brown and R. Stoyanova, “NMR spectral quantitation by principal-component analysis. II. Determination of frequency and phase shifts,” J. Magn. Reson. B. 112(1), 32–43 (1996).
[Crossref] [PubMed]

J. Pharm. Biomed. Anal. (1)

M. Spraul, P. Neidig, U. Klauck, P. Kessler, E. Holmes, J. K. Nicholson, B. C. Sweatman, S. R. Salman, R. D. Farrant, E. Rahr, C. R. Beddell, and J. C. Lindon, “Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples,” J. Pharm. Biomed. Anal. 12(10), 1215–1225 (1994).
[Crossref] [PubMed]

J. Raman Spectrosc. (2)

H. Nawaz, N. Rashid, M. Saleem, M. Asif Hanif, M. Irfan Majeed, I. Amin, M. Iqbal, M. Rahman, O. Ibrahim, S. M. Baig, M. Ahmed, F. Bonnier, and H. J. Byrne, “Prediction of viral loads for diagnosis of Hepatitis C infection in human plasma samples using Raman spectroscopy coupled with partial least squares regression analysis,” J. Raman Spectrosc. 48(5), 697–704 (2017).
[Crossref]

K. H. Wong, V. Razmovski‐Naumovski, K. M. Li, G. Q. Li, and K. Chan, “The quality control of two Pueraria species using Raman spectroscopy coupled with partial least squares analysis,” J. Raman Spectrosc. 46(4), 361–368 (2015).
[Crossref]

Opt. Express (1)

Photochem. Photobiol. (1)

C. Y. Wang, C. T. Chen, C. P. Chiang, S. T. Young, S. N. Chow, and H. K. Chiang, “A probability-based multivariate statistical algorithm for autofluorescence spectroscopic identification of oral carcinogenesis,” Photochem. Photobiol. 69(4), 471–477 (1999).
[Crossref] [PubMed]

RSC Advances (1)

T. Lu, Y. Yuan, X. He, M. Li, X. Pu, T. Xu, and Z. Wen, “Simultaneous determination of multiple components in explosives using ultraviolet spectrophotometry and a partial least squares method,” RSC Advances 5(17), 13021–13027 (2015).
[Crossref]

Sensors (Basel) (1)

V. Sikirzhytski, K. Virkler, and I. K. Lednev, “Discriminant analysis of Raman spectra for body fluid identification for forensic purposes,” Sensors (Basel) 10(4), 2869–2884 (2010).
[Crossref] [PubMed]

Spectrochim. Acta A Mol. Biomol. Spectrosc. (1)

S. He, W. Xie, W. Zhang, L. Zhang, Y. Wang, X. Liu, Y. Liu, and C. Du, “Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy,” Spectrochim. Acta A Mol. Biomol. Spectrosc. 137, 1092–1099 (2015).
[Crossref] [PubMed]

Talanta (3)

Q. Yang, S. S. Lin, J. T. Yang, L. J. Tang, and R. Q. Yu, “Detection of inborn errors of metabolism utilizing GC-MS urinary metabolomics coupled with a modified orthogonal partial least squares discriminant analysis,” Talanta 165, 545–552 (2017).
[Crossref] [PubMed]

J. Lim, G. Kim, C. Mo, M. S. Kim, K. Chao, J. Qin, X. Fu, I. Baek, and B. K. Cho, “Detection of melamine in milk powders using near-infrared hyperspectral imaging combined with regression coefficient of partial least square regression model,” Talanta 151, 183–191 (2016).
[Crossref] [PubMed]

C. Botella, J. Ferré, and R. Boqué, “Classification from microarray data using probabilistic discriminant partial least squares with reject option,” Talanta 80(1), 321–328 (2009).
[Crossref] [PubMed]

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Figures (8)

Fig. 1
Fig. 1 The Raman spectra of blood samples with and without spectral shift.
Fig. 2
Fig. 2 The modules of the Fourier transform of the Raman spectra in Fig. 1.
Fig. 3
Fig. 3 The prediction value. (a) is obtained by PLS when the Raman spectrum was shifted the pixel mathematically; (b) is obtained by FPLS when the Raman spectrum was shifted the pixel mathematically.
Fig. 4
Fig. 4 The prediction value obtained by PLS when the Raman spectra have a spectral shift. (a) shown prediction value of the Raman spectra without spectral shift; (b)-(e) were corresponding to prediction value of the Raman spectra with a shift of 2.82 cm−1, 6.36 cm−1, 9.18 cm−1 and 19.77 cm−1.
Fig. 5
Fig. 5 The average prediction value with different spectral shift when PLS model was used.
Fig. 6
Fig. 6 The prediction value obtained by FPLS when the Raman spectra have a spectral shift. (a) shown prediction value of the Raman spectra without spectral shift; (b)-(e) were corresponding to prediction value of the Raman spectra with a shift of 2.82 cm−1, 6.36 cm−1, 9.18 cm−1 and 19.77 cm−1
Fig. 7
Fig. 7 The average prediction value with different spectral shift when FPLS model was used
Fig. 8
Fig. 8 The prediction value. (a) is obtained by PLS when the Raman spectra in the training data set have spectral shift; (b) is obtained by FPLS model when the Raman spectra in the training data set have spectral shift

Equations (10)

Equations on this page are rendered with MathJax. Learn more.

k= 1 λ excitation 1 λ
Δy=( a( k 1 ) I ' ( k 1 )+a( k 2 ) I ' ( k 2 )+...+a( k n ) I ' ( k n ) )δk +( a( k 1 ) I ' ( k 1 ) 2! +a( k 2 ) I ' ( k 2 ) 2! +...+a( k n ) I ' ( k n ) 2! )δ k 2 +... +( a( k 1 ) I ' ( k 1 ) n! +a( k 2 ) I ' ( k 2 ) n! +...+a( k n ) I ' ( k n ) n! )δ k n
I sampled ( k )=I( k ) Π δξ/2 ( ξ )
FFT( I sampled ( k ) )=FFT( I( k ) Π δξ/2 ( ξ ) ) =FFT( I( k ) )×FFT( Π δξ/2 ( ξ ) ) =G( ω )×sinc( ωδξ/2 )
| FFT( I sampled ( k ) ) |=| G( ω )×sinc( ωδξ/2 ) | =A( ω )×| sinc( ωδξ/2 ) |
| FFT( I sampled ( k+δk ) ) |=| FFT( I( k+δk ) Π δξ/2 ( ξ ) ) | =| FFT( I( k+δk ) )×FFT( Π δξ/2 ( ξ ) ) | =| G( ω )exp( jωδk )×sinc( ωδξ/2 ) | =A( ω )×| exp( jωδk ) |×| sinc( ωδξ/2 ) | =A( ω )×| sinc( ωδξ/2 ) |
| FFT( I( k ) ) |=| FFT( I( k+δk ) ) |=A( ω )×| sinc( ωδξ/2 ) |
I sampled ( k )= i=1 n I sampled ( k i )
| FFT( I sampled ( k ) ) |=| i=1 n G i ( ω ) ×sinc( ωδξ/2 ) | =| i=1 n G i ( ω ) |×| sinc( ωδξ/2 ) |
| FFT( I sampled ' ( k ) ) |=| FFT( i=1 n I sampled ( k i +δ k i ) Π δξ/2 ( ξ ) ) | =| i=1 n ( G i ( ω )exp( jωδ k i ) ) ×sinc( ωδξ/2 ) | =| i=1 n ( G i ( ω )exp( jωδ k i ) ) |×| sinc( ωδξ/2 ) |

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