A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

Madan Kumar Lakshmanan; Birte Boelt; René Gislum

doi:10.1177/09670335221138955

A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

Madan Kumar Lakshmanan^*, Birte Boelt, René Gislum

^*Corresponding author for this work

Department of Agroecology - Crop Health

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

8 Citations (Scopus)

Abstract

This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.

Original language	English
Journal	Journal of Near Infrared Spectroscopy
Volume	31
Issue	1
Pages (from-to)	24-32
Number of pages	9
ISSN	0967-0335
DOIs	https://doi.org/10.1177/09670335221138955
Publication status	Published - Feb 2023

Keywords

seed quality assessment
near infrared spectroscopy
chemometrics
variable selection for data classification
successive projections algorithm

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jns-21-0075.r2_proof_hi-dftiz0rvsqh8j6fhk6l1pc33c42syl4xSubmitted manuscript, 957 KBLicence: CC BY-NC-ND

Cite this

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title = "A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm",

abstract = "This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds{\textquoteright} viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.",

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author = "Lakshmanan, {Madan Kumar} and Birte Boelt and Ren{\'e} Gislum",

year = "2023",

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language = "English",

volume = "31",

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A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm. / Lakshmanan, Madan Kumar; Boelt, Birte ; Gislum, René.
In: Journal of Near Infrared Spectroscopy, Vol. 31, No. 1, 02.2023, p. 24-32.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

AU - Lakshmanan, Madan Kumar

AU - Boelt, Birte

AU - Gislum, René

PY - 2023/2

Y1 - 2023/2

N2 - This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.

AB - This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.

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KW - near infrared spectroscopy

KW - chemometrics

KW - variable selection for data classification

KW - successive projections algorithm

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DO - 10.1177/09670335221138955

M3 - Journal article

SN - 0967-0335

VL - 31

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JO - Journal of Near Infrared Spectroscopy

JF - Journal of Near Infrared Spectroscopy

IS - 1

ER -

A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

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Keywords

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