René Gislum

Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR. / Shetty, Nisha; Gislum, René; Jensen, Anne Mette Dahl; Boelt, Birte.

In: Chemometrics and Intelligent Laboratory Systems, Vol. 111, No. 1, 15.02.2012, p. 34-38.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

APA

CBE

MLA

Vancouver

Author

Shetty, Nisha ; Gislum, René ; Jensen, Anne Mette Dahl ; Boelt, Birte. / Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR. In: Chemometrics and Intelligent Laboratory Systems. 2012 ; Vol. 111, No. 1. pp. 34-38.

Bibtex

@article{37cab265fe04404f9e2b27ef196121ad,
title = "Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR",
abstract = "Near- infrared (NIR) spectroscopy was used in combination with chemometrics to quantify total non-structural carbohydrates (TNC) in grass samples in order to overcome year-to-year variation. A total of 1103 above-ground plant and root samples were collected from different field and pot experiments and with various experimental designs in the period from 2001 to 2005. A calibration model was developed using partial least squares regression (PLSR). The calibration model on a large data set spanning five years demonstrated that quantification of TNC using NIR spectroscopy was possible with an acceptable low root mean square of prediction error (RMSEP) of 1.30. However, for some years the estimated RMSEP was too optimistic as year-to-year variation for new years was not included in the model. Interval partial least squares (iPLS) regression was applied to remove non-relevant spectral regions and in order to improve model performance. But still it was not possible to avoid year-to-year variation using iPLS, however iPLS simplified the interpretation of the regression model. The best option was to expand the database with samples from a new year, include these samples in the calibration model and to apply this on the remaining samples from the future year.",
keywords = "NIR, PLSR, iPLS, TNC, grasses, fructan",
author = "Nisha Shetty and Ren{\'e} Gislum and Jensen, {Anne Mette Dahl} and Birte Boelt",
year = "2012",
month = "2",
day = "15",
doi = "10.1016/j.chemolab.2011.11.004",
language = "English",
volume = "111",
pages = "34--38",
journal = "Chemometrics and Intelligent Laboratory Systems",
issn = "0169-7439",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Development of NIR calibration models to assess year-to-year variation in total non-structural carbohydrates in grasses using PLSR

AU - Shetty, Nisha

AU - Gislum, René

AU - Jensen, Anne Mette Dahl

AU - Boelt, Birte

PY - 2012/2/15

Y1 - 2012/2/15

N2 - Near- infrared (NIR) spectroscopy was used in combination with chemometrics to quantify total non-structural carbohydrates (TNC) in grass samples in order to overcome year-to-year variation. A total of 1103 above-ground plant and root samples were collected from different field and pot experiments and with various experimental designs in the period from 2001 to 2005. A calibration model was developed using partial least squares regression (PLSR). The calibration model on a large data set spanning five years demonstrated that quantification of TNC using NIR spectroscopy was possible with an acceptable low root mean square of prediction error (RMSEP) of 1.30. However, for some years the estimated RMSEP was too optimistic as year-to-year variation for new years was not included in the model. Interval partial least squares (iPLS) regression was applied to remove non-relevant spectral regions and in order to improve model performance. But still it was not possible to avoid year-to-year variation using iPLS, however iPLS simplified the interpretation of the regression model. The best option was to expand the database with samples from a new year, include these samples in the calibration model and to apply this on the remaining samples from the future year.

AB - Near- infrared (NIR) spectroscopy was used in combination with chemometrics to quantify total non-structural carbohydrates (TNC) in grass samples in order to overcome year-to-year variation. A total of 1103 above-ground plant and root samples were collected from different field and pot experiments and with various experimental designs in the period from 2001 to 2005. A calibration model was developed using partial least squares regression (PLSR). The calibration model on a large data set spanning five years demonstrated that quantification of TNC using NIR spectroscopy was possible with an acceptable low root mean square of prediction error (RMSEP) of 1.30. However, for some years the estimated RMSEP was too optimistic as year-to-year variation for new years was not included in the model. Interval partial least squares (iPLS) regression was applied to remove non-relevant spectral regions and in order to improve model performance. But still it was not possible to avoid year-to-year variation using iPLS, however iPLS simplified the interpretation of the regression model. The best option was to expand the database with samples from a new year, include these samples in the calibration model and to apply this on the remaining samples from the future year.

KW - NIR

KW - PLSR

KW - iPLS

KW - TNC

KW - grasses

KW - fructan

U2 - 10.1016/j.chemolab.2011.11.004

DO - 10.1016/j.chemolab.2011.11.004

M3 - Journal article

VL - 111

SP - 34

EP - 38

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

IS - 1

ER -