Evaluation of predictive performance of PLS regression models after being transferred from benchtop to handheld NIR spectrometers. (June 2022)
- Record Type:
- Journal Article
- Title:
- Evaluation of predictive performance of PLS regression models after being transferred from benchtop to handheld NIR spectrometers. (June 2022)
- Main Title:
- Evaluation of predictive performance of PLS regression models after being transferred from benchtop to handheld NIR spectrometers
- Authors:
- Rukundo, Isaac R.
Danao, Mary-Grace C.
Mitchell, Robert B.
Weller, Curtis L. - Abstract:
- Abstract : With increasing availability of handheld near infrared (NIR) spectrometers, it would be beneficial to transfer an existing calibration model for a benchtop NIR to another spectrometer with minimal loss in prediction performance. In this study, a benchtop FOSS XDS Rapid Content Analyzer (B1) was calibrated to estimate nitrogen content ( N ) of warm-season grasses using partial least squares (PLS) regression. This model had a coefficient of determination of validation ( r 2 ) of 0.928 and ratio of standard deviation to standard error of prediction ( R P D ) of 3.78. The calibration model was transferred to another benchtop, FOSS 6500 (B2) and two handheld spectrometers, ASD QualitySpec® Trek and Tellspec Enterprise (H1 and H2, respectively). B1, B2, and H1 had similar specifications, while H2 had a shorter spectral range with a wider spectral interval. When the calibration model for B1 was transferred to B2 and H1, both r 2 and RPD decreased slightly. However, transferring the model to H2 required truncating and standardizing the B1 spectra using piecewise reverse standardization and more preprocessing than was done for B2 and H1. The calibration model developed for the standardised B1 spectra had an r 2 = 0.889 and R P D = 1.79, which were lower than those for the original calibration model developed for B1. When this new model was transferred and applied to H2, r 2 decreased further to 0.726 while R P D remained 1.79. Based on these values, the model to H2Abstract : With increasing availability of handheld near infrared (NIR) spectrometers, it would be beneficial to transfer an existing calibration model for a benchtop NIR to another spectrometer with minimal loss in prediction performance. In this study, a benchtop FOSS XDS Rapid Content Analyzer (B1) was calibrated to estimate nitrogen content ( N ) of warm-season grasses using partial least squares (PLS) regression. This model had a coefficient of determination of validation ( r 2 ) of 0.928 and ratio of standard deviation to standard error of prediction ( R P D ) of 3.78. The calibration model was transferred to another benchtop, FOSS 6500 (B2) and two handheld spectrometers, ASD QualitySpec® Trek and Tellspec Enterprise (H1 and H2, respectively). B1, B2, and H1 had similar specifications, while H2 had a shorter spectral range with a wider spectral interval. When the calibration model for B1 was transferred to B2 and H1, both r 2 and RPD decreased slightly. However, transferring the model to H2 required truncating and standardizing the B1 spectra using piecewise reverse standardization and more preprocessing than was done for B2 and H1. The calibration model developed for the standardised B1 spectra had an r 2 = 0.889 and R P D = 1.79, which were lower than those for the original calibration model developed for B1. When this new model was transferred and applied to H2, r 2 decreased further to 0.726 while R P D remained 1.79. Based on these values, the model to H2 spectrometer can be used for rough screening purposes. Graphical abstract: Image 1 Highlights: Forage nitrogen concentration was predicted using benchtop and handheld NIR spectrometers. A calibration model was transferred from a benchtop NIR to other spectrometers. Transfer to a similar benchtop spectrometer required no standardization of spectra. Transfer to a handheld spectrometer required truncating and additional preprocessing of spectra. Transfer to a handheld spectrometer required applying fewer latent variables of the original model. … (more)
- Is Part Of:
- Biosystems engineering. Volume 218(2022)
- Journal:
- Biosystems engineering
- Issue:
- Volume 218(2022)
- Issue Display:
- Volume 218, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 218
- Issue:
- 2022
- Issue Sort Value:
- 2022-0218-2022-0000
- Page Start:
- 245
- Page End:
- 255
- Publication Date:
- 2022-06
- Subjects:
- Partial least squares regression -- Near infrared -- Portable spectroscopy -- Feed analysis -- Screening
Bioengineering -- Periodicals
Agricultural engineering -- Periodicals
Biological systems -- Periodicals
Génie rural -- Périodiques
Systèmes biologiques -- Périodiques
631 - Journal URLs:
- http://www.sciencedirect.com/science/journal/15375110 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.biosystemseng.2022.04.014 ↗
- Languages:
- English
- ISSNs:
- 1537-5110
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 2089.670500
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British Library HMNTS - ELD Digital store - Ingest File:
- 21518.xml