Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff
Two handheld near infrared (NIR) spectrometers were used to quantify crude protein (CP) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 n...
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-03-01
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| Series: | AIMS Agriculture and Food |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/agrfood.2021027?viewType=HTML |
| _version_ | 1818017106425282560 |
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| author | Isaac R. Rukundo Mary-Grace C. Danao James C. MacDonald Randy L. Wehling Curtis L. Weller |
| author_facet | Isaac R. Rukundo Mary-Grace C. Danao James C. MacDonald Randy L. Wehling Curtis L. Weller |
| author_sort | Isaac R. Rukundo |
| collection | DOAJ |
| description | Two handheld near infrared (NIR) spectrometers were used to quantify crude protein (CP) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 nm) with a spectral interval of 1 nm (H1). Second was a smartphone spectrometer, which measured from 900–1700 nm with a spectral interval of 4 nm (H2). Spectral data of 147 forage and feed samples were collected by both handheld instruments and split into calibration (n = 120) and validation (n = 27) sets. For H1, only absorbances in the NIR region (780–2500 nm) were used in the multivariate analyses, while for H2, absorbances in the second and third overtone regions (940–1660 nm) were used. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using mean-centered data that had been preprocessed using standard normal variate (SNV) or Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm. PCA models showed two major groups—one with Sweet Bran and distillers grains, and the other with corn silage and corn stalk. Using H1 spectra, the PLS regression model that best predicted CP followed SG1 preprocessing. This model had low root mean square error of prediction (RMSEP = 2.22%) and high ratio of prediction to deviation (RPD = 5.24). With H2 spectra, the model best predicting CP was based on SG2 preprocessing, returning RMSEP = 2.05% and RPD = 5.74. These values were not practically different than those of H1, indicating similar performance of the two devices despite having absorbance measurements only in the second and third overtone regions with H2. The result of this study showed that both handheld NIR instruments can accurately measure forage and feed CP during screening, quality, and process control applications. |
| first_indexed | 2024-04-14T07:21:31Z |
| format | Article |
| id | doaj.art-477777d336344354ba8a7daadc351b31 |
| institution | Directory Open Access Journal |
| issn | 2471-2086 |
| language | English |
| last_indexed | 2024-04-14T07:21:31Z |
| publishDate | 2021-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Agriculture and Food |
| spelling | doaj.art-477777d336344354ba8a7daadc351b312022-12-22T02:06:08ZengAIMS PressAIMS Agriculture and Food2471-20862021-03-016246347810.3934/agrfood.2021027Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuffIsaac R. Rukundo0Mary-Grace C. Danao1James C. MacDonald 2Randy L. Wehling 3Curtis L. Weller41. Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA1. Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA2. Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA1. Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA1. Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USATwo handheld near infrared (NIR) spectrometers were used to quantify crude protein (CP) content of mixed forage and feedstuff composed of Sweet Bran, distiller's grains, corn silage, and corn stalk. First was a transportable spectrometer, which measured in the visible and NIR ranges (320–2500 nm) with a spectral interval of 1 nm (H1). Second was a smartphone spectrometer, which measured from 900–1700 nm with a spectral interval of 4 nm (H2). Spectral data of 147 forage and feed samples were collected by both handheld instruments and split into calibration (n = 120) and validation (n = 27) sets. For H1, only absorbances in the NIR region (780–2500 nm) were used in the multivariate analyses, while for H2, absorbances in the second and third overtone regions (940–1660 nm) were used. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using mean-centered data that had been preprocessed using standard normal variate (SNV) or Savitzky-Golay first derivative (SG1) or second derivative (SG2) algorithm. PCA models showed two major groups—one with Sweet Bran and distillers grains, and the other with corn silage and corn stalk. Using H1 spectra, the PLS regression model that best predicted CP followed SG1 preprocessing. This model had low root mean square error of prediction (RMSEP = 2.22%) and high ratio of prediction to deviation (RPD = 5.24). With H2 spectra, the model best predicting CP was based on SG2 preprocessing, returning RMSEP = 2.05% and RPD = 5.74. These values were not practically different than those of H1, indicating similar performance of the two devices despite having absorbance measurements only in the second and third overtone regions with H2. The result of this study showed that both handheld NIR instruments can accurately measure forage and feed CP during screening, quality, and process control applications.https://www.aimspress.com/article/doi/10.3934/agrfood.2021027?viewType=HTMLanimal nutritionprincipal components analysispartial least squares regressionportable spectroscopy |
| spellingShingle | Isaac R. Rukundo Mary-Grace C. Danao James C. MacDonald Randy L. Wehling Curtis L. Weller Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff AIMS Agriculture and Food animal nutrition principal components analysis partial least squares regression portable spectroscopy |
| title | Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff |
| title_full | Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff |
| title_fullStr | Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff |
| title_full_unstemmed | Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff |
| title_short | Performance of two handheld NIR spectrometers to quantify crude protein of composite animal forage and feedstuff |
| title_sort | performance of two handheld nir spectrometers to quantify crude protein of composite animal forage and feedstuff |
| topic | animal nutrition principal components analysis partial least squares regression portable spectroscopy |
| url | https://www.aimspress.com/article/doi/10.3934/agrfood.2021027?viewType=HTML |
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