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Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model

View Article: PubMed Central - PubMed

ABSTRACT

Near-infrared spectroscopy combined with chemometrics was applied to construct a hybrid model for the non-invasive detection of protein content in different types of plant feed materials. In total, 829 samples of plant feed materials, which included corn distillers’ dried grains with solubles (DDGS), corn germ meal, corn gluten meal, distillers’ dried grains (DDG) and rapeseed meal, were collected from markets in China. Based on the different preprocessed spectral data, specific models for each type of plant feed material and a hybrid model for all the materials were built. Performances of specific model and hybrid model constructed with full spectrum (full spectrum model) and selected wavenumbers with VIP (variable importance in the projection) scores value bigger than 1.00 (VIP scores model) were also compared. The best spectral preprocessing method for this study was found to be the standard normal variate transformation combined with the first derivative. For both full spectrum and VIP scores model, the prediction performance of the hybrid model was slightly worse than those of the specific models but was nevertheless satisfactory. Moreover, the VIP scores model obtained generally better performances than corresponding full spectrum model. Wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were found to be the key wavenumbers in modeling protein content in these plant feed materials. The values for the root mean square error of prediction (RMSEP) and the relative prediction deviation (RPD) obtained with the VIP scores hybrid model were 1.05% and 2.53 for corn DDGS, 0.98% and 4.17 for corn germ meal, 0.75% and 6.99 for corn gluten meal, 1.54% and 4.59 for DDG, and 0.90% and 3.33 for rapeseed meal, respectively. The results of this study demonstrate that the protein content in several types of plant feed materials can be determined using a hybrid near-infrared spectroscopy model. And VIP scores method can be used to improve the general predictability of hybrid model.

No MeSH data available.


VIP scores curves for full spectrum specific and hybrid models.
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pone.0163145.g002: VIP scores curves for full spectrum specific and hybrid models.

Mentions: Fig 2 displays the VIP scores plots of the specific models for different plant feed materials. Clearly, the wavenumbers of approximately 4500 cm-1, 4660 cm-1, 4836 cm-1, 5684 cm-1, 5724 cm-1 and 6728 cm-1 contribute the most to modeling the protein content in these plant feed ingredients. These wavenumbers are closely related to the chemical structure of proteins; specifically, 4500 cm-1 and 4660 cm-1 are associated with the combination of the N-H, C-N and C = O vibrations of the amide group; 4836 cm-1 is associated with the N-H vibration of proteins; 5684 cm-1 and 5724 cm-1 are associated with the C-H vibration of lipids, respectively, and 6728 cm-1 is associated with the N-H vibration of aromatic amines [17].


Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model
VIP scores curves for full spectrum specific and hybrid models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036844&req=5

pone.0163145.g002: VIP scores curves for full spectrum specific and hybrid models.
Mentions: Fig 2 displays the VIP scores plots of the specific models for different plant feed materials. Clearly, the wavenumbers of approximately 4500 cm-1, 4660 cm-1, 4836 cm-1, 5684 cm-1, 5724 cm-1 and 6728 cm-1 contribute the most to modeling the protein content in these plant feed ingredients. These wavenumbers are closely related to the chemical structure of proteins; specifically, 4500 cm-1 and 4660 cm-1 are associated with the combination of the N-H, C-N and C = O vibrations of the amide group; 4836 cm-1 is associated with the N-H vibration of proteins; 5684 cm-1 and 5724 cm-1 are associated with the C-H vibration of lipids, respectively, and 6728 cm-1 is associated with the N-H vibration of aromatic amines [17].

View Article: PubMed Central - PubMed

ABSTRACT

Near-infrared spectroscopy combined with chemometrics was applied to construct a hybrid model for the non-invasive detection of protein content in different types of plant feed materials. In total, 829 samples of plant feed materials, which included corn distillers’ dried grains with solubles (DDGS), corn germ meal, corn gluten meal, distillers’ dried grains (DDG) and rapeseed meal, were collected from markets in China. Based on the different preprocessed spectral data, specific models for each type of plant feed material and a hybrid model for all the materials were built. Performances of specific model and hybrid model constructed with full spectrum (full spectrum model) and selected wavenumbers with VIP (variable importance in the projection) scores value bigger than 1.00 (VIP scores model) were also compared. The best spectral preprocessing method for this study was found to be the standard normal variate transformation combined with the first derivative. For both full spectrum and VIP scores model, the prediction performance of the hybrid model was slightly worse than those of the specific models but was nevertheless satisfactory. Moreover, the VIP scores model obtained generally better performances than corresponding full spectrum model. Wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were found to be the key wavenumbers in modeling protein content in these plant feed materials. The values for the root mean square error of prediction (RMSEP) and the relative prediction deviation (RPD) obtained with the VIP scores hybrid model were 1.05% and 2.53 for corn DDGS, 0.98% and 4.17 for corn germ meal, 0.75% and 6.99 for corn gluten meal, 1.54% and 4.59 for DDG, and 0.90% and 3.33 for rapeseed meal, respectively. The results of this study demonstrate that the protein content in several types of plant feed materials can be determined using a hybrid near-infrared spectroscopy model. And VIP scores method can be used to improve the general predictability of hybrid model.

No MeSH data available.