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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.


Related in: MedlinePlus

Raw and pretreated spectra for different types of plant feed materials.(A) Raw and mean spectra of different plant feed materials. Some minor differences were existed between them. (B) SNV with first derivative pretreated mean spectra of different plant feed materials. Their response values at wavenumber around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by their mean protein contents.
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pone.0163145.g001: Raw and pretreated spectra for different types of plant feed materials.(A) Raw and mean spectra of different plant feed materials. Some minor differences were existed between them. (B) SNV with first derivative pretreated mean spectra of different plant feed materials. Their response values at wavenumber around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by their mean protein contents.

Mentions: Raw and pretreated spectra for different types of plant feed materials were presented in Fig 1A and 1B, respectively. The raw spectra of each types of plant feed materials were generally similar but some minor differences were existed. For example, spectra of corn DDGS, corn germ meal and DDG were nearly flat from 4664 cm-1 to 4836 cm-1. While that of corn gluten meal and rapeseed meal were not flat, a valley can be visually observed at those wavenumbers. Moreover, some differences can be directly found with regard to their SNVD1 pretreated mean spectra. It’s interesting to see that the response values at wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by the mean protein content of different types of plant feed materials. Moreover, according to the reference [17], 4500 cm-1, 4664 cm-1 and 4836 cm-1 are closely associated with vibrations of proteins. These results indicated that aforementioned wavenumbers may be play important roles in modeling protein content in those samples.


Non-Invasive Detection of Protein Content in Several Types of Plant Feed Materials Using a Hybrid Near Infrared Spectroscopy Model
Raw and pretreated spectra for different types of plant feed materials.(A) Raw and mean spectra of different plant feed materials. Some minor differences were existed between them. (B) SNV with first derivative pretreated mean spectra of different plant feed materials. Their response values at wavenumber around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by their mean protein contents.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0163145.g001: Raw and pretreated spectra for different types of plant feed materials.(A) Raw and mean spectra of different plant feed materials. Some minor differences were existed between them. (B) SNV with first derivative pretreated mean spectra of different plant feed materials. Their response values at wavenumber around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by their mean protein contents.
Mentions: Raw and pretreated spectra for different types of plant feed materials were presented in Fig 1A and 1B, respectively. The raw spectra of each types of plant feed materials were generally similar but some minor differences were existed. For example, spectra of corn DDGS, corn germ meal and DDG were nearly flat from 4664 cm-1 to 4836 cm-1. While that of corn gluten meal and rapeseed meal were not flat, a valley can be visually observed at those wavenumbers. Moreover, some differences can be directly found with regard to their SNVD1 pretreated mean spectra. It’s interesting to see that the response values at wavenumbers around 4500 cm-1, 4664 cm-1 and 4836 cm-1 were somehow ordered by the mean protein content of different types of plant feed materials. Moreover, according to the reference [17], 4500 cm-1, 4664 cm-1 and 4836 cm-1 are closely associated with vibrations of proteins. These results indicated that aforementioned wavenumbers may be play important roles in modeling protein content in those samples.

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.


Related in: MedlinePlus