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

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.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

“…Whilst the RMSEP measures the accuracy of prediction, the SEP measures the precision of the projection (i.e., the difference between repeated measurements). Similarly, Fan and coworkers [ 260 ] used n = 829 samples to assess protein content from several feed ingredients from China’s markets. Ferrerira and coworkers [ 261 ] determined dry matter, acid and neutral detergent fiber (ADF and NDF), gross energy, crude fat, ash, and protein to validate a mathematical model then to assess the metabolizable energy of corn ( n = 99 samples) used in swine feed.…”

Detection of Nitrogen Content in Rubber Leaves Using Near-Infrared (NIR) Spectroscopy with Correlation-Based Successive Projections Algorithm (SPA)

“…Fan et al. 5 detect the protein content in several types of plant feed materials with a hybrid NIR model which combines a VIP score method. Allison et al.…”

“…3 Mistele et al 4 estimate the N content status of crops under the description of the nitrogen nutrition index (NNI) by NIR models to enable site-specific fertilizing in precision farming system. Fan et al 5 detect the protein content in several types of plant feed materials with a hybrid NIR model which combines a VIP score method. Allison et al 6 studied the feasibility of using partial least square regression (PLSR) of Fourier transform IR spectra to predict N of two dried grass species in different growing conditions.…”

Effects of cultivars and fertilization levels on the quality of brown and polished rice

“…Rice cultivars have responded satisfactorily in terms of productivity or postharvest grain quality. Increasing rice production can be obtained through fertilization management, regardless of the cultivar, while the industrialtechnological quality of rice can be achieved according to the type of milling (Cen & He, 2007;Fan et al, 2016;Pan et al, 2013).…”

“…Milling time can reduce the percentage of protein and interfere with the morphological structure of starch (Cen & He, 2007;Fan et al, 2016;Huang et al, 2020;Lin et al, 2009). Thus, the fertilizer levels and type of industrial milling could be correlated with the cultivars to define the best production and milling condition by understanding the variation of protein and starch quality in rice.…”