Prediction Of Crude Protein Content Research Articles

Prediction of crude protein content in rice grain with canopy spectral reflectance

Non-destructive and rapid monitoring methods for crude protein content (CPC) in rice grain are of significance in nitrogen diagnosis and grain quality monitoring, and in enhancing nutritional management and use efficiency. In this study, CPC and canopy spectra in rice were measured based on rice field experiment. Key spectral bands were selected by principal component analysis (PCA) method, and the predicted models were built by multiple linear regressions (MLR), artificial neural network (ANN) and partial least squares regression (PLSR). The results showed that there is a significant correlation between CPC content and key spectral bands. The results of prediction for the three models were in order of PLSR > ANN > MLR with correlation values of 0.96, 0.92 and 0.90, respectively, for the validation data. Therefore, it is implied that CPC in rice (grain quality) could be estimated by canopy spectral data.  

Simple prediction of crude protein content in vegetation growing on abandoned cultivated land

AbstractThe accuracy of two simple methods was compared for the prediction of crude protein (CP) content of above‐ground plant material of mixed‐species composition on abandoned cultivated land in Japan. The first method is based on standard CP values (in g kg−1 dry matter) for individual species (STV method) as listed in the literature. The second procedure (GLM method) was an application of the generalized linear model using the relative above‐ground biomass of monocots and legumes, total herbage mass, and day of year. Predictions were made at the quadrat scale, and for surveyed sites based on average of values for five or six quadrats in a single survey. A ‘leave‐site‐out’ method was adopted for model validation of the generalized linear model. The observed values of CP content ranged between 21·5 and 161·9 g kg−1 dry matter (DM). With the STV method, the values of root mean square error (RMSE indicates average estimation error) were 50·9 at the quadrat level and 53·8 at the surveyed‐site level (both g kg−1 DM). When a ‘leave‐site‐out’ validation was carried out, the RMSE‐values for the GLM method were 23·2 at the quadrat level and 13·2 at the surveyed‐site level (both g kg−1 DM). We therefore propose adoption of the GLM method for the purpose of estimating the CP content in herbage on abandoned sites.

Prediction of crude protein content in field peas using near infrared reflectance spectroscopy

A rapid, near-infrared spectroscopic method to predict the crude protein contents of 72 field pea lines grown in Saskatchewan, both whole seeds and ground samples, was established. Correlation coefficients between the laboratory and predicted values were 0.938 and 0.952 for whole seed and ground seed, respectively. Both methods developed are adequate to support our field pea breeding programme. Key words: Field pea, near-infrared reflectance spectroscopy, crude protein

Non-invasive body composition measurement of broiler chickens between 4–18 weeks of age by computer tomography

Abstract. Non-invasive computerized tomography was performed on commercial broiler chickens, in both sexes, between 4 to 18 weeks of age, with the aim of body composition measurement. On the basis of 16–32 scans gathered from each bird during the scanning procedure, volumetric measurement of the breast muscle was performed. These values were at the 259; 493; 786 cm3 and 195; 460; 668 cm3 in male and female birds at the age of 6, 12 and 18 weeks, respectively. The total body fat content was characterized by the so called „fat index”, a value independent of the live weight. This indices were 7,3; 7,14; 9,48 and 8,8; 13,3; 21,6 following the above order. Total body fat and crude protein was determined both by CT and chemical analysis. The variables of the prediction equations were taken from the density values of the Hounsfield scale between −140 and +150, by summarizing the frequencies within each interval of 10 values. Several models were developed with Principal Component Analysis. The R² values of the estimations were high (R2 = 0,89; 0,88) for total body fat but moderate (R2 = 0,39; 0,44) in the prediction of crude protein content. The investigation of the tissue development in the body was carried out by means of 3D histograms.