Prediction Of Protein Content Research Articles

Spectral Multivariable Selection and Calibration in Visible-Shortwave Near-Infrared Spectroscopy for Non-Destructive Protein Assessment of Spirulina Microalga Powder

Visible and shortwave-near-infrared spectroscopy was used to predict protein content in Spirulina powder. Three variable selection algorithms of partial least square based uninformative variable elimination, partial least square based genetic algorithm, and successive projections algorithm were analyzed. Results showed that it was necessary to operate partial least square based uninformative variable elimination before successive projections algorithm. Successive projections algorithm performed better than partial least square based genetic algorithm, because it had a better result and fewer selected variables. The results showed that partial least square based uninformative variable elimination-successive projections algorithm was a good hybrid algorithm for the multivariable selection, and it was feasible for using visible and shortwave-near-infrared spectroscopy to non-destructively estimate protein content in Spirulina powder.

Sequentially Integrated Optimization of the Conditions to Obtain a High-Protein and Low-Antinutritional Factors Protein Isolate from Edible Jatropha curcas Seed Cake.

Jatropha curcas seed cake is a protein-rich byproduct of oil extraction which could be used to produce protein isolates. The purpose of this study was the optimization of the protein isolation process from the seed cake of an edible provenance of J. curcas by an alkaline extraction followed by isoelectric precipitation method via a sequentially integrated optimization approach. The influence of four different factors (solubilization pH, extraction temperature, NaCl addition, and precipitation pH) on the protein and antinutritional compounds content of the isolate was evaluated. The estimated optimal conditions were an extraction temperature of 20°C, a precipitation pH of 4, and an amount of NaCl in the extraction solution of 0.6 M for a predicted protein content of 93.3%. Under these conditions, it was possible to obtain experimentally a protein isolate with 93.21% of proteins, 316.5 mg 100 g−1 of total phenolics, 2891.84 mg 100 g−1 of phytates and 168 mg 100 g−1 of saponins. The protein content of the this isolate was higher than the content reported by other authors.

Prediction of protein content in malting barley using proximal and remote sensing

This paper examines the prediction of within-field differences in protein in malting barley at a late growth stage using the Yara N-Sensor and prediction of its regional variation with medium resolution satellite images. Field predictions of protein in the crop at a late growth stage could be useful for harvest planning, whereas regional prediction of barley quality before harvest would be useful for the grain industry. The project was carried out in central Sweden where the variation in protein content of malting barley has been documented both within fields and regionally. Scanning with an N-sensor and crop sampling were carried out in 2007 and 2008 at several fields. The regional data used consisted of weather data, quality analyses of the malting barley delivered to the major farmers’ co-operative, crops grown and field boundaries. Satellite scenes (SPOT 5 and IRS-P6 LISS-III) were acquired from a date as close as possible to the N-sensor scans. Reasonable partial least squares (PLS) models could be constructed based on weather and reflectance data from either the N-sensor or satellite. The models used mainly reflectance data, but the weather data improved them. Better field models could be created with data from the N-sensor than from the satellite image, but a local satellite-based model based on a simple ratio (middle infrared/green) in combination with weather was useful in regional prediction of malting barley protein. A regional prediction model based only on the weather variables explained about half the variation in recorded protein.

A Model for Predicting Protein Content in Winter Wheat Grain Based on Land-Sat TM Image and Nitrogen Accumulation

A Model for Predicting Protein Content in Winter Wheat Grain Based on Land-Sat TM Image and Nitrogen Accumulation

Triticale Moisture and Protein Content Prediction by Near‐Infrared Spectroscopy (NIRS)

ABSTRACTThe use of near‐infrared spectroscopy (NIRS) for the prediction of whole‐grain triticale moisture and protein content was evaluated. Because triticale is genetically close to wheat, commercially available wheat prediction models for Foss Infratec analyzers were applied in a year‐by‐year basis to triticale samples harvested in Iowa between 2002 and 2006. Wheat models were not applicable to moisture prediction (SEPavg = 0.37% pt; expected SEP on wheat samples 0.15% pt), but usable for screening for protein (SEPavg = 0.38% pt; expected SEP on wheat samples 0.25% pt). Dedicated triticale calibrations were developed from 2002 to 2005 data. Prediction results for 2006 samples only were compared. Triticale calibrations performed better than wheat calibrations for 2006 samples (moisture SEPtriticale = 0.29% pt, SEPwheat = 0.50% pt; protein SEPtriticale = 0.30% pt, SEPwheat = 0.68% pt).

Effects of steeping conditions on wet‐milling attributes of amaranth

SummaryThe effect of temperature and SO2 concentration on fraction yields (starch, gluten and fibre yields) and protein content of starch during wet milling were investigated using response surface method. Soaking time was adequate to accomplish saturation of the grain. A 32 factorial design including triplicate of central point was performed. The starch and fibre yields decreased with temperature. The effect of SO2 on fraction yields was significant; for gluten, the effect was dependent on the level of temperature. The predicted optimum conditions required to maximise starch and gluten yields together with the minimisation of protein content in starch fraction included steeping at 53.9 °C (1.6 h) with 0.041 w/v% of SO2 concentration. The predicted starch and gluten yields were 45.0% ± 1.35% and 17.5% ± 0.80%, respectively. The predicted protein content for starch fraction was 0.60% ± 0.015%. These predicted values were satisfactorily checked by an experiment at optimal soaking conditions.

Predicting Wheat Quality Characteristics and Functionality Using Near‐Infrared Spectroscopy

ABSTRACTThe accuracy of using near‐infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b* values with accuracies suitable for process control (R2 > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a* values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R2 > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content.

NIR spectroscopy—a non-destructive analytical tool for protein quantification within lipid implants

Lipid implants have been proposed as promising sustained release devices for the parenteral application of pharmaceutical proteins. Near infrared spectroscopy (NIRS) has been reported in the literature to be a non-destructive tool for drug quantification within controlled release matrix systems based on poly-(lactic-co-glycolic) acid (PLGA). The objective of this study was to evaluate the potential application of NIRS for protein content determination within lipid matrices containing stabilizing and release modifying additives. Bovine serum albumin (BSA) and rh-interferon α-2a (IFN α-2a) were initially lyophilized with trehalose and then blended with tristearin (matrix material) and optionally with polyethylene glygol 6000 (PEG, release modifier). Implants were prepared by compression. NIR transmittance spectra were measured on a NIRTab® spectrometer. Partial least squares regression (PLSR) calibration models were developed to predict protein content in implants from the NIRS results. Additional samples were measured after performing release studies. It could be shown that NIRS allowed protein quantification in complex matrix systems with good accuracy after implant manufacture and during release studies [e.g., standard error of prediction (SEP) between 57 μg–176 μg]. In addition, small protein amounts down to 70 μg of incorporated protein per implant could be determined, thus demonstrating the low detection limit of NIRS.

The near Infrared Window—Glass or Quartz? A Study in Precision

All near infrared instruments are equipped with a “window” to protect the detectors from the atmosphere. Many types of sample cells are also fitted with a window between the sample surface and the detector. These windows may be composed of special optical glass or quartz. This paper describes variability of wheat flour spectra when using a series of glass and quartz windows. Differences among the windows of both glass and quartz exhibited differences of up to respectively 40% and 14% in the spectral data when they were used in the development of calibration models for the prediction of protein content in wheat flour. The differences among the windows did not affect the precision of spectral data when the same window was used for replicate testing, but induced absolute biases of up to over 3% (glass) and 1% (quartz) to the predicted protein results. Biases could be countered by use of derivatives or by combining spectral data from flours by using the different windows in the development of the calibration models. An anomaly was noted in the flour spectra obtained using quartz window number 4. The anomaly was found to cause significant biases in the prediction of protein content. The biases could be removed if the calibration model was developed using a wavelength range that avoided the anomaly.

“Plasmo2D”: An Ancillary Proteomic Tool to Aid Identification of Proteins from Plasmodium falciparum

Bioinformatics tools to aid gene and protein sequence analysis have become an integral part of biology in the post-genomic era. Release of the Plasmodium falciparum genome sequence has allowed biologists to define the gene and the predicted protein content as well as their sequences in the parasite. Using pI and molecular weight as characteristics unique to each protein, we have developed a bioinformatics tool to aid identification of proteins from Plasmodium falciparum. The tool makes use of a Virtual 2-DE generated by plotting all of the proteins from the Plasmodium database on a pI versus molecular weight scale. Proteins are identified by comparing the position of migration of desired protein spots from an experimental 2-DE and that on a virtual 2-DE. The procedure has been automated in the form of user-friendly software called "Plasmo2D". The tool can be downloaded from http://144.16.89.25/Plasmo2D.zip.

The use of NIRS for prediction of protein content in residues after incubation in the rumen

The use of NIRS for prediction of protein content in residues after incubation in the rumen

Low‐field1H nuclear magnetic resonance and chemometrics combined for simultaneous determination of water, oil, and protein contents in oilseeds

AbstractPrediction of the content of water, oil, and protein in rape and mustard seed was examined by a combination of low‐field1H nuclear magnetic resonance (LF‐NMR) and chemometrics, enabling utilization of the entire relaxation curves in the data evaluation. To increase the range of relative contents, the untreated seeds were wetted and dried; each treatment was followed by NMR analysis. The chemometric results are compared to traditional evoluation by multiexponential fitting of the relaxation curves. For this purpose a new jackKnife validation procedure was developed to evaluate the number of exponential components objectively. Classification of the two kinds of seeds was easily performed by LN‐NMR. Partial least squares regression to oil content in untreated rape and mustard seed yielded models with correlation coefficients ofr=0.88 and 0.89 with root mean square error of cross‐validation (RMSECV) of 0.84 and 0.45, respectively. The rapeseed model was based on one component, wheres the mustard seed model was based on two components. If the seeds were dried, the predictive performance improved tor=0.98 and RMSECV=0.38 for rapeseed and tor=0.95 and RMSECV=0.38 for mustard seed. Upon drying, prediction of protein content in mustard seed improved, whereas the prediction of protein for rapeseed deteriorated. Global models, including the combination of untreated, wet, and dry seeds, all resulted in a robust and good predictive performance with RMSECV in the range 0.8–1.3% to water, oil, and protein content. It was demonstrated that drying the seeds to simultaneously determine water and oil content was not necessary when chemometrics was applied on the relaxation curves.

Predicting Protein Content by near Infrared Reflectance Spectroscopy in Diverse Cereal Food Products

Simultaneous determination of constituents (e.g. dietary fibre, protein, fat) by near infrared (NIR) spectroscopy would increase the speed and efficiency of nutrient analysis while substantially reducing the cost. Previous work has described the development of NIR reflectance models for the prediction of dietary fibre in a diverse group of cereal food products. While NIR spectroscopy has been used to measure protein content in cereal samples comprised of a single grain type, the utility of the NIR technique would be greatly improved if it could be expanded to cereal products derived from a diverse cross-section of grains and formulations. The present study was conducted to investigate the potential of NIR spectroscopy for the analysis of protein in a data set that included products with numerous grains, such as wheat, oats, rice, rye, corn, millet, buckwheat and with a wide range of fat, sugar and fibre contents. In addition, numerous processing techniques and food additives were represented in the data set. Nitrogen content of dry-milled cereal products was measured by combustion analysis (AOAC Method 992.23) and the range in nitrogen values was from 0.65 to 3.31% of dry weight. Milled cereal products were scanned from 1100 to 2500nm with a scanning monochromator. A nitrogen calibration was developed, using a commercial analysis program, with modified partial least squares as the regression method. The standard error of cross validation and R2 for nitrogen ( n=147 calibration samples) were 0.090% and 0.973, respectively. Independent validation samples ( n=72) were predicted with a standard error of performance of 0.079% nitrogen and r2 of 0.984. Because of the diversity of grains in the data set, crude protein was calculated using two nitrogen-to-protein conversion methods and two PLS models were developed for the prediction of crude protein. Crude protein was predicted with a similar precision to nitrogen and the results for both protein models are within the precision required for US nutrition labelling legislation. In conclusion, NIR reflectance spectroscopy can be used for rapid and accurate prediction of nitrogen and crude protein content in a heterogeneous group of cereal products comprised of a wide cross-section of grains and formulations.

The prediction of percentage of protein in pork carcasses

Determination of percentage of protein in a pork carcass is a very costly and lengthy process. Therefore, we attempted to identify an easy and economical means to predict protein content of pork carcasses. Forty-seven market-weight pigs were slaughtered. The right side of each carcass was skinned, deboned and ground for composition analysis. Carcass data were collected on the left side and specific gravity analysis was performed on the side, ham and 8-9-10 rib loin section of each carcass. The hams and loin sections were dissected into skin, bone, subcutaneous fat, seam fat and individual muscles. After these components were weighted, they were analyzed for chemical composition. Percentage of chemical protein of each animal was determined and correlated with each of the weights and measurements taken on the carcass, ham and loin sections. Stepwise regression was used to predict decimal fraction of protein using either the carcass data, specific gravity ham measurements, loin section measurements or various combinations of these. In prediction equation, specific gravity of the carcass accounted for 73.4% of the variation found in percentage of protein. Prediction equations using indicators from the ham and loin section resulted in equations with R-square values as high as .783. These results suggest that specific gravity or indicators from the carcass, ham or loin section can be employed to predict percentage of protein in pork carcasses.

Attempts to predict forage yield and protein content of individual plants of tall wheatgrass [ Agropyron elongatum (Host) Beauv.

A study was conducted with individual plants from ten clones of tall wheatgrass, Agropyron elongatum (Host) Beauv., to determine if leaf length, leaf width, crown height, base circumference, and combinations (first order interactions) of these traits could be used to predict dry forage yield and protein content. Simple regressions indicated that leaf length, base circumference and crown height contributed substantially to the variation in forage yield. Multiple regressions gave coefficients of determination ( R 2′s) ranging from 43.8 to 81.0% using between three and six independent variables. Although most of these coefficients of determination were highly significant statistically, they were not large enough for practical use in predicting forage yield with sufficient precision. Similar simple regressions on protein content gave r 2 percentages for leaf length ranging between 15 and 39%. Lower values were obtained with the other three variables. The selected R 2′s accounted for 42.0 to 55.4% of the variation in protein content. These coefficients of determination are too low for the practical use of regression equations in predicting protein content in a tall wheatgrass breeding nursery.

Možnost prognózy obsahu bílkovin v zrnu ječmene.

Možnost prognózy obsahu bílkovin v zrnu ječmene.