Establishment of nondestructive testing model of the protein content in wheat flour by near infrared spectroscopy

2011 ◽  
Author(s):  
Jin Huali ◽  
Wang Jinshui ◽  
Yan Lihui ◽  
Guo Rui ◽  
Luo Li
Keyword(s):  
Infrared Spectroscopy ◽  
Nondestructive Testing ◽  
Protein Content ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Testing Model

Determination of the protein content in brine from salted herring using near-infrared spectroscopy

LWT ◽  
2004 ◽  
Vol 37 (7) ◽  
pp. 803-809 ◽  
Author(s):  
Vibeke T Svensson ◽  
Henrik Hauch Nielsen ◽  
Rasmus Bro
Keyword(s):  
Infrared Spectroscopy ◽  
Protein Content ◽  
Near Infrared Spectroscopy ◽  
Near Infrared

scholarly journals Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).

2020 ◽  
Author(s):  
Gokhan Hacisalihoglu ◽  
Jelani Freeman ◽  
Paul R. Armstrong ◽  
Brad W. Seabourn ◽  
Lyndon D. Porter ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Pisum Sativum ◽  
Protein Content ◽  
Near Infrared Spectroscopy ◽  
Seed Weight ◽  
Oil Content ◽  
Near Infrared ◽  
Seed Quality ◽  
Single Seed ◽  
Selection Of

Abstract Background: Pea (Pisum sativum) is a prevalent cool season crop that produces seeds valued for high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single-seed near-infrared spectroscopy (NIRS) for measuring pea seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single-seed NIRS and wet chemistry methods. To demonstrate field relevance, the single-seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on protein content of harvested dry peas in a field trial. Results: External validation of Partial Least Squares (PLS) regression models showed high prediction accuracy for protein and weight (R2 = 0.94 for both) and less accuracy for oil (R2 = 0.75). Single seed weight was not significantly correlated with protein or oil content in contrast to previous reports. In the field study, the single-seed NIRS predicted protein values were within 1% of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects. Conclusion: The high accuracy of protein and weight estimation show that single-seed NIRS could be used in the dual selection of high protein, high weight peas early in the breeding cycle allowing for faster genetic advancement toward improved pea nutritional quality.

scholarly journals Examination of wheat rheological characteristics by near-infrared spectroscopy

2012 ◽  
pp. 99-104
Author(s):  
Éva Kónya ◽  
Zoltán Győri
Keyword(s):  
Infrared Spectroscopy ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Food Industry ◽  
Near Infrared ◽  
Chemical Properties ◽  
Rheological Characteristics ◽  
Physical And Chemical ◽  
Calibration Equations ◽  
And Chemical Properties

Near-infrared spectroscopy has many advantages that make it a widely used analitical method in the different areas, like agricultural and food industry as well. In wheat quality control rheological characteristics of dough made from wheat flour are as important as physical and chemical properties too. In this work we examined rheological properties of wheat flour samples by alveograph, and spectral data of the same samples were collected by FOSS Infratec 1241 instrument. Modified partial least squares analyses on NIR spectra were developed for two alveograph parameter (P/L és W) to get calibration equations.

Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique

2019 ◽  
Vol 82 (10) ◽  
pp. 1655-1662
Author(s):  
YI LIU ◽  
LAIJUN SUN ◽  
ZHIYONG RAN ◽  
XUYANG PAN ◽  
SHUANG ZHOU ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rbf Neural Network ◽  
Scatter Correction ◽  
Set Partitioning ◽  
Characteristic Analysis ◽  
Detection Model ◽  
Infrared Spectral

ABSTRACT A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x–y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.

Experimentation of Detecting the Talc-Containing Wheat Flour in Solution by Near Infrared Spectroscopy

2013 ◽  
Vol 330 ◽  
pp. 426-429 ◽  
Author(s):  
Cui Ling Liu ◽  
Xiu Li Dong ◽  
Xiao Rong Sun ◽  
Jing Zhu Wu ◽  
Sheng Nan Wu
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Infrared Spectroscopy ◽  
Wheat Flour ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quantitative Detection

Do quantitative detection of talc-containing wheat flour using near infrared spectroscopy combined with BP neural network.Confect 50samples by adulterating talc to wheat flour,randomly selected nine samples as the prediction samples, formulated10 talc-free flour samples for qualitative analysis.The results show that:BP neural network combined with NIR for the determination of talc-containing flour is ideal, can be used for talc-containing flour; the result of cluster analysis should that it need to seek better methods for talc-containing wheat flour.

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