Rapid Prediction of Camelina Seed Oil Content Using Near-Infrared Spectroscopy

2017 ◽  
Vol 31 (5) ◽  
pp. 5629-5634 ◽  
Author(s):  
Ke Zhang ◽  
Zhenglin Tan ◽  
Chengci Chen ◽  
Xiuzhi Susan Sun ◽  
Donghai Wang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Oil Content ◽  
Near Infrared ◽  
Seed Oil ◽  
Seed Oil Content ◽  
Rapid Prediction ◽  
Camelina Seed

scholarly journals Rancidity Estimation of Perilla Seed Oil by Using Near-Infrared Spectroscopy and Multivariate Analysis Techniques

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Suk-Ju Hong ◽  
Shin-Joung Rho ◽  
Ah-Yeong Lee ◽  
Heesoo Park ◽  
Jinshi Cui ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Seed Oil ◽  
Prediction Models ◽  
Oxidation Process ◽  
Analysis Techniques ◽  
Peroxide Values ◽  
Perilla Seed

Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

scholarly journals Rapid Prediction of the Chemical Information of Wood Powder from Softwood Species Using Near-Infrared Spectroscopy

BioResources ◽  
2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Se-Yeong Park ◽  
Jong-Chan Kim ◽  
Seungheon Yeon ◽  
Sang-Yun Yang ◽  
Hwanmyeong Yeo ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Chemical Information ◽  
Wood Powder ◽  
Rapid Prediction ◽  
Softwood Species

Rapid determination of oil content in dried-ground oil palm mesocarp and kernel using near infrared spectroscopy

2017 ◽  
Vol 25 (5) ◽  
pp. 338-347 ◽  
Author(s):  
Sudarno ◽  
Divo D Silalahi ◽  
Tauvik Risman ◽  
Baiq L Widyastuti ◽  
F Davrieux ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Oil Palm ◽  
Standard Error ◽  
Oil Content ◽  
Near Infrared ◽  
Cross Validation ◽  
Calibration Model ◽  
Reliable Technique

Near infrared spectroscopy calibrations for rapid oil content determination of dried-ground oil palm mesocarp and kernel were developed. Samples were analyzed, one set using the Soxhlet extraction method for reference analysis and the other set scanned by near infrared spectroscopy instrument for calibration. Successful calibrations were obtained with good accuracy and precision for mesocarp and kernel, based on statistical models. Math treatment and scatter correction had significant effects on the fitting of the calibration model. The best obtained calibration models were demonstrated by multiple correlation coefficient (R2), standard error of calibration, standard error of cross validation, coefficient of determination in cross validation (1-VR) and relative predictive deviation of calibration, which respectively were 0.997, 1.21%, 1.23%, 0.997 and 17.89 for mesocarp and 0.952, 0.47%, 0.53%, 0.94 and 4.00 for kernel. The correlations between reference and predicted values for samples in the validation sets were in agreement with high linearity, high ratio performance to deviation of prediction (≥4.00) and low standard error of prediction samples for both samples. The results demonstrated that near infrared spectroscopy can be used as an alternative and reliable technique to estimate the mesocarp and kernel oil contents in dry matter basis accurately and rapidly.

Rapid prediction of wood crystallinity in Pinus elliotii plantation wood by near-infrared spectroscopy

2007 ◽  
Vol 53 (5) ◽  
pp. 449-453 ◽  
Author(s):  
Ze-Hui Jiang ◽  
Zhong Yang ◽  
Chi-Leung So ◽  
Chung-Yun Hse
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Rapid Prediction

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.

Sign in / Sign up

Export Citation Format

Share Document