Rapid estimation of single cell oil content of solid-state fermented mass using near-infrared spectroscopy

2008 ◽  
Vol 99 (18) ◽  
pp. 8869-8872 ◽  
Author(s):  
Xiaowei Peng ◽  
Hongzhang Chen
Author(s):  
Laurence Schimleck ◽  
Robert Evans ◽  
David Jones ◽  
Richard Daniels ◽  
Gary Peter ◽  
...  

2012 ◽  
Vol 482-484 ◽  
pp. 1515-1519
Author(s):  
Zhi Guo Zhang ◽  
Hong Zhang Chen

Recently, some solid state fermentation (SSF) processes of xanthan production were studied. However, quantitative analysis of the concentration of xanthan and biomass is more complicated than that of submerged fermentation. To facilitate the analysis of these components, near–infrared spectroscopy (NIRS) was used. A NIRS calibration models for rapidly estimating xanthan and biomass concentration in xanthan fermentation on inert support of polyurethane foam was established. The wavenumber and spectral pretreatment method were optimized. The data of cross validation and external validation shows that NIRS was suitable for rapid and accurate quantification of the concentration of xanthan and biomass in solid state fermentation on inert support. This method will provide much convenience for the research of solid state fermentation on inert support.


2017 ◽  
Vol 25 (5) ◽  
pp. 338-347 ◽  
Author(s):  
Sudarno ◽  
Divo D Silalahi ◽  
Tauvik Risman ◽  
Baiq L Widyastuti ◽  
F Davrieux ◽  
...  

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.


2020 ◽  
Author(s):  
Gokhan Hacisalihoglu ◽  
Jelani Freeman ◽  
Paul R. Armstrong ◽  
Brad W. Seabourn ◽  
Lyndon D. Porter ◽  
...  

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.


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