Validation of mass-transfer model for VIPS process using in situ measurements performed by near-infrared spectroscopy

AIChE Journal ◽  
2012 ◽  
Vol 59 (2) ◽  
pp. 671-686 ◽  
Author(s):  
D. Bouyer ◽  
C. Pochat-Bohatier
Keyword(s):  
Mass Transfer ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
In Situ Measurements ◽  
Transfer Model ◽  
Mass Transfer Model

Prediction of crude protein and neutral detergent fibre concentration in residues of in situ ruminal degradation of pasture samples by near-infrared spectroscopy (NIRS)

2016 ◽  
Vol 56 (9) ◽  
pp. 1504 ◽  
Author(s):  
J. P. Keim ◽  
H. Charles ◽  
D. Alomar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Crude Protein ◽  
Near Infrared ◽  
External Validation ◽  
Neutral Detergent Fibre ◽  
Insufficient Amount ◽  
Ruminal Degradation ◽  
Animal Feedstuffs

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.

In situ quantification of microcarrier animal cell cultures using near-infrared spectroscopy

2010 ◽  
Vol 45 (8) ◽  
pp. 1427-1431 ◽  
Author(s):  
Emma Petiot ◽  
Patrick Bernard-Moulin ◽  
Thierry Magadoux ◽  
Cécile Gény ◽  
Hervé Pinton ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Cell Cultures ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Animal Cell ◽  
Animal Cell Cultures

scholarly journals Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy

Talanta ◽  
2021 ◽  
Vol 222 ◽  
pp. 121511
Author(s):  
Dolores Pérez-Marín ◽  
Tom Fearn ◽  
Cecilia Riccioli ◽  
Emiliano De Pedro ◽  
Ana Garrido
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Classification Models ◽  
Probabilistic Classification ◽  
Iberian Pig ◽  
Pig Carcasses

scholarly journals In situ near-infrared spectroscopy for soil organic matter prediction in paddy soil, Pasak watershed, Thailand

2018 ◽  
Vol 64 (No. 2) ◽  
pp. 70-75 ◽  
Author(s):  
Romsonthi Chutipong ◽  
Tawornpruek Saowanuch ◽  
Watana Sumitra
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Soil Quality ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Partial Least Square

Soil organic matter (SOM) is a major index of soil quality assessment because it is one of the key soil properties controlling nutrient budgets in agricultural production systems. The aim of the in situ near-infrared spectroscopy (NIRS) for SOM prediction in paddy area is evaluation of the potential of SOM and prediction of other soil properties. There are keys for soil fertility and soil quality assessments. A spectral reflectance of 130 soil samples was collected by field spectroradiometer in a region of near-infrared. Spectral reflectance collections were processed by the first derivative transformation with the Savitsky-Golay algorithms. Partial least square regression method was used to develop a calibration model between soil properties and spectral reflectance, which was used for prediction and validation processes. Finally, the results of this study demonstrate that NIRS is an effective method that can be used to predict SOM (R<sup>2</sup> = 0.73, RPD (ratio of performance to deviation) = 1.82) and total nitrogen (R<sup>2</sup> = 0.72, RPD = 1.78). Therefore, NIRS is a potential tool for soil properties predictions. The use of these techniques will facilitate the implementation of soil management with a decreasing cost and time of soil study in a large scale. However, further works are necessary to develop more accurate soil properties prediction and to apply this method to other areas.

Near-Infrared Spectroscopy for in situ Cure Monitoring of Dimethacrylate-Based Networks

2000 ◽  
Vol 54 (1) ◽  
pp. 39-43 ◽  
Author(s):  
L. Rey ◽  
J. Galy ◽  
H. Sautereau ◽  
G. Lachenal ◽  
D. Henry ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cure Monitoring
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