Granule Characterization During Fluid Bed Drying by Development of a Near Infrared Method to Determine Water Content and Median Granule Size

Simultaneous real-time monitoring of water content and mean particle size in the powder bed of a fluidized-bed granulator was performed by near-infrared (NIR) spectroscopy through a window, and the findings were used to evaluate the granular properties.

Download Full-text

Freeze-dried meningococcal vaccine: Total error assessment of a near-infrared method for water content determination

Talanta ◽

10.1016/j.talanta.2019.120659 ◽

2020 ◽

Vol 211 ◽

pp. 120659 ◽

Cited By ~ 1

Author(s):

Ana Mandac Zubak ◽

Ana Horvat ◽

Dražen Čavužić ◽

Ines Fabijanić

Keyword(s):

Water Content ◽

Near Infrared ◽

Total Error ◽

Error Assessment ◽

Meningococcal Vaccine ◽

Content Determination ◽

Freeze Dried ◽

Infrared Method

Download Full-text

Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging

International Journal of Pharmaceutics ◽

10.1016/j.ijpharm.2020.120069 ◽

2021 ◽

pp. 120069

Author(s):

Michael Ghijs ◽

Brecht Vanbillemont ◽

Niels Nicolaï ◽

Thomas De Beer ◽

Ingmar Nopens

Keyword(s):

Moisture Content ◽

Near Infrared ◽

Chemical Imaging ◽

Two Dimensional ◽

Size Measurement ◽

Fluid Bed

Download Full-text

Water absorption thermodynamics in single wood pellets modelled by multivariate near-infrared spectroscopy

Holzforschung ◽

10.1515/hf.2008.071 ◽

2008 ◽

Vol 62 (4) ◽

Cited By ~ 7

Author(s):

Torbjörn A. Lestander

Keyword(s):

Water Content ◽

Near Infrared ◽

Calorific Value ◽

Least Squares Regression ◽

Wood Pellets ◽

Differential Heat ◽

On Line ◽

Explained Variation ◽

Test Sets ◽

Content Range

Abstract Samples of wood pellets were adjusted into six water content classes from 0% to 12%. The water content in single pellets varied between 0.1% and 14.2%. Three equations were constructed to estimate the differential heat of sorption (-ΔH) values from (1) fractal-geometry, (2) isosteric, and (3) calorimetric data. The ranges in calculated -ΔH of single pellets were (1) 133–1475, (2) 315–881, and (3) 195–1188 J g-1 water, respectively, across the studied moisture content range. Partial least squares regression was used to model near-infrared (NIR) spectra from single pellets and to predict -ΔH values and water content. The explained variation in test sets for the different models ranged from 97.1% to 99.9%. The shifts in peak absorbance for two water bands indicated that frequency in overtone vibration of O-H stretching and bending decreased, when water content was raised. Simulations of mixes between pellets of differential heat values showed that released heat was up to 0.03% of the gross calorific value of wood pellets. This heat may be a major contributor to initial temperature increases in pellet stacks during storage. The results indicate that on-line NIR based predictions of differential heat in wood pellets is possible to apply in the pellet industry.

Download Full-text

Near-Infrared Method for Analysis of Block and Random Ethylene-Propylene Copolymers.

Analytical Chemistry ◽

10.1021/ac60234a018 ◽

1966 ◽

Vol 38 (2) ◽

pp. 217-220 ◽

Cited By ~ 28

Author(s):

R. M. Bly ◽

P. E. Kiener ◽

B. A. Fries

Keyword(s):

Near Infrared ◽

Ethylene Propylene ◽

Infrared Method

Download Full-text

Water Properties of Soft Contact Lenses: A Comparative Near-Infrared Study of Two Hydrogel Materials

International Journal of Polymer Science ◽

10.1155/2016/3737916 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Jelena Munćan ◽

Ivana Mileusnić ◽

Jovana Šakota Rosić ◽

Aleksandra Vasić-Milovanović ◽

Lidija Matija

Keyword(s):

Water Content ◽

Near Infrared ◽

Contact Lenses ◽

Polymer Network ◽

The State ◽

Vinyl Pyrrolidone ◽

Soft Contact ◽

Infrared Study ◽

Soft Contact Lenses ◽

State Of Water

The functionality of soft contact lenses depends strongly on the water content and their water-transport ability. This study was conducted in order to examine the state of water in two sets of soft contact lenses: VSO38, pHEMA Filcon I 1, and VSO50, copolymer of HEMA and VP Filcon II 1 (HEMA = 2-hydroxy-ethyl methacrylate; VP = vinyl pyrrolidone). Hydrogel lenses were studied using near-infrared spectroscopy and the novel Aquaphotomics approach in order to determine the state of water in materials based on their near-infrared spectra. Aquaphotomics approach investigates absorption at specific vibrational bands of water’s covalent and hydrogen bonds which can provide information on how the water structure changes with the structural change of the polymer network. Principal component analysis and specific star-chart “aquagram” were used to analyse water spectral pattern in hydrogel materials. The findings show that material VSO38 has water predominantly organized in bound state, while material with higher water content, VSO50, has more free and weakly hydrogen bonded water. Our findings define in detail exact water species existing and interacting with the polymer network. The results show qualitative and quantitative possibilities of Aquaphotomics for better modelling and understanding water behaviour in hydrogel materials.

Download Full-text

Mapping of Leaf Water Content Using Near-Infrared Hyperspectral Imaging

Applied Spectroscopy ◽

10.1366/13-07028 ◽

2013 ◽

Vol 67 (11) ◽

pp. 1302-1307 ◽

Cited By ~ 8

Author(s):

Sakura Higa ◽

Hikaru Kobori ◽

Satoru Tsuchikawa

Keyword(s):

Water Content ◽

Hyperspectral Imaging ◽

Near Infrared ◽

Leaf Water ◽

Leaf Water Content

Download Full-text

Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Remote Sensing ◽

10.3390/rs13204125 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4125

Author(s):

Weiping Kong ◽

Wenjiang Huang ◽

Lingling Ma ◽

Lingli Tang ◽

Chuanrong Li ◽

...

Keyword(s):

Vertical Distribution ◽

Water Content ◽

Near Infrared ◽

Field Experiments ◽

Estimation Method ◽

Middle Layer ◽

Leaf Water ◽

Leaf Water Content ◽

Growth Stages ◽

Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

Download Full-text