Effects of Particle Size on the Near-Infrared Reflectance Spectra of Wheat and Rape Seed Meal Mixtures

Devaux, Marie-Françoise; Nathier-Dufour, Nathalie; Robert, Paul; Bertrand, Dominique

doi:10.1366/0003702953963256

Cited by 28 publications

(17 citation statements)

References 6 publications

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“…The wavelength ranges of 800-1,600, 700-1,500, and 600-1,700 nm yielded better models than other wavelength ranges and thus were used for further model development. For ground materials, particle size affects absorbance values of the entire spectrum (Osborne and Fearn 1986;Devaux et al 1995), which explains why wider wavelength regions have better predictive ability for particle size than narrow wavelength regions. Also, particle size effects are more pronounced in the longer wavelength regions of the NIR (Norris and Williams 1984;Chapelle et al 1989).…”

Section: Nir Reflectance-granulation Modelsmentioning

confidence: 99%

Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes

et al. 2001

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In flour milling, a granulation sensor for ground wheat is needed for automatic control of a roller mill's roll gap. A near‐infrared (NIR) reflectance spectrometer was evaluated as a potential granulation sensor of first‐break ground wheat using offline methods. Sixty wheat samples, ground independently, representing six classes and five roller mill gaps, were each used for calibration and validation sets. Partial least squares regression was used to develop the models with cumulative mass of size fraction as the reference value. Combinations of four data pretreatments (log (1/R), baseline correction, unit area normalization, and derivatives) and three wavelength regions (700–1,500, 800–1,600, and 600–1,700 nm) were evaluated. Unit area normalization combined with baseline correction or second derivative yielded models that predicted well each size fraction of first‐break ground wheat. Standard errors of performance of 4.07, 1.75, 1.03, and 1.40 and r2 of 0.93, 0.90, 0.88, and 0.38 for the >1,041‐, >375‐, >240‐, and >136‐μm size ranges, respectively, were obtained for the best model. Results indicate that the granulation sensing technique based on NIR reflectance is ready for online evaluation.

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Section: Nir Reflectance-granulation Modelsmentioning

confidence: 99%

Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes

et al. 2001

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“…Thus, particle size is an integral part of the NIR absorbance values at all wavelengths. 10 Suspension polymerization is carried out heterogeneously by suspending the monomer (discontinuous phase) as droplets (50 -500 m in diameter) in water (continuous phase). The monomer droplets (subsequently converted to polymer particles) are prevented from coalescing by agitation and the presence of suspension stabilizers (which may be water-soluble polymers or waterinsoluble inorganic powders), leading to the development of particles of different sizes.…”

Section: Introductionmentioning

confidence: 99%

In-line evaluation of average particle size in styrene suspension polymerizations using near-infrared spectroscopy

Santos

Lima

Pinto

1998

J. Appl. Polym. Sci.

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ABSTRACT:The main objective of this article is evaluating the influence of average polystyrene particle size upon the near-infrared (NIR) spectra collected during suspension polymerization experiments and observing whether NIR spectroscopy may be used for in-line monitoring and control of average particle size. It is shown that NIR spectra are sensitive to changes of the average particle size, and that standard empirical models (PLS-partial least squares-and NN-neural networks) may be built to correlate average particle size and light absorbance at certain wavelengths fairly well. Finally, it is shown that these models allow the in-line evaluation of average particle size in styrene suspension polymerizations with NIR spectroscopy.

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“…8,9,22 The better SRW wheat granulation models have fewer (one to four) PLS factors (Tables 3 and 4), nearly as few as those of HRW wheat granulation models (two or three). 13 As suggested by the results of Devaux et al, 20 factors beyond three could be describing effects other than particle size and surface effects. The most likely effect is the sieving behaviour of ground soft wheat.…”

Section: Nir Reflectance-granulation Modelsmentioning

confidence: 92%

Granulation sensing of first‐break ground wheat using a near‐infrared reflectance spectrometer: studies with soft red winter wheats

et al. 2003

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A near-infrared reflectance spectrometer, previously evaluated as a granulation sensor for first-break ground wheat from six wheat classes and hard red winter (HRW) wheats, was further evaluated for soft red winter (SRW) wheats. Two sets of 35 wheat samples, representing seven cultivars of SRW wheat ground by an experimental roller mill at five roll gap settings (0.38, 0.51, 0.63, 0.75 and 0.88 mm), were used for calibration and validation. Partial least squares regression was applied to develop the granulation models using combinations of four data pretreatments (log(1/R), baseline correction, unit area normalisation and derivatives) and subregions of the 400-1700 nm wavelength range. Cumulative mass of size fraction was used as reference value. Models that corrected for path length effects (those that used unit area normalisation) predicted the bigger size fractions well. The model based on unit area normalisation/first derivative predicted 34 out of 35 validation spectra with standard errors of prediction of 3.53, 1.83, 1.43 and 1.30 for the >1041, >375, >240 and >136 mm size fractions respectively. Because of less variation in mass of each size fraction, SRW wheat granulation models performed better than the previously reported models for six wheat classes. However, because of SRW wheat flour's tendency to stick to the underside of sieves, the finest size fraction of these models did not perform as well as the HRW wheat models.

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Effects of Particle Size on the Near-Infrared Reflectance Spectra of Wheat and Rape Seed Meal Mixtures

Cited by 28 publications

References 6 publications

Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes

Evaluation of a Near‐Infrared Reflectance Spectrometer as a Granulation Sensor for First‐Break Ground Wheat: Studies with Six Wheat Classes

In-line evaluation of average particle size in styrene suspension polymerizations using near-infrared spectroscopy

Granulation sensing of first‐break ground wheat using a near‐infrared reflectance spectrometer: studies with soft red winter wheats

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