On-Line Fat Content Classification of Inhomogeneous Pork Trimmings Using Multispectral near Infrared Interactance Imaging

2010 ◽  
Vol 18 (2) ◽  
pp. 135-145 ◽  
Author(s):  
Marion O'Farrell ◽  
Jens Petter Wold ◽  
Martin Høy ◽  
Jon Tschudi ◽  
Helene Schulerud
Keyword(s):  
Near Infrared ◽  
Fat Content ◽  
Content Classification ◽  
On Line

On-Line Classification of Synthetic Polymers Using near Infrared Spectral Imaging

2003 ◽  
Vol 11 (1) ◽  
pp. 71-81 ◽  
Author(s):  
A. Kulcke ◽  
C. Gurschler ◽  
G. Spöck ◽  
R. Leitner ◽  
M. Kraft
Keyword(s):  
Near Infrared ◽  
Spectral Imaging ◽  
Classification Systems ◽  
Polymer Materials ◽  
Shape Information ◽  
Polymer Waste ◽  
Spectral Angle Mapper ◽  
Line System ◽  
On Line

The lack of industrially-applicable, fast polymer classification systems is currently a major stumbling block in establishing both economically- and ecologically-useful waste recycling systems. With the advent of near infrared (NIR) spectral imaging for online classification, a method capable of distinguishing between different materials while simultaneously providing reliable size and shape information became available. In particular, polymer materials can be identified by their characteristic reflection spectra in the NIR without critical interferences from varying sample sizes and colours. A dedicated laboratory-scale prototype spectral imaging system has been developed and a number of classification algorithms have been evaluated for their applicability for polymer classification. Of the investigated algorithms, the Spectral Angle Mapper algorithm, supplemented by a threshold value and applied to the first derivatives of the normalised spectra, proved to be best suited for a rapid and reliable classification of polymers. Based on these achievements, an on-line system capable of classifying polymer parts delivered on a conveyor belt in real-time has been set up, which can be used, for example, as a sensor for fully-automated industrial polymer waste sorters.

Multivariate classification of pulp NIR spectra for end-product properties using discrete wavelet transform with orthogonal signal correction

2014 ◽  
Vol 6 (22) ◽  
pp. 8906-8914 ◽  
Author(s):  
N. Tavassoli ◽  
W. Tsai ◽  
P. Bicho ◽  
E. R. Grant
Keyword(s):  
Near Infrared ◽  
Natural Material ◽  
Discrete Wavelet ◽  
Orthogonal Signal ◽  
Paper Properties ◽  
Calibration Models ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
On Line

Natural material variations uncorrelated with physical properties of fibre networks hinder the development of robust calibration models by which to predict paper properties from on-line near-infrared (NIR) spectra of production pulps.

Validation of remote, on-line, near-infrared measurements for the classification of demolition waste

2002 ◽  
Vol 453 (1) ◽  
pp. 117-124 ◽  
Author(s):  
P.J de Groot ◽  
G.J Postma ◽  
W.J Melssen ◽  
L.M.C Buydens
Keyword(s):  
Near Infrared ◽  
Demolition Waste ◽  
Infrared Measurements ◽  
On Line

scholarly journals A New Two Dimensional Germinative Classification of Malting Barley Quality Based on Separate Estimates of Vigour and Viability

2011 ◽  
Vol 40 (No. 3) ◽  
pp. 102-108
Author(s):  
B. Møller ◽  
L. Munck
Keyword(s):  
Near Infrared ◽  
Two Dimensional ◽  
Least Squares Regression ◽  
Infrared Transmission ◽  
Beta Glucan ◽  
Fully Integrated ◽  
Integration Pattern ◽  
Dimensional Classification ◽  
On Line

It is surprising that not even today do germination data seem fully integrated with malting data in barley quality evaluation. In order to implement such an integration, pattern recognition multivariate data analysis (chemometrics) is essential. Inspired by the results from chemometric analyses of whole germination curves we tested a two-dimensional classification plot of barley samples based on separate estimates for “vigour” (g%1) germination energy (GE) as abscissa with limits at 70% and 30% and “viability” (g%3) as ordinate with limits at 98% and 92%. The seven barley classes obtained visualise the quality differences in a consistent and instructive way clearly differencing and ordering malting barleys with falling extract% and increasing wort β-glucan (mg/l) according to a subsequent validation analysis. “Vigour” g%1 could surprisingly be predicted by Partial Least Squares Regression (PLSR) correlation by Near Infrared Transmission (NIT) and by a separate set of ten physical-chemical analyses. Samples with “viability” g%3 lower than 92% were outliers. It was concluded that germination speed is connected with the structure of the seed, which regulates the availability of substrate for germ growth near connected to the speed of malt modification. It is suggested that a NIT PLSR prediction model for “vigour” can be used directly “on-line” for quality control in the grain industry and by plant breeders. A fast germinative classification plot can be established with NIT spectroscopy for “vigour” and the Tetrazolium germ-staining test for “viability” within two hours.    

scholarly journals New automated method for macrocontaminant analysis: Industrial applications

TAPPI Journal ◽  
2017 ◽  
Vol 16 (11) ◽  
pp. 623-631
Author(s):  
SYLVIE OSSARD ◽  
PATRICK HUBER ◽  
PASCAL BOREL ◽  
DAVY SOYSOUVANH ◽  
THIERRY DELAGOUTTE
Keyword(s):  
Near Infrared ◽  
Raw Materials ◽  
Process Analysis ◽  
Industrial Applications ◽  
Recycled Paper ◽  
Automated Method ◽  
On Line ◽  
Industrial Samples

In recycled paper processes, stickies are the origin of many production disturbances. In this paper, we present how the recently developed method for macrocontaminant analysis was used with industrial samples for process analysis. The new automated stickies measurement method allows (i) determination of the threedimensional (3D) morphology of screened particles (without any deformation) and (ii) classification of the particles as stickies among contaminants. This is achieved by a combination of laser triangulation and local near-infrared spectroscopy (NIR). Measurement of macrocontaminants in pulp samples and their classification allow meaningful evaluation of their specific removal. Chemical nature and amount of the macrocontaminants coming from different raw materials were studied and were shown to be very different in two different mills. In an Asian mill, a low removal of pressuresensitive adhesives (PSAs) in the process (46%) was found in comparison with high removal of other stickies (99%). Applications of this device are shown, while the new on-line sensor for macrocontaminant analysis is being developed.

Transportable Spectrophotometer System for On-Line Classification of Poultry Carcasses

1996 ◽  
Vol 50 (7) ◽  
pp. 910-916 ◽  
Author(s):  
Yud-Ren Chen ◽  
Roy Winfield Huffman ◽  
Bosoon Park ◽  
Minh Nguyen
Keyword(s):  
Real Time ◽  
Near Infrared ◽  
False Negative ◽  
Error Rates ◽  
Average Accuracy ◽  
False Negative Error ◽  
On Line ◽  
Poultry Carcass ◽  
Real Time Classification

This paper describes a transportable spectrophotometer system developed for real-time classification of poultry carcasses on-site at slaughter plants. The system measures the spectral reflectance of poultry carcasses in the visible/near-infrared regions (471 to 963.7 nm). An optimal neural network classifier for real-time classification of poultry carcasses into normal, septicemic, and cadaver classes with an average accuracy of 93% was obtained. When the classifier was used to classify the carcasses into two classes, normal and abnormal (septicemic and cadaver), the average accuracy was 97.4%. The percentages of the false positive and the false negative error rates were 2.4 and 2.9%, respectively. This paper also proposes implementing the system at the slaughter plants as a poultry carcass screening system (PCSS). Using two visible/NIR spectrophotometer systems, the PCSS tests both sides of the breast of each bird. With the PCSS, the inspection-passed-bird and inspection-rejected-bird error rates by the spectrophotometer systems would be minimal, and less than 5% of the incoming birds would require further inspection by human inspectors.

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