In-line moisture measurement during granulation with a four-wavelength near-infrared sensor: an evaluation of process-related variables and a development of non-linear calibration model

2001 ◽  
Vol 56 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Jukka Rantanen ◽  
Eetu Räsänen ◽  
Osmo Antikainen ◽  
Jukka-Pekka Mannermaa ◽  
Jouko Yliruusi
Keyword(s):  
Near Infrared ◽  
Calibration Model ◽  
Infrared Sensor ◽  
Linear Calibration ◽  
Moisture Measurement ◽  
Non Linear

Artificial Neural Networks in Multivariate Calibration

1993 ◽  
Vol 1 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Tormod Næs ◽  
Knut Kvaal ◽  
Tomas Isaksson ◽  
Charles Miller
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Calibration Method ◽  
Linear Calibration ◽  
Feed Forward Network ◽  
Non Linear ◽  
Artificial Neural

This paper is about the use of artificial neural networks for multivariate calibration. We discuss network architecture and estimation as well as the relationship between neural networks and related linear and non-linear techniques. A feed-forward network is tested on two applications of near infrared spectroscopy, both of which have been treated previously and which have indicated non-linear features. In both cases, the network gives more precise prediction results than the linear calibration method of PCR.

Dryer Effluent Monitoring in a Chemical Pilot Plant via Fiber-Optic Near-Infrared Spectroscopy

1998 ◽  
Vol 52 (5) ◽  
pp. 717-724 ◽  
Author(s):  
Charity Coffey ◽  
Alex Predoehl ◽  
Dwight S. Walker
Keyword(s):  
Real Time ◽  
Pilot Plant ◽  
Near Infrared ◽  
Fiber Optic ◽  
Tunable Filter ◽  
Calibration Model ◽  
Gas Cell ◽  
Rotary Dryer ◽  
Vapor Stream ◽  
Effluent Stream

The monitoring of the effluent of a rotary dryer has been developed and implemented. The vapor stream between the dryer and the vacuum is monitored in real time by a process fiber-optic coupled near-infrared (NIR) spectrometer. A partial least-squares (PLS) calibration model was developed on the basis of solvents typically used in a chemical pilot plant and uploaded to an acousto-optic tunable filter NIR (AOTF-NIR). The AOTF-NIR is well suited to process monitoring as it electrically scans a crystal and hence has no moving parts. The AOTF-NIR continuously fits the PLS model to the currently collected spectrum. The returned values can be used to follow the drying process and determine when the material can be unloaded from the dryer. The effluent stream was monitored by placing a gas cell in-line with the vapor stream. The gas cell is fiber-optic coupled to a NIR instrument located 20 m away. The results indicate that the percent vapor in the effluent stream can be monitored in real time and thus be used to determine when the product is free of solvent.

Calibration Transfer between Miniature Photodiode Array-Based Spectrometers in the near Infrared Assessment of Mandarin Soluble Solids Content

2002 ◽  
Vol 10 (1) ◽  
pp. 27-35 ◽  
Author(s):  
C.V. Greensill ◽  
K.B. Walsh
Keyword(s):  
Near Infrared ◽  
Model Updating ◽  
Soluble Solids ◽  
Soluble Solid Content ◽  
Calibration Model ◽  
Calibration Transfer ◽  
Orthogonal Signal Correction ◽  
Significant Difference ◽  
Photodiode Array ◽  
Direct Standardisation

The transfer of predictive models among photodiode array based, short wave near infrared spectrometers using the same illumination/detection optical geometry has been attempted using various chemometric techniques, including slope and bias correction (SBC), direct standardisation (DS), piecewise direct standardisation (PDS), double window PDS (DWPDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT). Additionally, an interpolation and photometric response correction method, a wavelength selection method and a model updating method were assessed. Calibration transfer was attempted across two populations of mandarin fruit. Model performance was compared in terms of root mean squared error of prediction ( RMSEP), using Fearn's significance testing, for calibration transfer (standardisation) between pairs of spectrometers from a group of four spectrometers. For example, when a calibration model (Root Mean Square Error of Cross-Validation [ RMSECV = 0.26% soluble solid content (SSC)], developed on one spectrometer, was used with spectral data collected on another spectrometer, a poor prediction resulted ( RMSEP = 2.5% SSC). A modified WT method performed significantly better (e.g. RMSEP = 0.25% SSC) than all other standardisation methods (10 of 12 cases), and almost on a par with model updating (MU) (nine cases with no significant difference, one case and two cases significantly better for WT and MU, respectively).

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