Infrared and near-infrared technology for the food industry and agricultural uses: on-line applications

Food Control ◽  
1994 ◽  
Vol 5 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Véronique Bellon ◽  
Jean Louis Vigneau ◽  
Francis Sévila
2021 ◽  
Author(s):  
Vishal Chorasiya ◽  
Qaleem Mohammed ◽  
Kausar Makki ◽  
Arif Ali khan ◽  
Furquan Ahmed ◽  
...  

Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.


2002 ◽  
Vol 16 (2) ◽  
pp. 89-98 ◽  
Author(s):  
B. J. A. Mertens ◽  
C. P. O'Donnell ◽  
D. J. O'Callaghan
Keyword(s):  

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Torbjörn A. Lestander

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.


2009 ◽  
Vol 103 (1) ◽  
pp. 144-152 ◽  
Author(s):  
A.M. Mouazen ◽  
M.R. Maleki ◽  
L. Cockx ◽  
M. Van Meirvenne ◽  
L.H.J. Van Holm ◽  
...  

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