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Optical monitoring and control of drying processes.
Abstract:
Colour is one of the most important criteria for the quality of food products. It does not only influence its subjective appreciation by the consumer but can also be used for the determination of quantitative quality criteria which depend on the drying process. Changes in shape and development of temperature are further criteria for the evaluation of the drying process.

The paper gives a synopsis of the techniques of measurement and quantification methods for colour, two dimensional shrinkage, infrared thermometry and control strategies based on data gained using methods of non destructive measurement. Special emphasis is given to the use of low cost hardware available as digital equipment and to the possibility of continuous measurement. The different methods of colour designation in terms of RGB, XYZ or [L.sup.*][a.sup.*][b.sup.*] -values are discussed. It is shown, that information relevant for the control of the product quality can be derived from the image data.

Key words: colourimetry, imaging, monitoring, drying, infrared thermography

Article Type:
Report
Subject:
Color science (Research)
Drying (Methods)
Thermography (Methods)
Thermography (Copying process) (Methods)
Authors:
Sturm, B.
Hofacker, W.
Pub Date:
01/01/2009
Publication:
Name: DAAAM International Scientific Book Publisher: DAAAM International Vienna Audience: Academic Format: Magazine/Journal Subject: Engineering and manufacturing industries Copyright: COPYRIGHT 2009 DAAAM International Vienna ISSN: 1726-9687
Issue:
Date: Annual, 2009
Topic:
Event Code: 310 Science & research
Geographic:
Geographic Scope: Germany Geographic Code: 4EUGE Germany
Accession Number:
224335558
Full Text:
1. Introduction

Drying is one of the oldest and most important means of preserving agricultural products. In the past little emphasis was put on quality attributes of the product. Recently optimisation of the drying process has become more important due to changed consumer demands on one hand and increasing energy costs on the other. One of the most important criteria influencing the consumers' decision is the appearance of a product, causing a positive or negative sensation.

2. Literature Overview and State of the Art

Visual sensation is a mix of colour, shape and size of a product (Fernandez et al., 2005). Drying of cellular tissue produces several chemical (browning and other reactions) and physical (colour, texture, shape, porosity etc.) changes which are related in some complex ways (Del Valle et al., 1998).

Lack of analytical understanding of physical changes in the product during drying typically results in a trial-and-error approach to drying process development, with associated economic costs (Ho et al., 2002).

Improving drying processes necessitates the knowledge of changes the product is undergoing during drying in order to find significant phases. Usually drying time, quality changes which can be easily determined, e.g. reduction of volume, sometimes product temperature and energy consumption are determined and used for process optimisation.

Naturally grown materials such as fruits and vegetables are heterogeneous systems (Brennan, 2006). Aim of a robust drying process must be the compensation of variations of raw material quality to gain a more or less homogeneous product (Berget & Naes, 2002; Sahin et al., 2001; Jorgensen & Naes, 2003).

Most biological products are heat sensitive. Therefore it is necessary to make sure the product does not exceed certain critical temperatures during the drying process. For example the ability of wheat kernels to germinate after drying is preserved up to a drying temperature of 70[degrees]C. Exceeding this temperature results in the complete loss of germination ability (Hofacker, 1986). (Lewicki & Jakubczyk, 2004) showed that up to a certain value (70[degrees]C), there is no significant influence of drying temperature on mechanical properties of apples. Exceeding this value a decrease of shrinkage was observed. They hypothesized that high temperature was responsible for hardening of the product surface, which in most cases should be avoided.

Colour is an important quality criterion. It does not only influence the product's subjective appreciation by the consumer but it c an also be used for the determination of quantitative quality criteria, which depend on the drying process. This is due to the correlation between colour, taste and aroma (Morries et al., 1953). Changes of colour and browning of fruits are effects of a multitude of chemical reactions like Maillard reactions (Lozano & Ibarz, 1997) and destruction of pigments. Recent developments allow continuous non invasive measurement of product colour during the process using low cost equipment (Sturm & Hofacker, 2008).

During drying shape and size of a particle are changing continuously. Shrinkage modifies both the dimensions and transport properties of individual particles as well as the thickness and porosity of the packed bed in the dryer (Ratti, 1994). Shrinkage does not depend on water content only. Drying conditions also influence the degree of shrinkage.

Colour changes are usually measured using a colourimeter. Particles are taken out of the dryer, colour is measured and afterwards particles are placed back into the dryer. The drying process is intermitted particles cool down and have to heat to the initial temperature again. This means colour changes measured are not identical to those gained in an uninterrupted process.

Consequently for gaining all information desired, more than one test has to be carried out. As mentioned above, naturally grown products are very heterogeneous and therefore no particle or fruit is alike another. Consequentially all measurements have to be carried out simultaneously using the same sample to get a valid result. This can only be achieved using non-invasive optical monitoring systems for determination of product temperature, colour and shape.

A lot of effort has been made to use machine vision systems when processing agricultural products. Generally the surface colour or the dimensions of the product particles are determined and evaluated. it is assumed that this information allows a quality assessment. Comprehensive studies of activities in these fields are given by (Brosnan & Sun, 2004) and (Du & Sun, 2004).

New developments in hardware, particularly the availability of cheap and robust imaging systems allow the development of systems which derive quality information from images or from a video stream.

(Fito et al., 2004) used image analysis of infrared thermographs during surface drying of citrus fruits to determine the final drying time. Drying is finished, when a uniform surface temperature is reached. it is important that water on the surface is completely removed whereas the skin of the fruit is not dried. The latter would lead to decay and citrus quality loss such as peel damage, undesirable flavours development etc.

Automated colour sorting is commercially used on packing lines for apples, peaches and several other horticultural commodities (Abbot, 1999).

3. Technological Background of Colour Measurement

Colour of an object can be described by several colour coordinate systems. Some of the most popular systems are RGB (red, green, blue) used in colour video monitors, Hunter L,a,b, CIE (Commision International de l'Eclairage) [L.sup.*][a.sup.*][b.sup.*] and CiE XYZ. For instrumental measurement CiE and Hunter Systems are of greatest importance. According to the CiE concepts, the human eye has three colour receptors and all colours are combinations of those three. Colour is not a physical value but depends on several physical factors (Figura, 2004). Those factors are source of light, brightness, angle of illumination, angle of observation and surface condition.

3.1 Human vision

The human eye evaluates visual stimuli according to different spectral perception functions. However single excitations are combined to a uniform sensation, colour stimulus specification. Colour vision is based on red (600 nm), blue (350 nm) and green (550nm) stimulus perception. Using the measurement of the three human cones' sensitiveness a physiological colour space can be determined.

Fig. 1 shows the so called tristimulus XYZ curves of the human colour perception.

[FIGURE 1 OMITTED]

If mixing red, green and blue (RGB) light in different ratios and intensities most of the visible colour spectrum may be displayed. The RGB colours are described as additive colours. If all three are overlaid, light is reflected back to the observer and the colour white accrues for the observer. In the RGB colour space every RGB component may have values between 0 (absent) and 255 (maximum).

3.2 [L.sup.*][a.sup.*][b.sup.*]-Colour Space

The [L.sup.*][a.sup.*][b.sup.*] colour space (Fig. 2) is an international standard, which was developed by the Commission Internationale d'Eclairage (CIE) in 1976 (Schurr, 2000). It is device independent and describes the human vision best. All other colour spaces are contained in it. Colours that are of great importance for human colour vision are red, green, blue, and yellow.

The [L.sup.*][a.sup.*][b.sup.*] colour space is described in a three dimensional coordinate system. The L-axis shows the lightness (0-100), the a axis shows the ratio between green and red (-120...+120) and the b axis the ratio between blue and yellow ( 120...+120). With this classification a colour space originates, in which all colours may be depicted.

This makes the [L.sup.*][a.sup.*][b.sup.*] colour space too extensive, to be captured by scanners and monitors. RGB colour data gained can be converted. The transformations necessary are exactly defined through mathematical operations and described in chapter 5.3.

In contrary to the RGB in the [L.sup.*][a.sup.*][b.sup.*] colour space a change of lightness does not result in a change of values of colours. Another advantage of the [L.sup.*][a.sup.*][b.sup.*] colour space is the consideration of human colour apperception (Wargalla, 2003).

[FIGURE 2 OMITTED]

4. Objectives of this work

For purpose of quality assurance, but also for process control, quality changes can be determined by continuously measuring the surface colour of a product. However, some problems have to be resolved:

* The functional link between the information derived from the image data, the quality criterion to be controlled and the external process parameters (e.g. temperature, humidity) must be known.

* The system hardware must provide for the images in requested rate and (constant) quality and must be able to perform the image analysis and information extraction in an acceptable delay.

Finally, the system should be simple, robust and reliable. The investment for the system should be as low as possible and comparable to a conventional system controller.

5. Equipment and Procedures

5.1 Hardware

Experiments were carried out with the experimental setup described in (Sturm et al., 2009). Air temperature can be adjusted between 5[degrees]C to 160[degrees]C, and dew point temperature between 4[degrees]C to 75[degrees]C. It can be used as an overflow dryer as well as a single layer dryer. Different drying strategies can be applied: one stage or multi stage air temperature controlled drying, product temperature controlled drying or a combination of the strategies mentioned.

Weight of the sample, pressure drop, surface colour and temperature are measured continuously. A digital camera (IC-Imaging, Model DFK 31BU03.H) supplies digital images to a PC running under Windows XP. The visual field of the camera covers one or several product particles depending of the material's nature. Special efforts were made to protect the camera from heat and humidity to guarantee for constant image quality.

Dryer and optical system were protected from daylight to avoid interference with the artificial illumination of the product samples. The images are taken continuously and are sent to the computer in pre set intervals. For the investigations discussed imaging rate was set to one picture per 5 minutes. Images are stored for later analysis or can be analysed immediately after exposure for the purpose of automatic control.

5.2 Procedure

Product temperature was determined continuously using the pyrometer placed in the drying chamber, inlet air temperature, water bath temperature, dry bulb and wet bulb as well as drying chamber temperature were gauged using thermocouples. Air flow rate was detected using a hot-wire anemometer. All values were transferred to the PLC and from there to the HMI (Human Machine Interface) WinCC. Weight was measured continuously using a precision balance and transferred to a LabView template for data acquisition.

Samples were dried until they reached a dry base water content of approximately 0.13 g/g.

At the end of drying experiments, samples were weighed and put into an electric oven for 48 hours at a constant temperature of 70[degrees]C for the dry matter's determination.

5.3 Image Analysis

Analysis of image data is carried out by a programme specially developed for this purpose. The area of the image to be analysed is defined by mouse operation. Information about the background colour and a white standard is put in. All pixels in the analysis region are evaluated: if they are part of the product surface, the RGBvalues are extracted and stored; if they are of the background colour, they are discarded. Glossy regions are identified by their luminosity. The minimum-maximum- and mean RGB values are determined. Pixels which undoubtedly are part of the particle surface are counted to get information of the particle size and the change of the particle size due to shrinkage.

The RGB-values are used to calculate the XYZ-Values and consequentially the [L.sup.*][a.sup.*]b values of the pixels by the procedures given by (Hunt & Chichester, 1991) and (Leon et al., 2006):

The first step is to transform the RGB-values in XYZ-(tristimulus)-values:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)

The second step is to transform the XYZ values in CIE-[L.sup.*][a.sup.*][b.sup.*] values (Schurr, 2000):

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (2)

The values given by CIE for the matrix coefficients and the constants in the transformation equation for a 2[degrees] observer and reference RAL white standard are used. Illumination is set on a low level to avoid reflections.

For determination of quality the Total Colour Difference (TCD) is calculated from the [L.sup.*][a.sup.*][b.sup.*] values with the function (Leon et al., 2006)

TCD = [square root of ([DELTA][L.sup.2] + [DELTA][a.sup.2] + [DELTA][b.sup.2] (3)

6. Results

The following figures show the results of drying experiments with apples, variety "Jonagold". Figures 3 and 4 depict the drying process with constant air temperature ([[??].sub.Air] = 85[degrees]C, 60[degrees]C, 35[degrees]C). Developments of product temperatures show different, significant phases: At the beginning of the drying process, the temperature rises due to the heat transferred from the air to the product surface. After this heating phase, the temperature rise is decelerated, as heat is partly absorbed by the evaporation of the product humidity. This phase lasts as long as enough moisture can diffuse to the surface. In the third phase, temperature rise is accelerated. Almost the whole heat transferred warms the product while there is only a small amount of water evaporated. This behaviour is typical for porous materials.

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

As the heat transferred to the product depends mainly on the temperature of the product surface, this parameter can be used to control the process: In figures 5 to 7 results for product temperature control are depicted. Product and drying air temperatures as functions of time and moisture content, moisture contents as functions of time and development of colour and shrinkage as functions of moisture content are shown.

Product temperature was kept constant and air temperature was adjusted continually. This resulted in a rapid rise of product temperature to the desired value. Air temperature decrease was analogue to product temperature rise during air temperature controlled drying. Variation of maximum air temperature for those tests showed, that a level of 100[degrees]C should not be exceeded to guarantee a good product quality. Higher temperature levels can lead to quality losses due to internal stress in the product.

At the beginning of the process TCD rises quickly to a value of about 1/3 of the maximum value. Afterwards there is a phase of almost constant values down to a moisture content of circa X=2. Below this level TCD changes accelerate again. When air temperature is controlled, the second phase of rising values starts at a moisture content of X=3.

Changes of quality and temperatures are very different for the strategies applied. Correlations between temperature changes and quality changes in the different phases could be seen.

Product temperature control resulted in shorter drying time to obtain the same moisture content as when controlling air temperature. Furthermore, product quality--expressed in colour change--was considerably improved. If comparing identical drying times temperature level is 13[degrees]C lower when product temperature is controlled. TCD is significantly lower. The lower temperature level guarantees for a higher content of heat sensitive components like vitamins and aromas.

[FIGURE 5 OMITTED]

[FIGURE 6 OMITTED]

[FIGURE 7 OMITTED]

Tests carried out showed that two dimensional shrinkage (visible cross section) cannot be used as quality criterion for products which shrivel when they shrink. Down to moisture content levels of approximately X=2 data gained is correct afterwards most of the apparent shrinkage is due to shrivelling of the particle.

7. Conclusion

Experiments have shown that the continuous measurement of colour and size of particles as well as development of product temperature during drying give valuable information on changes, a product undergoes during the different stages of drying.

The research presented uses colour change vicarious for quality changes. Chemical and physical properties were not investigated. For a better understanding of quality changes the functional links between chemical, physical, optical and sensory properties have to be determined. The experimental set up does not allow precise prediction of energy consumption.

Further work is to be done in the field of the determination of the functional links between colour changes and quality changes. Two more cameras will be installed in the dryer for investigation of the three dimensional shrinkage during drying.

DOI: 10.2507/daaam.scibook.2009.50

8. Acknowledgement

The authors acknowledge the German Federal Ministry of Economy and Technology and the University of Applied Sciences Konstanz for financial support.

9. References

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Sturm, B. & Hofacker, W. (2008). Determination of Quality Changes during the Production of Dried Food Products using Colorimetry (2008). Annals of DAAAM for 2008 & Proceedings of the 19th International DAAAM Symposium, Katalinic, B. (Ed.), pp 665, ISBN 978-3-901509-68-1, ISSN 1726-9679, Trnava Published by DAAAM International, Vienna, Austria 2008

Sturm, B.; Hofacker, W. & Hensel, O. (2009). Automatic Control of the Drying Process of Biological Materials using Optical Sensors to acquire Surface Temperature, Color and Shape, Annual International Meeting of the ASABE, Paper Number 096219 21.06.-24.06.2009, Reno, Nevada, USA

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This Publication has to be referred as: Sturm, B[arbara] & Hofacker, W[erner] (2009). Optical Monitoring and Control of Drying Processes, Chapter 50 in DAAAM International Scientific Book 2009, pp. 501-512, B. Katalinic (Ed.), Published by DAAAM International, ISBN 978-3-901509-69-8, ISSN 1726-9687, Vienna, Austria

Authors' data: Dipl.-Ing. (FH) Sturm, B[arbara] M.Eng.; Prof. Dr.-Ing. Hofacker, W[emer], Hochschule Konstanz, Technik, Wirtschaft und Gestaltung, Brauneggerstasse 55, 78462, Konstanz, Germany, bstrurm@htwg-konstanz.de, hofacker@htwg-konstanz.de
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