EVALUATION OF THE HUNTERLAB COLOUR MEASURING INSTRUMENT
Preliminary work done by the Sugar Milling Research Institute (SMRI) showed that the HunterLab colour measuring system could possibly be used to estimate the colour of raw sugar. With this system it was possible to measure the colour within a short time (less than one minute). The instrument (ColorQuest LAV 4S0/0") has been used for several seasons at the Transvaal Sugar Limited Malelane (ML) factory for measuring the colour of brown sugar. Work was also done on refined and C-sugars. This paper summarises the evaluation of this instrument by both the SMRI and ML. Introduction Due to market requirements, ML produces two types of raw sugar, namely light (1000 ICUMSA 420 colour) and dark ( 1 500 ICUMSA 420 colour) and the same equipment (pans, crystallisers, etc.) in the raw sugar factory is used to make the two types of sugar. In order to assist the packaging department personnel to decide which type of brown sugar has to be bagged, a rapid indication of raw sugar colour is required. The thirty minutes required to determine colour by Figure I. the ICUMSA method is too long and it was therefore decided to evaluate instruments that could measure the colour of The spectrally based system is considered to measure 'what sugar "on-line". the eye sccs' and utilises the measurement of three parameThere are a number of instruments available which are suitable for this task (Nielson, 1996 ; Anon, 1996). The SMRI evaluated two HunterLab instruments during 1995 and 1996, namely the Miniscan which is a portable instrument (Dunsmore, 1995) and the ColorQuest LAV 45"/0°, which is a laboratory model, with a view to their suitability for use in the South African sugar industry. The laboratory model was subsequently evaluated by the Malelane factory and used i n the factory during the past three seasons. Description and basis of operation of the ColorQ~iest system The ColorQuest spectrophotometer (Figure 1) is a colour measurement system with a choice of optical sensors depending on the application. Each sensor has its own illumination and viewing geometrics. The system evaluated was the ColorQuest LAV 45"/0°. ters, the tristimulus values. These parameters are represented by three axes, which are illustrated in Figure 2. The vertical axis depicts the lightness "L" of the sample expressed on a scale of 0 to l00 where black = 0 and white = 100, the horizontal axis runs left to right and depicts "a" the redness or greenness, where the redness is positive and the greenness is negative, and the third axis runs from front to back and represents "b", the yellowness (positive), or blueness (negative). In addition, the instrument's software allows these three parameters to be combined and expressed in one of three indices. These are "Z", the brightness index, "WI", the whiteness index and "YI", the yellowness index. The system consists of a sensor and a detector. The sensor uses circumferential illumination, which is provided by a fibre optic ring, to accommodate a variety of samples. Light from the fibres illuminates the sugar sample (400 grams) to be analysed at an angle of 45". The diffused light reflected from the sample is collected at 0" and then directed to a difProc S Afr Sug Teclznol Ass (1999) 73 Evaluation of the HunterLab colour measuring instrument M Moodley, A Dunsmore & L De Jager
Evaluation of the Saccharoflex 2000 reflectance measuring instrument for refined sugar colour estimation at Huletts refinery.
Due to the successful use of the Saccharoflex 2000 reflectance measurement instrument on the estimation of refined sugar colour elsewhere in the world, it was decided by Tongaat-Hulett Sugar to evaluate the instrument at the refinery in Durban. Tests were carried out on first, second, third and fourth refined sugars, the results of which showed a good correlation between the ICUMSA colour measurement and the reflectance reading obtained from the Saccharoflex 2000. The instrument offers a number of advantages, the main one being that a refined sugar colour value can be obtained in less than a minute. The refinery has therefore purchased one for process control.
EXPERIMENT ' PLANNING AND THE USE OF FACTORIAL DESIGNS
5. Interpret the experimental results. The importance of overall planning for efficient experimenAchieving efficient experimental work without applying at tation is discussed anti stressed. One of the most important least part of these procedures would be difficult, if not impossteps of this planning, the selection of an experimental design, sible. It is probable that they are used, at least intuitively, by is studied with particular reference to the use of factorial all experimenters. designs. The advantages of this type of design as well as a One of the most important steps in the above procedure is recent application are described. the selection of the experimental design. Introduction Factorially designed experiments This paper deals with four aspects of experimentation. Factorially designed experiments offer some definite advantages, the most valuable being tQe determination of interactions Firstly, it provides a. set of procedures considered essential between factors. T~~ factors are said to interact if the effect for efficient experimental planning. of one de~ends on the level of the other. These designs also Secondly, it highlights the advantages in using properly planned, statistically designed experiments instead of completing a series of experimental trials and then posing the questions "How do .we analyse the data?" "What conclusions can be made from the results?" Thirdly, it discusses in some detail, an experimental design. Finally, it gives an example of a problem where a factorial design has been employed as an initial step in the problem solving process. Procedures for efficient experimentation As efficient experimentation requires that the maximum useful information be obtained from the minimum outlay of money, time, effort, erc., some basic procedures have to be followed : 1. Define the problem. 2. Obtain background information. Design the experimental programme. In this paper, attention is focusetl on the design of the experimental programme, as follows :(a) Select the independent variables or factors to be studied and establish the values at which they are to be tested. These values are usually called "levels" of the factors. (b) Choose the dependent variables which are to be measured; the values thus obtained are the results of the experiments. These dependent variables are usually called "response variables". (c) Consider the probable precision and accuracy of the measurements to be taken and of the final results. (d) Consider the limitations, e.g. cost, labour, time, materials, etc. (e) Consider possible interrelationships of the factors, i.e. whether the effect of one factor is dependent on the level of another. (f) Consider the effects of factors not under study. For example a seasonal change could affect the results if the experiment covers a long time period, in which case the effect of time must be taken into consideration. (g) Select an experimental design and draw up a detailed chronological plan of action. 4. Carry out the experimental work. indicate the relative importance of each factor tcus they may be used for "screening", that is, selecting from a large set of factors those that are important. Statistical designs yield unambiguous results and an estimate of the experimental error at a minimum cost. Finally, it is usually possible to proceed sequentially, which allows constant adaptation of the experimentation in the light of results obtained. A common approach when investigating the effect of several factors on a response is the "one variable at a tinie method". In this kind of experiment the effect of varying one factor is measured while holding the others constant. This is repeated so that at each stage a different factor is varied while the others are held constant. The results of such an experiment are incomplete because it is impossible to determine whether the effect of one factor is dependent on the level of another, i.e. there may be an interaction between the two factors, which cannot be detected with this "one at a time" approach. The concept of interaction is presented graphically in Fig. 1. In a factorial experiment several factors are controlled, at each of 2 or more levels, and their effects on the response variable investigated. The response is measured at each one of all the possible combinations that can be formed for the different levels of the factors. Thus if 4 factors are chosen, each at 2 levels, the experiment will consist of 2 x 2 x 2 x 2 = 16 trials, i.e. a 24 factorial experiment, where 2 represents the number of levels and 4 the number of factors. This design allows not only the effect of each factor on the response to be calculated but also the effect of any interaction of factors. The way in which these are calculated is described in detail in the literature1. The magnitude of these effects should be compared to the experimental error inherent in the system, so that it may be judged whether they genuinely are a result of changes in the level of the factors or whether they are merely the effect of experimental error. Thus an estimate of the experimental error is required for the proper analysis of a factorial design. If there, is no idea of the size of the experimental error from previous experimentation it may be estimated by using either replication (certain or all trials are repeated and the variance of the repeated observations determined) or otherwise by combining the higher order interaction effects as an estimate of the experimental error. This last possibility may only be used if it can be assumed that these higher order interactions are negligible, which is often the case. Fractional factorial experiments A complete factorial experiment in which all possible combinations of all the levels of the different factors are investiProceedin~s of The South African Sugar Technologists' Association June 1977