sensory analysis for food and beverage quality control || sensory methods for quality control

© Woodhead Publishing Limited, 2010

4

Sensory methods for quality controlL. L. Rogers, Consultant, UK

Abstract: The chapter includes a reminder about the importance of choosing the right method, agreeing how the results will be used and the impact of choosing the wrong test for the objective. An introduction to the use of action standards is included. The chapter gives an overview of a large number of tests, giving advice on which are the best and most popular in the world of quality measurements. Each test section has: an introduction to the test including popularity, advantages and disadvantages, example uses; samples, including what format and how many, how much is required, balanced designs; panellists, number and level of training; example test set-up, for example, the ballot paper/test sheet; and references for further reading and information.

Key words: sensory science, action standards, quality methods.

4.1 Introduction

This chapter is concerned with the sensory methods available for quality control in the food and beverage industry. Several methods are described and discussed, including their popularity and applicability to quality control situations. Where methods are highly aligned to quality control, further detail is given in each method section:

• introduction (including popularity, advantages and disadvantages, example uses);

• samples (quantity, type, balanced designs);• panellists (number and level of training);• example test set-up (e.g. questionnaire/ballot paper/test sheet);• references for additional information.

Many industries are still not using sensory science to its full capabilities in quality control. There seems to be a continued reliance on experts to judge sensory quality and in some industries, where they have moved away from experts, they have moved to the use of small numbers of panellists

52 Sensory analysis for food and beverage quality control


making subjective rather than objective measurements. In the future it would be benefi cial if the food industry paid more attention to the sensory attributes of their products and how the changes in these attributes can affect consumer liking. This change would see industries using recom-mended objective sensory methods, linked to statistical process control and agreed action standards. Consumer-led quality methods mean that the iden-tifi cation of the sensory characteristics most important to the consumers’ view of ‘quality’ is the way forward. Consumers will not continue to buy a product if it does not meet their expectations – and they may not complain: in today’s busy environment they are more likely never to buy the product again. These consumer-led quality methods could also be linked to instru-mental measurements, to aid production facilities in making excellent prod-ucts time and time again.

It can be daunting when starting out in your sensory science career (and even later!) to see the huge number of sensory methods available. How can the right method be selected to meet the test objective? The objective of the test and the manner in which the results will be used are key to the selection of the correct test method. There may also be constraints due to facilities which mean that some methods will not always be available. Obvi-ously this needs to be taken into account when deciding which method to use.

One of the most important aspects to consider before selecting which method to use is to be clear of the objective of the sensory study. This will involve fi nding out different pieces of information to determine exactly why the sensory test is required so that the test can be designed to meet the objective. For example there is little point conducting a full sensory profi le if the client only wishes to know if there is a texture change because of the introduction of a new ingredient supplier for a thickening agent. Another important consideration before deciding which method to use is how the results will be collected, analysed and subsequently used. A useful tech-nique is the use of action standards as these can be incredibly helpful in designing the test. An action standard (AS) defi nes the aim of the overall experiment but also states the action (or next steps) to be taken dependent upon the results. In the example above, where a new supplier was under discussion for the thickening agent, the action standard might have read:

AS1: ‘If the sensory test confi rms that there is a textural difference between the new supplier and our existing supplier for product X, we will not proceed with the new supplier.’

The objective is clear: the client wishes to know if there is a difference in texture, but also will be rejecting the new supplier if there is a difference in texture. In this case a simple difference test could have been selected to determine if there was a texture difference.

The action standard below might have resulted in a totally different sensory approach:

Sensory methods for quality control 53


AS2: ‘If the sensory test confi rms that there is a textural difference between the new supplier and our existing supplier for product X, we will need to understand what the textural difference is and if there are any other changes to product X as a result of the supplier change. Our existing supplier will stop producing at the end of December so it is critical we fi nd a new supplier.’

The sensory scientist may well have chosen to ask the client how likely they thought a difference might be between the new supplier’s ingredient and the existing supplier. If it was likely that there would be a large difference they may have decided to go directly to conducting a full profi le to under-stand what the differences were. These differences would be critical in understanding the effect of this change on consumers’ reactions to product X. If the change threatened key drivers of liking, further discussions with the new supplier may be required to achieve a match.

AS3: ‘If the sensory test confi rms that there is a textural difference between the new supplier and our existing supplier for product X, we will need to understand what the textural difference is and if there are any other changes to product X as a result of the supplier change. We also need to understand how this difference is related to the natural variance in our product.’

In this case the sensory scientist may well decide to carry out some batch-to-batch variability tests and determine where the new ingredient batch fi ts. The difference from control method might be a useful starting point to gather data for this test as several batches may be included in one test.

AS4: ‘Is there a textural difference between the new supplier and our existing supplier for product X?’

The example above (AS4) is an example of a poor action standard. The next stage after understanding if there was a difference or not is not docu-mented and therefore the choice of test is a diffi cult one. It is more likely that the wrong test would be chosen and then, when the results are reported, the client would be questioning what the next steps should be: the sensory scientist will probably not have the information at hand to guide the client.

These examples indicate how knowing the objective and knowing what the next steps will be as an outcome of the sensory study, are vital in the choice of sensory test.

Some of the method categories below are not suitable for day-to-day quality assessments but can be used in the set up of a QC programme, so have been included in this discussion (Costell, 2002). In each case it is not the test method alone which will result in the desired outcome, but also the manner in which the test is conducted and how the results are analysed and used (Costell, 2002). Table 4.1 gives an overview of all the tests and the relative complexity of each. Table 4.2 gives the number of panellists and the level of training and experience they might require for each method.

© W

oodhead

Pub

lishing Lim

ited, 2010

Table 4.1 Overview of sensory methods

Section Method QC relevance Time to conduct test Time to set up methodology

Level of detail gained from results

4.2 Descriptive specifi cation High Medium High High4.3 ‘In/out’ (or pass/fail) High Low Medium Low4.4 Difference from control High Medium Low to medium Medium4.5 A not A Medium Low to medium Medium Low4.6 Paired comparison (e.g. 2AFC) Medium Low Low Low4.7 Scaling (including targeted scaling) Medium Low to high High Medium to high4.8 Ranking Medium Low Low Moderate4.9 Triangle test Low Low Low Low4.10 Quality scoring/grading/rating High Low Medium to high Medium to high4.11 Magnitude estimation Low Low Medium Low to medium4.11 Duo–trio Low Low Low Low4.12 In-house methods High Variable, generally low Variable, generally low Moderate4.12 DIY High Variable Variable Variable



4.2 Descriptive specifi cations (DS) method

The descriptive specifi cations (DS) method is also known as the compre-hensive descriptive method (Muñoz et al., 1992) and descriptive analysis method (Lawless and Heymann, 1999). The basis for this popular method lies in the development of sensory specifi cations for fi nished products. A sensory specifi cation is similar to other specifi cations and is a vital part of ensuring product quality. Specifi cations detail exactly what the product should look like, smell like and taste like and can easily be extended to texture measurements where necessary. An example semi-quantitative sensory specifi cation is given in Fig. 4.1 and a fully quantitative example is given in Fig. 4.2.

The sensory specifi cation is built around those attributes which are known to contribute to consumer acceptance of the product and it is this aspect of the method which requires large resources during its conception. The method is very objective as it does not require the panellists to make any subjective judgements on the product’s quality as such. This judgement is made by the sensory scientist in the interpretation of the data. The method gives very actionable results which can be correlated to both instru-mental and consumer measurements.

A well-trained sensory panel of around 10 screened panellists is required to measure the levels of a selection of attributes, generally for fi nished

Table 4.2 Recommended number of panellists

Section MethodRecommended number of panellists (highly trained panellists)

Panellist training and experience

4.2 Descriptive specifi cation (10) High4.3 ‘In/out’ (or pass/fail) 25 (10) Medium4.4 Difference from control 30 (18) Low to medium4.5 A not A 20 (10) Medium4.6 Paired comparison (e.g.

2AFC)30 (20) Low

4.7 Scaling (including targeted scaling)

Variable High

4.8 Ranking 30 (5) Low4.9 Triangle test* 24 (18) Low4.10 Quality scoring/grading/

rating8–12 (5) Medium to high

4.11 Magnitude estimation Variable Medium4.11 Duo–trio 32 (15) Low4.12 In-house methods Variable Variable generally

low4.12 DIY Variable Variable

* See ISO 4120:2004 for more details on number of panellists.



Fig. 4.1 Example specifi cation for a fruit drink.

products. Samples are usually taken from daily production batches. The quantity required is based on each panellist making one assessment of all the modalities under consideration in the specifi cation. This can be done with a fairly simple paper ballot or using standard sensory software. The method can be semi-quantitative (see example questionnaire given in

Date:

Product: Fruit drink 1

Pack type and size: 380 ml PET bottle

The product must be free from off-odours, taints and foreign particles

Intensity scale: None Slight Moderate Strong Very strong

Appearance Evaluated by looking at the product in a clear sampling cup under artificial daylight before and after swirling.A very strong orange coloured liquid that is bright, clear and still.The liquid appears thin and does not leave a residue upon swirling.

Aroma Evaluated by smelling from the sampling cup before and after swirling.A moderate lemon and moderate orange aroma that is moderately sweet. A slight aroma of vitamin C tablets and slightly floral.

Flavour Evaluated by taking sips from the sampling cup.A moderate lemon, moderate orange flavour that is also moderately acidic and moderately sweet. A slight flavour of vitamin C tablets and also slightly bitter in flavour.

Mouth-feel/Texture Evaluated at the same time as the flavour and by taking more sips from the sampling cup.Slightly drying mouth-feel. The liquid feels slightly thicker than water in the mouth.

Aftertaste/Afterfeel Evaluated after swallowing – no extra sips taken.A moderate citrus aftertaste that is also moderately acidic and moderately bitter. Moderately drying and a moderate teeth/mouth-coating afterfeel. A slightly sweet aftertaste.

Glossary of terms exampleFlavour/aftertasteOrange A bitter orange flavour. Like the flavour of Seville oranges.Lemon A sour lemon flavour. Like fresh lemonsAcidic The basic taste of citric acid solution.Sweet The basic taste of sucrose solution.Vitamin C tablets The flavour of vitamin C tablets. Like Haliborange.Bitter The basic taste of caffeine solution.Citrus The tangy flavour of general citrus fruits such as lemon, lime and

orange.



Fig. 4.2 Example data and specifi cation for the fully quantitative descriptive/specifi cations method.

Fig. 4.3) or fully quantitative by adding line scales for each attribute mea-sured (an example questionnaire is given in Fig. 4.4).

In the fully quantitative method, if the attribute measurements are out of specifi cation then the product is deemed unacceptable by the sensory scientist. The panellists are not aware of the attribute intensity levels built into the specifi cation and are therefore not making a judgement on product quality. They are acting as an instrument and the data they produce is used to help decide if the product meets specifi cation. In the semi-quantitative method the panellist is checking if each attribute is present at the correct level. The information from each panellist is passed to the sensory scientist to decide if the product meets specifi cation. The semi-quantitative method is useful for line-side assessments and can be particularly useful for check-ing preliminary products. The fully quantitative method is recommended for fi nal product evaluation.

The sensory specifi cation can be set by management or by the additional use of consumer data. The setting of sensory specifi cations with input from consumer data is incredibly useful as it gives information about the attri-butes that drive product liking (and also disliking) but also gives informa-tion about the tolerances consumers have to changes in the product. This allows the quality team to make recommendations to management based on these tolerances, rather than rejecting products that may have been acceptable or not rejecting products that were unacceptable. For full details on setting up consumer-led specifi cations please see Muñoz et al. (1992).

An additional dimension of this technique is that it lends itself very nicely to the use of statistical process control (SPC) (Oakland, 2007). This allows production to be monitored over time and drifts in quality highlighted before the product goes out of specifi cation. An example of this is given in Fig. 4.5. For more details on the use of SPC please see Oakland (2007).

Product 7, batch 123Example data and specification (used for decision making and not seen by panellists)

Attribute Results for sample 123 Sensory specification

Appearance: colour intensity 5.2 4.5–6.0Appearance: brightness 7.0 6.5–9.5Aroma: lemon 3.0 2.0–4.0Aroma: orange 4.3 4.0–6.0Aroma: vitamin C tablet 1.5 0–1.5Flavour: lemon 3.0 2.0–4.0Flavour: orange 6.1 5.0–7.0Flavour: vitamin C tablet 1.0 0–1.5Flavour: acidic 3.0 2.5–4.5Flavour: sweet 8.2 7.5–8.5



Fig. 4.3 Example of the ballot paper for the semi-quantitative descriptive/specifi -cations method.

Fig. 4.4 Example of the ballot paper for the fully quantitative descriptive/specifi ca-tions method.

Appearancevery strong orange coloured liquid bright clear still thin (like water) not leave a residue upon swirling

Aromamoderate lemon moderate orange slight vitamin C tablet

Flavourmoderate lemon moderate orange moderately acidic moderately sweet slight vitamin C tablet slight bitter

Texture in the mouthslightly drying feels slightly thicker than water in the mouth

Aftertastemoderate citrus moderately acidic slightly sweet moderately drying

If there are any additional descriptors or if any descriptors listed here are not present, please consult your manager

...................................................................

...................................................................

...................................................................

...................................................................

...................................................................

1. Refer to the sensory specification documentation before completing this assessment

2. Tick the box if the descriptor is present3. Product must be assessed as per make-up instructions

Product 7, batch 123Instructions:Please rate each of the attributes below according to the standard protocol for assessment, and with reference to the attribute definitions list and intensity training programme.

AppearanceColour intensity

| | 0 10

Brightness

| | 0 10

Aroma

Lemon

| | 0 10



Fig. 4.4 Continued

Orange

| | 0 10

Vitamin C tablet

| | 0 10

Flavour

Lemon

| | 0 10

Orange

| | 0 10

Vitamin C tablet

| | 0 10

Acidic

| | 0 10

Sweet

| | 0 10

The attribute list would continue with additional flavour, texture and aftertaste attributes

Example definitionsLemon: the fresh lemon aroma/flavour as found in freshly peeled lemon segmentsOrange: the orange juice aroma/flavour as found in freshly peeled Jaffa orange segmentsNatural sweetness: basic taste of a sucrose solutionAcidic: basic taste of a citric acid solution

Example data and specification (used for decision making and not seen by panellists)

Attribute Results for sample 124 Sensory specification

Appearance: colour intensity 5.2 4.5–6.0Appearance: brightness 7.0 6.5–9.5Aroma: lemon 3.0 2.0–4.0Aroma: orange 4.3 4.0–6.0Aroma: vitamin C tablet 1.5 0–1.5Flavour: lemon 3.0 2.0–4.0Flavour: orange 6.1 5.0–7.0Flavour: vitamin C tablet 1.0 0–1.5Flavour: acidic 3.0 2.5–4.5Flavour: sweet 8.2 7.5–8.5



1 2 3 4 5 6 7 8 9 10 11 12 13 14 150

1

2

3

4

5

6

7

8

Attribute intensityUpper control limitLower control limit

Batch number

Inte

nsity

of r

aspb

erry

flav

our

Fig. 4.5 Statistical process control (SPC) example.

4.3 ‘In/out’ (or pass/fail) method

The ‘in/out’ method is widely used in quality assurance (QA) and quality control (QC) sensory tests due to its ease of setting up and its simplicity in analysis (Muñoz et al., 1992). It can be used for a wide variety of purposes: raw materials, interim products and fi nished products. A trained panel assesses whether each sample type is ‘in’ or ‘out’ of specifi cation. An example ballot paper is given in Fig. 4.6. The specifi cations must be docu-mented to limit personal subjectivity. The method differs from the previous DS method in one main factor: the panellists actually make the decision on whether or not a sample is suitable or not. This is one of its main disadvan-tages as this decision can be quite subjective in nature and can cause prob-lems – especially when wrongly linked to production bonuses, for example. Another main disadvantage is the lack of information provided about the reason for the product failure although this may be built into the method where necessary.

Although the method is simple there are many industrial examples where its use could be improved. In some production facilities only one, or sometimes up to fi ve people, take part in these types of assessments. These are informal and conducted verbally based on each person’s experience of



production quality and deviations. Problems can occur when these panel-lists do not agree on whether a product is in or out of specifi cation (prob-ably the reason why in some companies this job falls to one person) and can tend to lead to people making decisions based on their own personal preferences – not a recommended situation. Relatively easy additions and changes can be made to vastly improve the results.

Firstly by documenting the production specifi cations, the deviations and personal preferences can be kept to a minimum (Carpenter et al., 2000). This is usually conducted by evaluation of a large number of samples and then determining important attributes: those which vary and those important for the product characteristics, and the limits of each of these attributes for suc-cessful production. The use of around 25 or more panellists will also improve the data collected by this method. Panel training, particularly in the form of examples of products both in and out of specifi cation, can hugely increase the analytical nature of this method. Documentation of sample preparation and consistent serving methods can also go along way to improving the use of this method. As for many QA and QC methods, the use of action stan-dards vastly improves the decision-making process. Generally the percent-age of panellists rating each batch in or out is used for decision making.

Fig. 4.6 Examples of the ballot papers for the ‘in/out’ or pass/fail method.

Instructions

Please evaluate the products below in the order shown. Evaluate each sample individually and mark whether it is ‘in’ or ‘out’ of specification in the box provided. Please use the specifications provided to help in your decision making.

Sample In Out871 902 376 299

Instructions

Please evaluate the products below in the order shown. Evaluate each sample individually and mark whether it is ‘in’ or ‘out’ of specification in the box provided. Please use the specifications provided to help in your decision making. Where you have marked the product ‘out’ of specification please comment why you have made your choice

Sample In Out Comment871 ..............................902 ..............................376 ..............................299 ..............................



Obviously in an ideal situation the panellists would all rate an individual sample in the same manner, for example 100% of panellists would rate the batch as ‘in’ specifi cation. However, in practice this does not happen and therefore action standards often take the form of ‘60% or more panellists accept the batch’ and therefore the batch is deemed to be in specifi cation, or ‘40% or less accept the batch’ and the batch is deemed to be out of speci-fi cation. Any batches falling in the area between 40 and 60% would be sent for further analysis to determine next steps (Muñoz et al., 1992).

The use of panel monitoring techniques is critical for this type of method and can be easily implemented by the use of ‘hidden control’ products – usually kept from previous rejected batches for this purpose. Further addi-tions, perhaps more complicated and expensive, can be made by the addition of consumer information. However, specifi cations for this method are gen-erally prepared by management, although the addition of any consumer data where available would be benefi cial.

4.4 Difference from control (DFC) method

The difference from control (DFC) method is another popular method in daily quality assessments (Muñoz et al., 1992; Lawless and Heymann, 1999) and can also be used for the assessment of batches on a regular basis for ambient products. However, one of the disadvantages is the need for a ‘control’ product. For some food products this is less of an issue as control products can be stored effectively for several months at a time and a new control selected at regular intervals. Where a control cannot be stored the control for each test must be representative of standard production. One solution for this can be to use the descriptive specifi cation method (DS method – see Section 4.2 above) to select the control product and then use this control to assess several batches over the usability period of the control using the DFC method. As the panellists for this test do not need to be as highly trained as those for the DS method, the DFC and the DS methods can be used in conjunction for resource and time saving in a production environment.

The test is fairly straightforward to set up and screened panellists require only a short training period to get used to the test: but it is recommended that panellists are monitored in each test by the use of a hidden control (see below). Data analysis can be diffi cult where statistical signifi cance is employed; however, many companies rely on the mean scores alone for each judgement. The test can be run by a technician but is best analysed with input from a sensory scientist where statistical analysis is required. Although a greater quantity of the control is required there only needs to be enough sample of each batch for the panellists to assess once. If statistical tests such as analysis of variance are to be conducted, the number of panel-lists required is around 18. However an understanding of batch conformity



can be gathered with around ten or more highly trained panellists if the data are validated by the measurements for the hidden control.

Each panellist is presented with the control product and several other batches coded with three-digit numbers. The number of batches that can be assessed in each test will depend on the product type, but if there is little carry-over or aftertaste issues, up to fi ve different batches could be assessed in one test. The panellist assesses the control product fi rst and is then asked to determine how different each individual sample compares with the control on a scale: generally 0 to 10 where 0 is no difference and 10 is an extreme difference. See Fig. 4.7 for an example questionnaire. The batches themselves are not compared – only compared with the control – therefore making it very resource friendly as there are two to fi ve comparisons to the control in just one test. The DFC method relies on the use of ‘hidden’ control to prove its validity. This hidden control is another portion of the control batch but instead of being identifi ed as such, it is hidden along with the various batches by a three-digit code. The hidden control should be rated by the panellists as being the same as the control with a score of 0 or 1, or perhaps 2 depending on the production variability between batches and between controls. If the hidden control is rated outside these limits the test results are rejected. The panellists must never be aware of the existence of the hidden control nor must its correct identifi cation be used as panel monitoring in a feedback situation. The reason for this is that the panellists

Fig. 4.7 Example of the ballot paper for the difference from control method.

Instructions

Assess the sample marked control first.

Assess the first sample marked with the 3-digit code.

Assess the overall sensory differences between the two samples using the scale below – mark the scale to indicate the size of the overall difference.

Difference No Mod ExtremeScale 0 2 4 6 8 10Code: 123



NotesFor a computerised system, each sample would be presented on a different ‘page’

or screen.For each coded sample a question about the difference may also be asked and this

can also be presented in the form of differences about all modalities if desired.



will be focused on identifying the hidden control rather than identifying any differences between batches: not the objective of the test at all.

This method can be used for production where there is inherent product variability due to its components, in preparation or in serving: for example baked and snack products. The use of standard difference tests would result in signifi cant differences where in fact the difference is just due to the product’s inherent variability. Where the control batches can also be vari-able, the method can be adapted (Pecore et al., 2006) and two controls are presented. For this example there are four pairs for each panellist: Control 1 versus Control 1 (also called the hidden control), Control 1 versus a second control, control 1 versus the test batch and the second control versus the test batch. The fi rst three comparisons are simply part of the original difference from control test and it is only the fourth comparison that makes up the control variability test. This allows for the test batch to be within the controls’ batch variability and easily detects where the test batch is outside the control batches variability. A further adaptation of this method also considers the variability of the test product, for example in ingredient sub-stitution, by the introduction of a balanced design to eliminate panellists’ fatigue and the need for more sampling (Young et al., 2008).

The DFC can also be used as a targeted DFC (TDFC). This is particularly useful if there is prior knowledge about the attributes or modalities that change within production batches. For example if differences are generally seen in sweetness level, then a TDFC can be employed to determine the difference in sweetness between a range of batches. See Fig. 4.8 for an example questionnaire.

Fig. 4.8 Example of the ballot paper for the targeted difference from control method.

Instructions

Assess the sample marked control first.

Assess the first sample marked with the 3-digit code.

Assess the sensory difference in sweetness between the two samples using the scale below – mark the scale to indicate the size of the difference in sweetness.

Difference in sweetnessNo Mod Extreme

Scale 0 2 4 6 8 10Code: 123

Difference in sweetnessNo Mod Extreme

Scale 0 2 4 6 8 10Code: 456Notes

For a computerised system, each sample would be presented on a different ‘page’ or screen.



4.5 ‘A’ not ‘A’ method

The ‘A’ not ‘A’ method is another popular method for QC and QA special-ists because it is simple to train panellists for, conduct and analyse. It is particularly useful for production facilities where a small number of differ-ent products are made on a regular basis, as panellists become very familiar with these products and hence very familiar with ‘A’. It is also useful where the two samples cannot be exactly the same in appearance (obviously where this modality is not essential to the product quality) but the differences are subtle and only obvious if the two samples were presented together (Lawless and Heymann, 1999). This method is only useful where the inherent vari-ability is low, otherwise it results in the rejection of too many production batches (Muñoz et al., 1992). The method only really gives the answer that the batch is different but does not give information as to the degree of dif-ference or in what format the difference takes – for example, that the batch is sweeter or with a higher fl avour intensity.

However, the test is very simple to set up and train panellists and easy to analyse the results. The panellists are presented with a sample labelled ‘A’ to familiarise themselves with and then presented with a series of three digit coded samples, some of which are A and some of which are test batches. In training the panellists must become familiar with A prior to taking part in the tests so that the ‘sensory profi le’ of A is familiar to them and the initial assessment during the tests just serves as a reminder (BSI, 1988). The presentation to each panellist should be random and different for each assessor. See Fig. 4.9 for an example questionnaire.

The number of panellists required depends upon the test objective and the required signifi cance level, but around 20 panellists would be used for

Fig. 4.9 Example of the ballot paper for the ‘A’ not ‘A’ method.

Instructions

Assess the sample marked ‘A’ first, then pass back to the test coordinator.

The coded samples consist of ‘A’ and ‘not A’ in a random order.

All the ‘not A’ samples are identical.

The respective number of each of the two kinds of samples is unknown to you.

Assess the coded samples one by one and complete the form below

Sample code The sample is‘A’ ‘Not A’

123456789234678



a typical situation and each would assess fi ve ‘A’ and fi ve ‘not A’ samples. The results can be tabulated to indicate the number of panellists identifying ‘A’ and ‘not A’ correctly and the ratios are then analysed using χ2 (BSI, 1988).

4.6 Paired comparison methods (e.g. 2AFC, n-AFC, simple difference test)

The paired comparison test is another simple method to set up, train and analyse but not always ideal for quality environments (Muñoz et al., 1992) as it can be very sensitive to small differences. The test can determine if two samples are different in a particular attribute (directional paired compari-son or 2-alternative forced choice (2-AFC) method) or simply if the samples are different (simple difference test). For example in a test with biscuits, the sensory scientist may know that they differ in texture and therefore the panellists would be asked which biscuit is softer in texture: this would be a 2-AFC method (Lawless and Heymann, 1999). The simple difference test has limited usefulness as generally the triangle test or duo–trio are more suitable. However, it can be useful where there is limited sample quantity or where the presentation of three samples is not possible, for example for chewing gums or certain curried products.

4.6.1 2-AFC methodThere are two presentation orders (AB and BA) and the test is designed so that both orders are presented an equal number of times. The samples are both presented at the same time and the panellist is asked to identify the sample which is higher in the specifi ed attribute. Figure 4.10 gives an example questionnaire for this test. The panellists must understand the attribute under consideration to be able to judge the difference effectively. The results give an indication of the direction of difference between the two samples; however, if the difference in one attribute affects several other attributes (for example the sugar level in biscuits can affect the sweetness and hardness) then this would not be the test of choice.

4.6.2 Simple difference testThe samples are again both presented at the same time. Little training is needed as people generally fi nd it easy to decide if the samples are the same or different. Around 20 to 50 presentations are required (Meilgaard et al., 1999) but each panellist assesses each sample only once. Therefore around 60 panellists for a QC application are sensible. There are four presentation orders in this example (AA, BB, AB, BA) and these will be randomised across panellists with an equal number of each order presented. Figure 4.10



gives an example questionnaire for this test. The results can only tell the sensory scientist if the samples are different or not – no reason for differ-ence or intensity of difference can be obtained from this method.

4.7 Scaling method (including targeted scaling)

Scaling can be useful where a quality unit is closely assigned to a Research and Development (R&D) unit and therefore has use of the R&D quan-titative descriptive profi ling panel. The method can be used with a quality panel if the time is available for training and panel monitoring. The method is closely linked to quantitative profi ling but generally the sensory scientist selects only the attributes that are known to change in produc-tion for daily monitoring of the key sensory characteristics. The method is not very popular due to the amount of panel training required when there is no access to an R&D panel, and the statistical data analysis element requires time and statistical training for the test coordinator. It can be useful though for the measurement of particular attributes that are known to change but yet are key to consumers’ liking of the product. This is

Fig. 4.10 Example of the ballot paper for the paired comparison methods.

2-Alternative forced choice method

Instructions

There are two samples for you to assess: please assess them in the order shown below.

Please assess the force required to bite off half the biscuit.

Please ring the code of the hardest biscuit.

123 456

Thank you for taking part.

Simple difference test method

Instructions

There are two samples for you to assess: please assess them in the order shown below.

Please bite off half of each biscuit.

Are the two samples the same or different? Please tick the relevant box.

Pair 123 and 456 SAME DIFFERENT




particularly helpful where the product’s characteristics may be changed by blending or reworking as there is quantitative data to help with these adjustments.

Screened and trained panellists rate the specifi c attributes on line scales for each sample. Samples are coded with three digit numbers and presented monadically to each panellist in a randomised balanced design. The rating can be performed using paper questionnaires but where sensory data col-lection systems are in use this method is much easier to gather and analyse data. An example questionnaire is given in Fig. 4.11. The panellist assesses each sample and marks on the line scale the intensity of the given attribute. For panellists that are not part of a quantitative panel it can be useful to give a warm-up sample with a given intensity and a further known-intensity sample can be used for panel monitoring.

A useful adaptation of this method is the scaling of only one attribute. This is known as targeted scaling. The method can be especially useful where an R&D panel is not available as QC panellists can become very skilled in the understanding of the attribute and its scaling.

Fig. 4.11 Example of the ballot paper for the scaling method.

Instructions:

Please rate each of the attributes below according to the standard protocol for assessment

Appearance

Colour intensity

| | 0 100

Brightness

| | 0 100

Aroma

Lemon (the fresh lemon aroma/flavour as found in freshly peeled lemon segments)

| | 0 100

Orange (the orange juice aroma/flavour as found in freshly peeled Jaffa orange segments)

| | 0 100

Vitamin C tablet (the aged orange aroma as found in Co-Op brand Vitamin C tablets)

| | 0 100



4.8 Ranking test

The ranking test is very simple to set up, panellists need little training and the analysis is simple, but in terms of quality assessments it is not especially popular. This is due to the manner in which the sensory question is asked in this test. Panellists are asked to put the samples in order (rank) of some attribute (BSI, 1989). For example they may be asked to rank the samples in order of sweetness or creaminess. As potential quality issues may be linked to several attributes (and not just creaminess alone, for example) this can limit the usefulness of this test. However, if the production vari-ability is known, and that generally creaminess variability is the main issue, then this method can be very useful.

Screened and trained panellists are presented with four or fi ve samples in a random order and asked to rank them in order of the specifi ed attribute or give the samples equal rank. See Fig. 4.12 for an example questionnaire. The use of data collection software can be very useful here as the panellists can simply drag and drop each sample in rank order or drop equal ranked samples into the same ‘box’. For quality methods one of the samples must be the verifi ed control and it can be useful to have a hidden control on certain occasions. The hidden sample can also be useful to check panel performance. Please see notes on the use of a hidden control in Section 4.4 above.

The minimum number of panellists required for this test is fi ve (BSI, 1989), but this is not recommended as the more panellists you have the

Fig. 4.12 Example of the ballot paper for the ranking method.

Instructions

Assess the samples in the order shown below.

Note the intensity of the creaminess of each sample.

Write ‘1’ in the box of the sample which is the least creamy.

Write ‘2’ for the next creamy and ‘3’ for the next and ‘4’ for the most creamy.

If two samples appear the same please rank them with the same number.

Handy tip: as you assess each sample, place it in front of you in the order of creaminess – this makes it easier to fill in the boxes below when you have finalised your decision.

Sample code Rank Order

123

456

789

234




greater the quality of the data. Carpenter et al. (2000), recommend a minimum of 30 panellists and analysis of the data with the Friedman rank test (O’Mahony, 1986).

4.9 Triangle test

Muñoz et al. (1992) state that the triangle test is not an ideal method in QC as it can be very sensitive to small differences and can therefore create too many false positives. However, where a product can withstand very little difference, and consumers require low variability, it can be useful.

Screened and trained panellists are presented with three coded samples. They are told that two of the samples are the same and one is different and then asked to identify the ‘odd’ sample. There are six possible presen-tation orders (AAB, ABA, BAA, BBA, BAB, ABB) and therefore it is recommended to conduct this test with groups of six assessors (6, 12, 18 . . .) to include all the presentation orders in each case. The ISO standard (ISO4120:2004) for triangle tests gives a very detailed overview of the number of panellists required dependent upon the objective of the test. An example questionnaire is given in Fig. 4.13. An additional question may

Fig. 4.13 Example of the ballot paper for the triangle test method.

Instructions

There are three samples for you to assess.

Two of the samples are the same and one is different.

Please assess them in the order shown below.

Please ring the code of the odd sample.

123 456 789


be added to the questionnaire to gain information about the nature of the difference. It is not recommended to give direct feedback on whether the panellist detected the ‘correct’ odd sample, as in the situation where samples are generally not different (as decided by the whole panel), this can lead to the panellists feeling that they always fail in this test situation.

4.10 Quality scoring/grading/rating method

The quality scoring method is a common method for quality control and is often developed for the company’s specifi c product(s). The training and



experience levels of the panellists taking part in these tests are generally high as the method relies in part on the panellist’s memory of the ideal product (often seen in scales of ‘typicality’) and also the panellist generally needs experience of the day-to-day issues and changes occurring within the product. Another factor in the panellist’s training requirements with this method is the fact that they will be making the decision about the product quality: usually by scoring, grading or rating the effect of the changing attributes on the end quality of the product.

This type of method can often be seen in use for commodity products such as milk and fi sh and are often supported by an industry consensus. For example the American Dairy Science Association developed a scale used for milk products: 10 and 9 (excellent), 8 (good), 7 (fair), 6 (poor), to less than 6 being ‘unacceptable, a probable consumer complaint’. In some cases specifi c modalities or attributes are measured and the scores or grades summed to give an overall indication of product quality. Figure 4.14 gives an example of a quality score based on ‘typicality’.

There can be many disadvantages of this method. Firstly, because it relies on the expertise of the panellist, the results may not be directly linkable to consumers’ opinions of the products. Panellists can also drift into their own scoring system based on their own likes and dislikes. For new product development it can cause issues as there is generally little understanding of consumers’ opinions of the new product at the fi rst production stage. In some cases the method is very poorly used with small numbers of panellists without the necessary training experience – this tends to lead to panellists making their own personal judgements on the products. If an overall scoring system is used this does not give the information required to fi x the problem for the next production run or if the product might be re-blended or used for a different product as there is no information about which sensory

Fig. 4.14 Example of the ballot paper for a quality score method.

Please assess each coded sample below and score according to the table below.

Scale Definition

1 Fresh, typical, full flavour, no aged, no stale, no rancid flavours2 Fresh, typical, slightly lacking flavour, no off notes3 Relatively fresh, typical, however dull flavour4 Flavour slightly unbalanced with ageing, stale notes5 Aged, stale, rancid, not typical

Code Score

123 _____

456 _____

789 _____



characteristics are causing the quality issues. With the correct controls in place (Muñoz et al., 1992) this method can be fast and economic to use and, when backed by industry standards and linked to consumer acceptability scores, can be useful for management in making quality decisions. However Muñoz et al. recommend that unless the rules are adhered to, companies would be better placed if they chose another recommended method (such as DS or in/out) owing to the inherent disadvantages of quality scoring and grading.

4.11 Magnitude estimation and duo–trio methods

The magnitude estimation method is very simple but rarely used for day-to-day quality ratings except for specifi c products such as the assessment of chilli peppers. The method is based on Steven’s law. Panellists are given a reference sample and told, for example, that the reference would score 50 for a specifi c attribute (e.g. sweetness, crispiness, chilli heat). Subsequent samples are then scored in comparison to this reference. For example if the next chilli was twice as hot as the reference, the sample would score 100. For more information see the ISO standard 11056:1999.

Muñoz et al. (1992) state that the duo–trio test is not an ideal method in QC as it can be very sensitive to small differences and can therefore create too many false positives. The test was developed by Peryam and Swartz in 1950 for quality control in distilleries. It was proposed as an advantage over the triangle test as it was thought to be easier for panellists to match rather than compare three unknowns (Stone and Sidel, 2004). The test measures if there are any differences between two products but three products are assessed. This method is similar to the ‘A’ not ‘A’ and the triangle test in that a reference sample (A) is given to the panellist and then they are pre-sented with a pair of samples and asked which of the pair matches ‘A’. Unlike the ‘A’ not ‘A’ test, all three samples are presented simultaneously. The test does not give any indication as to the nature of the difference. An advantage of this test is that the analysis of the data from this method is very simple: the scientist looks up the number of correct answers in statisti-cal tables and reports the result depending on the signifi cance. More than 15 panellists are required: the ideal minimum number being around 30 with equal numbers of each possible combination.

There are two formats to the duo–trio test: where the reference is the same for each panellist (constant reference) and where the reference is a balanced representation of both of the samples in the test (balanced refer-ence). Where panellists are familiar with the reference sample the fi rst option appears more sensitive (Lawless and Heymann, 1999). An example questionnaire for both methods is given in Fig. 4.15. The constant reference method, where panellists are familiar with the reference, can be very sensi-tive to differences attributable to ingredient substitution.



4.12 In-house and do-it-yourself (DIY) methods

There are many in-house sensory methods developed for quality control and some of these have become very popular and used by other industries: for example the duo–trio test. Many companies have internal grading systems based on typicality, product quality (using scales such as bad to good) and even undocumented assessments where the line-staff simply assess the product and tick a box to say if the product is satisfactory or not.

As mentioned in the introduction to this chapter, these subjective methods are not ideal, and the use of the objective, consumer-led methods are recommended. However, some in-house methods have been developed over many years, have detailed Standard Operating Procedures and Work Instructions and are based on sensory specifi cations and consumer data, resulting in more objective and controlled results.

Hybrids of the recommended methods can be used to produce a detailed programme of tests for the different situations a quality control sensory scientist might fi nd themselves in. For example the sensory scientist might

Fig. 4.15 Example of the ballot paper for the duo–trio test method. The question-naire is identical for both tests; it is the presentation of the samples that differs.

Constant reference duo–trio

Instructions


One of the coded pairs is the same as the reference.

Please assess the reference first, then the two coded samples in the order shown below.

Please ring the code of the sample which is most similar to the reference

Reference 456 789


Balanced reference duo–trio

Instructions


One of the coded pairs is the same as the reference.

Please assess the reference first, then the two coded samples in the order shown below.

Please ring the code of the sample which is most similar to the reference

Reference 456 789




choose the difference from control method for monitoring batch variation and for ingredient substitution tests, the descriptive specifi cations method for passing daily batches and the ‘in/out’ method for passing raw materials.

4.13 Referencesbsi (1988) British Standard Methods for Sensory Analysis of food, Part 5 ‘A’ not ‘A’

test, BS 5929: Part 5.bsi (1989) British Standard Methods for Sensory Analysis of food, Part 6 Ranking,

BS 5929: Part 6.carpenter, r p, lyon d h and hasdell, t a (2000) Guideline for Sensory Analysis in

Food Product Development and Quality Control, Aspen.costell, e (2002) ‘A comparison of sensory methods in quality control’, Food

Quality and Preference, 13, 341–353.iso Sensory Analysis – Methodology – Triangle test, 4120:2004. www.iso.org.iso Sensory Analysis – Methodology – Magnitude estimation method, 11056:1999.

www.iso.org.lawless, h t and heymann, h (1999) Sensory Evaluation of Food. Principles and

practices, Aspen.meilgaard, m, civille, g v and carr, b t (1999) Sensory Evaluation Techniques, CRC

Press.muñoz, m, civille, g v and carr, b t (1992) Sensory Evaluation in Quality Control,

Van Nostrand Reinhold.oakland, j (2007) Statistical Process Control, Butterworth-Heinemann.o’mahoney, m (1986) Sensory Evaluation of Food: Statistical methods and proce-

dures, Marcel Dekker.pecore, s et al. (2006) Degree of difference testing: a new approach incorporating

control lot variability, Food Quality and Preference, 17, 552–555.stone, h and sidel, j l (2004) Sensory Evaluation Practices, Academic Press.young, t a et al. (2008) ‘Incorporating test and control product variability in degree

of difference tests’, Food Quality and Preference, 19, 734–736, doi:10.1016/j.foodqual.2008.04.002.

sensory analysis for food and beverage quality control || sensory methods for quality control

Documents