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Sensory interpretation of instrumental measurements 2: sweet and acid taste of apple fruit
Postharvest Biology and Technology 24 (2002) 241– 250 www.elsevier.com/locate/postharvbio
Sensory interpretation of instrumental measurements 2: sweet and acid taste of apple fruit F.R. Harker *, K.B. Marsh, H. Young, S.H. Murray 1, F.A. Gunson, S.B. Walker Mt. Albert Research Centre, The Horticulture and Food Research Institute of New Zealand Ltd., Pri6ate Bag 92 169, Auckland, New Zealand Received 2 August 2000; accepted 30 June 2001
Abstract The relationship between objective and sensory measurements of apple taste and flavour was investigated. The aim was to determine the objective parameters that were best correlated with sensory evaluation, and then to identify the minimum objective difference that was required before a trained sensory panellist could detect a difference in apple taste and/or flavour. Objective measures included titratable acidity, °Brix (soluble solids content), levels of individual volatiles, sugars and acids, as well as calculations of °Brix/titratable acid ratio. Sensory panellists were trained to assess sweet taste, acid taste, apple flavour, and overall flavour. Titratable acidity was the best predictor of acid taste (correlation of 0.86 for the median panellist), and differences between apples of 800 mg kg − 1 (0.08% titratable acid) were required before the average trained panellist could detect a difference in acid taste (P =0.90). This value represented about a tenth of the range of titratable acidity values of the treatments presented to the trained panel. Sweet taste was difficult to predict using any of the objective methods. Indeed, the best objective predictor of sweetness was °Brix (correlation of 0.41 for the median panellist), which could predict a difference in taste when apples differed by more than 1 °Brix (P =0.90). This value represented about a third of the range of °Brix levels presented to the trained panel. Thus, while acid taste may be predicted on the basis of titratable acidity, we recommend that evaluation of sweet taste and flavour attributes continue to require assessment by trained sensory panels. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Sweet; Acid; Flavour; Volatiles; Apple; Malus; Brix; Titratable acidity
1. Introduction
* Corresponding author. Tel.: +64-9-815-4200; fax: + 649-815-4202. E-mail address: [email protected] (F.R. Harker). 1 Present address: Department of Applied Science, Faculty of Science and Engineering, Auckland University of Technology, PO Box 92006, Auckland 1020, New Zealand.
Studies on fruit quality have often found good relationships between °Brix levels and/or °Brix/titratable acidity ratio and consumer acceptability of fruit (Vangdal, 1985; Fellers, 1991; Mitchell et al., 1991). For apples, the sensory attributes sweet taste and acid taste are important drivers of con-
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sumer preference (Daillant-Spinnler et al., 1996; Jaeger et al., 1998). Furthermore, consumers of fresh apples consider quality more important than price (Market Review, 1996), and current food trends are placing an increasing emphasis on the use of sweet fruit flavours (Fischer, 1999). Against this background, postharvest researchers are increasingly being required to evaluate the flavour and texture of fruit. Since many researchers do not have ready access to sensory and/or consumer evaluation facilities, they must rely on instrumental and/or chemical measurements of quality. Indeed, measurements of °Brix and titratable acidity are often included in assessments of the postharvest quality of apples (Smith, 1985; Mitcham, 1997). In this study, we have examined the relationships between objective measurements and human perception of taste and flavour in apples. The approach was based on that of Harker et al. (2001), in that correlations were determined for individual people and statistical equations were re-arranged so as to calculate the objective difference that was required to ensure a sensory difference (P= 0.05). Objective measurements, including sugars, acids, °Brix, titratable acidity, and volatiles were correlated with the sensory responses to acid taste, sweet taste, apple flavour and overall flavour obtained from 20 panellists trained in sensory analysis. The aim of the study was to determine which objective parameters were the best predictors of taste and flavour, and how big a difference in objective parameter was required before a sensory difference would be perceived.
2. Methods
2.1. Plant material Apple fruit were harvested from seedlings from an F1 population of crosses between Fuji and Alkmene and between Fuji and Jonathan, growing at the Research Orchard in Havelock North. Fuji was used as a parent as it was considered to be a sweet low acid apple, while Alkmene and Jonathan were selected as parents on the basis
that they were high acid taste or low acid taste, respectively. Specific seedlings were selected on the basis of their acidity, °Brix and the absence of unusual flavour notes as determined in earlier studies (Marsh et al., 1995). In addition commercially harvested Fuji and Braeburn apples were used. The apples were harvested at the ‘tree ripe’ stage and stored at 0 °C until required for sensory studies (B 1 month). The fruit were warmed to 20 °C overnight prior to assessment.
2.2. Objecti6e assessments Whenever possible the exposed and shaded portion of each apple was identified from the location of the blush. Fruit were divided along the blushshade axis ensuring that both sides (objective and sensory) had similar blush coverage. Half of the fruit was used for objective measurements and the remainder used for sensory assessments. Juice was squeezed from the half-apple, and collected onto a digital refractometer (Model PR1 Atago, Tokyo, Japan) for measurement of °Brix. Two samples (approximately 10 g) of chopped outer cortical tissue were then frozen in liquid nitrogen and stored at − 20 °C for subsequent analysis of titratable acidity and quantitative measurements of sugars and acids. Titratable acidity was determined following maceration of one of the frozen tissue samples (10 g) in 35 ml distilled water using a polytron (Kinematica, Luzern, Switzerland). The homogenate was titrated to an endpoint at pH 8.1 using 0.1 N NaOH, and an automatic titrator (model 716 DMS Titrino, Metrohm, Herisau, Switzerland). The pH of the macerate before the start of the titration was recorded, and this value is described as the ‘suspension pH’. °Brix/titratable acidity ratio (Fellers, 1991) was calculated for individual fruit. Quantitative sugar and acid analysis was undertaken using gas chromatography (GC). The second tissue sample was ground in liquid nitrogen using a mortar and pestle and a subsample (40 mg) taken for extraction with 600 ml of methanol, chloroform, water (MCW, 12:5:3) in a 1 ml microfuge tube. Tartarate (400 mg) and adonitol (400 mg) were added as internal standards for acids and
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sugars, respectively. The samples were left overnight, then centrifuged at 10 000 rpm and the pellet resuspended in a further 400 ml MCW and centrifuged again. The supernatants were combined and 200 ml chloroform and 200 ml water added, the sample spun (13 000 rpm, 10 min) and the lower layer (chloroform) discarded. The upper layer was freeze dried and resuspended in 150 ml water and applied to Sephadex columns (QAE and SPC in series; Redgwell, 1980). Sugar samples were collected in a water eluant (800 ml), the columns were separated and acids eluted from QAE with 7% formic acid (800 ml). Aliquots of acids and sugars were derivatized with MSTFA (Pierce 48911) or silated with Tri-Z (Pierce 49230), respectively, before analysis and comparison with internal standards by capillary GC (DB1701, J&W Scientific). Quantitative data for individual sugars were converted to sucrose equivalents using mean relative sweetness values presented by Shallenberger (1993). Volatiles were assessed for a subset of five fruit per selection. Cylinders of cortical flesh were removed using a 9.5 mm diameter cork borer and cut into 50 mm lengths. Four cylinders of tissue were placed in a 50 ml Quickfit test-tube fitted with a headspace collection attachment. The test-tube was placed in a 25 °C waterbath, and the atmosphere in the test-tube flushed with N2 (25 ml min − 1 for 20 min) into a stainless steel cartridge (3.3 mm internal diameter× 100 mm length) packed with 100 mg of chromosorb 105. GC analysis was carried out by thermal desorption of the collected material on to a 30 m× 0.53 mm (internal diameter) DBWax (J & W Scientific) capillary column (Young, 1981). GC conditions were temperature programme 35 °C, 6 min hold, 3 °C min − 1 to 195 °C; H2 carrier at 28 cm s − 1; desorption temperature =175 °C; flame ionisation detection (FID). Levels of compounds were calculated using FID response factor as determined using ethyl butanoate, butyl acetate, 2-methylbutyl acetate, ethyl hexanoate, butanol and hexanol.
2.3. Sensory analysis At a pre-screening session the taste sensitivity of 40 panellists was screened using artificial solutions
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containing mixtures of sucrose (7–14 °Brix) and malic acid (0.08– 0.2% wt./v), as well as whole fruit. From this pool of panellists, the 20 that were most taste-sensitive were selected. This was necessary since previous studies had demonstrated that people vary in taste-sensitivity. The 20-member panel was trained in the descriptive analysis of apple taste and flavour. Key attributes and their definitions included, ‘sweet taste’ —the intensity of the taste sensation caused by sugars; ‘acid taste’ — the intensity of the taste sensation caused by acids’; ‘apple flavour’ —total apple flavour, i.e. the flavour that identifies the fruit as an apple; ‘overall flavour’ —the intensity of the overall flavour sensation i.e. flavour and taste combined. Upon completion of training, the panellists evaluated 12 of the possible 13 fruit in a balanced incomplete block design, where panellist was the block and selection was the treatment. In two sessions over two days, the panellists were given three half-fruit to evaluate and then following a break of approximately 20 min, evaluated the remaining three half-fruit. Samples were identified using three-digit codes, and presented monadically to panellists. Each panellist received a peeled halffruit, and water purified by reverse osmosis was provided as a palate cleaner between samples. Evaluations took place in individual sensory booths, in which environmental temperature was held at 20 °C and daylight corrected lighting was used. The samples were scored for the intensity of attributes using 150 mm unstructured line scales, where 0= absent and 150= extreme.
2.4. Statistical analysis The data was first analysed using Residual Maximum likelihood in the Genstat Statistical Package (REML, Payne et al., 1993). There was no detectable session effect on the results, and no outliers were detected. For each sensory attribute in turn, for each panellist, the correlations with each objective measurement were calculated. Boxplots were produced to show the spread of correlations for each objective measurement, and for each sensory attribute. Subsequent analysis was then based on the methodologies and calculations described by
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Fig. 1. Examples of the correlations between titratable acidity and acid taste, and °Brix and sweet taste for the best, median and worst panellists.
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Harker et al. (2001). The first question addressed was, do panellists on average tend to give sensory scores that correlate more or less closely with different objective measurements? To obtain a sensitive comparison the analysis needed to be made within panellists, and thus panellist was used as the blocking structure, in an analysis of transformed data. The resulting values are described as ‘degrees of association’ (between instrumental and sensory measurements; Harker et al., 2001). The higher the ‘degree of association’ the more closely the objective measurement is able to predict the sensory response. The second question addressed was, how large must the objective difference be, if we are to be reasonably sure that it will result in a perceived difference in a sensory taste and flavour attributes? An answer to this question can be derived statistically, using estimates of variance and the t-distribution (Harker et al., 2001). Essentially this calculation provides a value that
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represents the difference (an objective measurement) required before one can assume that it will result in a perceivable difference in sensory attributes (P= 0.9).
3. Results and discussion
3.1. Relationships between objecti6e and sensory measurements The seedlings provided fruit with a broad spread of sensory attributes that also differed in chemical composition (Table 1). Examples of the range of correlations between sensory attributes and objective measurements are provided in Fig. 1, and a summary of correlations for all objective measurements with all taste and flavour attributes is provided in Fig. 2. The highest correlations between sensory and objective measurements occurred for acid taste (Fig. 2). The basis of the calculation of the degree of associa-
Fig. 2. The range of correlations between objective and sensory measurements of apple taste and flavour. Each boxplot represents the correlations obtained from 20 individual trained panellists.
7.6 0.113 0.140 7.92 25 5.9 19.2 40.3 90.3 88.9
Acids Malate (mg g−1 FW) Citrate (mg g−1 FW) Quinate (mg g−1 FW) Total (mg g−1 FW)
Sugars Sucrose (mg g−1 FW) Sorbitol (mg g−1 FW) Glucose (mg g−1 FW) Fructose (mg g−1 FW) Total (mg g−1 FW) Total (sucrose equivalent)d 52 5.6 10.0 32.0 99.3 99.8
12.4 0.083 0.024 12.84
0.95 13.7
120 84 106 93
FA22
30 5.1 16.2 40.9 92.7 92.9
5.3 0.083 0.108 5.61
0.26 13.1
45 94 92 75
FA23
42 4.8 19.4 39.0 105.0 103.8
8.4 0.063 0.070 8.82
0.76 14.5
121 78 111 95
FA17
48 5.3 13.2 36.6 103.2 103.5
9.2 0.074 0.137 9.48
0.68 14.1
114 97 117 104
FA25
25 5.0 17.0 42.0 89.4 89.6
5.8 0.086 0.091 5.91
0.41 12.6
82 97 104 99
FA41
24 6.6 19.6 42.3 92.2 90.8
4.2 0.043 0.164 4.43
0.30 12.5
49 88 81 82
FJ4
40 3.8 14.3 39.1 97.4 98.5
6.8 0.130 0.137 7.19
0.47 13.2
80 85 101 74
FJ11
Values represent the mean concentrations and scores for each seedling, and the minimum and maximum values are in bold. a Seedlings FA =Fiesta×Alkmene; FJ= Fiesta×Jonathan. b LSDTukey (0.05). c Braeburn. d Conversion based on mean values for sweetness conversion from Shallenberger (1993), mg g−1 FW.
0.52 14.6
92 98 103 100
FA11
Seedlingsa and cultivars
Instrumental Titratable acidity (%) °Brix
Sensory Acid taste Sweet taste Overall flavour Apple flavour
Attribute
Table 1 Sensory and objective differences between apples
19 4.7 18.6 44.3 86.6 86.8
1.9 0.066 0.239 2.23
0.15 13.3
19 69 54 50
FJ33
19 3.8 18.2 38.6 79.6 79.2
3.5 0.149 0.156 3.87
0.24 12.7
57 80 82 78
FJ14
28 7.4 20.6 42.4 98.4 96.3
5.3 0.101 o.132 5.67
0.36 14.8
54 94 84 79
FJ21
40 3.1 13.8 31.3 88.6 88.7
5.3 0.061 0.112 5.60
0.40 12.1
82 93 101 88
Braec
17 3.8 14.3 34.9 76.5 74.5
4.1 0.050 0.137 4.28
0.47 11.7
64 63 74 70
Fuji
15 2.3 6.4 10.3 26.3 20.2
2.9 0.141 0.121 3.09
0.10 1.2
26 31 24 33
LSDb
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Table 2 Differences in the degree of association between objective predictors and the flavour of apples Predictor
Titratable acidity (%) Suspension pH °Brix °Brix/titratable acidity Malate Quinate Citrate Total acids Fructose Glucose Sorbitol Sucrose Total sugars Total (sucrose equivalent) LSD
Sensory attributes Acid taste
Sweet taste
Apple favour
Overall flavour
2.16 1.91 0.34 1.89 1.35 0.50 0.20 1.30 0.59 0.64 0.19 0.70 0.11 0.03 0.37
0.10 0.05 0.69 0.16 0.19 0.51 0.02 0.14 0.28 0.47 0.13 0.19 0.05 0.27 0.43
1.02 1.09 0.44 1.16 0.66 0.51 0.06 0.63 0.29 0.24 0.02 0.25 0.03 0.02 0.37
1.20 0.91 0.66 0.95 1.02 0.61 0.03 0.98 0.47 0.55 0.19 0.67 0.19 0.33 0.36
Large values have a strong association.
tion is described in the accompanying paper (Harker et al., 2001). Calculations of the degree of association indicated that titratable acidity was always amongst the best predictors of sensory attributes acid taste, overall flavour, and apple flavour (Table 2). However, titratable acidity was generally not significantly different from °Brix/titratable acidity or suspension pH in its ability to predict these sensory attributes. Quantitative measurements of acids were generally poorer predictors of taste and flavour in apples, with the exception of the high degree of association between the attribute overall flavour and both malate and total acid concentration (Table 2). Although sweet taste was the sensory attribute that was most difficult to predict using objective measurements, °Brix, glucose and quinate provided the highest degrees of association (Table 2). Conversion of sugars into sucrose equivalents based on relative sweetness values (Shallenberger, 1993) did not result in particularly high values for the degree of association with sweet taste (Table 2). This is explained by the quantitative dominance of fructose in sugar profile of the apples (Table 1), and the high relative sweetness of fructose (Shallenberger, 1993; Koehler and Kays, 1991). However, there is considerable variability
in the values for relative sweetness of sugars, which reflects differences in the methodology used to determine thresholds (Coultate, 1989). BrimA is a scale that has recently been proposed as a more sensitive prediction of consumer acceptability in fruit (Jordan et al., 2001). It is calculated using the formula: BrimA= °Brix− k× titratable acidity; where k is a constant that may vary between fruit species/cultivars due to differing mixes of acids and sugars, but is usually about five for citrus and grapes (Jordan et al., 2001). Research on Golden Delicious apples, suggested that calculations of total sugars (g l − 1 juice)+ 10×malic acid (g l − 1 juice) provided a better prediction of flavour acceptability than total sugars alone (Thiault, 1970). In the present study, BrimA did not improve the prediction of sensory attributes over those obtained using standard approaches of titratable acidity, °Brix, and °Brix/titratable acidity (data not presented). During determination of the magnitude of the constant (k) using the entire data set (i.e. not blocked by panellist) we found the correlation improved progressively as k decreased to such an extent that it became negative. In fact when the magnitude became − 10 the taste score was still increasing. The magnitude of k suggested that titratable acid-
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ity had a far more dominant role in the prediction of taste of apples than in prediction of flavour acceptability in fruit such as citrus and grape. It is noteworthy that acid has a positive correlation with taste in apples as shown here and with the data of Thiault (1970), yet is negative with citrus and grapes as shown by Jordan et al. (2001). On the basis of these preliminary findings we did not attempt to use BrimA on the data from individual panellists. It should be noted that our predictions related to sensory measurements by trained panellists and not to consumer acceptability (i.e. liking of the product). Our panelist’s were trained to distinguish between separate sweet and sour tastes, while consumers tend to integrate these tastes along with flavours into an overall sweet or sour sensation (Prescott, 1999). Thus, one might argue that Brix and Brix/titratable acidity may be better at explaining consumer perception of sweetness (or preference) than they are at explaining sweet scores obtained from by sensory panels. However, the evidence for such a relationship between Brix (and Brix/titratable acidity) and consumer acceptability and perception of sweetness is often unreliable in the case of apples. In some studies the relationship has probably been clouded by the influence of fruit maturity and starch content (e.g. Yuen et al., 1995), while other studies show good relationship between Brix or Brix/titratable acidity and acceptability (Thiault, 1970). Overall, there seem to be few studies that have used a reasonable number of consumers (i.e. minimum of 100 according to Cliff et al., 1998) to examine this issue.
3.2. Volatiles Studies with fruit have found that some volatiles are correlated with acceptability, as well as sweet taste, and sour taste (Baldwin et al., 1998). Thus, it was anticipated that volatiles may also have an influence on sensory assessment of taste and flavour of apples in the present study. For logistical reasons, volatile analysis could not be undertaken on the same fruit tasted by the sensory panel. Therefore, additional samples of fruit were collected for volatile analysis, and the results related back to the general differences in taste and flavour attributes identified for individual seedlings in Table 1. Principle component analysis indicated that the main contributors to the first two principal components, which accounted for 61.4% of the variance, were hexyl acetate, butyl acetate, propyl acetate, 2-methylbutyl acetate and propanol to a lesser extent (data not shown). However, it was not possible to explain sensory responses using the volatile data collected (while the volatiles data is not provided here, it is available upon request to corresponding author). This result differs from previous studies, which have demonstrated that volatiles have an impact on flavour of apples (Young et al., 1996).
3.3. Thresholds for perception of fla6our and taste differences Analysis of the magnitude of objective differences required before panellists perceived a difference in the taste and flavour of apples was undertaken. Only the objective measurements that
Table 3 Differences in objective measurement required before sensory differences become apparent Predictor
Titratable acidity (%) Suspension pH °Brix °Brix/titratable acidity
Sensory attribute Acid taste
Sweet taste
Apple flavour
Overall flavour
0.08 0.14 1.38 10.17
0.17 0.25 0.99 19.82
0.21 0.32 1.64 24.84
0.20 0.34 2.07 16.25
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showed the highest correlation and/or degree of association with acid taste, sweet taste, apple flavour and overall flavour are presented (Table 3). The most useful of these was titratable acidity, where differences as low as 0.08% (i.e. 800 mg kg − 1) between apples could evoke a response in perceived acid taste for the median panellist (P = 0.9). This value represents about a tenth of the range of titratable acidity values for individual selections, which varied from 0.15% titratable acidity for seedling FJ33 to 0.95% titratable acidity for seedling FA22. Titratable acidity of the parental phenotypes is between 0.9% (cv. Fiesta) and 0.35% (cv. Jonathon) at maturity (Marsh, unpublished). Both ‘Fiesta’ and ‘Jonathon’ are apple cultivars that are available commercially, and thus the range of titratable acidity studied here is similar to that available in the marketplace. Sweet taste is an important, but difficult, attribute to predict using objective measurements. The best and most convenient predictor was °Brix (Table 2). However, two apples needed to differ in °Brix by more than 1 (i.e. 1 g sucrose in 100 g aqueous solution) before evoking a response in perceived sweet taste for the median panellist (Table 3). This value represents about a third of the range of °Brix values for individual selections, which varied from 11.6 °Brix for ‘Fuji’ to 14.8 °Brix for seedling FJ21. Caution should be used when/if applying these taste threshold values. Previous studies (Harker et al., 2001) have demonstrated that sensory attributes of apples are not always adequately predicted by instrumental tests. In the accompanying paper (Harker et al., 2001), we demonstrate that while sensory differences were always found when the firmness-threshold was exceeded, sensory differences did sometimes exist in treatments were no significant difference in instrumental values was apparent. Thus, we recommend that assessment of fruit by trained and/or consumer panels remain a critical part of fruit quality assessment.
3.4. Implications for posthar6est quality assessment In this study, we have demonstrated that while it is possible to identify taste and flavour differ-
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ences using objective measurements, the differentiation is relatively imprecise for attributes other than acid taste. Differences in acid taste were perceived when apples differed by more than 800 mg kg − 1 titratable acidity. Thus, we suggest that titratable acidity may be an important tool in predicting taste of apples. This may be important during the assessment of fruit quality, since consumers often have distinct preferences for acid or sweet tasting apples (Daillant-Spinnler et al., 1996). Acknowledgements We thank ENZA New Zealand (International) and the New Zealand Foundation for Research Science and Technology for funding this study, Allan White for having the foresight to make the crosses, and Rosemary Weskett for her advice and help. References Baldwin, E.A., Scott, J.W., Einstein, M.A., Malundo, T.M.M., Carr, B.T., Shewfelt, R.L., Tandon, K.S., 1998. Relationship between sensory and instrumental analysis for tomato. J. Am. Soc. Hortic. Sci. 123, 906 – 915. Cliff, M.A., King, M.C., Hampson, C., 1998. Comparison of mean scores and R-indices for consumer preferences of apple cultivars. Hortscience 33, 1239 – 1240. Coultate, T.P., 1989. Food: The Chemistry of Its Components, second ed. The Royal Society of Chemistry, Cambridge. Daillant-Spinnler, B., MacFie, H.J.H., Beyts, P.K., Hedderley, D., 1996. Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Qual. Preference 7, 113 – 126. Fellers, P.J., 1991. The relationship between the ratio of degrees Brix to percent acid and sensory flavor in grapefruit juice. Food Technol. 75, 68 – 75. Fischer, L., 1999. Flavour trends. Food Product Design Magazine. www.foodproductdesign.com. Harker, F.R., Maindonald, J., Murray, S.H., Gunson, F.A., Hallett, I.C., Walker, S.B., 2001. Sensory interpretation of instrumental measurements 1: texture of apple fruit, Postharvest Biol. Technol., submitted for publication. Jaeger, S.R., Andani, Z., Wakeling, I.N., MacFie, H.J.H., 1998. Consumer preferences for fresh and aged apples: a cross-cultural comparison. Food Qual. Preference 9, 355 – 366.
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Jordan, R.B., Seelye, R.J., McGlone, V.A., 2001. A sensorybased alternative to brix/acid ratio. Food Technol. 55 (6), 36– 44. Koehler, P.E., Kays, S.J., 1991. Sweet potato flavor: quantitative and qualitative assessment of optimum sweetness. J. Food. Qual. 14, 241 –249. Market Review, 1996. UK fruit and vegetable market review 1995 – 1996. Fresh Fruit and Vegetable Information Bureau, London. Marsh, K.B., MacRae, E.A., Weskett, R.H., White, A.G., 1995. Inheritance of acidity in apple progeny. Programme and Abstracts of 35th Annual General Meeting of Australian Society Physiologists, September 26 –29, 1995, Sydney, Australia, p. 79. Mitcham, E.J. (Ed.), 1997. Proceedings of the Seventh International Controlled Atmosphere Research Conference, volume 2, Apples and Pears. Postharvest Horticulture Series No. 16. University of California, Davis, 308 pp. Mitchell, F.G., Mayer, G., Biasi, W., 1991. Effect of harvest maturity on storage performance of ‘Hayward’ kiwifruit. Acta Hortic. 297, 617 –625. Payne, R.W., et al., 1993. Genstat 5 Release 3 Reference Manual. Clarendon Press, Oxford. Prescott, J., 1999. Flavour as a psychological construct: impli-
cations for perceiving and measuring the sensory qualities of foods. Food Qual. Preference 10, 349 – 356. Redgwell, R.J., 1980. Fractionation of plant extracts using ion-exchange Sephadex. Anal. Biochem. 107, 44 – 50. Shallenberger, R.L., 1993. Taste Chemistry. Blackie Academic, London. Smith, S.M., 1985. Measurement of the quality of apples: recommendations of an EEC working group. Commission of the European Communities, Brussels. Thiault, J., 1970. Etude de criteres objectifs de la qualite gustative de pommes Golden Delicious. Bull. Tech. Inf. Minist. Agric. Paris 248, 191 – 201. Vangdal, E., 1985. Quality criteria for fruit for fresh consumption. Acta Agric. Scand. 35, 41 – 47. Yuen, C.M.C., Haynes, Y., Warton, M., 1995. Consumer acceptance of Jonathan and Delecious apples in relation to fruit maturity and physico-chemical attributes. ASEAN Food J. 10, 139 – 144. Young, H., 1981. Direct desorption of traps for capillary column gas chromatography. J. Chromatogr. 214, 197 – 201. Young, H., Gilbert, G.M., Murray, S.H., Ball, R.D., 1996. Causal effects of aroma compounds on Royal Gala flavours. J. Sci. Food Agric. 71, 329 – 336.
Sensory interpretation of instrumental measurements 2: sweet and acid taste of apple fruit F.R. Harker *, K.B. Marsh, H. Young, S.H. Murray 1, F.A. Gunson, S.B. Walker Mt. Albert Research Centre, The Horticulture and Food Research Institute of New Zealand Ltd., Pri6ate Bag 92 169, Auckland, New Zealand Received 2 August 2000; accepted 30 June 2001
Abstract The relationship between objective and sensory measurements of apple taste and flavour was investigated. The aim was to determine the objective parameters that were best correlated with sensory evaluation, and then to identify the minimum objective difference that was required before a trained sensory panellist could detect a difference in apple taste and/or flavour. Objective measures included titratable acidity, °Brix (soluble solids content), levels of individual volatiles, sugars and acids, as well as calculations of °Brix/titratable acid ratio. Sensory panellists were trained to assess sweet taste, acid taste, apple flavour, and overall flavour. Titratable acidity was the best predictor of acid taste (correlation of 0.86 for the median panellist), and differences between apples of 800 mg kg − 1 (0.08% titratable acid) were required before the average trained panellist could detect a difference in acid taste (P =0.90). This value represented about a tenth of the range of titratable acidity values of the treatments presented to the trained panel. Sweet taste was difficult to predict using any of the objective methods. Indeed, the best objective predictor of sweetness was °Brix (correlation of 0.41 for the median panellist), which could predict a difference in taste when apples differed by more than 1 °Brix (P =0.90). This value represented about a third of the range of °Brix levels presented to the trained panel. Thus, while acid taste may be predicted on the basis of titratable acidity, we recommend that evaluation of sweet taste and flavour attributes continue to require assessment by trained sensory panels. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Sweet; Acid; Flavour; Volatiles; Apple; Malus; Brix; Titratable acidity
1. Introduction
* Corresponding author. Tel.: +64-9-815-4200; fax: + 649-815-4202. E-mail address: [email protected] (F.R. Harker). 1 Present address: Department of Applied Science, Faculty of Science and Engineering, Auckland University of Technology, PO Box 92006, Auckland 1020, New Zealand.
Studies on fruit quality have often found good relationships between °Brix levels and/or °Brix/titratable acidity ratio and consumer acceptability of fruit (Vangdal, 1985; Fellers, 1991; Mitchell et al., 1991). For apples, the sensory attributes sweet taste and acid taste are important drivers of con-
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sumer preference (Daillant-Spinnler et al., 1996; Jaeger et al., 1998). Furthermore, consumers of fresh apples consider quality more important than price (Market Review, 1996), and current food trends are placing an increasing emphasis on the use of sweet fruit flavours (Fischer, 1999). Against this background, postharvest researchers are increasingly being required to evaluate the flavour and texture of fruit. Since many researchers do not have ready access to sensory and/or consumer evaluation facilities, they must rely on instrumental and/or chemical measurements of quality. Indeed, measurements of °Brix and titratable acidity are often included in assessments of the postharvest quality of apples (Smith, 1985; Mitcham, 1997). In this study, we have examined the relationships between objective measurements and human perception of taste and flavour in apples. The approach was based on that of Harker et al. (2001), in that correlations were determined for individual people and statistical equations were re-arranged so as to calculate the objective difference that was required to ensure a sensory difference (P= 0.05). Objective measurements, including sugars, acids, °Brix, titratable acidity, and volatiles were correlated with the sensory responses to acid taste, sweet taste, apple flavour and overall flavour obtained from 20 panellists trained in sensory analysis. The aim of the study was to determine which objective parameters were the best predictors of taste and flavour, and how big a difference in objective parameter was required before a sensory difference would be perceived.
2. Methods
2.1. Plant material Apple fruit were harvested from seedlings from an F1 population of crosses between Fuji and Alkmene and between Fuji and Jonathan, growing at the Research Orchard in Havelock North. Fuji was used as a parent as it was considered to be a sweet low acid apple, while Alkmene and Jonathan were selected as parents on the basis
that they were high acid taste or low acid taste, respectively. Specific seedlings were selected on the basis of their acidity, °Brix and the absence of unusual flavour notes as determined in earlier studies (Marsh et al., 1995). In addition commercially harvested Fuji and Braeburn apples were used. The apples were harvested at the ‘tree ripe’ stage and stored at 0 °C until required for sensory studies (B 1 month). The fruit were warmed to 20 °C overnight prior to assessment.
2.2. Objecti6e assessments Whenever possible the exposed and shaded portion of each apple was identified from the location of the blush. Fruit were divided along the blushshade axis ensuring that both sides (objective and sensory) had similar blush coverage. Half of the fruit was used for objective measurements and the remainder used for sensory assessments. Juice was squeezed from the half-apple, and collected onto a digital refractometer (Model PR1 Atago, Tokyo, Japan) for measurement of °Brix. Two samples (approximately 10 g) of chopped outer cortical tissue were then frozen in liquid nitrogen and stored at − 20 °C for subsequent analysis of titratable acidity and quantitative measurements of sugars and acids. Titratable acidity was determined following maceration of one of the frozen tissue samples (10 g) in 35 ml distilled water using a polytron (Kinematica, Luzern, Switzerland). The homogenate was titrated to an endpoint at pH 8.1 using 0.1 N NaOH, and an automatic titrator (model 716 DMS Titrino, Metrohm, Herisau, Switzerland). The pH of the macerate before the start of the titration was recorded, and this value is described as the ‘suspension pH’. °Brix/titratable acidity ratio (Fellers, 1991) was calculated for individual fruit. Quantitative sugar and acid analysis was undertaken using gas chromatography (GC). The second tissue sample was ground in liquid nitrogen using a mortar and pestle and a subsample (40 mg) taken for extraction with 600 ml of methanol, chloroform, water (MCW, 12:5:3) in a 1 ml microfuge tube. Tartarate (400 mg) and adonitol (400 mg) were added as internal standards for acids and
F.R. Harker et al. / Posthar6est Biology and Technology 24 (2002) 241–250
sugars, respectively. The samples were left overnight, then centrifuged at 10 000 rpm and the pellet resuspended in a further 400 ml MCW and centrifuged again. The supernatants were combined and 200 ml chloroform and 200 ml water added, the sample spun (13 000 rpm, 10 min) and the lower layer (chloroform) discarded. The upper layer was freeze dried and resuspended in 150 ml water and applied to Sephadex columns (QAE and SPC in series; Redgwell, 1980). Sugar samples were collected in a water eluant (800 ml), the columns were separated and acids eluted from QAE with 7% formic acid (800 ml). Aliquots of acids and sugars were derivatized with MSTFA (Pierce 48911) or silated with Tri-Z (Pierce 49230), respectively, before analysis and comparison with internal standards by capillary GC (DB1701, J&W Scientific). Quantitative data for individual sugars were converted to sucrose equivalents using mean relative sweetness values presented by Shallenberger (1993). Volatiles were assessed for a subset of five fruit per selection. Cylinders of cortical flesh were removed using a 9.5 mm diameter cork borer and cut into 50 mm lengths. Four cylinders of tissue were placed in a 50 ml Quickfit test-tube fitted with a headspace collection attachment. The test-tube was placed in a 25 °C waterbath, and the atmosphere in the test-tube flushed with N2 (25 ml min − 1 for 20 min) into a stainless steel cartridge (3.3 mm internal diameter× 100 mm length) packed with 100 mg of chromosorb 105. GC analysis was carried out by thermal desorption of the collected material on to a 30 m× 0.53 mm (internal diameter) DBWax (J & W Scientific) capillary column (Young, 1981). GC conditions were temperature programme 35 °C, 6 min hold, 3 °C min − 1 to 195 °C; H2 carrier at 28 cm s − 1; desorption temperature =175 °C; flame ionisation detection (FID). Levels of compounds were calculated using FID response factor as determined using ethyl butanoate, butyl acetate, 2-methylbutyl acetate, ethyl hexanoate, butanol and hexanol.
2.3. Sensory analysis At a pre-screening session the taste sensitivity of 40 panellists was screened using artificial solutions
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containing mixtures of sucrose (7–14 °Brix) and malic acid (0.08– 0.2% wt./v), as well as whole fruit. From this pool of panellists, the 20 that were most taste-sensitive were selected. This was necessary since previous studies had demonstrated that people vary in taste-sensitivity. The 20-member panel was trained in the descriptive analysis of apple taste and flavour. Key attributes and their definitions included, ‘sweet taste’ —the intensity of the taste sensation caused by sugars; ‘acid taste’ — the intensity of the taste sensation caused by acids’; ‘apple flavour’ —total apple flavour, i.e. the flavour that identifies the fruit as an apple; ‘overall flavour’ —the intensity of the overall flavour sensation i.e. flavour and taste combined. Upon completion of training, the panellists evaluated 12 of the possible 13 fruit in a balanced incomplete block design, where panellist was the block and selection was the treatment. In two sessions over two days, the panellists were given three half-fruit to evaluate and then following a break of approximately 20 min, evaluated the remaining three half-fruit. Samples were identified using three-digit codes, and presented monadically to panellists. Each panellist received a peeled halffruit, and water purified by reverse osmosis was provided as a palate cleaner between samples. Evaluations took place in individual sensory booths, in which environmental temperature was held at 20 °C and daylight corrected lighting was used. The samples were scored for the intensity of attributes using 150 mm unstructured line scales, where 0= absent and 150= extreme.
2.4. Statistical analysis The data was first analysed using Residual Maximum likelihood in the Genstat Statistical Package (REML, Payne et al., 1993). There was no detectable session effect on the results, and no outliers were detected. For each sensory attribute in turn, for each panellist, the correlations with each objective measurement were calculated. Boxplots were produced to show the spread of correlations for each objective measurement, and for each sensory attribute. Subsequent analysis was then based on the methodologies and calculations described by
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Fig. 1. Examples of the correlations between titratable acidity and acid taste, and °Brix and sweet taste for the best, median and worst panellists.
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Harker et al. (2001). The first question addressed was, do panellists on average tend to give sensory scores that correlate more or less closely with different objective measurements? To obtain a sensitive comparison the analysis needed to be made within panellists, and thus panellist was used as the blocking structure, in an analysis of transformed data. The resulting values are described as ‘degrees of association’ (between instrumental and sensory measurements; Harker et al., 2001). The higher the ‘degree of association’ the more closely the objective measurement is able to predict the sensory response. The second question addressed was, how large must the objective difference be, if we are to be reasonably sure that it will result in a perceived difference in a sensory taste and flavour attributes? An answer to this question can be derived statistically, using estimates of variance and the t-distribution (Harker et al., 2001). Essentially this calculation provides a value that
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represents the difference (an objective measurement) required before one can assume that it will result in a perceivable difference in sensory attributes (P= 0.9).
3. Results and discussion
3.1. Relationships between objecti6e and sensory measurements The seedlings provided fruit with a broad spread of sensory attributes that also differed in chemical composition (Table 1). Examples of the range of correlations between sensory attributes and objective measurements are provided in Fig. 1, and a summary of correlations for all objective measurements with all taste and flavour attributes is provided in Fig. 2. The highest correlations between sensory and objective measurements occurred for acid taste (Fig. 2). The basis of the calculation of the degree of associa-
Fig. 2. The range of correlations between objective and sensory measurements of apple taste and flavour. Each boxplot represents the correlations obtained from 20 individual trained panellists.
7.6 0.113 0.140 7.92 25 5.9 19.2 40.3 90.3 88.9
Acids Malate (mg g−1 FW) Citrate (mg g−1 FW) Quinate (mg g−1 FW) Total (mg g−1 FW)
Sugars Sucrose (mg g−1 FW) Sorbitol (mg g−1 FW) Glucose (mg g−1 FW) Fructose (mg g−1 FW) Total (mg g−1 FW) Total (sucrose equivalent)d 52 5.6 10.0 32.0 99.3 99.8
12.4 0.083 0.024 12.84
0.95 13.7
120 84 106 93
FA22
30 5.1 16.2 40.9 92.7 92.9
5.3 0.083 0.108 5.61
0.26 13.1
45 94 92 75
FA23
42 4.8 19.4 39.0 105.0 103.8
8.4 0.063 0.070 8.82
0.76 14.5
121 78 111 95
FA17
48 5.3 13.2 36.6 103.2 103.5
9.2 0.074 0.137 9.48
0.68 14.1
114 97 117 104
FA25
25 5.0 17.0 42.0 89.4 89.6
5.8 0.086 0.091 5.91
0.41 12.6
82 97 104 99
FA41
24 6.6 19.6 42.3 92.2 90.8
4.2 0.043 0.164 4.43
0.30 12.5
49 88 81 82
FJ4
40 3.8 14.3 39.1 97.4 98.5
6.8 0.130 0.137 7.19
0.47 13.2
80 85 101 74
FJ11
Values represent the mean concentrations and scores for each seedling, and the minimum and maximum values are in bold. a Seedlings FA =Fiesta×Alkmene; FJ= Fiesta×Jonathan. b LSDTukey (0.05). c Braeburn. d Conversion based on mean values for sweetness conversion from Shallenberger (1993), mg g−1 FW.
0.52 14.6
92 98 103 100
FA11
Seedlingsa and cultivars
Instrumental Titratable acidity (%) °Brix
Sensory Acid taste Sweet taste Overall flavour Apple flavour
Attribute
Table 1 Sensory and objective differences between apples
19 4.7 18.6 44.3 86.6 86.8
1.9 0.066 0.239 2.23
0.15 13.3
19 69 54 50
FJ33
19 3.8 18.2 38.6 79.6 79.2
3.5 0.149 0.156 3.87
0.24 12.7
57 80 82 78
FJ14
28 7.4 20.6 42.4 98.4 96.3
5.3 0.101 o.132 5.67
0.36 14.8
54 94 84 79
FJ21
40 3.1 13.8 31.3 88.6 88.7
5.3 0.061 0.112 5.60
0.40 12.1
82 93 101 88
Braec
17 3.8 14.3 34.9 76.5 74.5
4.1 0.050 0.137 4.28
0.47 11.7
64 63 74 70
Fuji
15 2.3 6.4 10.3 26.3 20.2
2.9 0.141 0.121 3.09
0.10 1.2
26 31 24 33
LSDb
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Table 2 Differences in the degree of association between objective predictors and the flavour of apples Predictor
Titratable acidity (%) Suspension pH °Brix °Brix/titratable acidity Malate Quinate Citrate Total acids Fructose Glucose Sorbitol Sucrose Total sugars Total (sucrose equivalent) LSD
Sensory attributes Acid taste
Sweet taste
Apple favour
Overall flavour
2.16 1.91 0.34 1.89 1.35 0.50 0.20 1.30 0.59 0.64 0.19 0.70 0.11 0.03 0.37
0.10 0.05 0.69 0.16 0.19 0.51 0.02 0.14 0.28 0.47 0.13 0.19 0.05 0.27 0.43
1.02 1.09 0.44 1.16 0.66 0.51 0.06 0.63 0.29 0.24 0.02 0.25 0.03 0.02 0.37
1.20 0.91 0.66 0.95 1.02 0.61 0.03 0.98 0.47 0.55 0.19 0.67 0.19 0.33 0.36
Large values have a strong association.
tion is described in the accompanying paper (Harker et al., 2001). Calculations of the degree of association indicated that titratable acidity was always amongst the best predictors of sensory attributes acid taste, overall flavour, and apple flavour (Table 2). However, titratable acidity was generally not significantly different from °Brix/titratable acidity or suspension pH in its ability to predict these sensory attributes. Quantitative measurements of acids were generally poorer predictors of taste and flavour in apples, with the exception of the high degree of association between the attribute overall flavour and both malate and total acid concentration (Table 2). Although sweet taste was the sensory attribute that was most difficult to predict using objective measurements, °Brix, glucose and quinate provided the highest degrees of association (Table 2). Conversion of sugars into sucrose equivalents based on relative sweetness values (Shallenberger, 1993) did not result in particularly high values for the degree of association with sweet taste (Table 2). This is explained by the quantitative dominance of fructose in sugar profile of the apples (Table 1), and the high relative sweetness of fructose (Shallenberger, 1993; Koehler and Kays, 1991). However, there is considerable variability
in the values for relative sweetness of sugars, which reflects differences in the methodology used to determine thresholds (Coultate, 1989). BrimA is a scale that has recently been proposed as a more sensitive prediction of consumer acceptability in fruit (Jordan et al., 2001). It is calculated using the formula: BrimA= °Brix− k× titratable acidity; where k is a constant that may vary between fruit species/cultivars due to differing mixes of acids and sugars, but is usually about five for citrus and grapes (Jordan et al., 2001). Research on Golden Delicious apples, suggested that calculations of total sugars (g l − 1 juice)+ 10×malic acid (g l − 1 juice) provided a better prediction of flavour acceptability than total sugars alone (Thiault, 1970). In the present study, BrimA did not improve the prediction of sensory attributes over those obtained using standard approaches of titratable acidity, °Brix, and °Brix/titratable acidity (data not presented). During determination of the magnitude of the constant (k) using the entire data set (i.e. not blocked by panellist) we found the correlation improved progressively as k decreased to such an extent that it became negative. In fact when the magnitude became − 10 the taste score was still increasing. The magnitude of k suggested that titratable acid-
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ity had a far more dominant role in the prediction of taste of apples than in prediction of flavour acceptability in fruit such as citrus and grape. It is noteworthy that acid has a positive correlation with taste in apples as shown here and with the data of Thiault (1970), yet is negative with citrus and grapes as shown by Jordan et al. (2001). On the basis of these preliminary findings we did not attempt to use BrimA on the data from individual panellists. It should be noted that our predictions related to sensory measurements by trained panellists and not to consumer acceptability (i.e. liking of the product). Our panelist’s were trained to distinguish between separate sweet and sour tastes, while consumers tend to integrate these tastes along with flavours into an overall sweet or sour sensation (Prescott, 1999). Thus, one might argue that Brix and Brix/titratable acidity may be better at explaining consumer perception of sweetness (or preference) than they are at explaining sweet scores obtained from by sensory panels. However, the evidence for such a relationship between Brix (and Brix/titratable acidity) and consumer acceptability and perception of sweetness is often unreliable in the case of apples. In some studies the relationship has probably been clouded by the influence of fruit maturity and starch content (e.g. Yuen et al., 1995), while other studies show good relationship between Brix or Brix/titratable acidity and acceptability (Thiault, 1970). Overall, there seem to be few studies that have used a reasonable number of consumers (i.e. minimum of 100 according to Cliff et al., 1998) to examine this issue.
3.2. Volatiles Studies with fruit have found that some volatiles are correlated with acceptability, as well as sweet taste, and sour taste (Baldwin et al., 1998). Thus, it was anticipated that volatiles may also have an influence on sensory assessment of taste and flavour of apples in the present study. For logistical reasons, volatile analysis could not be undertaken on the same fruit tasted by the sensory panel. Therefore, additional samples of fruit were collected for volatile analysis, and the results related back to the general differences in taste and flavour attributes identified for individual seedlings in Table 1. Principle component analysis indicated that the main contributors to the first two principal components, which accounted for 61.4% of the variance, were hexyl acetate, butyl acetate, propyl acetate, 2-methylbutyl acetate and propanol to a lesser extent (data not shown). However, it was not possible to explain sensory responses using the volatile data collected (while the volatiles data is not provided here, it is available upon request to corresponding author). This result differs from previous studies, which have demonstrated that volatiles have an impact on flavour of apples (Young et al., 1996).
3.3. Thresholds for perception of fla6our and taste differences Analysis of the magnitude of objective differences required before panellists perceived a difference in the taste and flavour of apples was undertaken. Only the objective measurements that
Table 3 Differences in objective measurement required before sensory differences become apparent Predictor
Titratable acidity (%) Suspension pH °Brix °Brix/titratable acidity
Sensory attribute Acid taste
Sweet taste
Apple flavour
Overall flavour
0.08 0.14 1.38 10.17
0.17 0.25 0.99 19.82
0.21 0.32 1.64 24.84
0.20 0.34 2.07 16.25
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showed the highest correlation and/or degree of association with acid taste, sweet taste, apple flavour and overall flavour are presented (Table 3). The most useful of these was titratable acidity, where differences as low as 0.08% (i.e. 800 mg kg − 1) between apples could evoke a response in perceived acid taste for the median panellist (P = 0.9). This value represents about a tenth of the range of titratable acidity values for individual selections, which varied from 0.15% titratable acidity for seedling FJ33 to 0.95% titratable acidity for seedling FA22. Titratable acidity of the parental phenotypes is between 0.9% (cv. Fiesta) and 0.35% (cv. Jonathon) at maturity (Marsh, unpublished). Both ‘Fiesta’ and ‘Jonathon’ are apple cultivars that are available commercially, and thus the range of titratable acidity studied here is similar to that available in the marketplace. Sweet taste is an important, but difficult, attribute to predict using objective measurements. The best and most convenient predictor was °Brix (Table 2). However, two apples needed to differ in °Brix by more than 1 (i.e. 1 g sucrose in 100 g aqueous solution) before evoking a response in perceived sweet taste for the median panellist (Table 3). This value represents about a third of the range of °Brix values for individual selections, which varied from 11.6 °Brix for ‘Fuji’ to 14.8 °Brix for seedling FJ21. Caution should be used when/if applying these taste threshold values. Previous studies (Harker et al., 2001) have demonstrated that sensory attributes of apples are not always adequately predicted by instrumental tests. In the accompanying paper (Harker et al., 2001), we demonstrate that while sensory differences were always found when the firmness-threshold was exceeded, sensory differences did sometimes exist in treatments were no significant difference in instrumental values was apparent. Thus, we recommend that assessment of fruit by trained and/or consumer panels remain a critical part of fruit quality assessment.
3.4. Implications for posthar6est quality assessment In this study, we have demonstrated that while it is possible to identify taste and flavour differ-
249
ences using objective measurements, the differentiation is relatively imprecise for attributes other than acid taste. Differences in acid taste were perceived when apples differed by more than 800 mg kg − 1 titratable acidity. Thus, we suggest that titratable acidity may be an important tool in predicting taste of apples. This may be important during the assessment of fruit quality, since consumers often have distinct preferences for acid or sweet tasting apples (Daillant-Spinnler et al., 1996). Acknowledgements We thank ENZA New Zealand (International) and the New Zealand Foundation for Research Science and Technology for funding this study, Allan White for having the foresight to make the crosses, and Rosemary Weskett for her advice and help. References Baldwin, E.A., Scott, J.W., Einstein, M.A., Malundo, T.M.M., Carr, B.T., Shewfelt, R.L., Tandon, K.S., 1998. Relationship between sensory and instrumental analysis for tomato. J. Am. Soc. Hortic. Sci. 123, 906 – 915. Cliff, M.A., King, M.C., Hampson, C., 1998. Comparison of mean scores and R-indices for consumer preferences of apple cultivars. Hortscience 33, 1239 – 1240. Coultate, T.P., 1989. Food: The Chemistry of Its Components, second ed. The Royal Society of Chemistry, Cambridge. Daillant-Spinnler, B., MacFie, H.J.H., Beyts, P.K., Hedderley, D., 1996. Relationships between perceived sensory properties and major preference directions of 12 varieties of apples from the southern hemisphere. Food Qual. Preference 7, 113 – 126. Fellers, P.J., 1991. The relationship between the ratio of degrees Brix to percent acid and sensory flavor in grapefruit juice. Food Technol. 75, 68 – 75. Fischer, L., 1999. Flavour trends. Food Product Design Magazine. www.foodproductdesign.com. Harker, F.R., Maindonald, J., Murray, S.H., Gunson, F.A., Hallett, I.C., Walker, S.B., 2001. Sensory interpretation of instrumental measurements 1: texture of apple fruit, Postharvest Biol. Technol., submitted for publication. Jaeger, S.R., Andani, Z., Wakeling, I.N., MacFie, H.J.H., 1998. Consumer preferences for fresh and aged apples: a cross-cultural comparison. Food Qual. Preference 9, 355 – 366.
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