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Sensory evaluation by small postharvest teams and the relationship with instrumental measurements of apple texture
Postharvest Biology and Technology 59 (2011) 179–186
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Sensory evaluation by small postharvest teams and the relationship with instrumental measurements of apple texture Paul L. Brookfield a,∗ , Sara Nicoll a , F. Anne Gunson b , F. Roger Harker b , Mark Wohlers b a b
The New Zealand Institute for Plant & Food Research Limited (Plant & Food Research), Private Bag 1401, Havelock North, Hastings 4157, New Zealand Plant & Food Research, Private Bag 92 169, Auckland 1142, New Zealand
a r t i c l e
i n f o
Article history: Received 13 August 2009 Accepted 30 August 2010 Keywords: Apple Malus domestica (Borkh.) Texture Juiciness Crispness SENB test Trained sensory assessors
a b s t r a c t Effective prediction of sensory properties of apples is a critical component of the evaluation of fruit quality. Small postharvest teams (<4 individuals) are often faced with the question as to whether they should taste the fruit they are evaluating or rely on instrumental measurements. In this study, we have evaluated the relative advantage of both approaches in a project in which measurements were made on the fruit of nine apple cultivars with a wide range of textures. Instrumental measures comprised the puncture test, the single-edge notched bend (SENB) test and juiciness based on juice absorption. Sensory assessments of juiciness and crispness were carried out by three trained assessors using 150-mm intensity line scales marked with reference standards. The largest number of cultivars (7/9) was distinguished by sensory juiciness, which dominated the first linear discriminant, explaining 77.4% of the variation in the data. Sensory crispness was second most discriminating of the measurements, distinguishing six of nine cultivars. The instrumental measures each discriminated four cultivars, which was fewer than for the sensory measures. The highest correlation between sensory and instrumental measures was between sensory crispness and puncture force (r = 0.7), which exhibited less variance than the other instrumental measures. Instrumental juiciness measured by juice absorption was poorly related to sensory and instrumental measures. The results showed that small panels can be used effectively for postharvest assessments of sensory properties of apples, where they focus on a small number of attributes. Sensory assessments improved discrimination of textures among different cultivars compared with that obtained from simple instrumentally based methods. Sensory juiciness is a key measure that, in combination with instrumental measures such as the puncture test, could account relatively efficiently for a large proportion of product variability when applied to the postharvest evaluation of apple texture. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The assessment of produce quality is one of the core aspects of applied postharvest biology. While protocols for assessing quality from an instrumental and physiological perspective are well established, it is common for small teams of postharvest scientists to assess the texture, taste, odour and flavour of produce. Traditionally, such sensory attributes are assessed using a panel consisting of eight to sixteen members who have been trained in sensory evaluation methodologies, or alternatively, if information on hedonic liking or purchase intentions is required, a panel consisting of about 100 consumers is recruited. Detailed accounts of the training and use of sensory panels can be found in texts such as Meilgaard et al. (2006) and Lawless and Heymann (1999). However, many postharvest biologists do not have access to consumer or trained
∗ Corresponding author. Tel.: +64 6 9758880; fax: +64 6 9758881. E-mail address: paul.brookfi[email protected] (P.L. Brookfield). 0925-5214/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.postharvbio.2010.08.021
panels, and they are forced to consider alternative ways for assessing sensory properties of the produce they are evaluating. It is not uncommon in such situations to be reliant on a single ‘expert taster’ for carrying out sensory assessments. This current study considers (1) the extent that it is possible to develop a more precise approach to these types of postharvest assessments of sensory properties of fruits, (2) whether or not such sensory evaluations provide insights over and above those provided by simple instrumentally based methods and (3) whether there are combinations of instrumental and sensory assessments that maximise the amount of product variability that is described, while retaining high efficiency. The research focus in this study was to assess the extent to which apple texture could be described by combinations of sensory and instrumental measurements. Texture is particularly important in apples, with clear evidence that hardness, juiciness and absence of mealiness are important drivers of consumer preference (Dalliant-Spinnler et al., 1996; Jaeger et al., 1998; Andani et al., 2001). Instrumental measures of hardness such as the puncture or penetrometer test have been shown to predict consumer accept-
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ability of apples (Harker et al., 2008; Péneau et al., 2007) and there is substantial research demonstrating close relationships between these instrumental measures and sensory attributes such as ‘hardness’, ‘crispness’ and ‘crunchiness’ (Barreiro et al., 1998; Harker et al., 2002; Ioannnides et al., 2007; Mehinagic et al., 2004). The singleedge notched bend (SENB) test is a fundamental measure of texture from which the tissue fracture parameters Kc (fracture toughness) and Gc (fracture energy) are derived (Alvarez et al., 2000). Fracture toughness refers to the critical stress at initiation of fracture and relates to the forces required to cause rupture in the tissue, whereas fracture energy is more a function of overall toughness and the energy required for material fracture. The cell size and intercellular air spaces of biological material influence both parameters (Alvarez et al., 2000). Fundamental measures such as the SENB test have been promoted as providing more realistic measures of texture (Alvarez et al., 2000), and there is evidence from studies on apples that support this. For example, Harker et al. (2006a) noted that the relationship between puncture force and SENB measures of fracture parameters Kc and Gc differed between lines of fruit such that some cultivars produced fruit that were interpreted as being crisper than suggested by measures of puncture force. The other key aspect of fruit texture is juiciness. Juiciness in apples is not related to the water content so much as it is a function of the way cells break open during mastication (Allan-Wojtas et al., 2003; Harker et al., 1997) For this reason, juiciness is influenced by the mechanical properties of tissue and often predicted by measures such as puncture force (Harker et al., 2006b; Mehinagic et al., 2004). Instrumental predictors of juiciness such as apparent juice content and juice absorption from cut surfaces have provided inconsistent results in apples (Harker et al., 2006b; Barreiro et al., 1998). The current study explores the possibilities of using a small team of trained sensory assessors to supplement instrumental measurement of apple texture.
2. Materials and methods 2.1. Plant materials Nine apple cultivars of commercial quality representing a wide range of season of harvest maturity and storage potential were selected for the study. The cultivars, located at the Plant & Food Research orchard or on commercial orchards in Hawke’s Bay, New Zealand were harvested on one date during their respective commercial harvest periods in 2003 as follows: ‘Sciearly’ and ‘Cox’s Orange Pippin’ 20 February, ‘Royal Gala’ and ‘Honeycrisp’ 27 February, ‘Red Delicious’ and ‘Scifresh’ 14 March, ‘Fuji’ and ‘Sciros’ 10 April and ‘Cripps Pink’ 23 April. The harvest maturity characteristics of fruit were determined from a sample of 20 fruit per cultivar (Table 1). One hundred fruit of each cultivar was placed in cold storage at 0.5 ◦ C (90% RH) except for ‘Cox’s Orange Pippin’, which was stored at 3 ◦ C (90% RH) as practiced commercially, because of susceptibility of fruit to internal breakdown at lower storage temperatures. For each cultivar, textural measurements were spread over two d (50 fruit assessed on each day) following 45 and 46 d of cold storage and 1 d at 20 ◦ C after harvest. On removal from cool storage, all fruit were numbered to maintain their identification throughout the instrumental and sensory assessments. Instrumental measurements were made on all 100-fruit per cultivar, with one measurement of each instrumental method per fruit. A subset of 50 fruit per cultivar was used for sensory assessments. Three assessors assessed each fruit (3 sensory measurements per fruit × 50 fruit per cultivar) for each sensory attribute. Assessment of the fruit of each cultivar was structured over a period of two d. This was to ensure that assessors were not overwhelmed or fatigued by the
number of samples that needed to be tasted. On each assessment day, instrumental measurements were made on 18, 16 and 16 fruit immediately before three corresponding sensory assessments. The sensory assessments were made on 25 of these fruit at the following times: 9 fruit at 0930 h (first sensory evaluation), 8 fruit at 1130 h (second sensory evaluation) and 8 fruit at 1420 h (third sensory evaluation). 2.2. Instrumental assessments For each fruit, one of each of the instrumental measurements was obtained as outlined below: 2.2.1. Penetrometer measurement A small area of skin was removed from the equatorial region on the least blushed side of the fruit and a puncture test made at this location using a materials testing machine (model 4443, Instron, Canton, MA) with a 1 kN load cell. The materials testing machine was fitted with an 11.1 mm diameter Effegi penetrometer head with a convex tip, which was driven 8 mm into the fruit at a speed of 4 mm/s. 2.2.2. Juice absorption A plug of tissue was removed from a location adjacent to site of the penetrometer test using a 14.7 mm cork borer. Core tissue at the inner end of the plug was cut off and the surface blotted dry. The remaining plug of tissue was cut in half using a new razor blade and the two cut surfaces representing mid-cortex tissue (3.394 cm2 ) immediately placed on two layers of 3-ply tissue paper, which were tared on a balance. After 60 s, the apple tissue was removed and the weight of released juice absorbed by the tissue paper recorded to an accuracy of 0.001 g (Harker et al., 1997). 2.2.3. SENB test Two parallel cuts between stem and calyx were made toward the core of the apple in the same quadrant used for the penetrometer and juice absorption tests. The slice of tissue between the cuts was extracted from which a beam for the SENB test was prepared as described by Alvarez et al. (2000). A jig was used to cut a beam of tissue 40 mm long, 8 mm deep and 4 mm wide. A 4-mm deep cut (notch) was made into the lower edge mid-way along the length of the beam with a razor blade. The notched beam was then positioned across two horizontal steel rod supports (6 mm diameter) placed 32 mm apart on the base plate of the materials testing machine, which was fitted with a 50 N load cell. A probe with a horizontal steel rod (6 mm diameter) at the tip was driven down at a speed of 2 mm/s to bend the beam at the position above the notch and mid-way between the supports. The critical load at the initiation of crack propagation upwards from the notch and the area under the force-displacement curve to that point, were obtained from the recorded data (Alvarez et al., 2000). These values were then used to derive measures of fracture toughness (Kc ) and fracture energy (Gc ) using equations described by Harker et al. (2006a). 2.3. Sensory analysis Based on previous experience in sensory evaluation of apples (Harker et al., 2002), we selected two key attributes, juiciness and crispness for evaluation. The sensory evaluations were carried out by three female assessors (ages 26–50 years) who were Plant & Food Research technicians. They had 3–7 years experience in fruit research and representative of staff commonly available in small postharvest research teams for participation in sensory assessment of fruit. These assessors had undertaken a 7 h Plant & Food Research training course on sensory evaluation practices which is run by the consumer science group prior to apple harvest each season. This
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Table 1 Harvest maturity characteristics of apples from nine cultivars used in sensory and instrumental assessments of texture. Assessments were made on 20 apples per cultivar. Cultivar
Fruit weight (g)
IECa (L L−1 )
Bk colorb (1–10)
Firmness (N)
Soluble solids (%)
SPIc (0–6)
Titratable acidity (%)
‘Sciearly’ ‘Cox’s Orange Pippin’ ‘Royal Gala’ ‘Honeycrisp’ ‘Red Delicious’ ‘Scifresh’ ‘Sciros’ ‘Fuji’ ‘Cripps Pink’
226 164 191 189 156 187 243 245 191
1.46 0.09 1.37 1.81 0.14 2.96 0.98 0.79 0.56
4.4 2.7 5.2 2.7 4.3 4.7 6.9 4.2 4.8
79.5 78.3 74.8 63.3 79.1 103.6 72.7 73.6 82.1
11.4 10.4 10.8 10.9 9.9 12.3 12.6 12.9 13.3
1.9 1.6 3.0 2.4 1.1 2.2 3.9 4.6 3.8
0.35 0.83 0.34 0.56 0.27 0.64 – – 0.69
a b c
Fruit internal ethylene concentration. Fruit skin background color where 1 = green and 10 = yellow. Starch pattern index based on staining with iodine solution where 0 = fully stained and 6 = no staining.
was followed by three 1 h training sessions in the week before commencement of the study to familiarize assessors with the intensities of reference standards and in relation to apple samples of differing texture and to provide feedback on scoring of apple samples to develop consistency between assessors. A further 1 h training session with standards and samples of apple was carried out by the assessors the day before the assessment of each cultivar. Samples of the standards were made available for reference during each session of apple texture evaluation. The first author, who was not involved in tasting the fruit, prepared all the samples and coded them for presentation to the assessors, to minimise prior knowledge of the samples by the assessors. Crispness was defined as ‘the amount and pitch of sound on first bite with the front teeth’. Juiciness was defined as ‘the amount of juice released during chewing using the back teeth to chew the sample until it was ready to be swallowed’. The reference standards were placed on a 150-mm horizontal line scale anchored by absent at 0 and extreme at 150. The line scale was marked at each end by a short vertical line. The reference standards were determined by being readily available and representative of the range of intensities that could be found in the apples. For crispness, whole canned water chestnut (‘Golden Sun’, Acton International Marketing Ltd., Lower Hutt, New Zealand), fresh carrot and fresh celery were placed at 50, 100 and 150 mm, respectively, on the line scale. For juiciness, fresh carrot, fresh celery and canned mandarin (‘John West’ brand used under license, Simplot Australia Pty Ltd., Victoria, Australia) were placed at 10, 50 and 85 mm, respectively. During the evaluations, the assessors rated the intensity of crispness and juiciness of each apple sample on the intensity scales marked with the position of the reference standards. On each assessment day (two assessment days per cultivar), the sensory evaluations were carried out on 9 fruit at 0930 h, 8 fruit at 1130 h and a further 8 fruit at 1430 h. Each session lasted about 0.5 h. To provide a manageable number of fruit for sensory assessment that was also representative of the total sample from each cultivar the sub-samples for sensory assessment were obtained as follows. Before each sensory session, the instrumentally tested fruit were ranked highest to lowest according to Gc , and every second fruit according to this ranking were selected for the sensory assessments, in an attempt to ensure that the sensory sample of 50 fruit per cultivar was representative of the textural variation in the total 100-fruit sample per cultivar. Apple fruit have variable sensory properties whereby one side can differ from the other (Dever et al., 1995). For this reason, we attempted to make all sensory and instrumental measurements within the same third of each apple on the non-blushed side of the fruit. For each of these fruit, three longitudinal slices about 20 mm wide at the outer edge were cut with the skin left on, from the same section as used for the instrumental measures, to enable three assessors to each assess a sample from each fruit. The slices were numbered 1–3 according to their relative location on the sampled section, which was the same for each apple.
The presentation of samples within each session was balanced for order and carry-over effects taking account of their ranking of Gc , assessor and slice. This design provided three measures of sensory juiciness and crispness per fruit (one score per assessor per fruit) for each of 50 fruits per cultivar. Samples were presented to the assessors in an air-conditioned evaluation room at 20 ◦ C and under daylight balanced lighting. Filtered water and plain water crackers were provided for palate cleansing between samples. The data were recorded on computers using an in-house software application that enabled assessors to view the intensity line scales on-screen and record their responses by clicking on the line using a computer mouse. 2.4. Statistical analysis Three-way analysis of variance (ANOVA) was carried out using Genstat 6.1 for Windows (Payne et al., 2007) with dependent variables being the texture attributes and independent variables being cultivar, assessor and their interaction. The linear discriminant analysis function (LDA) was carried out in SAS 9.1 using the CANDISC procedure (SAS Institute Inc., 2000–2004). The LDA function performs multidimensional scaling and is suitable for use where there are repeated measures on the same products, such as by different sensory analysts. LDA allows the finding of product points in a space, where the ratio of the between-products sum of squares to the within product sum of squares has been maximised. Principal component analysis (PCA) was done in R 2.90 using the FactoMiner package version 1.1 (R Development Core team, 2009). The PCA was unrotated. 3. Results 3.1. Differences in sensory scores between assessors There were significant differences between assessors in ratings of juiciness and crispness, as is frequently found in sensory studies. Statistical analysis that used ‘assessor’ as a treatment (rather than a block) showed significant (P < 0.001) effects and interactions for ‘fruit cultivar’ and ‘assessor’ for juiciness and crispness. However, examination of the estimated variances of the three fixed effects, being ‘fruit cultivar’, ‘assessor’, and ‘fruit cultivar’ × ‘assessor’ (data not shown) and correlations (Fig. 1), showed that ‘fruit cultivar’ was the dominant influence for the crispness and juiciness scores. The results demonstrated good consistency between assessors for many but not all cultivars (Fig. 1). Instances of low consistency in scores between at least two assessors were most evident for the two earliest season cultivars ‘Cox’s Orange Pippin’ and ‘Sciearly’ (Fig. 1). Given that some variability was associated with assessors, each assessor was treated as an ‘independent analytical measure’ in subsequent multivariate LDA and PCA statistical approaches.
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Fig. 1. Relationships between individual panelists (F, I, S) in their sensory scores for juiciness and crispness made on fruit from nine apple cultivars. Each point represents an individual fruit (n = 50). The cultivars are ‘Sciearly’ (red), ‘Cripps Pink’ (body pink), ‘Fuji’ (magenta), ‘Sciros’ (dark blue), ‘Scifresh’ (light blue) ‘Red Delicious’ (dark green), ‘Honeycrisp’ (light green), ‘Royal Gala’ (yellow) ‘Cox’s Orange Pippin’ (orange). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
3.2. Discrimination between cultivars by ANOVA of textural measures ANOVA revealed differences in the number of cultivars that were distinguished by the different measures of texture (Table 2). All measures segregated ‘Cox’s Orange Pippin’ apples with the lowest values and all measures except Gc and instrumental juiciness segregated the texture of ‘Scifresh’ apples with the highest values. For Gc , ‘Scifresh’ shared the highest ranking with ‘Cripps Pink’, and for instrumental juiciness, ‘Fuji’ and ‘Sciros’ had the highest values. The highest number of cultivars could be discriminated (separated by LSD (0.05)) by sensory juiciness (7) and sensory crispness (6), followed by the instrumental measures Kc and Gc , instrumental juiciness and puncture test – each with 4 (Table 2). As might be expected, no measure of texture ranked the cultivars in the same order as another, although there were similarities in order for the sensory measures crispness and juiciness in one group and for Kc , Gc , and the puncture test in another. Sensory crispness and juiciness ranked ‘Honeycrisp’ and ‘Sciearly’ relatively higher among the cultivars than these were ranked by Kc , Gc , and the puncture test. 3.3. Relationships between measures of texture Sensory measures of juiciness and crispness were highly correlated (r = 0.83, Fig. 2). For relationships between instrumental and sensory measures, the highest correlation (r = 0.70) was
between puncture force and sensory crispness. Instrumental juiciness was poorly correlated with sensory juiciness and crispness. This appeared to arise mainly from the relatively higher instrumental juiciness values for ‘Fuji’ and ‘Sciros’ than were indicated from the instrumental–sensory relationship derived from the remaining varieties (Fig. 2). Among the instrumental measures, Kc and Gc were highly correlated (r = 0.90). Kc was more highly correlated with puncture force (r = 0.71) than Gc (r = 0.58). Instrumental juiciness had low correlations with puncture force (r = 0.11), Kc (r = 0.24) and Gc (r = 0.13). All correlations were significantly different from 0 at the 5% level. The relationship between one measure of texture and another based on cultivar mean values differed more for certain cultivars than others. For example, the sensory juiciness of ‘Honeycrisp’ and ‘Sciearly’ was higher than was indicated from the puncture test, based on the relationship established with the other cultivars (Fig. 2). Using Kc as the predictor, the sensory juiciness of ‘Cripps Pink’ was lower than expected, although there was no basis of prediction with the exclusion of the extreme opposing cultivars ‘Cox’s Orange Pippin’ and ‘Scifresh’. The sensory juiciness of ‘Fuji’ and ‘Sciros’ was lower than predicted by instrumental juiciness using the relationship based on the other cultivars. Broadly comparable cultivar differences in relationships between measures were also apparent when Gc was substituted for Kc and sensory crispness was substituted for sensory juiciness, as might be expected from their high respective correlation coefficients.
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Table 2 Textural measurements of apple cultivars ranked according to the number of cultivars each measurement could discriminate (first being most and third equal being least) using LSD (Tukey, ˛ = 0.05) in ANOVA. Measurementa
Sensory juicinessb
Sensory crispness
Kc (kPa m½ )
Gc (J m−2 )
Instr. juiciness (g × 10c )
Puncture force (N)
Rank ‘Sciearly’ ‘Cox’s Orange Pippin’ ‘Royal Gala’ ‘Honeycrisp’ ‘Red Delicious’ ‘Scifresh’ ‘Sciros’ ‘Fuji’ ‘Cripps Pink’
1 69.4cc 22.2g 58.6d 71.8b 56.4d 82.4a 51.4f 54.0e 47.6f
2 68.3c 43.1f 62.8d 71.5b 57.3e 96.2a 57.1e 58.6e 66.7c
3= 12.6e 6.9f 13.4de 13.8cde 14.7bcd 19.3a 15.1bc 16.1b 18.4a
3= 86.8d 38.4e 93.1cd 85.6d 99.7cd 117.7ab 96.5c 103.2bc 127.6a
3= 30.0cde 23.4f 28.2de 33.4bc 27.0e 31.2cd 36.8ab 37.8a 28.4de
3= 65.1c 49.4d 69.9b 62.1c 72.8b 95.4a 71.3b 73.0b 73.3b
a
N = 50 for sensory measurements (scale 0–150) and 100 for instrumental measurements. Sensory juiciness was defined as ‘the amount of juice released during chewing, using the back teeth to chew the sample until it was ready to be swallowed’. Sensory crispness was defined as ‘the amount and pitch of sound on first bite with the front teeth’. Fracture toughness (Kc ) and fracture energy (Gc ) are parameters derived from the single-edge notched bend test. Instrumental juiciness was determined by the weight of juice absorption by tissue paper from the cut surface of mid-cortex apple tissue samples. Puncture force was measured with a materials testing machine (11.1 mm head) at the equatorial region of the fruit after first removing a small amount of the apple skin. c Within each column, values followed by the same letters are not significantly different (˛ = 0.05). b
Fig. 2. Relationships between instrumental measures (puncture test, fracture toughness (Kc ), fracture energy (Gc ), instrumental juiciness) and sensory measures (panelist scores for juiciness and crispness on a scale of 0–150) of texture made on nine apple cultivars. The cultivar means (n = 50 per cultivar) are derived from one instrumental measure and three sensory measures (one from each of three assessors) per fruit. The cultivars are ‘Sciearly’ (red), ‘Cripps Pink’ (body pink), ‘Fuji’ (magenta), ‘Sciros’ (dark blue), ‘Scifresh’ (light blue), ‘Red Delicious’ (dark green), ‘Honeycrisp’ (light green), ‘Royal Gala’ (yellow) ‘Cox’s Orange Pippin’ (orange). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
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Fig. 3. Linear discriminant analysis of relationships between instrumental (puncture test, fracture toughness (Kc ), fracture energy (Gc ) and instrumental juiciness) and sensory measures (juiciness and crispness) of texture of nine apple cultivars. The points represent the value for each fruit (n = 50 per cultivar). F, I and S are the responses of three individual panelists for sensory crispness and juiciness. The arrows show the direction of increasing measurements for each attribute. Their lengths have been re-scaled and are relative to each other rather than the scale on the X-axis.
3.4. Linear discriminant analysis (LDA) LDA searches for that combination of variables which best discriminates between the treatment groups, rather than that combination of variables which are closely correlated, as is the case in principal component analysis. LDA explained about 88% of variability in the data (LD1 = 77.4% and LD2 = 10.7%; Fig. 3). Cultivars were distributed along LD1 between ‘Cox’s Orange Pippin’ at the extreme left and ‘SciFresh’ at the extreme right, while LD2 differentiated between ‘Scifresh’ and ‘Cripps Pink’ at one extreme and ‘Honeycrisp’ and ‘Sciearly’ at the other extreme (Fig. 3). Sensory juiciness vectors were dominant in explaining variability along LD1 and LD2 and were orthogonal to vectors associated with sensory crispness and mechanical properties of fruit (puncture force, Kc ; Fig. 3). This multivariate analysis in its simplest form indicated that cultivars and individual fruit within each cultivar were distributed such that moving from left to right apples were firmer (puncture force, Kc ), crisper and more juicy, and that there was some separation along the vertical axis into those that differed in terms of the balance between juiciness and crispness. For example, cultivars with high juiciness (‘Sciearly’, ‘Honeycrisp’, ‘Scifresh’) were differentiated in that ‘Scifresh’ was highest in terms of sensory crispness and associated measures of instrumental firmness (Fig. 3).
3.5. Principal component analysis PCA explained about 74% of the variability in the data (60% in PC1 and 13.8% in PC2; Fig. 4). Cultivars were distributed along PC1 with ‘Cox’s Orange Pippin’ at the extreme left and ‘Scifresh’ at the extreme right and a dominant component of all the sensory and instrumental vectors was aligned positively along this axis (Fig. 4). Instrumental vectors tended to diverge from sensory vectors in that they were generally aligned more positively along PC2 (Fig. 4). Overall the PCA suggests that the apple cultivars could be differentiated from each other on the basis of the combined influence of juiciness and crispness, as well as the associated instrumental measures of firmness.
Fig. 4. Principal component analysis of the relationships between instrumental (puncture test, fracture toughness (Kc ), fracture energy (Gc ) and instrumental juiciness) and sensory measures (juiciness and crispness) of texture of nine apple cultivars. The points represent the value for each fruit (n = 50 per cultivar). F, I and S are the responses of three individual panelists for sensory crispness and juiciness. The arrows show the direction of increasing measurements for each attribute. Their lengths have been re-scaled and are relative to each other rather than the scale on the X-axis.
4. Discussion 4.1. Assessor consistency A thorough protocol was used in an attempt to minimise sources of variation in sensory assessment and to enable assessment of the potential for discriminating texture using a small trained panel. Although agreement between assessors was developed during training, some inconsistent scoring between assessors was associated with the first two cultivars in the study. Inconsistency in assessor scores associated with a particular cultivar could result from of a day of poor judgement for an assessor, or differential assessor acuity in assessing a particular cultivar. Differences between paired assessor scores were most evident in the two earliest season cultivars. Being the first assessments of the study following initial training, this result may indicate that the assessors were relatively less experienced than when assessing mid or late season apples. Higher inherent within-fruit variability associated with rapid rates of fruit development in these early season fruit may also have contributed to higher variation in scores between assessors. Nevertheless, after taking into account differences in assessor consistency, in terms of discriminating the texture of different cultivars, the results support the validity of using small panels to assess apple texture when trained to assess a small number of fruit attributes. 4.2. Discrimination between cultivars by analysis of variance of textural measures The approach for sensory assessment in this study was to use a small trained team of assessors in contrast to either the single ‘expert taster’ as often relied on in fruit breeding programmes, or a sensory panel of much larger size commonly used in formal sensory analysis (Hampson et al., 2000). Sensory assessment of juiciness by the trained panel of three assessors provided greater discrimination of cultivars than any of the instrumental measures. This result implies the need for caution in relying on instrumental measures at the expense of sensory approaches, a conclusion also drawn by Harker et al. (2002). Nevertheless, instrumental mea-
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sures that could substitute for sensory assessment should continue to be evaluated because of the constraints to sensory assessment and the perceived benefits of instrumental measures. Instrumental measures are also routinely used as standards in markets – for example, a cultivar with low puncture force is less likely to be of acceptable condition, regardless of sensory characteristics (Harker et al., 2008; Washington State Department of Agriculture, 1999). Comparing the instrumental measures used in the current study indicated that they have broadly similar powers of discrimination with Kc and Gc identifying between four of the nine cultivars using ANOVA, the same as for puncture force and instrumental juiciness. This result provides some support for the potential of the SENB test. In an earlier study comparing the SENB test with the penetrometer, it was suggested the SENB test may be a more accurate measure of texture (Harker et al., 2006a), although high fruit-to-fruit variability could influence the decision on its use. The cultivar mean values for Kc derived from the SENB test in this study appeared to be more widely separated than those for puncture force, suggesting greater sensitivity to textural differences. However, higher fruit-to-fruit variance for Kc than that for puncture force resulted in no practical advantage for discrimination on this basis. 4.3. Relationships between measures The high correlation between sensory juiciness and sensory crispness of apple found in this study was also found by Barreiro et al. (1998). A number of studies using different approaches have found relationships of varying significance between instrumental and sensory measures of texture (Barreiro et al., 1998; Harker et al., 2002; Ioannnides et al., 2007; Mann et al., 2005; King et al., 2000). The relationships in our study appeared to be anchored by the contrasting textural properties of ‘Scifresh’ (firm, crisp and juicy) and ‘Cox’s Orange Pippin’ (soft and tending mealy). The highest correlation between instrumental and sensory measures was between puncture force and sensory crispness, supporting the puncture test as a useful indicator of fruit texture. However, differences in the instrumental–sensory relationship for certain cultivars found in this study could indicate one reason for the wide range of correlations found among fruit texture studies; correlations derived for prediction purposes depend on the textural diversity among the cultivars used. These results indicate that relationships between instrumental and sensory measures cannot be reliably extrapolated to predict the textural characteristics of new cultivars. They also highlight the importance of developing complementary instrumental tests to provide more reliable assessment of texture than revealed by puncture force, when considering textural measurements for apple. While sensory juiciness was found to be a key parameter of texture for cultivar discrimination, the instrumental measurement of juiciness had a poor relationship with the other measures of texture. Instrumental measurement of juiciness by the technique of juice absorption exhibited relatively high fruit-to-fruit variance compared with most of the other measures used in this study and this could have contributed to the weaker relationship of this measure with the other measures. In comparison, Harker et al. (1997) found that measurement of juiciness by absorption provided a relatively good discrimination and relationship with sensory assessment when applied across different fruit types and assessed by a panel of 16. In another study, juice absorption measured under confined compression was highly correlated with mealiness in apple determined by a sensory panel of 12 and based on assessment of three cultivars (Barreiro et al., 1998). However, the high discrimination among textures revealed by sensory assessment of juiciness in the current study highlights that development of more reliable
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yet convenient instrumental measures of this attribute could prove very useful for postharvest evaluation of apples. 4.4. Principal component analysis and linear discriminant analysis Whereas the PCA demonstrated a similarity between sensory juiciness and crispness in the first principal component, the LDA analysis further revealed sensory juiciness as the dominant discriminant of textural differences between the cultivars. A time-intensity study by Harker et al. (2002) also highlighted that juiciness is a textural attribute that defines many fruits. This might be explained by the juiciness sensation being an integrated representation of the structural properties of fruit texture, since these determine juice release during chewing (Szczesniak and Ilker, 1988; Harker et al., 1997; Allan-Wojtas et al., 2003; Mann et al., 2005). Sensory crispness and puncture force comprised significant vectors of the second linear discriminant. A number of studies have already identified that juiciness and crispness are key apple textural attributes because they have a dominant influence on consumer liking and perception of apple freshness (Dalliant-Spinnler et al., 1996; Barreiro et al., 1998; Jaeger et al., 1998; Hampson et al., 2000; Allan-Wojtas et al., 2003; Péneau et al., 2007). These studies reinforce the potential of sensory juiciness, which in this study showed high discriminating power, as a key measure in the evaluation of apple texture. However, the current study further identifies sensory juiciness as a dominant discriminator of apple texture among cultivars, which has been determined within the constraints of a small trained sensory panel. 5. Conclusions Small panels can be effective in contributing a more precise approach to postharvest assessments of sensory properties of apple cultivars, where they focus on a small number of attributes. The inclusion of such panels appears to improve characterisation of the fruit texture compared with the use of instrumental methods alone, when applied to the discrimination of texture among apple cultivars. In particular, sensory juiciness when combined with the puncture test, could account relatively efficiently for a large proportion of product variability in the textural assessment of apple cultivars. Acknowledgements This research was funded by the New Zealand Foundation for Research Science and Technology (contract CO6X0705). We thank Dr Chris Triggs for useful discussion on use of LDA. References Allan-Wojtas, P., Sanford, K.A., McRae, K.B., Carbyn, S., 2003. An integrated microstructural and sensory approach to describe apple texture. J. Am. Soc. Hort. Sci. 128, 381–390. Alvarez, M.D., Saunders, D.E.J., Vincent, J.F.V., Jeronimidis, G., 2000. An engineering method to evaluate the crisp texture of fruit and vegetables. J. Texture Stud. 31, 457–473. Andani, Z., Jaeger, S.R., Wakeling, I.N., MacFie, H.J.H., 2001. Mealiness in apples: towards a multilingual consumer vocabulary. J. Food Sci. 66, 872–879. Barreiro, P., Ortiz, C., Ruiz-Altisent, M., De Smedt, V., Schotte, S., Andani, Z., Wakeling, I., Beyts, P.K., 1998. Comparison between sensory and instrumental measurements for mealiness assessment in apples: a collaborative test. J. Texture Stud. 29, 509–525. Dalliant-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. Prefer. 7, 113–126. Dever, M.C., Cliff, A., Hall, J.W., 1995. Analysis of variation and multivariate relationships among analytical and sensory characteristics in whole apple evaluation. J. Sci. Food Agric. 69, 329–338.
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Hampson, C.R., Quamme, H.A., Hall, J.W., MacDonald, R.A., King, M.C., Cliff, M.A., 2000. Sensory evaluation as a selection tool in apple breeding. Euphytica 111, 79–90. Harker, F.R., Stec, M.G.H., Hallett, I.C., Bennett, C.L., 1997. Texture of parenchymatous plant tissue: a comparison between tensile and other instrumental and sensory measurements of tissue strength and juiciness. Postharvest Biol. Technol. 11, 63–72. Harker, F.R., Maindonald, J., Murray, S.H., Gunson, F.A., Hallett, I.C., Walker, S.B., 2002. Sensory interpretation of instrumental measurements 1: texture of apple fruit. Postharvest Biol. Technol. 24, 225–239. Harker, F.R., White, A., Gunson, F.A., Hallett, I.C., De Silva, H.N., 2006a. Instrumental measurement of apple texture: a comparison of the single-edge notched bend test and the penetrometer. Postharvest Biol. Technol. 39, 185–192. Harker, F.R., Amos, R.L., Echeverriaa, G., Gunson, F.A., 2006b. Influence of texture on taste: insights gained during studies of hardness, juiciness and sweetness of apple fruit. J. Food Sci. 71, S77–S82. Harker, F.R., Kupferman, E.M., Marin, A.B., Gunson, A., Triggs, C.M., 2008. Eating quality standards for apples based on consumer preferences. Postharvest Biol. Technol. 50, 70–78. Ioannnides, Y., Howarth, M.S., Raithatha, C., Defernez, M., Kemsley, E.K., Smith, A.C., 2007. Texture analysis of red delicious fruit: towards multiple measurements on individual fruit. Food Qual. Prefer. 18, 825–833. 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. Prefer. 9, 355–366. King, G.J., Maliepaard, C., Lynn, J.R., Alston, F.H., Durel, C.E., Evans, K.M., Griffon, B., Laurens, F., Manganaris, A.G., Schrevens, E., Tartarini, S., Verhaegh, J., 2000.
Quantitative genetic analysis and comparison of physical and sensory descriptors relating to fruit flesh firmness in apple (Malus pumila Mill.). Theor. Appl. Genet. 100, 1074–1084. Lawless, H.T., Heymann, H., 1999. Sensory Evaluation of Food: Principles and practices. Aspen publishers, Gaithersburg, MD. Mann, H., Bedford, D., Luby, J., Vickers, Z., Tong, C., 2005. Relationship of instrumental and sensory texture measurements of fresh and stored apples to cell number and size. HortScience 40, 1815–1820. Meilgaard, M.C., Civille, G.V., Carr, B.T., 2006. Sensory Evaluation Techniques, 4th ed. CRC Press. Mehinagic, E., Royer, G., Symoneaux, R., Bertrand, D., Jourjon, F., 2004. Prediction of the sensory quality of apples by physical measurements. Postharvest Biol. Technol. 34, 257–269. Payne, R.W., Murray, D.A., Harding, S.A., Baird, D.B., Soutar, D.M., 2007. GenStat for Windows (10th Edition) Introduction. VSN International, Hemel Hempstead. Péneau, P.B., Brockhoff, P.B., Hoehn, E., Escher, F., Nuessli, J., 2007. Relating consumer evaluation of apple freshness to sensory and physiochemical measurements. J. Sens. Stud. 22, 313–335. R Development Core team, 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN: 3900051-07-0, URL: http://www.R-project.org. SAS Institute Inc., 2000–2004. 1.3 Help and Documentation. Cary, NC. Szczesniak, A.S., Ilker, R., 1988. The meaning of textural characteristics – juiciness in plant food stuffs. J. Texture Stud. 19, 61–78. Washington State Department of Agriculture, 1999. Clarifying new Red Delicious inspections. Good fruit Grower. May 15.
Contents lists available at ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Sensory evaluation by small postharvest teams and the relationship with instrumental measurements of apple texture Paul L. Brookfield a,∗ , Sara Nicoll a , F. Anne Gunson b , F. Roger Harker b , Mark Wohlers b a b
The New Zealand Institute for Plant & Food Research Limited (Plant & Food Research), Private Bag 1401, Havelock North, Hastings 4157, New Zealand Plant & Food Research, Private Bag 92 169, Auckland 1142, New Zealand
a r t i c l e
i n f o
Article history: Received 13 August 2009 Accepted 30 August 2010 Keywords: Apple Malus domestica (Borkh.) Texture Juiciness Crispness SENB test Trained sensory assessors
a b s t r a c t Effective prediction of sensory properties of apples is a critical component of the evaluation of fruit quality. Small postharvest teams (<4 individuals) are often faced with the question as to whether they should taste the fruit they are evaluating or rely on instrumental measurements. In this study, we have evaluated the relative advantage of both approaches in a project in which measurements were made on the fruit of nine apple cultivars with a wide range of textures. Instrumental measures comprised the puncture test, the single-edge notched bend (SENB) test and juiciness based on juice absorption. Sensory assessments of juiciness and crispness were carried out by three trained assessors using 150-mm intensity line scales marked with reference standards. The largest number of cultivars (7/9) was distinguished by sensory juiciness, which dominated the first linear discriminant, explaining 77.4% of the variation in the data. Sensory crispness was second most discriminating of the measurements, distinguishing six of nine cultivars. The instrumental measures each discriminated four cultivars, which was fewer than for the sensory measures. The highest correlation between sensory and instrumental measures was between sensory crispness and puncture force (r = 0.7), which exhibited less variance than the other instrumental measures. Instrumental juiciness measured by juice absorption was poorly related to sensory and instrumental measures. The results showed that small panels can be used effectively for postharvest assessments of sensory properties of apples, where they focus on a small number of attributes. Sensory assessments improved discrimination of textures among different cultivars compared with that obtained from simple instrumentally based methods. Sensory juiciness is a key measure that, in combination with instrumental measures such as the puncture test, could account relatively efficiently for a large proportion of product variability when applied to the postharvest evaluation of apple texture. © 2010 Elsevier B.V. All rights reserved.
1. Introduction The assessment of produce quality is one of the core aspects of applied postharvest biology. While protocols for assessing quality from an instrumental and physiological perspective are well established, it is common for small teams of postharvest scientists to assess the texture, taste, odour and flavour of produce. Traditionally, such sensory attributes are assessed using a panel consisting of eight to sixteen members who have been trained in sensory evaluation methodologies, or alternatively, if information on hedonic liking or purchase intentions is required, a panel consisting of about 100 consumers is recruited. Detailed accounts of the training and use of sensory panels can be found in texts such as Meilgaard et al. (2006) and Lawless and Heymann (1999). However, many postharvest biologists do not have access to consumer or trained
∗ Corresponding author. Tel.: +64 6 9758880; fax: +64 6 9758881. E-mail address: paul.brookfi[email protected] (P.L. Brookfield). 0925-5214/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.postharvbio.2010.08.021
panels, and they are forced to consider alternative ways for assessing sensory properties of the produce they are evaluating. It is not uncommon in such situations to be reliant on a single ‘expert taster’ for carrying out sensory assessments. This current study considers (1) the extent that it is possible to develop a more precise approach to these types of postharvest assessments of sensory properties of fruits, (2) whether or not such sensory evaluations provide insights over and above those provided by simple instrumentally based methods and (3) whether there are combinations of instrumental and sensory assessments that maximise the amount of product variability that is described, while retaining high efficiency. The research focus in this study was to assess the extent to which apple texture could be described by combinations of sensory and instrumental measurements. Texture is particularly important in apples, with clear evidence that hardness, juiciness and absence of mealiness are important drivers of consumer preference (Dalliant-Spinnler et al., 1996; Jaeger et al., 1998; Andani et al., 2001). Instrumental measures of hardness such as the puncture or penetrometer test have been shown to predict consumer accept-
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ability of apples (Harker et al., 2008; Péneau et al., 2007) and there is substantial research demonstrating close relationships between these instrumental measures and sensory attributes such as ‘hardness’, ‘crispness’ and ‘crunchiness’ (Barreiro et al., 1998; Harker et al., 2002; Ioannnides et al., 2007; Mehinagic et al., 2004). The singleedge notched bend (SENB) test is a fundamental measure of texture from which the tissue fracture parameters Kc (fracture toughness) and Gc (fracture energy) are derived (Alvarez et al., 2000). Fracture toughness refers to the critical stress at initiation of fracture and relates to the forces required to cause rupture in the tissue, whereas fracture energy is more a function of overall toughness and the energy required for material fracture. The cell size and intercellular air spaces of biological material influence both parameters (Alvarez et al., 2000). Fundamental measures such as the SENB test have been promoted as providing more realistic measures of texture (Alvarez et al., 2000), and there is evidence from studies on apples that support this. For example, Harker et al. (2006a) noted that the relationship between puncture force and SENB measures of fracture parameters Kc and Gc differed between lines of fruit such that some cultivars produced fruit that were interpreted as being crisper than suggested by measures of puncture force. The other key aspect of fruit texture is juiciness. Juiciness in apples is not related to the water content so much as it is a function of the way cells break open during mastication (Allan-Wojtas et al., 2003; Harker et al., 1997) For this reason, juiciness is influenced by the mechanical properties of tissue and often predicted by measures such as puncture force (Harker et al., 2006b; Mehinagic et al., 2004). Instrumental predictors of juiciness such as apparent juice content and juice absorption from cut surfaces have provided inconsistent results in apples (Harker et al., 2006b; Barreiro et al., 1998). The current study explores the possibilities of using a small team of trained sensory assessors to supplement instrumental measurement of apple texture.
2. Materials and methods 2.1. Plant materials Nine apple cultivars of commercial quality representing a wide range of season of harvest maturity and storage potential were selected for the study. The cultivars, located at the Plant & Food Research orchard or on commercial orchards in Hawke’s Bay, New Zealand were harvested on one date during their respective commercial harvest periods in 2003 as follows: ‘Sciearly’ and ‘Cox’s Orange Pippin’ 20 February, ‘Royal Gala’ and ‘Honeycrisp’ 27 February, ‘Red Delicious’ and ‘Scifresh’ 14 March, ‘Fuji’ and ‘Sciros’ 10 April and ‘Cripps Pink’ 23 April. The harvest maturity characteristics of fruit were determined from a sample of 20 fruit per cultivar (Table 1). One hundred fruit of each cultivar was placed in cold storage at 0.5 ◦ C (90% RH) except for ‘Cox’s Orange Pippin’, which was stored at 3 ◦ C (90% RH) as practiced commercially, because of susceptibility of fruit to internal breakdown at lower storage temperatures. For each cultivar, textural measurements were spread over two d (50 fruit assessed on each day) following 45 and 46 d of cold storage and 1 d at 20 ◦ C after harvest. On removal from cool storage, all fruit were numbered to maintain their identification throughout the instrumental and sensory assessments. Instrumental measurements were made on all 100-fruit per cultivar, with one measurement of each instrumental method per fruit. A subset of 50 fruit per cultivar was used for sensory assessments. Three assessors assessed each fruit (3 sensory measurements per fruit × 50 fruit per cultivar) for each sensory attribute. Assessment of the fruit of each cultivar was structured over a period of two d. This was to ensure that assessors were not overwhelmed or fatigued by the
number of samples that needed to be tasted. On each assessment day, instrumental measurements were made on 18, 16 and 16 fruit immediately before three corresponding sensory assessments. The sensory assessments were made on 25 of these fruit at the following times: 9 fruit at 0930 h (first sensory evaluation), 8 fruit at 1130 h (second sensory evaluation) and 8 fruit at 1420 h (third sensory evaluation). 2.2. Instrumental assessments For each fruit, one of each of the instrumental measurements was obtained as outlined below: 2.2.1. Penetrometer measurement A small area of skin was removed from the equatorial region on the least blushed side of the fruit and a puncture test made at this location using a materials testing machine (model 4443, Instron, Canton, MA) with a 1 kN load cell. The materials testing machine was fitted with an 11.1 mm diameter Effegi penetrometer head with a convex tip, which was driven 8 mm into the fruit at a speed of 4 mm/s. 2.2.2. Juice absorption A plug of tissue was removed from a location adjacent to site of the penetrometer test using a 14.7 mm cork borer. Core tissue at the inner end of the plug was cut off and the surface blotted dry. The remaining plug of tissue was cut in half using a new razor blade and the two cut surfaces representing mid-cortex tissue (3.394 cm2 ) immediately placed on two layers of 3-ply tissue paper, which were tared on a balance. After 60 s, the apple tissue was removed and the weight of released juice absorbed by the tissue paper recorded to an accuracy of 0.001 g (Harker et al., 1997). 2.2.3. SENB test Two parallel cuts between stem and calyx were made toward the core of the apple in the same quadrant used for the penetrometer and juice absorption tests. The slice of tissue between the cuts was extracted from which a beam for the SENB test was prepared as described by Alvarez et al. (2000). A jig was used to cut a beam of tissue 40 mm long, 8 mm deep and 4 mm wide. A 4-mm deep cut (notch) was made into the lower edge mid-way along the length of the beam with a razor blade. The notched beam was then positioned across two horizontal steel rod supports (6 mm diameter) placed 32 mm apart on the base plate of the materials testing machine, which was fitted with a 50 N load cell. A probe with a horizontal steel rod (6 mm diameter) at the tip was driven down at a speed of 2 mm/s to bend the beam at the position above the notch and mid-way between the supports. The critical load at the initiation of crack propagation upwards from the notch and the area under the force-displacement curve to that point, were obtained from the recorded data (Alvarez et al., 2000). These values were then used to derive measures of fracture toughness (Kc ) and fracture energy (Gc ) using equations described by Harker et al. (2006a). 2.3. Sensory analysis Based on previous experience in sensory evaluation of apples (Harker et al., 2002), we selected two key attributes, juiciness and crispness for evaluation. The sensory evaluations were carried out by three female assessors (ages 26–50 years) who were Plant & Food Research technicians. They had 3–7 years experience in fruit research and representative of staff commonly available in small postharvest research teams for participation in sensory assessment of fruit. These assessors had undertaken a 7 h Plant & Food Research training course on sensory evaluation practices which is run by the consumer science group prior to apple harvest each season. This
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Table 1 Harvest maturity characteristics of apples from nine cultivars used in sensory and instrumental assessments of texture. Assessments were made on 20 apples per cultivar. Cultivar
Fruit weight (g)
IECa (L L−1 )
Bk colorb (1–10)
Firmness (N)
Soluble solids (%)
SPIc (0–6)
Titratable acidity (%)
‘Sciearly’ ‘Cox’s Orange Pippin’ ‘Royal Gala’ ‘Honeycrisp’ ‘Red Delicious’ ‘Scifresh’ ‘Sciros’ ‘Fuji’ ‘Cripps Pink’
226 164 191 189 156 187 243 245 191
1.46 0.09 1.37 1.81 0.14 2.96 0.98 0.79 0.56
4.4 2.7 5.2 2.7 4.3 4.7 6.9 4.2 4.8
79.5 78.3 74.8 63.3 79.1 103.6 72.7 73.6 82.1
11.4 10.4 10.8 10.9 9.9 12.3 12.6 12.9 13.3
1.9 1.6 3.0 2.4 1.1 2.2 3.9 4.6 3.8
0.35 0.83 0.34 0.56 0.27 0.64 – – 0.69
a b c
Fruit internal ethylene concentration. Fruit skin background color where 1 = green and 10 = yellow. Starch pattern index based on staining with iodine solution where 0 = fully stained and 6 = no staining.
was followed by three 1 h training sessions in the week before commencement of the study to familiarize assessors with the intensities of reference standards and in relation to apple samples of differing texture and to provide feedback on scoring of apple samples to develop consistency between assessors. A further 1 h training session with standards and samples of apple was carried out by the assessors the day before the assessment of each cultivar. Samples of the standards were made available for reference during each session of apple texture evaluation. The first author, who was not involved in tasting the fruit, prepared all the samples and coded them for presentation to the assessors, to minimise prior knowledge of the samples by the assessors. Crispness was defined as ‘the amount and pitch of sound on first bite with the front teeth’. Juiciness was defined as ‘the amount of juice released during chewing using the back teeth to chew the sample until it was ready to be swallowed’. The reference standards were placed on a 150-mm horizontal line scale anchored by absent at 0 and extreme at 150. The line scale was marked at each end by a short vertical line. The reference standards were determined by being readily available and representative of the range of intensities that could be found in the apples. For crispness, whole canned water chestnut (‘Golden Sun’, Acton International Marketing Ltd., Lower Hutt, New Zealand), fresh carrot and fresh celery were placed at 50, 100 and 150 mm, respectively, on the line scale. For juiciness, fresh carrot, fresh celery and canned mandarin (‘John West’ brand used under license, Simplot Australia Pty Ltd., Victoria, Australia) were placed at 10, 50 and 85 mm, respectively. During the evaluations, the assessors rated the intensity of crispness and juiciness of each apple sample on the intensity scales marked with the position of the reference standards. On each assessment day (two assessment days per cultivar), the sensory evaluations were carried out on 9 fruit at 0930 h, 8 fruit at 1130 h and a further 8 fruit at 1430 h. Each session lasted about 0.5 h. To provide a manageable number of fruit for sensory assessment that was also representative of the total sample from each cultivar the sub-samples for sensory assessment were obtained as follows. Before each sensory session, the instrumentally tested fruit were ranked highest to lowest according to Gc , and every second fruit according to this ranking were selected for the sensory assessments, in an attempt to ensure that the sensory sample of 50 fruit per cultivar was representative of the textural variation in the total 100-fruit sample per cultivar. Apple fruit have variable sensory properties whereby one side can differ from the other (Dever et al., 1995). For this reason, we attempted to make all sensory and instrumental measurements within the same third of each apple on the non-blushed side of the fruit. For each of these fruit, three longitudinal slices about 20 mm wide at the outer edge were cut with the skin left on, from the same section as used for the instrumental measures, to enable three assessors to each assess a sample from each fruit. The slices were numbered 1–3 according to their relative location on the sampled section, which was the same for each apple.
The presentation of samples within each session was balanced for order and carry-over effects taking account of their ranking of Gc , assessor and slice. This design provided three measures of sensory juiciness and crispness per fruit (one score per assessor per fruit) for each of 50 fruits per cultivar. Samples were presented to the assessors in an air-conditioned evaluation room at 20 ◦ C and under daylight balanced lighting. Filtered water and plain water crackers were provided for palate cleansing between samples. The data were recorded on computers using an in-house software application that enabled assessors to view the intensity line scales on-screen and record their responses by clicking on the line using a computer mouse. 2.4. Statistical analysis Three-way analysis of variance (ANOVA) was carried out using Genstat 6.1 for Windows (Payne et al., 2007) with dependent variables being the texture attributes and independent variables being cultivar, assessor and their interaction. The linear discriminant analysis function (LDA) was carried out in SAS 9.1 using the CANDISC procedure (SAS Institute Inc., 2000–2004). The LDA function performs multidimensional scaling and is suitable for use where there are repeated measures on the same products, such as by different sensory analysts. LDA allows the finding of product points in a space, where the ratio of the between-products sum of squares to the within product sum of squares has been maximised. Principal component analysis (PCA) was done in R 2.90 using the FactoMiner package version 1.1 (R Development Core team, 2009). The PCA was unrotated. 3. Results 3.1. Differences in sensory scores between assessors There were significant differences between assessors in ratings of juiciness and crispness, as is frequently found in sensory studies. Statistical analysis that used ‘assessor’ as a treatment (rather than a block) showed significant (P < 0.001) effects and interactions for ‘fruit cultivar’ and ‘assessor’ for juiciness and crispness. However, examination of the estimated variances of the three fixed effects, being ‘fruit cultivar’, ‘assessor’, and ‘fruit cultivar’ × ‘assessor’ (data not shown) and correlations (Fig. 1), showed that ‘fruit cultivar’ was the dominant influence for the crispness and juiciness scores. The results demonstrated good consistency between assessors for many but not all cultivars (Fig. 1). Instances of low consistency in scores between at least two assessors were most evident for the two earliest season cultivars ‘Cox’s Orange Pippin’ and ‘Sciearly’ (Fig. 1). Given that some variability was associated with assessors, each assessor was treated as an ‘independent analytical measure’ in subsequent multivariate LDA and PCA statistical approaches.
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Fig. 1. Relationships between individual panelists (F, I, S) in their sensory scores for juiciness and crispness made on fruit from nine apple cultivars. Each point represents an individual fruit (n = 50). The cultivars are ‘Sciearly’ (red), ‘Cripps Pink’ (body pink), ‘Fuji’ (magenta), ‘Sciros’ (dark blue), ‘Scifresh’ (light blue) ‘Red Delicious’ (dark green), ‘Honeycrisp’ (light green), ‘Royal Gala’ (yellow) ‘Cox’s Orange Pippin’ (orange). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
3.2. Discrimination between cultivars by ANOVA of textural measures ANOVA revealed differences in the number of cultivars that were distinguished by the different measures of texture (Table 2). All measures segregated ‘Cox’s Orange Pippin’ apples with the lowest values and all measures except Gc and instrumental juiciness segregated the texture of ‘Scifresh’ apples with the highest values. For Gc , ‘Scifresh’ shared the highest ranking with ‘Cripps Pink’, and for instrumental juiciness, ‘Fuji’ and ‘Sciros’ had the highest values. The highest number of cultivars could be discriminated (separated by LSD (0.05)) by sensory juiciness (7) and sensory crispness (6), followed by the instrumental measures Kc and Gc , instrumental juiciness and puncture test – each with 4 (Table 2). As might be expected, no measure of texture ranked the cultivars in the same order as another, although there were similarities in order for the sensory measures crispness and juiciness in one group and for Kc , Gc , and the puncture test in another. Sensory crispness and juiciness ranked ‘Honeycrisp’ and ‘Sciearly’ relatively higher among the cultivars than these were ranked by Kc , Gc , and the puncture test. 3.3. Relationships between measures of texture Sensory measures of juiciness and crispness were highly correlated (r = 0.83, Fig. 2). For relationships between instrumental and sensory measures, the highest correlation (r = 0.70) was
between puncture force and sensory crispness. Instrumental juiciness was poorly correlated with sensory juiciness and crispness. This appeared to arise mainly from the relatively higher instrumental juiciness values for ‘Fuji’ and ‘Sciros’ than were indicated from the instrumental–sensory relationship derived from the remaining varieties (Fig. 2). Among the instrumental measures, Kc and Gc were highly correlated (r = 0.90). Kc was more highly correlated with puncture force (r = 0.71) than Gc (r = 0.58). Instrumental juiciness had low correlations with puncture force (r = 0.11), Kc (r = 0.24) and Gc (r = 0.13). All correlations were significantly different from 0 at the 5% level. The relationship between one measure of texture and another based on cultivar mean values differed more for certain cultivars than others. For example, the sensory juiciness of ‘Honeycrisp’ and ‘Sciearly’ was higher than was indicated from the puncture test, based on the relationship established with the other cultivars (Fig. 2). Using Kc as the predictor, the sensory juiciness of ‘Cripps Pink’ was lower than expected, although there was no basis of prediction with the exclusion of the extreme opposing cultivars ‘Cox’s Orange Pippin’ and ‘Scifresh’. The sensory juiciness of ‘Fuji’ and ‘Sciros’ was lower than predicted by instrumental juiciness using the relationship based on the other cultivars. Broadly comparable cultivar differences in relationships between measures were also apparent when Gc was substituted for Kc and sensory crispness was substituted for sensory juiciness, as might be expected from their high respective correlation coefficients.
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Table 2 Textural measurements of apple cultivars ranked according to the number of cultivars each measurement could discriminate (first being most and third equal being least) using LSD (Tukey, ˛ = 0.05) in ANOVA. Measurementa
Sensory juicinessb
Sensory crispness
Kc (kPa m½ )
Gc (J m−2 )
Instr. juiciness (g × 10c )
Puncture force (N)
Rank ‘Sciearly’ ‘Cox’s Orange Pippin’ ‘Royal Gala’ ‘Honeycrisp’ ‘Red Delicious’ ‘Scifresh’ ‘Sciros’ ‘Fuji’ ‘Cripps Pink’
1 69.4cc 22.2g 58.6d 71.8b 56.4d 82.4a 51.4f 54.0e 47.6f
2 68.3c 43.1f 62.8d 71.5b 57.3e 96.2a 57.1e 58.6e 66.7c
3= 12.6e 6.9f 13.4de 13.8cde 14.7bcd 19.3a 15.1bc 16.1b 18.4a
3= 86.8d 38.4e 93.1cd 85.6d 99.7cd 117.7ab 96.5c 103.2bc 127.6a
3= 30.0cde 23.4f 28.2de 33.4bc 27.0e 31.2cd 36.8ab 37.8a 28.4de
3= 65.1c 49.4d 69.9b 62.1c 72.8b 95.4a 71.3b 73.0b 73.3b
a
N = 50 for sensory measurements (scale 0–150) and 100 for instrumental measurements. Sensory juiciness was defined as ‘the amount of juice released during chewing, using the back teeth to chew the sample until it was ready to be swallowed’. Sensory crispness was defined as ‘the amount and pitch of sound on first bite with the front teeth’. Fracture toughness (Kc ) and fracture energy (Gc ) are parameters derived from the single-edge notched bend test. Instrumental juiciness was determined by the weight of juice absorption by tissue paper from the cut surface of mid-cortex apple tissue samples. Puncture force was measured with a materials testing machine (11.1 mm head) at the equatorial region of the fruit after first removing a small amount of the apple skin. c Within each column, values followed by the same letters are not significantly different (˛ = 0.05). b
Fig. 2. Relationships between instrumental measures (puncture test, fracture toughness (Kc ), fracture energy (Gc ), instrumental juiciness) and sensory measures (panelist scores for juiciness and crispness on a scale of 0–150) of texture made on nine apple cultivars. The cultivar means (n = 50 per cultivar) are derived from one instrumental measure and three sensory measures (one from each of three assessors) per fruit. The cultivars are ‘Sciearly’ (red), ‘Cripps Pink’ (body pink), ‘Fuji’ (magenta), ‘Sciros’ (dark blue), ‘Scifresh’ (light blue), ‘Red Delicious’ (dark green), ‘Honeycrisp’ (light green), ‘Royal Gala’ (yellow) ‘Cox’s Orange Pippin’ (orange). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
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Fig. 3. Linear discriminant analysis of relationships between instrumental (puncture test, fracture toughness (Kc ), fracture energy (Gc ) and instrumental juiciness) and sensory measures (juiciness and crispness) of texture of nine apple cultivars. The points represent the value for each fruit (n = 50 per cultivar). F, I and S are the responses of three individual panelists for sensory crispness and juiciness. The arrows show the direction of increasing measurements for each attribute. Their lengths have been re-scaled and are relative to each other rather than the scale on the X-axis.
3.4. Linear discriminant analysis (LDA) LDA searches for that combination of variables which best discriminates between the treatment groups, rather than that combination of variables which are closely correlated, as is the case in principal component analysis. LDA explained about 88% of variability in the data (LD1 = 77.4% and LD2 = 10.7%; Fig. 3). Cultivars were distributed along LD1 between ‘Cox’s Orange Pippin’ at the extreme left and ‘SciFresh’ at the extreme right, while LD2 differentiated between ‘Scifresh’ and ‘Cripps Pink’ at one extreme and ‘Honeycrisp’ and ‘Sciearly’ at the other extreme (Fig. 3). Sensory juiciness vectors were dominant in explaining variability along LD1 and LD2 and were orthogonal to vectors associated with sensory crispness and mechanical properties of fruit (puncture force, Kc ; Fig. 3). This multivariate analysis in its simplest form indicated that cultivars and individual fruit within each cultivar were distributed such that moving from left to right apples were firmer (puncture force, Kc ), crisper and more juicy, and that there was some separation along the vertical axis into those that differed in terms of the balance between juiciness and crispness. For example, cultivars with high juiciness (‘Sciearly’, ‘Honeycrisp’, ‘Scifresh’) were differentiated in that ‘Scifresh’ was highest in terms of sensory crispness and associated measures of instrumental firmness (Fig. 3).
3.5. Principal component analysis PCA explained about 74% of the variability in the data (60% in PC1 and 13.8% in PC2; Fig. 4). Cultivars were distributed along PC1 with ‘Cox’s Orange Pippin’ at the extreme left and ‘Scifresh’ at the extreme right and a dominant component of all the sensory and instrumental vectors was aligned positively along this axis (Fig. 4). Instrumental vectors tended to diverge from sensory vectors in that they were generally aligned more positively along PC2 (Fig. 4). Overall the PCA suggests that the apple cultivars could be differentiated from each other on the basis of the combined influence of juiciness and crispness, as well as the associated instrumental measures of firmness.
Fig. 4. Principal component analysis of the relationships between instrumental (puncture test, fracture toughness (Kc ), fracture energy (Gc ) and instrumental juiciness) and sensory measures (juiciness and crispness) of texture of nine apple cultivars. The points represent the value for each fruit (n = 50 per cultivar). F, I and S are the responses of three individual panelists for sensory crispness and juiciness. The arrows show the direction of increasing measurements for each attribute. Their lengths have been re-scaled and are relative to each other rather than the scale on the X-axis.
4. Discussion 4.1. Assessor consistency A thorough protocol was used in an attempt to minimise sources of variation in sensory assessment and to enable assessment of the potential for discriminating texture using a small trained panel. Although agreement between assessors was developed during training, some inconsistent scoring between assessors was associated with the first two cultivars in the study. Inconsistency in assessor scores associated with a particular cultivar could result from of a day of poor judgement for an assessor, or differential assessor acuity in assessing a particular cultivar. Differences between paired assessor scores were most evident in the two earliest season cultivars. Being the first assessments of the study following initial training, this result may indicate that the assessors were relatively less experienced than when assessing mid or late season apples. Higher inherent within-fruit variability associated with rapid rates of fruit development in these early season fruit may also have contributed to higher variation in scores between assessors. Nevertheless, after taking into account differences in assessor consistency, in terms of discriminating the texture of different cultivars, the results support the validity of using small panels to assess apple texture when trained to assess a small number of fruit attributes. 4.2. Discrimination between cultivars by analysis of variance of textural measures The approach for sensory assessment in this study was to use a small trained team of assessors in contrast to either the single ‘expert taster’ as often relied on in fruit breeding programmes, or a sensory panel of much larger size commonly used in formal sensory analysis (Hampson et al., 2000). Sensory assessment of juiciness by the trained panel of three assessors provided greater discrimination of cultivars than any of the instrumental measures. This result implies the need for caution in relying on instrumental measures at the expense of sensory approaches, a conclusion also drawn by Harker et al. (2002). Nevertheless, instrumental mea-
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sures that could substitute for sensory assessment should continue to be evaluated because of the constraints to sensory assessment and the perceived benefits of instrumental measures. Instrumental measures are also routinely used as standards in markets – for example, a cultivar with low puncture force is less likely to be of acceptable condition, regardless of sensory characteristics (Harker et al., 2008; Washington State Department of Agriculture, 1999). Comparing the instrumental measures used in the current study indicated that they have broadly similar powers of discrimination with Kc and Gc identifying between four of the nine cultivars using ANOVA, the same as for puncture force and instrumental juiciness. This result provides some support for the potential of the SENB test. In an earlier study comparing the SENB test with the penetrometer, it was suggested the SENB test may be a more accurate measure of texture (Harker et al., 2006a), although high fruit-to-fruit variability could influence the decision on its use. The cultivar mean values for Kc derived from the SENB test in this study appeared to be more widely separated than those for puncture force, suggesting greater sensitivity to textural differences. However, higher fruit-to-fruit variance for Kc than that for puncture force resulted in no practical advantage for discrimination on this basis. 4.3. Relationships between measures The high correlation between sensory juiciness and sensory crispness of apple found in this study was also found by Barreiro et al. (1998). A number of studies using different approaches have found relationships of varying significance between instrumental and sensory measures of texture (Barreiro et al., 1998; Harker et al., 2002; Ioannnides et al., 2007; Mann et al., 2005; King et al., 2000). The relationships in our study appeared to be anchored by the contrasting textural properties of ‘Scifresh’ (firm, crisp and juicy) and ‘Cox’s Orange Pippin’ (soft and tending mealy). The highest correlation between instrumental and sensory measures was between puncture force and sensory crispness, supporting the puncture test as a useful indicator of fruit texture. However, differences in the instrumental–sensory relationship for certain cultivars found in this study could indicate one reason for the wide range of correlations found among fruit texture studies; correlations derived for prediction purposes depend on the textural diversity among the cultivars used. These results indicate that relationships between instrumental and sensory measures cannot be reliably extrapolated to predict the textural characteristics of new cultivars. They also highlight the importance of developing complementary instrumental tests to provide more reliable assessment of texture than revealed by puncture force, when considering textural measurements for apple. While sensory juiciness was found to be a key parameter of texture for cultivar discrimination, the instrumental measurement of juiciness had a poor relationship with the other measures of texture. Instrumental measurement of juiciness by the technique of juice absorption exhibited relatively high fruit-to-fruit variance compared with most of the other measures used in this study and this could have contributed to the weaker relationship of this measure with the other measures. In comparison, Harker et al. (1997) found that measurement of juiciness by absorption provided a relatively good discrimination and relationship with sensory assessment when applied across different fruit types and assessed by a panel of 16. In another study, juice absorption measured under confined compression was highly correlated with mealiness in apple determined by a sensory panel of 12 and based on assessment of three cultivars (Barreiro et al., 1998). However, the high discrimination among textures revealed by sensory assessment of juiciness in the current study highlights that development of more reliable
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yet convenient instrumental measures of this attribute could prove very useful for postharvest evaluation of apples. 4.4. Principal component analysis and linear discriminant analysis Whereas the PCA demonstrated a similarity between sensory juiciness and crispness in the first principal component, the LDA analysis further revealed sensory juiciness as the dominant discriminant of textural differences between the cultivars. A time-intensity study by Harker et al. (2002) also highlighted that juiciness is a textural attribute that defines many fruits. This might be explained by the juiciness sensation being an integrated representation of the structural properties of fruit texture, since these determine juice release during chewing (Szczesniak and Ilker, 1988; Harker et al., 1997; Allan-Wojtas et al., 2003; Mann et al., 2005). Sensory crispness and puncture force comprised significant vectors of the second linear discriminant. A number of studies have already identified that juiciness and crispness are key apple textural attributes because they have a dominant influence on consumer liking and perception of apple freshness (Dalliant-Spinnler et al., 1996; Barreiro et al., 1998; Jaeger et al., 1998; Hampson et al., 2000; Allan-Wojtas et al., 2003; Péneau et al., 2007). These studies reinforce the potential of sensory juiciness, which in this study showed high discriminating power, as a key measure in the evaluation of apple texture. However, the current study further identifies sensory juiciness as a dominant discriminator of apple texture among cultivars, which has been determined within the constraints of a small trained sensory panel. 5. Conclusions Small panels can be effective in contributing a more precise approach to postharvest assessments of sensory properties of apple cultivars, where they focus on a small number of attributes. The inclusion of such panels appears to improve characterisation of the fruit texture compared with the use of instrumental methods alone, when applied to the discrimination of texture among apple cultivars. In particular, sensory juiciness when combined with the puncture test, could account relatively efficiently for a large proportion of product variability in the textural assessment of apple cultivars. Acknowledgements This research was funded by the New Zealand Foundation for Research Science and Technology (contract CO6X0705). We thank Dr Chris Triggs for useful discussion on use of LDA. References Allan-Wojtas, P., Sanford, K.A., McRae, K.B., Carbyn, S., 2003. An integrated microstructural and sensory approach to describe apple texture. J. Am. Soc. Hort. Sci. 128, 381–390. Alvarez, M.D., Saunders, D.E.J., Vincent, J.F.V., Jeronimidis, G., 2000. An engineering method to evaluate the crisp texture of fruit and vegetables. J. Texture Stud. 31, 457–473. Andani, Z., Jaeger, S.R., Wakeling, I.N., MacFie, H.J.H., 2001. Mealiness in apples: towards a multilingual consumer vocabulary. J. Food Sci. 66, 872–879. Barreiro, P., Ortiz, C., Ruiz-Altisent, M., De Smedt, V., Schotte, S., Andani, Z., Wakeling, I., Beyts, P.K., 1998. Comparison between sensory and instrumental measurements for mealiness assessment in apples: a collaborative test. J. Texture Stud. 29, 509–525. Dalliant-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. Prefer. 7, 113–126. Dever, M.C., Cliff, A., Hall, J.W., 1995. Analysis of variation and multivariate relationships among analytical and sensory characteristics in whole apple evaluation. J. Sci. Food Agric. 69, 329–338.
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