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Relationship between sensory analysis, penetrometry and visible–NIR spectroscopy of apples belonging to different cultivars
Food Quality and Preference 14 (2003) 473–484 www.elsevier.com/locate/foodqual
Relationship between sensory analysis, penetrometry and visible– NIR spectroscopy of apples belonging to different cultivars Emira Mehinagica,*, Gae¨lle Royera, Dominique Bertrandb, Ronan Symoneauxa, Franc¸ois Laurensc, Fre´de´rique Jourjona a
Ecole Supe´rieure d’Agriculture, Groupe de recherche en agro-industrie sur les produits, les proce´de´s et leur environnement (GRAPPE), 55 rue Rabelais, B.P. 748, 49007 Angers Cedex 01, France b INRA/ENITIAA Unite´ de Sensome´trie et de Chimiome´trie, Rue de la Ge´raudie`re, B.P. 82225, 44322 Nantes Cedex 03, France c INRA, Centre d’Angers, 42, Rue Georges Morel, BP 57, 49071, Beaucouze´ Cedex, France Received 1 August 2002; received in revised form 31 October 2002; accepted 28 November 2002
Abstract Nineteen French apple cultivars were characterised by sensory profiling, penetrometry and visible/near infrared (vis/NIR) spectroscopy. The main purpose was to investigate the relationships between sensory attributes, including apple texture and flavour, and destructive penetrometric measurements and non-destructive vis/NIR spectroscopy. Sensory crunchiness, touch resistance and crispness correlated well with total puncture force, flesh rupture breakdown force, and the work associated with that force. Juiciness and mealiness, two very important quality indices, were strongly correlated with the slope of force-deformation curves. A relationship was also found between different vis/NIR wavelengths and sensory attributes for apples (roughness, crunchiness, mealiness, sour and sweet taste). Subsequent studies will investigate the possible benefit of using vis/NIR spectroscopy to estimate certain sensory attributes measured by trained panellists. # 2003 Elsevier Science Ltd. All rights reserved. Keywords: Apple; Texture; Sensory analysis; Penetrometry; Visible-near infrared spectroscopy; Correlation
1. Introduction The sensory quality of apples, an important consideration for consumers, can be estimated by sensory analysis, although the technique involved is destructive, subjective and time-consuming. Stow (1995) determined that texture and flavour are the most important attributes for the consumer, and numerous studies have been performed to find significant correlations between these sensory attributes and objective instrumental measurements. Harker and Maindonald (2002) considered the correlations between different instrumental techniques and sensory analysis and determined that the best correlation coefficients were obtained with penetrometric, twist-test and acoustic measurements. Karlsen, Aaby, Sivertsen, Baardseth, and Ellekjaer (1999) showed that sensory hardness, chewiness and mushiness * Corresponding author. Tel.: +33-2-41-23-55-55; fax: +33-2-4123-55-65. E-mail address: [email protected] (E. Mehinagic).
correlated well with the instrumentally measured force and work required for penetration of apple flesh. All these studies were carried out exclusively on peeled apples and did not consider the effect of the mechanical behaviour of apple skin. More recently, Duprat, Grotte, Loonis, and Pietri (2000) proposed a new technique (puncture testing) for simultaneous measurement of the flesh and skin firmness of unpeeled apples. It would seem useful to study the correlations between parameters measured with this technique and sensory attributes measured by trained panellists. As penetrometric measurements constitute destructive tests, attempts are being made to develop new reliable, non-destructive techniques. Among available nondestructive techniques, those based on visible/nearinfrared spectroscopy (vis/NIR) are widely applied for the characterisation and analysis of food products (Osborne, Fearn, & Hindle, 1993). Previous vis/NIR research on apples concerned the measurement of quality attributes of soluble solid contents, firmness, or acidity (Lammertyn, Nicolaı¨, Ooms, De Smedt, & De
0950-3293/03/$ - see front matter # 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00012-0
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Baerdemaeker, 1998). In addition, McGlone and Jordan (2002) used vis/NIR measurements to predict the starch pattern index, quantitative starch, penetrometric firmness, and titrable acidity. Their predictive models, based on regression methods, account for 50–80% of total data set variance, but provide very poor predictive results. The purpose of the present study was to correlate sensory attributes with destructive penetrometric measurements and non-destructive vis/NIR measurements. The experiment was performed on apples belonging to 19 French cultivars.
2. Materials and methods 2.1. Sample collection
Table 1 Descriptors used for apples Attribute
Definition
External touch sensations Touch resistance Resistance of fruit to thumb pressure Roughness Degree of apple peel roughness as measured by touch External odour Odour intensity Soil odour Cellar odour Fresh leaf odour
Strength of all odours in the same sample Strength of soil odour Strength of mushroom and mouldy odour Associated with the odour of fresh leaves
Internal odour Aroma intensity
Aroma released during chewing
Texture Crunchiness
Force required for the first bite plus the noise resulting from this bite Time and number of chewing movements needed to grind the sample prior to swallowing Amount of liquid released on mastication Mealiness Force required to crush a piece of unpeeled apple between the tongue and palate
The present study is part of a general project sponsored by a French administrative region (Pays de La Loire) to characterise typical ancient apple cultivars from the Angers area. In this context, 19 French apple cultivars were studied: Rose aigre (ai), Alfred Jolibois (aj), Rouge des Antis (an), Bastien (ba), Be´ne´dictin (be), De Bouet (bo), Conard (cn), Reinette de Comminges (co), Coquette (cq), Reinette Grand-me`re (gm), Patte de loup (pl), Reinette d’Angleterre (ra), Reinette de Blain (rb), Rambour d’e´te´ (re), Reinette Franche (rf), Rilan (ri), Reinette Martin du Bec (rm), Reinette rose (ro), and Teint frais (tf). These cultivars were selected to ensure considerable variation in sensory quality. The apples used came from trees in the experimental orchard of the Institut National de la Recherche Agronomique (INRA, National Institute of Agronomic Research) located near Angers. Twenty fruits of each cultivar were picked in autumn 2001 at commercial maturity and stored at a mean temperature of 2 C before being brought to room temperature 24 h before analysis.
Chewiness
2.2. Sensory evaluation
2.3. Penetrometry
The 19 different apple cultivars were analysed by conventional sensory profiling, using 16 permanent trained panellists from the Ecole Supe´rieure d’Agriculture (ESA). Since 1999, these panellists have been selected and trained according to the recommendations of AFNOR (1995) and Fortin and Desplancke (1951). The sensory attributes studied were: odour intensity, fresh leaf odour, soil odour, cellar odour, aroma intensity, touchresistance, roughness, sour taste, sweet taste, astringency, juiciness, crispness, mealiness, chewiness and fondant. Each panellist analysed one fruit from each cultivar. Definitions of the sensory attributes are given in Table 1. Washed apple quarters were presented to the panellists unpeeled and at room temperature. A continuous non-structured
A cylindrical probe with a 4-mm-diameter convex tip was used to perforate unpeeled apples in an MTS (Synergie 200H) traction machine (Duprat et al., 2000). Two perforations were made on opposite paired sides of each apple. Penetration speed was set at 20 cm min1, and the test was stopped after penetration to 10 mm. Force/deformation curves were analysed and seven parameters were studied (Fig. 1): total puncture force (Fs), flesh rupture breakdown force (Ff), slope of the force-deformation curve (Grad), deformation (D), work associated with Fs (Ws), work associated with Ff (Wf), and flesh limit compression force (FLC). Definitions of these parameters are given in Table 2. Twenty specimens for each cultivar were analysed.
Juiciness Mealiness Fondant
Flavour during chewing Sour flavour Sweet flavour After swallowing Astringency
One of the basic tastes (e.g. malic acid) One of the basic tastes (e.g. sucrose)
Taste in the mouth after swallowing the sample
scale was used for evaluation. The left side of the scale corresponded to the lowest intensity (value 0) and the right side to the highest intensity (value 10). Four sessions were organised, and 4 or 5 cultivars were analysed during each session. Each panellist rinsed his mouth with mineral water between sample analyses.
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Fig. 1. Force/deformation curve obtained during a penetration test on unpeeled apple, using the MTS (Synergie 200H) traction machine (cylindrical probe with a 4-mm diameter convex tip, penetration speed of 20 cm min1, depth of 10 mm).
2.4. Visible/near infrared measurements
2.5. Statistical analysis
For vis/NIR measurements, 380 apples (20 specimens for each cultivar) were analysed. VisNIR spectra (light wavelength ranging from 400 to 2100 nanometers) were acquired directly on two opposite faces of intact fruit using a vis/NIR spectrometer (NIR Systems 6500, Perstorp Analytical) fitted with an optic probe device (OptiProbe Systems, Perstorp Analytical) (Fig. 2). Digitised spectra, formed by 900 individual absorbances, were corrected using Standard Normal Variate correction (Barnes, Dhaona, & Lister, 1989) and averaged according to the cultivars. In this way, it was possible to obtain a matrix of visible/NIR spectral measurements dimensioned 19900.
Two-way analysis of variance (ANOVA) was performed to determine significant differences in sensory quality between apples (Statgraphics1Plus5.0). The two studied factors were the panellist and the cultivar. Oneway ANOVA was carried out on instrumental measurements (penetrometry and vis/NIR spectroscopy) to test the effect of the cultivars. The significant sensory and instrumental attributes identified by ANOVA were then studied by principal component analysis (PCA). Before PCA, the data were averaged according to the cultivars. The PCA rely on the correlation matrix for all data. The correlation coefficients between instrumental and sensory data were calculated between the average values.
Table 2 Definitions of penetrometric parameters Parameter
Calculation
Definitions (Dobrzanski & Rybczynski, 1999; Duprat et al., 2000)
Fs (N) Ff (N) D (mm) Ws (Nmm) Wf (Nmm) Grad (Nmm1) FLC (N)
Maximal force on the curve (N) Force measured at 7 mm of deformation (N) Deformation associated with Fs (mm) Area under the curve between 0 and D (J) Area under the curve between 0 and 7 mm (J) Gradient on the curve (between 0 and Fs) (Nm1) Intersection between the vertical axis at D and the gradient from the second part of the curve
Force needed for rupture of apple skin and flesh Force needed for rupture of the flesh after the skin is broken Work required for the rupture of apple skin and flesh Work required for rupture of the flesh Gradient measuring firmness Force representing the limit of flesh elasticity during rupture
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E. Mehinagic et al. / Food Quality and Preference 14 (2003) 473–484 Table 3 Two-way ANOVA results for sensory attributes Descriptor
Fig. 2. Visible-NIR spectrometer (NIRSystems 6500, Perstorp Analytical) fitted with an optic probe device.
A PLS1 (Partial least square, model 1) regression method was applied for attempting to predict sensory attributes from the NIR measurements. As the sensory evaluation was carried out on batches corresponding to each cultivar, it was not possible to establish a model using the full set of NIR spectra. For this reason, the predictive model was assessed using the NIR averaged spectra as predictive variables X and some of the sensory attributes (namely roughness; crunchiness; mealiness; sour and sweet taste) as variables to be predicted y. As the number of observations was small (19 cultivars), the PLS model was validated by a ‘‘leave one out’’ crossvalidation. The crossvalidation makes it possible to choose the dimensions of the predictive model. It is therefore possible to obtain a prediction represented by a vector ypred for each of the studied dimensions. The quality of the prediction was appreciated by the correlation coefficient between the predicted (ypred) and the observed (y) variables and by the root mean square error of cross validation (RMSECV) given by: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP 2 un u yi ypredi ti¼1 RMSECV ¼ n where n is the number of observations.
3. Results and discussion 3.1. Sensory analysis Analyses of variance were assessed independently for each of the 15 sensory descriptors studied. For each analysis, a significance level of 5% was considered. At this level, the ‘‘panellist effect’’ (Table 3) was significant for all descriptors. This can be due to the heterogeneity of fruits because each panellist had to analyse a different fruit from the batch. At the same time, at the 5% level, the effect was considered significant for 13 descriptors
Astringency Sour taste Sweet taste Chewiness Juiciness Roughness Fondant Mealiness Crunchiness Touch resistance Global odour Fresh leaf odour Soil odour Cellar odour Aroma intensity
Panellist effect
Cultivar effect
F-ratio
P-value
F-ratio
P-value
17.57 6.78 21.91 13.69 14.4 4.17 20.01 8.5 10.59 14.45 10.06 17.06 8.88 7.59 23.94
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
3.3 13.8 4.67 7.57 8.57 25.87 9.29 9.94 13.02 11.31 6.64 4.3 1.6 1.15 4.14
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.056 0.300 <0.001
applying to apple cultivars (Table 3). Two descriptors, cellar and soil odour, were not significant. The significant descriptors were then characterised by PCA in which the first four principal components described 39.3, 23.8, 16.6 and 7.7% of the total sums of squares, respectively. The sensory data were normalised for the PCA because we wanted to give the same importance to all descriptors. In Fig. 3, cultivars pl, aj, tf, co, rm and bo are separated from ro, cn, ba, re, an and ri, on the first axis, and cultivars ri and cq from rb, gm, an and ai on the second axis. The first axis made it possible to separate the cultivars according to texture descriptors, i.e. chewiness, crunchiness, mealiness, touch resistance, fondant and juiciness. Accordingly, mealiness and fondant were negatively correlated with the first axis, contrary to the other texture descriptors. This distribution of the texture descriptors was in accordance with the repartition of the cultivars according to the first component. Cultivars co, tf, aj were very resistant to touch, crispy, juicy and had good chewiness, but were not mealy or fondant; whereas cultivars ro and ri showed just the opposite textural features. Aroma intensity (measured during chewing), odour intensity (measured for the whole apple), fresh leaf odour and sweet taste were mainly represented by the second component. For example, ri and cq were very sweet and had strong odour intensity and aroma. The biplot of components Nos. 3 and 4 is shown in Fig. 4. The third component is highly positively correlated with roughness, which allows apples with rough skin, such as pl and ba, to be distinguished from nonrough ones (e.g. an and rb). The fourth axis (Fig. 4) is strongly correlated with sour taste and astringency. The cultivars with a very strong sour taste were co, ai and tf, and these cultivars were also very astringent, contrary to cq and an.
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Fig. 3. PCA plot of the 19 unpeeled cultivars and 13 sensory descriptors in the plane defined by the first two principal components.
3.2. Penetrometry The analysis of variance showed a significant effect of apple cultivars at a 5% level for all parameters. These parameters were then used in PCA. The penetrometry data were normalised for the PCA because we have to deal with the variables having very different units. As PCA showed that the first two principal components described 93.8% of the total sums of squares, only these two dimensions were interpreted. PC1 described 66.4% of the variation among samples, and PC2 27.4%. In Fig. 5, Fs, Ff, Wf and FLC mainly describe the variation among samples on the first axis, which indicates that cultivars co, tf, aj and gm were very hard apples, contrary to ro or re that were very soft. These results are comparable with those for sensory profiling (Fig. 3). The second axis (Fig. 5) is described by three parameters: D, Grad and Ws. One means of explaining this axis is to correlate these parameters with sensory parameters.
3.3. Correlations between sensory analysis and penetrometry Parameters Fs and Ff were correlated positively with touch resistance (R=0.77 and R=0.73, respectively), crispness (R=0.78 and R=0.88) and chewiness (R=0.82 and R=0.90), and negatively with mealiness (R=0.61 and R=0.60) and fondant (R=0.67 and R=0.87). Similar correlations were performed with Wf, FLC and Grad (Table 4). The best correlation coefficients for crispness, chewiness and touch resistance (attributes measuring the sensory firmness of apples) were obtained with Ff and Wf. This is in accordance with the findings of Harker and Maindonald (2002), who reported that penetrometric measurements are good predictors of sensory attributes such as firmness, crunchiness and crispness. Karlsen et al. (1999) showed that sensory hardness, chewiness and mushiness correlate well with instrumentally measured force and the work required for penetration of the flesh. Juiciness and mealiness, two very important quality indices, were strongly correlated with Grad. Touch resistance was correlated negatively with D, and roughness showed a slight positive correlation with Ws.
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Fig. 4. PCA plot of the 19 unpeeled cultivars and 13 sensory descriptors in the plane defined by the third and fourth principal components.
The sensory attributes for taste and flavour (odour intensity, fresh leaf odour, sour and sweet taste and aroma intensity) were poorly correlated with penetrometric parameters, which is understandable since penetrometry measures only the mechanical properties of apple tissue. However, texture (cellular structure, cohesiveness and water content) influences the sensory perception of certain odour and flavour attributes (Karlsen et al., 1999). 3.4. Visible/NIR spectroscopy The spectra formed by 900 individual absorbances were subjected to Standard Normal Variate correction and averaged according to the cultivars. Fig. 6 shows the corrected averaged spectra of 19 cultivars. In the 400–800 nm range (visible range), all spectra were different, which means that the cultivars were discriminated by their colour, whereas they were more similar in the 800–2100 nm range (near infrared). In the NIR range, two large peaks of 1440 and 1940 nm appeared, possibly corresponding to water content. In order to investigate the effect of the cultivar on spectral data, one-way ANOVA was performed for each
of the 900 absorbances considered responses of the model. These analyses showed significant effects of the cultivar at a 5% level for all wavelengths. The largest F-values (Fisher test) were observed at wavelengths of 457, 1087 and 1908 nm. PCA was performed on the whole collection of averaged spectra. Components 1–3 accounted for 66.7, 18.9 and 9.9% of the total sums of squares, respectively. The biplot of the first two components, representing 85.6% of the total sums of squares, is shown in Fig. 7. For example, the first principal component distinguishes cultivars ai, cn, and bo from an and ba, while the second principal component distinguishes ri and bo from pl, rf, rb and gm. An attempt was then made to explain these data by correlating them with the sensory descriptors. 3.5. Correlations between sensory analysis and spectroscopy The data provided by sensory analysis were gathered in an S matrix dimensioned 1913 in which the rows represented the cultivars and the columns the descriptors. An element sij of S gave the score of the cultivar i for the descriptor j. In the same way, spectral data were
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Fig. 5. PCA plot of the 19 unpeeled cultivars and seven parameters measured by penetrometry in the plane defined by the first two principal components. Table 4 Pearson’s correlation coefficient between sensory and penetrometric data
Odour intensity Fresh leaf odour Roughness Touch resistance Crispness Juiciness Mealiness Chewiness Sweet Sour Aroma intensity Fondant Astringency
Fs
D
Wf
Ws
Ff
Grad 0–Fs
FLC
0.206 0.266 0.320 0.768 0.778 0.462 0.606 0.822 0.377 0.358 0.284 0.669 0.185
0.024 0.456 0.488 0.605 0.459 0.289 0.315 0.376 0.266 0.237 0.475 0.236 0.119
0.376 0.060 0.015 0.833 0.894 0.486 0.670 0.945 0.302 0.375 0.161 0.837 0.251
0.369 0.348 0.502 0.223 0.356 0.050 0.208 0.519 0.018 0.009 0.172 0.483 0.254
0.443 0.195 0.338 0.731 0.883 0.370 0.603 0.901 0.091 0.233 0.032 0.873 0.261
0.173 0.270 0.350 0.841 0.846 0.670 0.739 0.802 0.344 0.481 0.190 0.701 0.182
0.437 0.145 0.300 0.762 0.831 0.500 0.646 0.916 0.137 0.368 0.070 0.819 0.309
Bold type denotes highest correlations.
represented by an N matrix dimensioned 19900 containing the absorbances. To investigate a possible relationship between sensory and spectral data, a correlation matrix C between S and N was constructed, with 13 rows (descriptors) and 900 columns (wavelengths). An element cij of C represented the correlation coefficient of the sensory descriptor i and wavelength j. A graph ‘‘correlogram’’) was then plotted for each C
row as a function of wavelength. Among the 13 correlograms, only the five associated with the descriptors roughness, crunchiness, mealiness, acid taste and sweet taste showed significant correlation coefficient values and are presented here. The correlogram associated with roughness (Fig. 8) shows high correlation coefficients at a few interesting wavelengths, such as 1886 nm, the absorbance band
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Fig. 6. Vis/NIR spectra, with absorbancies corrected by Standard Normal Variate and averaged for 19 cultivars.
Fig. 7. PCA plot of the 19 unpeeled cultivars and variables measured by spectroscopy (absorbancies) in the plane defined by the first two principal components.
probably corresponding to starch, and 2050 nm, the absorbance band probably corresponding to proteins (Osborne et al., 1993). Crunchiness is correlated positively (Fig. 9), and mealiness negatively (Fig. 10), with spectroscopic data in the 680–710 nm range, which corresponds
to the chlorophyll absorbance bands, and with 980 nm, another wavelength corresponding to starch. The absorbing components responsible for the correlations between spectroscopic and textural data may not be textural factors (Watada, Massie, & Abbott, 1985). For
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Fig. 8. Correlation coefficients between spectroscopic data and the sensory attribute ‘‘roughness’’.
Fig. 9. Correlation coefficients between spectroscopic data and the sensory ‘‘mealiness’’.
example, as chlorophyll and starch change with texture during the maturation of apples, a correlation between some textural attributes such as crunchiness or mealiness exists. These results are in agreement with those of Watada et al. (1985), who found significant correlations between some textural attributes (crispness toughness
and juiciness) and optical data at wavelengths in the 670–710 and 920–950 nm ranges. The most correlated odour attribute was fresh leaf odour (R=0.60 at 1084, 1888 and 2036 nm). No information is currently available concerning possible correlations between spectral data and volatile components of apples.
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Fig. 10. Correlation coefficients between spectroscopic data and the sensory ‘‘crunchiness’’.
Fig. 11. Correlation coefficients between spectroscopic data and the sensory ‘‘sweet taste’’.
The sweetness attribute was correlated negatively with spectroscopic data at 1440 nm (R=0.58) and 1890 nm (R=0.48), i.e. absorbance bands corresponding to starch again (Fig. 11), while the sourness attribute was correlated positively with the same wavelengths (Fig. 12). These results seem logical,
because starch degradation produces sugars responsible for sweet taste, which explains why the correlations between starch absorbance bands and sweet taste are negative. There are two possible explanations why sour taste is correlated positively with starch:
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Fig. 12. Correlation coefficients between spectroscopic data and the sensory ‘‘sour taste’’.
1. The quantity of starch in apples is reduced at the same time as the malic acid concentration, i.e. during the fruit maturity stage. This phenomenon could account for the positive correlation coefficients between starch absorbance bands and sour taste, which is directly linked to the concentration of malic acid in apples. 2. Figs. 3 and 4 clearly show that sour and sweet attributes were always correlated on opposite sides of PC axes, which means that panellists never perceived very ‘‘sour apples’’ to be very ‘‘sweet’’. It is likely that the sour taste ‘‘masked’’ the sweet taste, or vice versa, although new sensory experiments are necessary to check this hypothesis. Rossiter, Young, Walker, Miller, and Dawson (2000) have shown in kiwi fruit that there is a significant decrease in perceived ‘‘acid taste’’ as Brix (which measures sugar quantity)
The sensory attributes which gave the highest correlation coefficient with the NIR data were studied by PLS regression. Only the attributes roughness; crunchiness; mealiness; sour and sweet taste were examined in this way. The results are given in Table 5. The correlation coefficients between the observed and the predicted variables were rather low and showed that it was not possible to establish a useful regression model between sensory attributes and NIR data. The roughness attribute gave a higher correlation coefficient, leading with a RMSECV equal to 0.97.
4. Conclusion
Table 5 PLS regression between sensory attributes and NIR data
Roughness Crunchiness Mealiness Sweet taste Sour taste
increases. The suppression of sweetness by acids and of acidity by sweeteners was previously demonstrated by McBride and Johnson (1987) in a lemon juice drink, but no studies have been done on apples.
Average
Standard deviation
PLS dimensionsa
Correlation coefficient
1.59 6.03 2.73 6.00 4.22
1.84 1.46 1.52 0.82 1.67
3 6 6 12 14
0.84 0.49 0.41 0.65 0.63
a PLS dimensions: dimension of the PLS model giving the highest correlation coefficient between predicted and observed values in the validation set (‘‘leave one out‘‘ validation).
This study shows that the parameters measured by penetrometry in unpeeled apples were highly correlated with sensory textural attributes. The most interesting parameters for measuring sensory firmness (described by crunchiness, touch resistance and crispness) were force Ff and work Wf. Juiciness and mealiness, two very important quality indices, were strongly correlated with Grad. These objective parameters for measurement of apple texture will be used in future studies concerning the relationships between texture, taste and aroma.
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Another conclusion of this study is that there is actually some statistically significant relationship between different visible/NIR wavelengths and some sensory attributes for apple. However, this relationship is not currently sufficient to allow the prediction of sensory attributes from NIR data. It must be due to the fact that the NIR data were averaged and that the sensory evaluation was carried out on batches of apples belonging to each cultivar, and not on individual fruits. In further work, we will investigate the possible interest of visible/NIR spectroscopy for estimating some sensory attributes as measured by experimented assessors. In the sensory evaluation tests, we will attempt to individually characterise each face of the fruits.
Acknowledgements This research was supported by the Conseil Re´gional du Pays de la Loire. The authors are grateful to Stephanie Khaldi and Roland Robic for skilful technical assistance. Special thanks are due to the members of the trained panel, who worked very seriously and never missed a sensory session. We would like to thank to anonymous referees for their helpful remarks that led to an improvement of the manuscript.
References AFNOR. (1995). V 09-021. Analyse sensorielle—Recherche et se´lection de descripteurs pour l’e´laboration d’un profil sensoriel, par approche multidimensionnelle. In Controˆle de la qualite´ des produits
alimentaires—Analyse sensorielle (pp. 276–310). 5e`me e´dition, AFNOR Paris. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Applied Spectroscopy, 45, 772–777. Dobrzanski, B., & Rybczynski, R. (1999). Stress–strain relationship for fruit firmness estimation. Acta Horticulturae, 485, 117–123. Duprat, F., Grotte, M., Loonis, D., & Pietri, E. (2000). Etude de la possibilite´ de mesurer simultane´ment la fermete´ et la chair de l’e´piderme des pommes. Sciences des Aliments, 20, 253–265. Fortin, J., & Desplancke, C. (1951). Guide d’entraıˆnement d’un jury de de´gustation. Edisen. Harker, F. R., & Maindonald, J. (2002). Sensory interpretation of instrumental measurements. 1: texture of apple fruit. Postharvest Biology and Technology, 24(3), 225–239. Karlsen, A. M., Aaby, K., Sivertsen, H., Baardseth, P., & Ellekjaer, M. R. (1999). Instrumental and sensory analysis of fresh Norwegian and imported apples. Food Quality and Preference, 10, 305–314. Lammertin, J., Nicolaı¨, B., Ooms, K., De Smedt, V., & De Baerdemaeker, J. (1998). Non-destructive measurement of acidity, soluble solids and firmness of Jonagold apples using NIR-spectroscopy. American Society of Agricultural Engineers, 41(4), 1089–1094. McBride, R. L., & Johnson, R. L. (1987). Perception of sugar-acid mixtures in lemon juice drink. International Journal of Food Science and Technology, 22, 399–408. McGlone, V. A., & Jordan, R. B. (2002). Vis/NIR estimation at harvest of pre- and post-storage quality indices for Royal Gala apple. Postharvest Biology and Technology, 25(2), 135–144. Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy with applications in food and beverage analysis. Essex, England: Longman Scientific and Technical. Rossiter, K. L., Young, H., Walker, S. B., & Miller, M. (2000). The effects of sugars and acids on consumer acceptability of kiwifruit. Journal of Sensory Studies, 15, 241–250. Stow, J. (1995). Quality measurements of apples. Postharvest News and Information, 6(3), 32–33. Watada, A. E., Massie, D. R., & Abbott, J. A. (1985). Relationship between sensory evaluations and nondestructive optical measurements of apple quality. Journal of Food Quality, 7, 219–226.
Relationship between sensory analysis, penetrometry and visible– NIR spectroscopy of apples belonging to different cultivars Emira Mehinagica,*, Gae¨lle Royera, Dominique Bertrandb, Ronan Symoneauxa, Franc¸ois Laurensc, Fre´de´rique Jourjona a
Ecole Supe´rieure d’Agriculture, Groupe de recherche en agro-industrie sur les produits, les proce´de´s et leur environnement (GRAPPE), 55 rue Rabelais, B.P. 748, 49007 Angers Cedex 01, France b INRA/ENITIAA Unite´ de Sensome´trie et de Chimiome´trie, Rue de la Ge´raudie`re, B.P. 82225, 44322 Nantes Cedex 03, France c INRA, Centre d’Angers, 42, Rue Georges Morel, BP 57, 49071, Beaucouze´ Cedex, France Received 1 August 2002; received in revised form 31 October 2002; accepted 28 November 2002
Abstract Nineteen French apple cultivars were characterised by sensory profiling, penetrometry and visible/near infrared (vis/NIR) spectroscopy. The main purpose was to investigate the relationships between sensory attributes, including apple texture and flavour, and destructive penetrometric measurements and non-destructive vis/NIR spectroscopy. Sensory crunchiness, touch resistance and crispness correlated well with total puncture force, flesh rupture breakdown force, and the work associated with that force. Juiciness and mealiness, two very important quality indices, were strongly correlated with the slope of force-deformation curves. A relationship was also found between different vis/NIR wavelengths and sensory attributes for apples (roughness, crunchiness, mealiness, sour and sweet taste). Subsequent studies will investigate the possible benefit of using vis/NIR spectroscopy to estimate certain sensory attributes measured by trained panellists. # 2003 Elsevier Science Ltd. All rights reserved. Keywords: Apple; Texture; Sensory analysis; Penetrometry; Visible-near infrared spectroscopy; Correlation
1. Introduction The sensory quality of apples, an important consideration for consumers, can be estimated by sensory analysis, although the technique involved is destructive, subjective and time-consuming. Stow (1995) determined that texture and flavour are the most important attributes for the consumer, and numerous studies have been performed to find significant correlations between these sensory attributes and objective instrumental measurements. Harker and Maindonald (2002) considered the correlations between different instrumental techniques and sensory analysis and determined that the best correlation coefficients were obtained with penetrometric, twist-test and acoustic measurements. Karlsen, Aaby, Sivertsen, Baardseth, and Ellekjaer (1999) showed that sensory hardness, chewiness and mushiness * Corresponding author. Tel.: +33-2-41-23-55-55; fax: +33-2-4123-55-65. E-mail address: [email protected] (E. Mehinagic).
correlated well with the instrumentally measured force and work required for penetration of apple flesh. All these studies were carried out exclusively on peeled apples and did not consider the effect of the mechanical behaviour of apple skin. More recently, Duprat, Grotte, Loonis, and Pietri (2000) proposed a new technique (puncture testing) for simultaneous measurement of the flesh and skin firmness of unpeeled apples. It would seem useful to study the correlations between parameters measured with this technique and sensory attributes measured by trained panellists. As penetrometric measurements constitute destructive tests, attempts are being made to develop new reliable, non-destructive techniques. Among available nondestructive techniques, those based on visible/nearinfrared spectroscopy (vis/NIR) are widely applied for the characterisation and analysis of food products (Osborne, Fearn, & Hindle, 1993). Previous vis/NIR research on apples concerned the measurement of quality attributes of soluble solid contents, firmness, or acidity (Lammertyn, Nicolaı¨, Ooms, De Smedt, & De
0950-3293/03/$ - see front matter # 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00012-0
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Baerdemaeker, 1998). In addition, McGlone and Jordan (2002) used vis/NIR measurements to predict the starch pattern index, quantitative starch, penetrometric firmness, and titrable acidity. Their predictive models, based on regression methods, account for 50–80% of total data set variance, but provide very poor predictive results. The purpose of the present study was to correlate sensory attributes with destructive penetrometric measurements and non-destructive vis/NIR measurements. The experiment was performed on apples belonging to 19 French cultivars.
2. Materials and methods 2.1. Sample collection
Table 1 Descriptors used for apples Attribute
Definition
External touch sensations Touch resistance Resistance of fruit to thumb pressure Roughness Degree of apple peel roughness as measured by touch External odour Odour intensity Soil odour Cellar odour Fresh leaf odour
Strength of all odours in the same sample Strength of soil odour Strength of mushroom and mouldy odour Associated with the odour of fresh leaves
Internal odour Aroma intensity
Aroma released during chewing
Texture Crunchiness
Force required for the first bite plus the noise resulting from this bite Time and number of chewing movements needed to grind the sample prior to swallowing Amount of liquid released on mastication Mealiness Force required to crush a piece of unpeeled apple between the tongue and palate
The present study is part of a general project sponsored by a French administrative region (Pays de La Loire) to characterise typical ancient apple cultivars from the Angers area. In this context, 19 French apple cultivars were studied: Rose aigre (ai), Alfred Jolibois (aj), Rouge des Antis (an), Bastien (ba), Be´ne´dictin (be), De Bouet (bo), Conard (cn), Reinette de Comminges (co), Coquette (cq), Reinette Grand-me`re (gm), Patte de loup (pl), Reinette d’Angleterre (ra), Reinette de Blain (rb), Rambour d’e´te´ (re), Reinette Franche (rf), Rilan (ri), Reinette Martin du Bec (rm), Reinette rose (ro), and Teint frais (tf). These cultivars were selected to ensure considerable variation in sensory quality. The apples used came from trees in the experimental orchard of the Institut National de la Recherche Agronomique (INRA, National Institute of Agronomic Research) located near Angers. Twenty fruits of each cultivar were picked in autumn 2001 at commercial maturity and stored at a mean temperature of 2 C before being brought to room temperature 24 h before analysis.
Chewiness
2.2. Sensory evaluation
2.3. Penetrometry
The 19 different apple cultivars were analysed by conventional sensory profiling, using 16 permanent trained panellists from the Ecole Supe´rieure d’Agriculture (ESA). Since 1999, these panellists have been selected and trained according to the recommendations of AFNOR (1995) and Fortin and Desplancke (1951). The sensory attributes studied were: odour intensity, fresh leaf odour, soil odour, cellar odour, aroma intensity, touchresistance, roughness, sour taste, sweet taste, astringency, juiciness, crispness, mealiness, chewiness and fondant. Each panellist analysed one fruit from each cultivar. Definitions of the sensory attributes are given in Table 1. Washed apple quarters were presented to the panellists unpeeled and at room temperature. A continuous non-structured
A cylindrical probe with a 4-mm-diameter convex tip was used to perforate unpeeled apples in an MTS (Synergie 200H) traction machine (Duprat et al., 2000). Two perforations were made on opposite paired sides of each apple. Penetration speed was set at 20 cm min1, and the test was stopped after penetration to 10 mm. Force/deformation curves were analysed and seven parameters were studied (Fig. 1): total puncture force (Fs), flesh rupture breakdown force (Ff), slope of the force-deformation curve (Grad), deformation (D), work associated with Fs (Ws), work associated with Ff (Wf), and flesh limit compression force (FLC). Definitions of these parameters are given in Table 2. Twenty specimens for each cultivar were analysed.
Juiciness Mealiness Fondant
Flavour during chewing Sour flavour Sweet flavour After swallowing Astringency
One of the basic tastes (e.g. malic acid) One of the basic tastes (e.g. sucrose)
Taste in the mouth after swallowing the sample
scale was used for evaluation. The left side of the scale corresponded to the lowest intensity (value 0) and the right side to the highest intensity (value 10). Four sessions were organised, and 4 or 5 cultivars were analysed during each session. Each panellist rinsed his mouth with mineral water between sample analyses.
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Fig. 1. Force/deformation curve obtained during a penetration test on unpeeled apple, using the MTS (Synergie 200H) traction machine (cylindrical probe with a 4-mm diameter convex tip, penetration speed of 20 cm min1, depth of 10 mm).
2.4. Visible/near infrared measurements
2.5. Statistical analysis
For vis/NIR measurements, 380 apples (20 specimens for each cultivar) were analysed. VisNIR spectra (light wavelength ranging from 400 to 2100 nanometers) were acquired directly on two opposite faces of intact fruit using a vis/NIR spectrometer (NIR Systems 6500, Perstorp Analytical) fitted with an optic probe device (OptiProbe Systems, Perstorp Analytical) (Fig. 2). Digitised spectra, formed by 900 individual absorbances, were corrected using Standard Normal Variate correction (Barnes, Dhaona, & Lister, 1989) and averaged according to the cultivars. In this way, it was possible to obtain a matrix of visible/NIR spectral measurements dimensioned 19900.
Two-way analysis of variance (ANOVA) was performed to determine significant differences in sensory quality between apples (Statgraphics1Plus5.0). The two studied factors were the panellist and the cultivar. Oneway ANOVA was carried out on instrumental measurements (penetrometry and vis/NIR spectroscopy) to test the effect of the cultivars. The significant sensory and instrumental attributes identified by ANOVA were then studied by principal component analysis (PCA). Before PCA, the data were averaged according to the cultivars. The PCA rely on the correlation matrix for all data. The correlation coefficients between instrumental and sensory data were calculated between the average values.
Table 2 Definitions of penetrometric parameters Parameter
Calculation
Definitions (Dobrzanski & Rybczynski, 1999; Duprat et al., 2000)
Fs (N) Ff (N) D (mm) Ws (Nmm) Wf (Nmm) Grad (Nmm1) FLC (N)
Maximal force on the curve (N) Force measured at 7 mm of deformation (N) Deformation associated with Fs (mm) Area under the curve between 0 and D (J) Area under the curve between 0 and 7 mm (J) Gradient on the curve (between 0 and Fs) (Nm1) Intersection between the vertical axis at D and the gradient from the second part of the curve
Force needed for rupture of apple skin and flesh Force needed for rupture of the flesh after the skin is broken Work required for the rupture of apple skin and flesh Work required for rupture of the flesh Gradient measuring firmness Force representing the limit of flesh elasticity during rupture
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E. Mehinagic et al. / Food Quality and Preference 14 (2003) 473–484 Table 3 Two-way ANOVA results for sensory attributes Descriptor
Fig. 2. Visible-NIR spectrometer (NIRSystems 6500, Perstorp Analytical) fitted with an optic probe device.
A PLS1 (Partial least square, model 1) regression method was applied for attempting to predict sensory attributes from the NIR measurements. As the sensory evaluation was carried out on batches corresponding to each cultivar, it was not possible to establish a model using the full set of NIR spectra. For this reason, the predictive model was assessed using the NIR averaged spectra as predictive variables X and some of the sensory attributes (namely roughness; crunchiness; mealiness; sour and sweet taste) as variables to be predicted y. As the number of observations was small (19 cultivars), the PLS model was validated by a ‘‘leave one out’’ crossvalidation. The crossvalidation makes it possible to choose the dimensions of the predictive model. It is therefore possible to obtain a prediction represented by a vector ypred for each of the studied dimensions. The quality of the prediction was appreciated by the correlation coefficient between the predicted (ypred) and the observed (y) variables and by the root mean square error of cross validation (RMSECV) given by: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP 2 un u yi ypredi ti¼1 RMSECV ¼ n where n is the number of observations.
3. Results and discussion 3.1. Sensory analysis Analyses of variance were assessed independently for each of the 15 sensory descriptors studied. For each analysis, a significance level of 5% was considered. At this level, the ‘‘panellist effect’’ (Table 3) was significant for all descriptors. This can be due to the heterogeneity of fruits because each panellist had to analyse a different fruit from the batch. At the same time, at the 5% level, the effect was considered significant for 13 descriptors
Astringency Sour taste Sweet taste Chewiness Juiciness Roughness Fondant Mealiness Crunchiness Touch resistance Global odour Fresh leaf odour Soil odour Cellar odour Aroma intensity
Panellist effect
Cultivar effect
F-ratio
P-value
F-ratio
P-value
17.57 6.78 21.91 13.69 14.4 4.17 20.01 8.5 10.59 14.45 10.06 17.06 8.88 7.59 23.94
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
3.3 13.8 4.67 7.57 8.57 25.87 9.29 9.94 13.02 11.31 6.64 4.3 1.6 1.15 4.14
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.056 0.300 <0.001
applying to apple cultivars (Table 3). Two descriptors, cellar and soil odour, were not significant. The significant descriptors were then characterised by PCA in which the first four principal components described 39.3, 23.8, 16.6 and 7.7% of the total sums of squares, respectively. The sensory data were normalised for the PCA because we wanted to give the same importance to all descriptors. In Fig. 3, cultivars pl, aj, tf, co, rm and bo are separated from ro, cn, ba, re, an and ri, on the first axis, and cultivars ri and cq from rb, gm, an and ai on the second axis. The first axis made it possible to separate the cultivars according to texture descriptors, i.e. chewiness, crunchiness, mealiness, touch resistance, fondant and juiciness. Accordingly, mealiness and fondant were negatively correlated with the first axis, contrary to the other texture descriptors. This distribution of the texture descriptors was in accordance with the repartition of the cultivars according to the first component. Cultivars co, tf, aj were very resistant to touch, crispy, juicy and had good chewiness, but were not mealy or fondant; whereas cultivars ro and ri showed just the opposite textural features. Aroma intensity (measured during chewing), odour intensity (measured for the whole apple), fresh leaf odour and sweet taste were mainly represented by the second component. For example, ri and cq were very sweet and had strong odour intensity and aroma. The biplot of components Nos. 3 and 4 is shown in Fig. 4. The third component is highly positively correlated with roughness, which allows apples with rough skin, such as pl and ba, to be distinguished from nonrough ones (e.g. an and rb). The fourth axis (Fig. 4) is strongly correlated with sour taste and astringency. The cultivars with a very strong sour taste were co, ai and tf, and these cultivars were also very astringent, contrary to cq and an.
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Fig. 3. PCA plot of the 19 unpeeled cultivars and 13 sensory descriptors in the plane defined by the first two principal components.
3.2. Penetrometry The analysis of variance showed a significant effect of apple cultivars at a 5% level for all parameters. These parameters were then used in PCA. The penetrometry data were normalised for the PCA because we have to deal with the variables having very different units. As PCA showed that the first two principal components described 93.8% of the total sums of squares, only these two dimensions were interpreted. PC1 described 66.4% of the variation among samples, and PC2 27.4%. In Fig. 5, Fs, Ff, Wf and FLC mainly describe the variation among samples on the first axis, which indicates that cultivars co, tf, aj and gm were very hard apples, contrary to ro or re that were very soft. These results are comparable with those for sensory profiling (Fig. 3). The second axis (Fig. 5) is described by three parameters: D, Grad and Ws. One means of explaining this axis is to correlate these parameters with sensory parameters.
3.3. Correlations between sensory analysis and penetrometry Parameters Fs and Ff were correlated positively with touch resistance (R=0.77 and R=0.73, respectively), crispness (R=0.78 and R=0.88) and chewiness (R=0.82 and R=0.90), and negatively with mealiness (R=0.61 and R=0.60) and fondant (R=0.67 and R=0.87). Similar correlations were performed with Wf, FLC and Grad (Table 4). The best correlation coefficients for crispness, chewiness and touch resistance (attributes measuring the sensory firmness of apples) were obtained with Ff and Wf. This is in accordance with the findings of Harker and Maindonald (2002), who reported that penetrometric measurements are good predictors of sensory attributes such as firmness, crunchiness and crispness. Karlsen et al. (1999) showed that sensory hardness, chewiness and mushiness correlate well with instrumentally measured force and the work required for penetration of the flesh. Juiciness and mealiness, two very important quality indices, were strongly correlated with Grad. Touch resistance was correlated negatively with D, and roughness showed a slight positive correlation with Ws.
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Fig. 4. PCA plot of the 19 unpeeled cultivars and 13 sensory descriptors in the plane defined by the third and fourth principal components.
The sensory attributes for taste and flavour (odour intensity, fresh leaf odour, sour and sweet taste and aroma intensity) were poorly correlated with penetrometric parameters, which is understandable since penetrometry measures only the mechanical properties of apple tissue. However, texture (cellular structure, cohesiveness and water content) influences the sensory perception of certain odour and flavour attributes (Karlsen et al., 1999). 3.4. Visible/NIR spectroscopy The spectra formed by 900 individual absorbances were subjected to Standard Normal Variate correction and averaged according to the cultivars. Fig. 6 shows the corrected averaged spectra of 19 cultivars. In the 400–800 nm range (visible range), all spectra were different, which means that the cultivars were discriminated by their colour, whereas they were more similar in the 800–2100 nm range (near infrared). In the NIR range, two large peaks of 1440 and 1940 nm appeared, possibly corresponding to water content. In order to investigate the effect of the cultivar on spectral data, one-way ANOVA was performed for each
of the 900 absorbances considered responses of the model. These analyses showed significant effects of the cultivar at a 5% level for all wavelengths. The largest F-values (Fisher test) were observed at wavelengths of 457, 1087 and 1908 nm. PCA was performed on the whole collection of averaged spectra. Components 1–3 accounted for 66.7, 18.9 and 9.9% of the total sums of squares, respectively. The biplot of the first two components, representing 85.6% of the total sums of squares, is shown in Fig. 7. For example, the first principal component distinguishes cultivars ai, cn, and bo from an and ba, while the second principal component distinguishes ri and bo from pl, rf, rb and gm. An attempt was then made to explain these data by correlating them with the sensory descriptors. 3.5. Correlations between sensory analysis and spectroscopy The data provided by sensory analysis were gathered in an S matrix dimensioned 1913 in which the rows represented the cultivars and the columns the descriptors. An element sij of S gave the score of the cultivar i for the descriptor j. In the same way, spectral data were
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Fig. 5. PCA plot of the 19 unpeeled cultivars and seven parameters measured by penetrometry in the plane defined by the first two principal components. Table 4 Pearson’s correlation coefficient between sensory and penetrometric data
Odour intensity Fresh leaf odour Roughness Touch resistance Crispness Juiciness Mealiness Chewiness Sweet Sour Aroma intensity Fondant Astringency
Fs
D
Wf
Ws
Ff
Grad 0–Fs
FLC
0.206 0.266 0.320 0.768 0.778 0.462 0.606 0.822 0.377 0.358 0.284 0.669 0.185
0.024 0.456 0.488 0.605 0.459 0.289 0.315 0.376 0.266 0.237 0.475 0.236 0.119
0.376 0.060 0.015 0.833 0.894 0.486 0.670 0.945 0.302 0.375 0.161 0.837 0.251
0.369 0.348 0.502 0.223 0.356 0.050 0.208 0.519 0.018 0.009 0.172 0.483 0.254
0.443 0.195 0.338 0.731 0.883 0.370 0.603 0.901 0.091 0.233 0.032 0.873 0.261
0.173 0.270 0.350 0.841 0.846 0.670 0.739 0.802 0.344 0.481 0.190 0.701 0.182
0.437 0.145 0.300 0.762 0.831 0.500 0.646 0.916 0.137 0.368 0.070 0.819 0.309
Bold type denotes highest correlations.
represented by an N matrix dimensioned 19900 containing the absorbances. To investigate a possible relationship between sensory and spectral data, a correlation matrix C between S and N was constructed, with 13 rows (descriptors) and 900 columns (wavelengths). An element cij of C represented the correlation coefficient of the sensory descriptor i and wavelength j. A graph ‘‘correlogram’’) was then plotted for each C
row as a function of wavelength. Among the 13 correlograms, only the five associated with the descriptors roughness, crunchiness, mealiness, acid taste and sweet taste showed significant correlation coefficient values and are presented here. The correlogram associated with roughness (Fig. 8) shows high correlation coefficients at a few interesting wavelengths, such as 1886 nm, the absorbance band
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Fig. 6. Vis/NIR spectra, with absorbancies corrected by Standard Normal Variate and averaged for 19 cultivars.
Fig. 7. PCA plot of the 19 unpeeled cultivars and variables measured by spectroscopy (absorbancies) in the plane defined by the first two principal components.
probably corresponding to starch, and 2050 nm, the absorbance band probably corresponding to proteins (Osborne et al., 1993). Crunchiness is correlated positively (Fig. 9), and mealiness negatively (Fig. 10), with spectroscopic data in the 680–710 nm range, which corresponds
to the chlorophyll absorbance bands, and with 980 nm, another wavelength corresponding to starch. The absorbing components responsible for the correlations between spectroscopic and textural data may not be textural factors (Watada, Massie, & Abbott, 1985). For
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Fig. 8. Correlation coefficients between spectroscopic data and the sensory attribute ‘‘roughness’’.
Fig. 9. Correlation coefficients between spectroscopic data and the sensory ‘‘mealiness’’.
example, as chlorophyll and starch change with texture during the maturation of apples, a correlation between some textural attributes such as crunchiness or mealiness exists. These results are in agreement with those of Watada et al. (1985), who found significant correlations between some textural attributes (crispness toughness
and juiciness) and optical data at wavelengths in the 670–710 and 920–950 nm ranges. The most correlated odour attribute was fresh leaf odour (R=0.60 at 1084, 1888 and 2036 nm). No information is currently available concerning possible correlations between spectral data and volatile components of apples.
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Fig. 10. Correlation coefficients between spectroscopic data and the sensory ‘‘crunchiness’’.
Fig. 11. Correlation coefficients between spectroscopic data and the sensory ‘‘sweet taste’’.
The sweetness attribute was correlated negatively with spectroscopic data at 1440 nm (R=0.58) and 1890 nm (R=0.48), i.e. absorbance bands corresponding to starch again (Fig. 11), while the sourness attribute was correlated positively with the same wavelengths (Fig. 12). These results seem logical,
because starch degradation produces sugars responsible for sweet taste, which explains why the correlations between starch absorbance bands and sweet taste are negative. There are two possible explanations why sour taste is correlated positively with starch:
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Fig. 12. Correlation coefficients between spectroscopic data and the sensory ‘‘sour taste’’.
1. The quantity of starch in apples is reduced at the same time as the malic acid concentration, i.e. during the fruit maturity stage. This phenomenon could account for the positive correlation coefficients between starch absorbance bands and sour taste, which is directly linked to the concentration of malic acid in apples. 2. Figs. 3 and 4 clearly show that sour and sweet attributes were always correlated on opposite sides of PC axes, which means that panellists never perceived very ‘‘sour apples’’ to be very ‘‘sweet’’. It is likely that the sour taste ‘‘masked’’ the sweet taste, or vice versa, although new sensory experiments are necessary to check this hypothesis. Rossiter, Young, Walker, Miller, and Dawson (2000) have shown in kiwi fruit that there is a significant decrease in perceived ‘‘acid taste’’ as Brix (which measures sugar quantity)
The sensory attributes which gave the highest correlation coefficient with the NIR data were studied by PLS regression. Only the attributes roughness; crunchiness; mealiness; sour and sweet taste were examined in this way. The results are given in Table 5. The correlation coefficients between the observed and the predicted variables were rather low and showed that it was not possible to establish a useful regression model between sensory attributes and NIR data. The roughness attribute gave a higher correlation coefficient, leading with a RMSECV equal to 0.97.
4. Conclusion
Table 5 PLS regression between sensory attributes and NIR data
Roughness Crunchiness Mealiness Sweet taste Sour taste
increases. The suppression of sweetness by acids and of acidity by sweeteners was previously demonstrated by McBride and Johnson (1987) in a lemon juice drink, but no studies have been done on apples.
Average
Standard deviation
PLS dimensionsa
Correlation coefficient
1.59 6.03 2.73 6.00 4.22
1.84 1.46 1.52 0.82 1.67
3 6 6 12 14
0.84 0.49 0.41 0.65 0.63
a PLS dimensions: dimension of the PLS model giving the highest correlation coefficient between predicted and observed values in the validation set (‘‘leave one out‘‘ validation).
This study shows that the parameters measured by penetrometry in unpeeled apples were highly correlated with sensory textural attributes. The most interesting parameters for measuring sensory firmness (described by crunchiness, touch resistance and crispness) were force Ff and work Wf. Juiciness and mealiness, two very important quality indices, were strongly correlated with Grad. These objective parameters for measurement of apple texture will be used in future studies concerning the relationships between texture, taste and aroma.
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Another conclusion of this study is that there is actually some statistically significant relationship between different visible/NIR wavelengths and some sensory attributes for apple. However, this relationship is not currently sufficient to allow the prediction of sensory attributes from NIR data. It must be due to the fact that the NIR data were averaged and that the sensory evaluation was carried out on batches of apples belonging to each cultivar, and not on individual fruits. In further work, we will investigate the possible interest of visible/NIR spectroscopy for estimating some sensory attributes as measured by experimented assessors. In the sensory evaluation tests, we will attempt to individually characterise each face of the fruits.
Acknowledgements This research was supported by the Conseil Re´gional du Pays de la Loire. The authors are grateful to Stephanie Khaldi and Roland Robic for skilful technical assistance. Special thanks are due to the members of the trained panel, who worked very seriously and never missed a sensory session. We would like to thank to anonymous referees for their helpful remarks that led to an improvement of the manuscript.
References AFNOR. (1995). V 09-021. Analyse sensorielle—Recherche et se´lection de descripteurs pour l’e´laboration d’un profil sensoriel, par approche multidimensionnelle. In Controˆle de la qualite´ des produits
alimentaires—Analyse sensorielle (pp. 276–310). 5e`me e´dition, AFNOR Paris. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Applied Spectroscopy, 45, 772–777. Dobrzanski, B., & Rybczynski, R. (1999). Stress–strain relationship for fruit firmness estimation. Acta Horticulturae, 485, 117–123. Duprat, F., Grotte, M., Loonis, D., & Pietri, E. (2000). Etude de la possibilite´ de mesurer simultane´ment la fermete´ et la chair de l’e´piderme des pommes. Sciences des Aliments, 20, 253–265. Fortin, J., & Desplancke, C. (1951). Guide d’entraıˆnement d’un jury de de´gustation. Edisen. Harker, F. R., & Maindonald, J. (2002). Sensory interpretation of instrumental measurements. 1: texture of apple fruit. Postharvest Biology and Technology, 24(3), 225–239. Karlsen, A. M., Aaby, K., Sivertsen, H., Baardseth, P., & Ellekjaer, M. R. (1999). Instrumental and sensory analysis of fresh Norwegian and imported apples. Food Quality and Preference, 10, 305–314. Lammertin, J., Nicolaı¨, B., Ooms, K., De Smedt, V., & De Baerdemaeker, J. (1998). Non-destructive measurement of acidity, soluble solids and firmness of Jonagold apples using NIR-spectroscopy. American Society of Agricultural Engineers, 41(4), 1089–1094. McBride, R. L., & Johnson, R. L. (1987). Perception of sugar-acid mixtures in lemon juice drink. International Journal of Food Science and Technology, 22, 399–408. McGlone, V. A., & Jordan, R. B. (2002). Vis/NIR estimation at harvest of pre- and post-storage quality indices for Royal Gala apple. Postharvest Biology and Technology, 25(2), 135–144. Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy with applications in food and beverage analysis. Essex, England: Longman Scientific and Technical. Rossiter, K. L., Young, H., Walker, S. B., & Miller, M. (2000). The effects of sugars and acids on consumer acceptability of kiwifruit. Journal of Sensory Studies, 15, 241–250. Stow, J. (1995). Quality measurements of apples. Postharvest News and Information, 6(3), 32–33. Watada, A. E., Massie, D. R., & Abbott, J. A. (1985). Relationship between sensory evaluations and nondestructive optical measurements of apple quality. Journal of Food Quality, 7, 219–226.