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Assessment of pomegranate postharvest quality using nuclear magnetic resonance
Postharvest Biology and Technology 77 (2013) 59–66
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Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Assessment of pomegranate postharvest quality using nuclear magnetic resonance Lu Zhang a , Michael J. McCarthy a,b,∗ a b
Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States
a r t i c l e
i n f o
Article history: Received 9 February 2012 Accepted 15 November 2012 Keywords: Pomegranate Quality Brix Acidity pH NMR
a b s t r a c t Fruit quality parameters, soluble solids content (Brix), total titratable acidity, pH, and Brix/acid ratio, are often used as indicators of fruit maturity and palatability. Measurement of these fruit quality parameters requires a series of destructive methods, which can only be conducted on extracted fruit juice. The aim of this study is to investigate the relationship between spin–spin relaxation time and pomegranate quality attributes and the potential of MRI for quantitative analysis of pomegranate quality. Spin–spin relaxation time, T2 , measured using a low magnetic field (0.04 T) showed correlation with the soluble solids content of pomegranate. The T2 relaxation time ranged from 837 ms to 1024 ms for the fruit with soluble solids content from 15.3 ◦ Brix to 18.7 ◦ Brix. However, accurate prediction was not achieved. In the MRI experiment, six MR images with varying contribution to total signal intensity from proton density, relaxation rates, and diffusion weighing were obtained for pomegranate fruit using a 1 T MR imaging system with 0.22 T/m gradient strength. The pH, Brix, total titratable acidity, and Brix/acid ratio of pomegranate were also measured by traditional destructive methods. Partial least square (PLS) analysis was applied to the statistical features of the voxel signal intensities in the MR images and quality parameters to examine the correlation between MR images results and destructive measurements. The MR image based PLS model have a R2 of 0.54, 0.6, and 0.63 for predicting titratable acidity, pH, and soluble solids/acidity levels, respectively. The correlation between MR image statistical features and soluble solids content of pomegranate was poor. In these models, T2 weighted Fast Spin Echo, diffusion weighted image, and Spin Echo image with short TE and moderate TR are the most important images in predicting the pomegranate quality attributes. Unlike traditional destructive methods, MR imaging is capable of evaluating multiple quality parameters in a single measurement. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The recognition of health benefits of consuming pomegranate fruit has drawn more and more interest from consumers and producers. The increasing popularity of pomegranate resulted in a booming market for pomegranate growers and processers. Pomegranate is consumed as a fresh fruit or processed into readyto-eat arils and juice. Although the intended use of the fruit may be different, the quality of pomegranate is always important to growers, processors, and consumers. To ensure the minimum acceptability of the quality to consumers, California mandatory quality standards for fresh fruit include indices, such as soluble solids content, titratable acidity
∗ Corresponding author at: Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States. Tel.: +1 530 752 8921; fax: +1 530 752 4759. E-mail address: [email protected] (M.J. McCarthy). 0925-5214/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.postharvbio.2012.11.006
concentration, and ratio of soluble solids to titratable acidity (Kader, 1999). These quality indexes are often used as indicators of fruit maturity and palatability. Characterization of the quality indices involves a series of standard methods, which can only be conducted on extracted fruit juice. The destructive nature of the methods made the measurement labor intensive, time consuming and inapplicable to grading and sorting. Attempts have been made to find alternative non-destructive methods for assessing the internal quality parameters of fruit. Nuclear Magnetic Resonance (NMR) is one of the techniques that have received considerable attention from scientists. NMR has been used to study the physiological changes in plant tissue induced by different treatments or natural factors. The properties of water in the fruit tissue detected by NMR are related to the chemical composition and structure of the tissue. At high magnetic field (200 MHz), the sugar peak intensity in the NMR spectrum following water suppression was correlated with the sugar content in muskmelon (Cho et al., 1991). More scientists chose to work at low field to avoid the high cost of high field system. Bellon et al. (1992) investigated
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the relationship between spin–spin relaxation time T2 and sugar content in pears, cherries, and grapes using a 20 MHz NMR spectrometer. A linear relationship between T2 and sugar content was found in cherries and grape at a 10 MHz field (Cho et al., 1993). The relationship between soluble solids and T2 was investigated in several apple varieties (Keener et al., 1998). Significant correlation was only observed in Golden delicious apples. The self diffusion rate of water in apples was also measured with a Pulse Field Gradient Spin-Echo sequence. In Granny Smith apples, the water self diffusion rate was strongly dependent on the soluble solids content. Wai et al. (1995) used a water self-diffusion weighted Hahn Echo sequence to measure the sugar content in fruit at a 5.35 MHz field. The sugar content was correlated with the echo amplitude ratio in both apples and cherries. Taking advantage of the difference in the self diffusion coefficient of water and sugar, Marigheto et al. (2006) used a Pulsed Field Gradient Spin Echo to suppress the water signal from the sample. The obtained echo amplitude was highly correlated with the soluble solids content in apples and strawberries. All the previous studies proved that NMR has great potential for assessing sugar content nondestructively in fruit. Sugar content is not the only factor determining the fruit quality, but it has drawn the most attention in the area of NMR quality assessment. Very limited research has been conducted on the application of NMR to measurement of other important fruit quality indices such as maturity, texture, titratable acidity, pH, and ratio of soluble solids to titratable acidity. Tu et al. (2007) showed a statistically significant difference in mean values of the T2 for tomatoes of different maturity and defect level. In apples the measured T2 showed dependence on pH, titratable acids, and insoluble solids besides soluble solids (Keener et al., 1998), thus a single NMR parameter like T2 may not be enough to characterize several quality attributes accurately. Magnetic resonance imaging (MRI) of fruit provides proton density and relaxation time encoded in the image signal intensity, maximizing the amount of information obtained from the fruit. The combination of multiple parameter weighted images may yield improved predictions of internal quality factors. Letal et al. (2003) found significant correlations between acidity, soluble solids and texture analysis parameters of MR images of apples. Multiple MR images were used to measure the maturity of processing tomato (Zhang and McCarthy, 2012), mechanical damage to processing tomatoes (Milczarek et al., 2009), and predict processing tomato peelability (Milczarek and McCarthy, 2011) with the aid of multivariate data analysis. The objectives of this study were to investigate the relationship between T2 and pomegranate quality attributes such as soluble solids content, titratable acidity, pH, and ratio of soluble solids content to titratable acidity, to correlate the quality attributes with information from MR images, and to determine the accuracy and precision of MRI derived models.
2. Materials and methods 2.1. Fruit samples Pomegranate fruit, variety “wonderful”, were provided by POM Wonderful (Del Ray, CA). The pomegranates were hand picked at the ripe stage on a weekly basis throughout the harvest season. The fruit were not sorted and represent an “as picked” sample set. In order to expand the range of titratable acidity in the study, fruit stored under controlled atmosphere (CA) for three months were obtained after the harvest season. The CA fruit were sorted prior to storage. For NMR relaxation measurement, 133 fresh fruit and 27 CA fruit were used. A total of 169 fresh fruit and 37 CA fruit were used in the MR imaging experiment.
Table 1 MRI pulse sequence parameters. Pulse sequence SE 1 SE 2 FSE DW 0 DW 60 a
TE (ms) 7.3 7.3 209a 38 38
TR (ms)
FOV (mm2 )
Slice thickness (mm)
130 600 6000 268 268
95 95 95 130 130
5 5 5 5 5
TE for FSE is the effective TE (TEeff ).
2.2. NMR relaxation time measurement A 0.04 T (1.7 MHz) permanent magnet NMR spectrometer (Quantum Magnetics, San Diego, CA) was used to measure the T2 relaxation time for each whole pomegranate. The diameter of the coil was 25 cm, large enough for the fruit. T2 relaxation time was acquired by Carr-Purcell-Meiboom-Gill (CPMG) sequence with 2048 echoes and an echo time of 2.03 ms. The T2 value was calculated using a mono-exponential curve fitting function in MatLab 2010a (The Mathworks, Natick, MA). 2.3. Magnetic resonance imaging MRI data was acquired on a 1 T permanent magnet NMR spectrometer (Aspect Imaging, Industrial Area Hevel Modi’in, Shoham, Israel) with a 60 mm × 90 mm elliptical RF coil. The coil accommodates samples up to 56 mm in height and 85 mm in diameter. Samples were trimmed by removing primarily the rind to fit the coil. Trimmed fruit was placed on a plastic sample holder and centered so that the sample was fixed in a predetermined position in the coil. Fruit was scanned to obtain images of the equatorial slice. A set of 5 MR images was acquired using 5 different sequences and an addition image was generated from the 2 diffusion weighted images. The 5 MRI sequences were selected so that water proton properties, including proton density, T1 and T2 relaxation time, and diffusion rate, contribute differently to the MR image (Table 1). In spin echo images, the signal intensity is described by (Bernstein et al., 2004): S = M0 (1 − 2e−(TR−TE)/T1 + e−TR/T1 )e−TE/T2
(1)
where M0 is the net magnetization proportional to the proton density, T1 is the spin-lattice relaxation time, and T2 is the spin–spin relaxation time. Repetition time (TR) and Echo time (TE) are sequence parameters manipulating the interval between pulses. The TE and TR of spin echo sequence (SE) could be adjusted to obtain spin echo image with different weighting. In Fast Spin Echo (FSE) images, the signal intensity is given by (Bernstein et al., 2004): S = M0 e−TEeff /T2
(2)
The image contrast is predominantly determined by the central area in k-space. The effective echo time (TEeff ) is the time when the central k-space data is acquired, so the signal intensity in FSE image is primarily a function of TEeff . The long TEeff used in imaging makes the FSE images T2 -weighted. In diffusion weighted imaging (DW 60), a pair of gradient pulse, with ı = 10.2 ms, = 23.938 ms, and G = 0.1206 T/m, were added to a spin echo sequence. Movement of molecules within the time interval between the two pulses, i.e., 23.938 ms, results in signal intensity attenuation. DW 0 is a normal spin echo sequence with the same TE and TR as DW 60, but without the diffusion gradient pulses. The signal attenuation caused by the pulse gradient is determined by the apparent diffusion coefficient of water. To quantify the diffusion induced signal attenuation, a new image was obtained by taking the ratio of signal intensity of each voxel in DW 60 to that
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Table 2 Correlation coefficient between T2 and quality attributes of pomegranates. SSC
TA
pH
SSC/TA
−0.41a 0.38a 0.42a
−0.14 −0.20 0.41a
−0.14 −0.12 −0.52a
0.05 0.19 −0.46a
T2 Fresh CA All a
Values are significant at P = 0.01.
the subgroups have been used once as the validation data. The 5-fold random cross-validation was iterated for 20 times. 3. Results and discussion 3.1. NMR relaxation measurement
Fig. 1. Region of interest (highlighted) in an FSE image of a pomegranate fruit.
of DW 0. The new image is a diffusion coefficient weighted intensity image. 2.4. Chemical analyses After NMR measurement or MR imaging, each fruit, including the arils and the rind, was sliced and hand pressed with a juicer to obtain a juice sample. The juice was allowed to settle for 15 min to precipitate suspended solids, and then the clarified supernatant was used for chemical analysis. Soluble solids content (SSC) was measured using a benchtop temperature compensating refractometer (RFM730, Bellingham and Stanley, UK). A pH meter (Accumet AP71, Fisher Scientific, Pittsburgh, PA) was used to acquire the pH value of the juice. The titratable acidity content was determined according to AOAC method 942.15 (AOAC, 2002). Aliquot of 5 g juice sample was diluted in 100 ml distilled water and well mixed. The diluted juice was titrated with 0.1 M NaOH to an end point of pH 8.2 as indicated by a pH meter. TA was reported as % citric acid. All the measurements were done in duplicate, and the average values were used. 2.5. Data analysis Signal intensity of the MR images is a function of water proton properties in the sample, so quantitative analysis of the images is based on extracting the signal intensity of the voxels in the fruit area in the image. Due to the large size of the pomegranate fruit, some artifacts occurred on the edge of the fruit in the images. A region of interest (ROI) was selected to cover the fruit region in the image and omit the periphery of the sample (Fig. 1). Nine statistical features of the signal intensity of the ROI were calculated for each MR image, including mean, median, mode, variance, standard deviation, skewness, kurtosis, range, and coefficient of variation. Partial Least Square (PLS) was applied to establish the relationships between the 54 features of 6 images and the measured chemical properties of the pomegranates. The model was developed using Matlab 2010a (The Mathworks, Natick, MA) and PLS toolbox (Eigenvector Research Inc., Wenatchee, WA). A 5-fold random cross-validation was performed to validate the calibration model. The whole dataset was divided into 5 subgroups with equal size randomly. Four subsets were used as training data to build a calibration model, and the model was tested using the remaining one subset. The cross-validation was repeated until all
A mono-exponential fit of the CPMG data yielded a T2 for each relaxation measurement. The relationships between T2 relaxation time and quality parameters, SSC, pH, TA, and SSC/TA, were investigated on the basis of fresh in-season pomegranate fruit. T2 inversely correlated with SSC (r = −0.41, Table 2). The decrease of T2 with increasing soluble solids content was also observed in Granny Smith and Golden Delicious apples (Keener et al., 1998). The SSC is a measure of sugar content in the sample. Fast proton exchange between water and exchangeable proton on hydroxyl group of sugars would facilitate the relaxation process of water (Hills, 2006). As the sugar content increases, more hydroxyl groups were available to exchange with water protons, leading to decrease in T2 relaxation time. The correlations between T2 and pH, TA, SSC/TA were insignificant, which may be a result of the small range of the quality parameters. CA storage causes reduction in TA and SSC in pomegranate (Artés et al., 1996), thus CA stored samples were included in the experiment to extend the range of the quality parameters. However, distinctive relationship between T2 and SSC were observed in CA stored fruit (Table 2). The T2 relaxation time increased with increasing SSC. The reason for the divergence in the correlations was not clear. The inclusion of the CA stored fruit enhanced the correlation between T2 and quality parameters (Table 2). However, the improvement of the correlation was mainly driven by the difference between the fresh and CA stored pomegranates. In Fig. 2, CA stored fruit showed relative lower SSC and TA than fresh fruit, but the T2 relaxation time was significantly shorter in CA stored fruit. The two groups of fruit were partitioned into separated clusters in the bivariate plots of T2 with quality parameters. Because T2 is sensitive to cell morphology and membrane permeability (Hills, 2006), any change in cell structure during CA storage may strongly affect T2 and the correlation between T2 and quality parameters. As water content affects the T2 , moisture loss from the rind during storage was also a possible contributing factor to the difference between fresh and CA stored fruit. On the other hand, the coefficient of correlation between T2 and SSC of fresh pomegranates was lower than the value (∼0.9) reported in previous studies on grapes and cherries (Bellon et al., 1992). The large sample size used in this experiment introduced much more biological variations, which disturbed the linear relationship between SSC and T2 . The dependence of T2 on factors other than SSC posed difficulty for developing the relationship between T2 and SSC. Keener et al. (1998) also found poor correlation between T2 and SSC in apple tissues of several varieties. A high correlation between T2 and SSC was obtained in orange juice, apple juice, and grape juice in the same study. The presence of insoluble solids and cellular structure in whole fruit complicated the relationship between T2 and SSC. The insoluble solids would serve as relaxation reservoirs, providing exchangeable protons. The diffusion of water across the
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2.8 CA Fresh
a
2.6
18.5
CA Fresh
b
2.4 18 2.2
TA (% citric acid)
SSC(Brix)
17.5
17
16.5
2 1.8 1.6 1.4
16 1.2 15.5
15 700
1
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0.8 700
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T2 (ms)
900
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T2 (ms)
Fig. 2. Bivariate plots of pomegranate fruit: (a) SSC vs T2 and (b) TA vs T2 .
membrane defined cellular compartments has considerable effect on the relaxation process as well. 3.2. MR imaging 3.2.1. MR images MRI of fruit yields proton density and relaxation time weighted image signal intensity. The spatially resolved 2D image offers view into the distribution of tissue with different water proton properties in the fruit rather than a T2 value or signal intensity averaged over the whole fruit. The combination of multiple parameter weighted images generates information on several water proton
properties of the same fruit. The increase in the amount of data may help to predict the internal quality parameters of pomegranates. The same pomegranate had distinctive appearance in the 6 MR images (Fig. 3). The images have different contrast among different parts of the pomegranate, arils, seeds, and rind. Arils were visible in DW 0, DW 60/DW 0, and FSE images. In SE 1, seeds were distinguished from the rest of the fruit. The signal intensity in DW 60/DW 0 was directly related to the water self diffusion coefficient in the sample. The low signal intensity of arils in DW 60/DW 0 was a result of higher water self diffusion coefficient in the arils than in the rind and seeds. In the T2 weighted FSE image, longer T2 relaxation time of arils caused higher signal intensity in the areas corresponding to
Fig. 3. Example of 6 MR images of the same pomegranate.
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19
a
3.8
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Predicted SSC (º Brix)
3.7
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3.1 3
16 2.9 15.5 15.5
16
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18.5
2.8 2.8
19
3
Measured SSC (º Brix)
3.2
3.4
3.6
3.8
Measured pH 30
2.6
c
d
2.4 25
2
Predicted SSC/TA
Predicted TA (% citric acid)
2.2
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1.2 1 10 0.8 0.6 0.5
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Measured TA (% citric acid)
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Measured SSC/TA
Fig. 4. Plots of measured quality attributes and predicted quality attributes of pomegranate by MR images: (a) SSC, (b) pH, (c) TA and (d) SSC/TA. The diagonal line represented points where the predicted and measured values are equal.
arils. The higher moisture content of arils compared to seeds and rind was the main reason for the larger apparent water self diffusion coefficient and longer T2 relaxation time. According to Eq. (1), the short TR and TE made SE 1 a T1 weighted image. The high signal intensity of seeds in SE 1 indicated that seeds had the shortest T1 relaxation time in the pomegranate. The different origin of contrast in the 6 images ensured that multiple intrinsic NMR properties of the protons were measured. 3.2.2. PLS models for quality parameters prediction All fruit, fresh and CA stored, were used to investigate the correlation between MR images and internal quality attributes. The 54 variables used in the PLS model captured up to 47% of the variance in SSC. The first four latent variables were selected to build the model based on the criteria of minimizing Root Mean Square Error of Cross-Validation (RMSECV) with the least number of latent variables. The model captured only 35% of the variance in SSC. The Root Mean Square Error of Calibration (RMSEC) and RMSECV were 0.50 and 0.57. Fig. 4a demonstrated the relationship between measured SSC and predicted SSC from the model. Although there is a tendency for the predicted SSC to increase with the rise in measured SSC, the
data points loosely scattered in the plot. Some samples with the same measured SSC had significantly different predicted SSC. A R2 of 0.17 was obtained from the cross-validated model. Considering the successfulness of previous studies on using NMR to measure the SSC in fruit, the poor correlation between SSC and MR images was unexpected. For prediction of pH, a model was constructed based on 5 latent variables, capturing 68% of the variance in pH. The R2 of the crossvalidated model was 0.6. The RMSEC and RMSCEV of the model were 0.12 and 0.13, respectively. The RMSECV was very close to RMSEC, which means the loss in the accuracy was very small when the calibration models were applied to the test data. The low value of the error indicated that the PLS model provided fairly accurate prediction of pH. The prediction performance was illustrated in Fig. 4b. Unlike the loose scattering of data around the diagonal line, where predicted value equals to measured value, observed in SSC prediction model, points stayed much closer to the diagonal line, implying better prediction. Similar performance was achieved in the PLS model for TA prediction as shown in Fig. 4c. The 6-latent variable model accounted for about 64% of the variance in TA. The R2 of the cross-validated
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5 4.5
FSE_mean
a
FSE_median FSE_mode
4
VIP Scores for pH
3.5 FSE_standard deviation 3 FSE_variance 2.5
SE2_standard deviation SE2_variance
2
FSE_range SE1_coeff icient of variation SE2_coeff icient of variation SE1_skewness
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Variable 4 3.5 FSE_mean
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FSE_variance 2.5
DW 60/DW 0_median SE2_standard deviation
2
DW 60/DW 0_mean
FSE_range
SE2_variance SE1_skewness SE2_coeff icent of variation
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DW 60_median SE1_variance
DW 60_mean
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1 0.5
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FSE_standard deviation 3
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2.5 SE2_variance
SE1_coeff icient of variation
SE2_standard deviation
2 DW 60/DW 0_median 1.5
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DW 60/DW 0_mean
SE2_coeff icient of variation
1 0.5 0
5
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Variable Fig. 5. VIP scores of variables for quality attributes prediction model: (a) pH, (b) TA and (c) SSC/TA. Only variables with a VIP score larger than one were labeled.
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TA model (R2 = 0.54) was slightly lower than the pH model. The RMSECV was 0.29 compared to the RMSEC of 0.26. While prediction of SSC was not successful, the SSC/TA demonstrated good correlation with the information from MR images. A cross-validated R2 of 0.63 was obtained. The good agreement in the RMSEC (2.29) and RMSECV (2.57) indicated the robustness of the model. The inclusion of the CA stored samples extended the range of pH, TA, and SSC/TA, but correlations between the quality attributes and MR images were not affected by the CA stored samples as occurred in T2 relaxation time. The cross-validation procedure verifies the validity of the model on the whole dataset. If the discrepancy between fresh and CA stored samples was so big that prediction of internal qualities of both groups cannot be achieved using the same models, the calibration model will fail the cross-validation test. The small difference between RMSEC and RMSECV of all the PLS models indicated that the developed model can be applied to any subset of the data. The effectiveness of the prediction models was also evaluation by RPD (Residual Prediction Deviation). The RPD values were 1.58, 1.49, and 1.65 for prediction models of pH, TA, and SSC/TA, respectively. Previous literature gave interpretations of the RPD values in terms of the model accuracy. A RPD between 1.5 and 2 indicates that the model can distinguish low from high values of the response variable and a model with RPD between 2 and 2.5 can make coarse quantitative predictions (Nicolaï et al., 2007), while Viscarra Rossel et al. (2007) concluded that a RPD value between 1.4 and 1.8 means that the model is fair and can be used for assessment and correlation. Although a high RPD value indicates a good model, the standard deviation of the response variable affects the RPD significantly. When the variance in the sample set is low, the RPD cannot be very high (Williams and Sobering, 1996). The standard deviation of pH, TA, and TA/SSC of pomegranate samples were 0.21, 0.44, and 4.32, the accuracy of the prediction model may be improved further by increasing the variability of the pomegranates. On the other hand, the performance of obtained prediction models is satisfactory for sorting purpose, which requires rough assessment of fruit quality instead of accurate quantification.
3.2.3. Identification of important predictors Variable importance in projection (VIP) scores for each independent variable were calculated to evaluate their importance in the PLS prediction models for pH, TA, and SSC/TA (Fig. 5). Variables with VIP scores bigger than one are considered as the most influential predictors for the model (Eriksson et al., 2006). Several statistical features of FSE and SE 2 had great contribution to the pH prediction model. The variance terms and skewness of signal intensity in SE 1 had relatively large VIP scores. The VIP score of the median of the signal intensity in DW 60/DW 0 was slightly above one. For TA prediction model, the five most influential variables were the same as the pH prediction model. In addition, mean and median of signal intensity in DW 60/DW 0 became important predictors. The relative importance of variables in the SSC/TA prediction model was similar to the pH prediction model, except for that the mean, median of DW 60/DW 0 were well above one and less features of SE 1 appeared to be important. The same set of statistical features, mean, median, mode, standard deviation, variance, and range, of signal intensity in FSE had large VIP scores in all the models. The variance, standard deviation, and coefficient of variation of SE 2, skewness and coefficient of variation of SE1, and DW 60/DW 0 were also considered to be important in all three models. The importance of the variance and distribution attributes of signal intensity in MR image implied that the variation pattern of NMR proton properties within the fruit correlated with the pomegranate quality attributes.
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The importance of each image type in prediction models represents the contribution of corresponding water proton properties to quality prediction. The T2 weighted FSE was the most important image among the 6 images in all prediction models, so T2 was the most important factor related to the internal quality of pomegranate even though no significant correlation were observed between T2 value and the quality parameters excluding SSC in T2 relaxation time study of pomegranate. The short TE and medium TR introduced T1 and proton density contrast to SE 2. Compared with the T1 weighted SE 1, SE 2 had more contribution to the pH and SSC/TA prediction. The extra proton density contrast in SE 2 may reflect more variance in pH and SSC/TA of pomegranate. The importance of SE 1 was mainly derived from the high VIP score of coefficient of variation and skewness of its signal intensity. The T1 contrast highlighted seeds within the arils in SE 1. The variation in the ratio of aril/rind or seed/aril would result in change in coefficient of variation and skewness of signal intensity in SE 1. It is possible that the internal quality of pomegranate is a function of the ratio among different parts of the fruit tissue. The water diffusion coefficient weighted DW 60/DW 0 was a crucial image in TA and SSC/TA models, indicating the water self diffusion rate was an essential indicator of TA and SSC/TA in pomegranate. Although a valid prediction model for SSC cannot be derived from the information from MR images, the relationship between statistical features and measured SSC was investigated to identify any promising feature and image. The correlation between SSC and mean signal intensity in DW 60 achieved 0.41, significant at P < 0.01. Other significant terms (P < 0.01) were median or mean of DW 60, DW 60/DW 0, and DW 0. The value of NMR water diffusion coefficient measurement in SSC prediction was proved by several previous studies. The water diffusion coefficient was found to be a better predictor of SSC than T2 (Keener et al., 1998). In addition, the echo amplitude obtained using a Pulsed Field Gradient Spin Echo, was highly correlated with the SSC in apples and strawberries. In the sequence, the water proton signal was suppressed by a gradient pulse to measure the signal solely from sugar utilizing the difference in the diffusion coefficient of water proton and sugar proton (Marigheto et al., 2006). Therefore, the usefulness of DW images in SSC measurement mainly originated from the enhanced contribution from the sugar proton to the signal intensity and the water diffusion weighting in DW images.
4. Conclusion T2 relaxation time was correlated with the SSC in pomegranates, but no correlation was established between pH, TA, SSC/TA and T2 . Subject to the effect of factors other than SSC, the relationship between T2 and SSC did not provide an accurate estimation of SSC. These results are consistent with those in the literature for other fruit. CA storage had significant effect on T2 values of pomegranates, thus different relationships between T2 and quality attributes were observed in CA stored fruit. NMR should prove to be useful for monitoring changes in CA stored fruit. The use of multiple NMR water proton property weighted MR images enable acquisition of a rich body of data from each fruit. PLS models based on the MR images proved to be effective for measuring pH, TA, and SSC/TA in pomegranate fruit. T2 weighted FSE was identified as the most important image in the prediction model for the three quality attributes. T1 weighted, proton density weighted, and diffusion weighted image also had various contribution to the prediction model for different attributes. The PLS analysis of MR images did not yield a valid model for SSC prediction, but diffusion weighted image demonstrated its value in SSC measurement. The non-destructive MR imaging technique is an alternative compared to the destructive chemical analysis of pH, TA, and SSC/TA in pomegranate. In
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summary, given the literature results and those in this paper, lowfield NMR based approaches to measure physical defects in fruit like bruising, rot, insect damage and browning are very effective, but low-field NMR based approaches to measure chemical based quality factors are not as uniformly effective. Efforts in developing low-field NMR for measurement of chemical based fruit quality factors should likely be focused on applying alternate pulse sequences than previously applied in most studies. The work by Hills and coworkers (2006) provides an example using a diffusion filter to more precisely measure sugar content. Acknowledgements This work was partially supported by POM Wonderful LLC and by National Research Initiative Award 2007-02632 from the USDA National Institute of Food and Agriculture. References AOAC, 2002. Official Methods of Analysis of AOAC International, 17th edition ed. AOAC International, Gaithersburg, MD. Artés, F., Marín, J.G., Martínez, J.A., 1996. Controlled atmosphere storage of pomegranate. Zeitschrift für Lebensmittel-Untersuchung und -Forschung 203, 33–37. Bellon, V., Cho, S.I., Krutz, G.W., Davenel, A., 1992. Ripeness sensor development based on nuclear magnetic resonance. Food Control 3, 45–48. Bernstein, M., King, K., Zhou, X., 2004. Handbook of MRI Pulse Sequences. Academic Press, Burlington, MA. Cho, S.I., Bellon, V., Eads, T.M., Stroshine, R.L., Krutz, G.W., 1991. Sugar content measurement in fruit tissue using water peak suppression in high resolution 1H magnetic resonance. Journal of Food Science 56, 1091–1094. Cho, S.I., Stroshine, R.L., Baianu, I.C., Krutz, G.W., 1993. Nondestructive sugar content measurements of intact fruit using spin–spin relaxation time (T2 ) measurements by pulsed 1 H magnetic resonance. Transactions of the ASAE 36, 1217–1221.
Eriksson, L., Kettaneh-Wold, E.J.N., Wold, S., 2006. Multivariate and Megavariate Data Analysis Basic Principles and Applications Umetrics AB. Umea, Sweden. Hills, B.P., 2006. NMR relaxation and diffusion studies of horticultural products. In: Webb, G.A. (Ed.), Modern Magnetic Resonance. Springer, Netherlands, pp. 1721–1727. Kader, A.A., 1999. Fruit maturity ripening, and quality relationships. Acta Horticulturae (ISHS) 485, 203–208. Keener, K.M., Stroshine, R.L., Nyenhuis, J.A., 1998. Proton magnetic resonance measurement of self-diffusion coefficient of water in sucrose solutions, citric acid solutions, fruit juices, and apple tissue. Transactions of the ASAE 40, 1633–1641. Letal, J., Jirak, D., Suderlova, L., Hajek, M., 2003. MRI ‘texture’ analysis of MR images of apples during ripening and storage. LWT-Food Science and Technology 36, 719–727. Marigheto, N., Wright, K., Hills, B., 2006. NMR protocol for on-line Brix determination. Applied Magnetic Resonance 30, 13–23. Milczarek, R.R., McCarthy, M.J., 2011. Prediction of processing tomato peeling outcomes. Journal of Food Processing and Preservation 35, 631–638. Milczarek, R.R., Saltveit, M.E., Garvey, T.C., McCarthy, M.J., 2009. Assessment of tomato pericarp mechanical damage using multivariate analysis of magnetic resonance images. Postharvest Biology and Technology 52, 189–195. Nicolaï, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Lammertyn, J., 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology 46, 99–118. Tu, S.S., Choi, Y.J., McCarthy, M.J., McCarthy, K.L., 2007. Tomato quality evaluation by peak force and NMR spin–spin relaxation time. Postharvest Biology and Technology 44, 157–164. Viscarra Rossel, R.A., Taylor, H.J., McBratney, A.B., 2007. Multivariate calibration of hyperspectral ␥-ray energy spectra for proximal soil sensing. European Journal of Soil Science 58, 343–353. Wai, W.-K., Stroshine, R.L., Krutz, G.W., 1995. A modified Hahn echo pulse sequence for proton magnetic resonance (1 H-MR) measurements of percent soluble solids of fruits. Transactions of the ASAE 38, 849–855. Williams, P.C., Sobering, D.C., 1996. How we do it? A brief summary of the methods we use in developing near infrared calibrations. In: Williams, P.C., Davies, A.M.L. (Eds.), Near Infrared Spectroscopy: The Future Waves. Nir Publications, Chichester. Zhang, L., McCarthy, M.J., 2012. Measurement and evaluation of tomato maturity using magnetic resonance imaging. Postharvest Biology and Technology 67, 37–43.
Contents lists available at SciVerse ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Assessment of pomegranate postharvest quality using nuclear magnetic resonance Lu Zhang a , Michael J. McCarthy a,b,∗ a b
Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States Department of Biological and Agricultural Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States
a r t i c l e
i n f o
Article history: Received 9 February 2012 Accepted 15 November 2012 Keywords: Pomegranate Quality Brix Acidity pH NMR
a b s t r a c t Fruit quality parameters, soluble solids content (Brix), total titratable acidity, pH, and Brix/acid ratio, are often used as indicators of fruit maturity and palatability. Measurement of these fruit quality parameters requires a series of destructive methods, which can only be conducted on extracted fruit juice. The aim of this study is to investigate the relationship between spin–spin relaxation time and pomegranate quality attributes and the potential of MRI for quantitative analysis of pomegranate quality. Spin–spin relaxation time, T2 , measured using a low magnetic field (0.04 T) showed correlation with the soluble solids content of pomegranate. The T2 relaxation time ranged from 837 ms to 1024 ms for the fruit with soluble solids content from 15.3 ◦ Brix to 18.7 ◦ Brix. However, accurate prediction was not achieved. In the MRI experiment, six MR images with varying contribution to total signal intensity from proton density, relaxation rates, and diffusion weighing were obtained for pomegranate fruit using a 1 T MR imaging system with 0.22 T/m gradient strength. The pH, Brix, total titratable acidity, and Brix/acid ratio of pomegranate were also measured by traditional destructive methods. Partial least square (PLS) analysis was applied to the statistical features of the voxel signal intensities in the MR images and quality parameters to examine the correlation between MR images results and destructive measurements. The MR image based PLS model have a R2 of 0.54, 0.6, and 0.63 for predicting titratable acidity, pH, and soluble solids/acidity levels, respectively. The correlation between MR image statistical features and soluble solids content of pomegranate was poor. In these models, T2 weighted Fast Spin Echo, diffusion weighted image, and Spin Echo image with short TE and moderate TR are the most important images in predicting the pomegranate quality attributes. Unlike traditional destructive methods, MR imaging is capable of evaluating multiple quality parameters in a single measurement. © 2012 Elsevier B.V. All rights reserved.
1. Introduction The recognition of health benefits of consuming pomegranate fruit has drawn more and more interest from consumers and producers. The increasing popularity of pomegranate resulted in a booming market for pomegranate growers and processers. Pomegranate is consumed as a fresh fruit or processed into readyto-eat arils and juice. Although the intended use of the fruit may be different, the quality of pomegranate is always important to growers, processors, and consumers. To ensure the minimum acceptability of the quality to consumers, California mandatory quality standards for fresh fruit include indices, such as soluble solids content, titratable acidity
∗ Corresponding author at: Department of Food Science and Technology, University of California, Davis, One Shields Avenue, Davis, CA 95616, United States. Tel.: +1 530 752 8921; fax: +1 530 752 4759. E-mail address: [email protected] (M.J. McCarthy). 0925-5214/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.postharvbio.2012.11.006
concentration, and ratio of soluble solids to titratable acidity (Kader, 1999). These quality indexes are often used as indicators of fruit maturity and palatability. Characterization of the quality indices involves a series of standard methods, which can only be conducted on extracted fruit juice. The destructive nature of the methods made the measurement labor intensive, time consuming and inapplicable to grading and sorting. Attempts have been made to find alternative non-destructive methods for assessing the internal quality parameters of fruit. Nuclear Magnetic Resonance (NMR) is one of the techniques that have received considerable attention from scientists. NMR has been used to study the physiological changes in plant tissue induced by different treatments or natural factors. The properties of water in the fruit tissue detected by NMR are related to the chemical composition and structure of the tissue. At high magnetic field (200 MHz), the sugar peak intensity in the NMR spectrum following water suppression was correlated with the sugar content in muskmelon (Cho et al., 1991). More scientists chose to work at low field to avoid the high cost of high field system. Bellon et al. (1992) investigated
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the relationship between spin–spin relaxation time T2 and sugar content in pears, cherries, and grapes using a 20 MHz NMR spectrometer. A linear relationship between T2 and sugar content was found in cherries and grape at a 10 MHz field (Cho et al., 1993). The relationship between soluble solids and T2 was investigated in several apple varieties (Keener et al., 1998). Significant correlation was only observed in Golden delicious apples. The self diffusion rate of water in apples was also measured with a Pulse Field Gradient Spin-Echo sequence. In Granny Smith apples, the water self diffusion rate was strongly dependent on the soluble solids content. Wai et al. (1995) used a water self-diffusion weighted Hahn Echo sequence to measure the sugar content in fruit at a 5.35 MHz field. The sugar content was correlated with the echo amplitude ratio in both apples and cherries. Taking advantage of the difference in the self diffusion coefficient of water and sugar, Marigheto et al. (2006) used a Pulsed Field Gradient Spin Echo to suppress the water signal from the sample. The obtained echo amplitude was highly correlated with the soluble solids content in apples and strawberries. All the previous studies proved that NMR has great potential for assessing sugar content nondestructively in fruit. Sugar content is not the only factor determining the fruit quality, but it has drawn the most attention in the area of NMR quality assessment. Very limited research has been conducted on the application of NMR to measurement of other important fruit quality indices such as maturity, texture, titratable acidity, pH, and ratio of soluble solids to titratable acidity. Tu et al. (2007) showed a statistically significant difference in mean values of the T2 for tomatoes of different maturity and defect level. In apples the measured T2 showed dependence on pH, titratable acids, and insoluble solids besides soluble solids (Keener et al., 1998), thus a single NMR parameter like T2 may not be enough to characterize several quality attributes accurately. Magnetic resonance imaging (MRI) of fruit provides proton density and relaxation time encoded in the image signal intensity, maximizing the amount of information obtained from the fruit. The combination of multiple parameter weighted images may yield improved predictions of internal quality factors. Letal et al. (2003) found significant correlations between acidity, soluble solids and texture analysis parameters of MR images of apples. Multiple MR images were used to measure the maturity of processing tomato (Zhang and McCarthy, 2012), mechanical damage to processing tomatoes (Milczarek et al., 2009), and predict processing tomato peelability (Milczarek and McCarthy, 2011) with the aid of multivariate data analysis. The objectives of this study were to investigate the relationship between T2 and pomegranate quality attributes such as soluble solids content, titratable acidity, pH, and ratio of soluble solids content to titratable acidity, to correlate the quality attributes with information from MR images, and to determine the accuracy and precision of MRI derived models.
2. Materials and methods 2.1. Fruit samples Pomegranate fruit, variety “wonderful”, were provided by POM Wonderful (Del Ray, CA). The pomegranates were hand picked at the ripe stage on a weekly basis throughout the harvest season. The fruit were not sorted and represent an “as picked” sample set. In order to expand the range of titratable acidity in the study, fruit stored under controlled atmosphere (CA) for three months were obtained after the harvest season. The CA fruit were sorted prior to storage. For NMR relaxation measurement, 133 fresh fruit and 27 CA fruit were used. A total of 169 fresh fruit and 37 CA fruit were used in the MR imaging experiment.
Table 1 MRI pulse sequence parameters. Pulse sequence SE 1 SE 2 FSE DW 0 DW 60 a
TE (ms) 7.3 7.3 209a 38 38
TR (ms)
FOV (mm2 )
Slice thickness (mm)
130 600 6000 268 268
95 95 95 130 130
5 5 5 5 5
TE for FSE is the effective TE (TEeff ).
2.2. NMR relaxation time measurement A 0.04 T (1.7 MHz) permanent magnet NMR spectrometer (Quantum Magnetics, San Diego, CA) was used to measure the T2 relaxation time for each whole pomegranate. The diameter of the coil was 25 cm, large enough for the fruit. T2 relaxation time was acquired by Carr-Purcell-Meiboom-Gill (CPMG) sequence with 2048 echoes and an echo time of 2.03 ms. The T2 value was calculated using a mono-exponential curve fitting function in MatLab 2010a (The Mathworks, Natick, MA). 2.3. Magnetic resonance imaging MRI data was acquired on a 1 T permanent magnet NMR spectrometer (Aspect Imaging, Industrial Area Hevel Modi’in, Shoham, Israel) with a 60 mm × 90 mm elliptical RF coil. The coil accommodates samples up to 56 mm in height and 85 mm in diameter. Samples were trimmed by removing primarily the rind to fit the coil. Trimmed fruit was placed on a plastic sample holder and centered so that the sample was fixed in a predetermined position in the coil. Fruit was scanned to obtain images of the equatorial slice. A set of 5 MR images was acquired using 5 different sequences and an addition image was generated from the 2 diffusion weighted images. The 5 MRI sequences were selected so that water proton properties, including proton density, T1 and T2 relaxation time, and diffusion rate, contribute differently to the MR image (Table 1). In spin echo images, the signal intensity is described by (Bernstein et al., 2004): S = M0 (1 − 2e−(TR−TE)/T1 + e−TR/T1 )e−TE/T2
(1)
where M0 is the net magnetization proportional to the proton density, T1 is the spin-lattice relaxation time, and T2 is the spin–spin relaxation time. Repetition time (TR) and Echo time (TE) are sequence parameters manipulating the interval between pulses. The TE and TR of spin echo sequence (SE) could be adjusted to obtain spin echo image with different weighting. In Fast Spin Echo (FSE) images, the signal intensity is given by (Bernstein et al., 2004): S = M0 e−TEeff /T2
(2)
The image contrast is predominantly determined by the central area in k-space. The effective echo time (TEeff ) is the time when the central k-space data is acquired, so the signal intensity in FSE image is primarily a function of TEeff . The long TEeff used in imaging makes the FSE images T2 -weighted. In diffusion weighted imaging (DW 60), a pair of gradient pulse, with ı = 10.2 ms, = 23.938 ms, and G = 0.1206 T/m, were added to a spin echo sequence. Movement of molecules within the time interval between the two pulses, i.e., 23.938 ms, results in signal intensity attenuation. DW 0 is a normal spin echo sequence with the same TE and TR as DW 60, but without the diffusion gradient pulses. The signal attenuation caused by the pulse gradient is determined by the apparent diffusion coefficient of water. To quantify the diffusion induced signal attenuation, a new image was obtained by taking the ratio of signal intensity of each voxel in DW 60 to that
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Table 2 Correlation coefficient between T2 and quality attributes of pomegranates. SSC
TA
pH
SSC/TA
−0.41a 0.38a 0.42a
−0.14 −0.20 0.41a
−0.14 −0.12 −0.52a
0.05 0.19 −0.46a
T2 Fresh CA All a
Values are significant at P = 0.01.
the subgroups have been used once as the validation data. The 5-fold random cross-validation was iterated for 20 times. 3. Results and discussion 3.1. NMR relaxation measurement
Fig. 1. Region of interest (highlighted) in an FSE image of a pomegranate fruit.
of DW 0. The new image is a diffusion coefficient weighted intensity image. 2.4. Chemical analyses After NMR measurement or MR imaging, each fruit, including the arils and the rind, was sliced and hand pressed with a juicer to obtain a juice sample. The juice was allowed to settle for 15 min to precipitate suspended solids, and then the clarified supernatant was used for chemical analysis. Soluble solids content (SSC) was measured using a benchtop temperature compensating refractometer (RFM730, Bellingham and Stanley, UK). A pH meter (Accumet AP71, Fisher Scientific, Pittsburgh, PA) was used to acquire the pH value of the juice. The titratable acidity content was determined according to AOAC method 942.15 (AOAC, 2002). Aliquot of 5 g juice sample was diluted in 100 ml distilled water and well mixed. The diluted juice was titrated with 0.1 M NaOH to an end point of pH 8.2 as indicated by a pH meter. TA was reported as % citric acid. All the measurements were done in duplicate, and the average values were used. 2.5. Data analysis Signal intensity of the MR images is a function of water proton properties in the sample, so quantitative analysis of the images is based on extracting the signal intensity of the voxels in the fruit area in the image. Due to the large size of the pomegranate fruit, some artifacts occurred on the edge of the fruit in the images. A region of interest (ROI) was selected to cover the fruit region in the image and omit the periphery of the sample (Fig. 1). Nine statistical features of the signal intensity of the ROI were calculated for each MR image, including mean, median, mode, variance, standard deviation, skewness, kurtosis, range, and coefficient of variation. Partial Least Square (PLS) was applied to establish the relationships between the 54 features of 6 images and the measured chemical properties of the pomegranates. The model was developed using Matlab 2010a (The Mathworks, Natick, MA) and PLS toolbox (Eigenvector Research Inc., Wenatchee, WA). A 5-fold random cross-validation was performed to validate the calibration model. The whole dataset was divided into 5 subgroups with equal size randomly. Four subsets were used as training data to build a calibration model, and the model was tested using the remaining one subset. The cross-validation was repeated until all
A mono-exponential fit of the CPMG data yielded a T2 for each relaxation measurement. The relationships between T2 relaxation time and quality parameters, SSC, pH, TA, and SSC/TA, were investigated on the basis of fresh in-season pomegranate fruit. T2 inversely correlated with SSC (r = −0.41, Table 2). The decrease of T2 with increasing soluble solids content was also observed in Granny Smith and Golden Delicious apples (Keener et al., 1998). The SSC is a measure of sugar content in the sample. Fast proton exchange between water and exchangeable proton on hydroxyl group of sugars would facilitate the relaxation process of water (Hills, 2006). As the sugar content increases, more hydroxyl groups were available to exchange with water protons, leading to decrease in T2 relaxation time. The correlations between T2 and pH, TA, SSC/TA were insignificant, which may be a result of the small range of the quality parameters. CA storage causes reduction in TA and SSC in pomegranate (Artés et al., 1996), thus CA stored samples were included in the experiment to extend the range of the quality parameters. However, distinctive relationship between T2 and SSC were observed in CA stored fruit (Table 2). The T2 relaxation time increased with increasing SSC. The reason for the divergence in the correlations was not clear. The inclusion of the CA stored fruit enhanced the correlation between T2 and quality parameters (Table 2). However, the improvement of the correlation was mainly driven by the difference between the fresh and CA stored pomegranates. In Fig. 2, CA stored fruit showed relative lower SSC and TA than fresh fruit, but the T2 relaxation time was significantly shorter in CA stored fruit. The two groups of fruit were partitioned into separated clusters in the bivariate plots of T2 with quality parameters. Because T2 is sensitive to cell morphology and membrane permeability (Hills, 2006), any change in cell structure during CA storage may strongly affect T2 and the correlation between T2 and quality parameters. As water content affects the T2 , moisture loss from the rind during storage was also a possible contributing factor to the difference between fresh and CA stored fruit. On the other hand, the coefficient of correlation between T2 and SSC of fresh pomegranates was lower than the value (∼0.9) reported in previous studies on grapes and cherries (Bellon et al., 1992). The large sample size used in this experiment introduced much more biological variations, which disturbed the linear relationship between SSC and T2 . The dependence of T2 on factors other than SSC posed difficulty for developing the relationship between T2 and SSC. Keener et al. (1998) also found poor correlation between T2 and SSC in apple tissues of several varieties. A high correlation between T2 and SSC was obtained in orange juice, apple juice, and grape juice in the same study. The presence of insoluble solids and cellular structure in whole fruit complicated the relationship between T2 and SSC. The insoluble solids would serve as relaxation reservoirs, providing exchangeable protons. The diffusion of water across the
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2.8 CA Fresh
a
2.6
18.5
CA Fresh
b
2.4 18 2.2
TA (% citric acid)
SSC(Brix)
17.5
17
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2 1.8 1.6 1.4
16 1.2 15.5
15 700
1
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0.8 700
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Fig. 2. Bivariate plots of pomegranate fruit: (a) SSC vs T2 and (b) TA vs T2 .
membrane defined cellular compartments has considerable effect on the relaxation process as well. 3.2. MR imaging 3.2.1. MR images MRI of fruit yields proton density and relaxation time weighted image signal intensity. The spatially resolved 2D image offers view into the distribution of tissue with different water proton properties in the fruit rather than a T2 value or signal intensity averaged over the whole fruit. The combination of multiple parameter weighted images generates information on several water proton
properties of the same fruit. The increase in the amount of data may help to predict the internal quality parameters of pomegranates. The same pomegranate had distinctive appearance in the 6 MR images (Fig. 3). The images have different contrast among different parts of the pomegranate, arils, seeds, and rind. Arils were visible in DW 0, DW 60/DW 0, and FSE images. In SE 1, seeds were distinguished from the rest of the fruit. The signal intensity in DW 60/DW 0 was directly related to the water self diffusion coefficient in the sample. The low signal intensity of arils in DW 60/DW 0 was a result of higher water self diffusion coefficient in the arils than in the rind and seeds. In the T2 weighted FSE image, longer T2 relaxation time of arils caused higher signal intensity in the areas corresponding to
Fig. 3. Example of 6 MR images of the same pomegranate.
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Measured SSC (º Brix)
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Measured pH 30
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Fig. 4. Plots of measured quality attributes and predicted quality attributes of pomegranate by MR images: (a) SSC, (b) pH, (c) TA and (d) SSC/TA. The diagonal line represented points where the predicted and measured values are equal.
arils. The higher moisture content of arils compared to seeds and rind was the main reason for the larger apparent water self diffusion coefficient and longer T2 relaxation time. According to Eq. (1), the short TR and TE made SE 1 a T1 weighted image. The high signal intensity of seeds in SE 1 indicated that seeds had the shortest T1 relaxation time in the pomegranate. The different origin of contrast in the 6 images ensured that multiple intrinsic NMR properties of the protons were measured. 3.2.2. PLS models for quality parameters prediction All fruit, fresh and CA stored, were used to investigate the correlation between MR images and internal quality attributes. The 54 variables used in the PLS model captured up to 47% of the variance in SSC. The first four latent variables were selected to build the model based on the criteria of minimizing Root Mean Square Error of Cross-Validation (RMSECV) with the least number of latent variables. The model captured only 35% of the variance in SSC. The Root Mean Square Error of Calibration (RMSEC) and RMSECV were 0.50 and 0.57. Fig. 4a demonstrated the relationship between measured SSC and predicted SSC from the model. Although there is a tendency for the predicted SSC to increase with the rise in measured SSC, the
data points loosely scattered in the plot. Some samples with the same measured SSC had significantly different predicted SSC. A R2 of 0.17 was obtained from the cross-validated model. Considering the successfulness of previous studies on using NMR to measure the SSC in fruit, the poor correlation between SSC and MR images was unexpected. For prediction of pH, a model was constructed based on 5 latent variables, capturing 68% of the variance in pH. The R2 of the crossvalidated model was 0.6. The RMSEC and RMSCEV of the model were 0.12 and 0.13, respectively. The RMSECV was very close to RMSEC, which means the loss in the accuracy was very small when the calibration models were applied to the test data. The low value of the error indicated that the PLS model provided fairly accurate prediction of pH. The prediction performance was illustrated in Fig. 4b. Unlike the loose scattering of data around the diagonal line, where predicted value equals to measured value, observed in SSC prediction model, points stayed much closer to the diagonal line, implying better prediction. Similar performance was achieved in the PLS model for TA prediction as shown in Fig. 4c. The 6-latent variable model accounted for about 64% of the variance in TA. The R2 of the cross-validated
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5 4.5
FSE_mean
a
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3.5 FSE_standard deviation 3 FSE_variance 2.5
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1 0.5 0
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Variable Fig. 5. VIP scores of variables for quality attributes prediction model: (a) pH, (b) TA and (c) SSC/TA. Only variables with a VIP score larger than one were labeled.
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TA model (R2 = 0.54) was slightly lower than the pH model. The RMSECV was 0.29 compared to the RMSEC of 0.26. While prediction of SSC was not successful, the SSC/TA demonstrated good correlation with the information from MR images. A cross-validated R2 of 0.63 was obtained. The good agreement in the RMSEC (2.29) and RMSECV (2.57) indicated the robustness of the model. The inclusion of the CA stored samples extended the range of pH, TA, and SSC/TA, but correlations between the quality attributes and MR images were not affected by the CA stored samples as occurred in T2 relaxation time. The cross-validation procedure verifies the validity of the model on the whole dataset. If the discrepancy between fresh and CA stored samples was so big that prediction of internal qualities of both groups cannot be achieved using the same models, the calibration model will fail the cross-validation test. The small difference between RMSEC and RMSECV of all the PLS models indicated that the developed model can be applied to any subset of the data. The effectiveness of the prediction models was also evaluation by RPD (Residual Prediction Deviation). The RPD values were 1.58, 1.49, and 1.65 for prediction models of pH, TA, and SSC/TA, respectively. Previous literature gave interpretations of the RPD values in terms of the model accuracy. A RPD between 1.5 and 2 indicates that the model can distinguish low from high values of the response variable and a model with RPD between 2 and 2.5 can make coarse quantitative predictions (Nicolaï et al., 2007), while Viscarra Rossel et al. (2007) concluded that a RPD value between 1.4 and 1.8 means that the model is fair and can be used for assessment and correlation. Although a high RPD value indicates a good model, the standard deviation of the response variable affects the RPD significantly. When the variance in the sample set is low, the RPD cannot be very high (Williams and Sobering, 1996). The standard deviation of pH, TA, and TA/SSC of pomegranate samples were 0.21, 0.44, and 4.32, the accuracy of the prediction model may be improved further by increasing the variability of the pomegranates. On the other hand, the performance of obtained prediction models is satisfactory for sorting purpose, which requires rough assessment of fruit quality instead of accurate quantification.
3.2.3. Identification of important predictors Variable importance in projection (VIP) scores for each independent variable were calculated to evaluate their importance in the PLS prediction models for pH, TA, and SSC/TA (Fig. 5). Variables with VIP scores bigger than one are considered as the most influential predictors for the model (Eriksson et al., 2006). Several statistical features of FSE and SE 2 had great contribution to the pH prediction model. The variance terms and skewness of signal intensity in SE 1 had relatively large VIP scores. The VIP score of the median of the signal intensity in DW 60/DW 0 was slightly above one. For TA prediction model, the five most influential variables were the same as the pH prediction model. In addition, mean and median of signal intensity in DW 60/DW 0 became important predictors. The relative importance of variables in the SSC/TA prediction model was similar to the pH prediction model, except for that the mean, median of DW 60/DW 0 were well above one and less features of SE 1 appeared to be important. The same set of statistical features, mean, median, mode, standard deviation, variance, and range, of signal intensity in FSE had large VIP scores in all the models. The variance, standard deviation, and coefficient of variation of SE 2, skewness and coefficient of variation of SE1, and DW 60/DW 0 were also considered to be important in all three models. The importance of the variance and distribution attributes of signal intensity in MR image implied that the variation pattern of NMR proton properties within the fruit correlated with the pomegranate quality attributes.
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The importance of each image type in prediction models represents the contribution of corresponding water proton properties to quality prediction. The T2 weighted FSE was the most important image among the 6 images in all prediction models, so T2 was the most important factor related to the internal quality of pomegranate even though no significant correlation were observed between T2 value and the quality parameters excluding SSC in T2 relaxation time study of pomegranate. The short TE and medium TR introduced T1 and proton density contrast to SE 2. Compared with the T1 weighted SE 1, SE 2 had more contribution to the pH and SSC/TA prediction. The extra proton density contrast in SE 2 may reflect more variance in pH and SSC/TA of pomegranate. The importance of SE 1 was mainly derived from the high VIP score of coefficient of variation and skewness of its signal intensity. The T1 contrast highlighted seeds within the arils in SE 1. The variation in the ratio of aril/rind or seed/aril would result in change in coefficient of variation and skewness of signal intensity in SE 1. It is possible that the internal quality of pomegranate is a function of the ratio among different parts of the fruit tissue. The water diffusion coefficient weighted DW 60/DW 0 was a crucial image in TA and SSC/TA models, indicating the water self diffusion rate was an essential indicator of TA and SSC/TA in pomegranate. Although a valid prediction model for SSC cannot be derived from the information from MR images, the relationship between statistical features and measured SSC was investigated to identify any promising feature and image. The correlation between SSC and mean signal intensity in DW 60 achieved 0.41, significant at P < 0.01. Other significant terms (P < 0.01) were median or mean of DW 60, DW 60/DW 0, and DW 0. The value of NMR water diffusion coefficient measurement in SSC prediction was proved by several previous studies. The water diffusion coefficient was found to be a better predictor of SSC than T2 (Keener et al., 1998). In addition, the echo amplitude obtained using a Pulsed Field Gradient Spin Echo, was highly correlated with the SSC in apples and strawberries. In the sequence, the water proton signal was suppressed by a gradient pulse to measure the signal solely from sugar utilizing the difference in the diffusion coefficient of water proton and sugar proton (Marigheto et al., 2006). Therefore, the usefulness of DW images in SSC measurement mainly originated from the enhanced contribution from the sugar proton to the signal intensity and the water diffusion weighting in DW images.
4. Conclusion T2 relaxation time was correlated with the SSC in pomegranates, but no correlation was established between pH, TA, SSC/TA and T2 . Subject to the effect of factors other than SSC, the relationship between T2 and SSC did not provide an accurate estimation of SSC. These results are consistent with those in the literature for other fruit. CA storage had significant effect on T2 values of pomegranates, thus different relationships between T2 and quality attributes were observed in CA stored fruit. NMR should prove to be useful for monitoring changes in CA stored fruit. The use of multiple NMR water proton property weighted MR images enable acquisition of a rich body of data from each fruit. PLS models based on the MR images proved to be effective for measuring pH, TA, and SSC/TA in pomegranate fruit. T2 weighted FSE was identified as the most important image in the prediction model for the three quality attributes. T1 weighted, proton density weighted, and diffusion weighted image also had various contribution to the prediction model for different attributes. The PLS analysis of MR images did not yield a valid model for SSC prediction, but diffusion weighted image demonstrated its value in SSC measurement. The non-destructive MR imaging technique is an alternative compared to the destructive chemical analysis of pH, TA, and SSC/TA in pomegranate. In
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summary, given the literature results and those in this paper, lowfield NMR based approaches to measure physical defects in fruit like bruising, rot, insect damage and browning are very effective, but low-field NMR based approaches to measure chemical based quality factors are not as uniformly effective. Efforts in developing low-field NMR for measurement of chemical based fruit quality factors should likely be focused on applying alternate pulse sequences than previously applied in most studies. The work by Hills and coworkers (2006) provides an example using a diffusion filter to more precisely measure sugar content. Acknowledgements This work was partially supported by POM Wonderful LLC and by National Research Initiative Award 2007-02632 from the USDA National Institute of Food and Agriculture. References AOAC, 2002. Official Methods of Analysis of AOAC International, 17th edition ed. AOAC International, Gaithersburg, MD. Artés, F., Marín, J.G., Martínez, J.A., 1996. Controlled atmosphere storage of pomegranate. Zeitschrift für Lebensmittel-Untersuchung und -Forschung 203, 33–37. Bellon, V., Cho, S.I., Krutz, G.W., Davenel, A., 1992. Ripeness sensor development based on nuclear magnetic resonance. Food Control 3, 45–48. Bernstein, M., King, K., Zhou, X., 2004. Handbook of MRI Pulse Sequences. Academic Press, Burlington, MA. Cho, S.I., Bellon, V., Eads, T.M., Stroshine, R.L., Krutz, G.W., 1991. Sugar content measurement in fruit tissue using water peak suppression in high resolution 1H magnetic resonance. Journal of Food Science 56, 1091–1094. Cho, S.I., Stroshine, R.L., Baianu, I.C., Krutz, G.W., 1993. Nondestructive sugar content measurements of intact fruit using spin–spin relaxation time (T2 ) measurements by pulsed 1 H magnetic resonance. Transactions of the ASAE 36, 1217–1221.
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