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Prediction of mechanical properties of blueberry using hyperspectral interactance imaging
Postharvest Biology and Technology 115 (2016) 122–131
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Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Prediction of mechanical properties of blueberry using hyperspectral interactance imaging Meng-Han Hua , Qing-Li Donga,* , Bao-Lin Liua,* , Umezuruike Linus Oparab,c a
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Rd., Shanghai 200093, PR China Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Horticultural Science, Stellenbosch University, Stellenbosch 7602, South Africa c Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Food Science, Stellenbosch University, Stellenbosch 7602, South Africa b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 April 2015 Received in revised form 22 November 2015 Accepted 23 November 2015 Available online xxx
The purpose of this investigation was to develop and validate a hyperspectral interactance imaging system to non-destructively estimate blueberry mechanical properties. Four texture profile analysis (TPA) and four puncture analysis (PA) parameters were predicted. A region growing based algorithm was used to segment the acquired interactance hypercubes and to assist in extracting mean spectra. Subsequently, the spectra were smoothed by Standard Normal Variate (SNV) and Savitzky-Golay first derivative (Der). Least squares support vector machines integrated with Monte Carlo uninformative variable elimination (MC-UVE) models were developed for mechanical parameters. Based on the MCUVE selected wavelengths, the SNV model performed best for cohesiveness with Rp (Rc) value of 0.91 (0.91). The SNV models of springiness, resilience, max force strain and final force resulted in Rp (Rc) values of 0.84 (0.85), 0.86 (0.87), 0.65 (0.76) and 0.62 (0.72), respectively. Using Der spectra, the Rp (Rc) values were found to be 0.77 (0.86), 0.71 (0.73) and 0.58 (0.69) for hardness, maximum force and gradient, respectively. Generally, the overall performances of MC-UVE based models were similar to those with full spectra. The above results showed the potential of hyperspectral interactance imaging coupled with MCUVE approach for predicting the mechanical properties of blueberry and the other small fruit. ã 2015 Elsevier B.V. All rights reserved.
Keywords: Interactance imaging Fruit quality Texture Monte Carlo Wavelength selection
1. Introduction Blueberry (Vaccinium corymbosum) is considered as a soft fruit with great economic value owing to its health-promoting properties and flavor (Gilbert et al., 2014; Flores et al., 2014). Blueberry mechanical properties have been found to be a significant factor associated with fruit quality and freshness, which in turn affect consumer preference and acceptability (Giongo et al., 2013; Saftner et al., 2008). Giongo et al. (2013) reported the application of puncture analysis (PA) to characterize the mechanical properties of commercial blueberry cultivars during fruit development, ripening and storage. Other studies have shown that mechanical properties influenced variety selection in breeding programs and transportability as well as postharvest life (Blaker et al., 2014; Li et al., 2011). For example, the texture profile analysis (TPA) parameters were successfully related to changes of blueberry quality attributes during postharvest
* Corresponding authors. Fax: +86 21 5527 1117. E-mail addresses: [email protected] (Q.-L. Dong), [email protected] (B.-L. Liu). http://dx.doi.org/10.1016/j.postharvbio.2015.11.021 0925-5214/ ã 2015 Elsevier B.V. All rights reserved.
storage (Chiabrando et al., 2009). Mechanical properties are usually detected using invasive and contact methods, such as puncture, penetrate and compression (Paniagua et al., 2014; Chen and Opara, 2013a,b; Retamales and Hancock, 2012; Chiabrando et al., 2009). However, such traditional approaches are laborious, time-consuming and a sampling examination. Considering the importance of mechanical properties as quality cues during consumption of blueberry and the desire for non-destructive measurements, it is necessary to develop accurate, rapid, efficient and non-contact techniques for determining their mechanical parameters. Hyperspectral imaging technique has been efficiently used to evaluate food quality. Compared with other non-destructive techniques, hyperspectral imaging requires minimal sample preparation and acquires both spatial and spectral information (Lorente et al., 2012). In contrast to traditional spectroscopy, hyperspectral imaging can capture more representative spectra and is thus more suitable for application in online detection and measurement. Furthermore, hyperspectral techniques can facilitate deeper understanding of the structure and quality of nonhomogeneous products (Wang et al., 2013; Schaare and Fraser,
M.-H. Hu et al. / Postharvest Biology and Technology 115 (2016) 122–131
Nomenclature
Texture profile analysis Puncture analysis Standard Normal Variate Savitzky-Golay of the first derivative Monte Carlo-uninformative variable elimination LV Latent variable LS-SVM Partial-least support vector machine Rc, Rp Pearson correlation coefficients of calibration and prediction RMSEC, RMSEp Root mean square error of calibration and prediction Sr Relative sample image SR Sample image SD Dark image of operated exposure time Rw Reference white image of corrected exposure time RD Reference dark image of corrected exposure time Sj Stability of jth wavelength bj PLS regression coefficients of jth wavelength k An arbitrary value in Eq. (3) TPA PA SNV Der MC-UVE
2000). Based on imaging architecture, this optical technique can be categorized into reflectance, transmittance, interactance and scattering sensing modes. Among these modes, the scattering imaging was found not feasible for small fruit such as blueberry in our preliminary experiments. The reflectance mode is widely used in food quality detection as it is easy to construct and perform (Wu and Sun, 2013a), and Leiva-Valenzuela et al. (2013) applied this mode to estimate the blueberry firmness and soluble solids content with correlation coefficients of prediction of 0.87 and 0.79, respectively. In contrast, the transmittance mode is considered to be better than the reflectance mode for internal quality evaluation because it allows the acquisition of information inside the food materials via analyzing the transmitted light (Magwaza et al., 2012). However, this mode is frequently used for qualitative
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analysis rather than quantitative analysis (Coelho et al., 2013; Huang et al., 2012). A hyperspectral imaging system combining reflectance and transmittance modes was reported by Ariana and Lu (2008a,b), and this integrated architecture led to satisfactory performance for quality evaluation of pickling cucumbers (Lu and Ariana, 2013; Ariana and Lu, 2010). Another group of investigators inspected embedded bone fragments in chicken fillets simultaneously using hyperspectral transmittance and reflectance modes (Seung et al., 2008). In terms of blueberry, a comparison of reflectance and transmittance and their integrated modes was reported by Leiva-Valenzuela et al. (2014), and the authors concluded that reflectance was superior to transmittance mode in predicting firmness and soluble solids content, and the combined mode did not result in improved predictions. Furthermore, our previous study assessed the blueberry comprehensive mechanical properties using similar imaging setup, and results showed that such system was acceptable for estimating most mechanical parameters (Hu et al., 2015). The interactance sensing is considered a compromise between reflectance and transmittance modes for near-infrared (NIR) spectroscopy (Magwaza et al., 2012). The difference among three sensing modes is the position of the light source and optical detector (Wu and Sun, 2013b). For interactance, the illumination and imaging unit are located on the same side of the sample and parallel to each other in such a way that specular reflection does not reach the detector (Fig. 1, left). In the case of interactance imaging, some literature had been published on the quality assessment of meat products (Gou et al., 2013; O’Farrell et al., 2010; ElMasry and Wold, 2008). In comparative studies using reflectance, transmittance and interactance modes, interactance has been reported to have more advantages than the others such as carrying deeper information about sample structure and composition (Wang et al., 2013; Schaare and Fraser, 2000) due in part to less effects of product surface and thickness on performance (Wu and Sun, 2013b). However, there are limited publications using interactance imaging for food quality estimation because of its relatively complicated structure. Results reported by Leiva-Valenzuela et al. (2014) suggested that the hyperspectral interactance measurement might be unsuitable for small fruits, especially for blueberry. In our preliminary experiments, images with good quality were successfully acquired by adjusting the imaging conditions, and this highlighted the scope for further study investigating the application of hyperspectral interactance imaging for evaluating blueberry quality.
Fig. 1. Photographs of hyperspectral reflectance/transmittance/interactance/scattering imaging system. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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(2014) on pear soluble solids content estimation using visible and NIR spectra. Nevertheless, the potential use of this method needs to be explored in hyperspectral data analysis. This study aims to investigate the feasibility of hyperspectral interactance imaging for predicting mechanical properties of blueberry measured by texture profile analysis (TPA) and puncture analysis (PA). The specific steps of the current study are as follows: (1) acquire hyperspectral interactance images of blueberry with good quality, (2) eliminate uninformative variables using MC-UVE algorithm, and (3) develop least squares support vector machines (LS-SVM) models of blueberry mechanical properties using the whole and selected wavelengths. 2. Materials and methods 2.1. Blueberry samples
Fig. 2. Distribution of incident light in hyperspectral interactance mode.
Hyperspectal images comprise a large number of datasets with redundant information, and, therefore, it is necessary to choose spectra that are particularly informative with respect to the relevant quality attributes of the product under study. Several investigators had reviewed research on wavelength selection methods in hyperspectral image analysis (Dai et al., 2014b; Liu et al., 2014; Firtha, 2007). These include interval partial least squares (PLS) (Leiva-Valenzuela et al., 2014), uninformative variable elimination (UVE) (Wang et al., 2012), competitive adaptive reweighted sampling (He et al., 2014) and artificial neural networks (ElMasry et al., 2009). Among these approaches, UVE can eliminate useless variables through assessing their stability (Centner et al., 1996) and has been shown to perform well in several hyperspectral imaging applications, such as estimating the chemical composition of mutton (Pu et al., 2014) and detecting gelatin adulteration in prawn (Wu et al., 2013). To overcome the problem of low efficiency encountered with standard UVE, Cai et al. (2008) proposed a procedure based on Monte Carlo random sampling, called MC-UVE, which was verified to outperform traditional PLS methods as well as standard UVE on NIR data. The feasibility of MC-UVE was further validated by Li et al.
A total of 429 and 383 blueberries (V. corymbosum) with size of 11–13 mm in height were used for texture profile analysis (TPA) and puncture analysis (PA), respectively. The first batch of blueberries was imported from Frutera San Fernando S.A., Chile and the latter batch from TAL S.A., Peru in October, 2014. Berries were stored at 4 C and the experiments were carried out within 6 days after transportation to the lab. To guarantee the model robustness, both blueberries with little visible physical damage and sound surface were analyzed. 2.2. Hyperspectral interactance image acquisition A pushbroom Vis–NIR hyperspectral interactance imaging system (Fig. 1, left) was constructed based on a hyperspectral reflectance and transmittance imaging system (Fig. 1, right) by Isuzu Optics Corp., Taiwan according to our design. This system mainly comprised of an imaging spectrograph (Imspector V10E, Spectral Imaging Ltd., Finland) attached to a 16-bit electronmagnifying charge-coupled detector (EMCCD) camera of 1004 1002 (spatial spectral) active pixels (Falcon EM285CL, Raptor Photonics Led., U.K.) and a C-mount lens (Xenoplan 1.4/17, Jos. Schneider Optische Werke GmbH, Germany), a halogen light source and control unit (3900-ER, 21 V/150 W, Illumination Technologies, Inc., USA) connected to line lights (9130-HT, Illumination Technologies, Inc., USA) assembling collector lenses (9560, Illumination Technologies, Inc., USA) by optic fibers (9145HT, Illumination Technologies, Inc., USA), and a mobile sample stage controlled by a linear travel translation stage controller (IRCP0076-1COMB, Isuzu Optics Corp., Taiwan).
Table 1 Pearson correlation matrices for TPA and PA parameters of blueberry.
Hardness Springiness Resilience Gumminess Cohesiveness Chewiness Hardness2
Maximum Force Max Force Strain Force Area Force Linear Distance Gradient Final Force
Hardness
Springiness
Resilience
Gumminess
Cohesiveness
Chewiness
Hardness2
1.0000 0.3523 0.5286 0.7613 0.5738 0.4080 0.9887
1.0000 0.3589 -0.0451 0.3413 0.2837 0.2961
1.0000 0.1165 0.2538 0.9659 0.4339
1.0000 0.9219 0.2230 0.8251
1.0000 0.3264 0.6556
1.0000 0.3263
1.0000
Force Max
Max Force Strain
Force Area
Force Linear Distance
Gradient
Final Force
1.0000 0.5557 0.9005 0.9048 0.3212 0.3995
1.0000 0.5369 0.4782 0.1572 0.3653
1.0000 0.8413 0.3184 0.5983
1.0000 0.3243 0.6232
1.0000 0.0018
1.0000
Note that the parameters in bold are selected for further analysis.
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In order to achieve interactance imaging, a new unit was suspended over a mobile sample stage, allowing the field of hypercube collection was parallel to the illuminated field. This unit mainly consisted of a line light with a collector lens, a focusing mirror and an adjustable light shield. The light shield, made of two black metal plates with adjustable height, could reduce unwanted reflected light from both the sample surface and light source (O’Farrell et al., 2010). Fig. 2 shows the distribution of incident light in interactance mode. For the interactance mode, the motor speed was set at 1.5 mm/ s and the exposure time was 100 ms. The actual light intensities at the surface of sample stage were 20 kLux. After the interactance with sample, it was difficult to detect the interacted light using the EMCCD camera, and the gain of EMCCD was, therefore, adjusted to obtain values close to 80% of the maximum pixel output of the EMCCD camera. The hyperspectral images of blueberries were captured with the stem scar facing vertically toward the lens. A total of 406 wavelengths between 675.33 nm and 1000.76 nm with a spectral resolution of 0.80 nm were selected for further analysis. 2.3. Mechanical parameters measurement A texture analyzer (TA.XTPlus, Stable Micro Systems, Inc., Surrey, U.K.) was used to obtain the mechanical parameters. The texture analyzer was equipped with a 490 N load cell and a blueberry was placed between a compression/puncture cylindrical probe and a cylindrical stainless flat platform with its stem scar facing vertically toward the probe. An auto force trigger of 5 g and a digital data acquisition resolution of 500 points per second were applied for both tests. The force and height measurements of the texture analyzer were calibrated prior to the tests. The calculation of mechanical parameters has been described in our previous study (Hu et al., 2015; Fig. S2 for review)
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See Supp Figure S1 as supplementary file. Supplementary material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.postharvbio.2015.11.021. For TPA tests, the samples were compressed twice to 30% deformation by a cylindrical plate of 50 mm diameter at the test speed of 0.8 mm/s with a pre- and post-test speed of 1.6 mm/s and 2 mm/s, respectively. After the first compression, the probe returned to the trigger position and held at this position for 10 s. Seven TPA mechanical parameters viz. hardness, hardness2, resilience, cohesiveness, gumminess, chewiness and springiness were derived from the generated curve. Owing to the mechanical characters of blueberry and the setting of deformation degree, fracturability, adhesiveness and stringiness in the typical TPA curve presented by Bourne (2002) were not calculated. In the PA tests, samples were punctured to 80% of their initial height using a probe of 5 mm diameter at the test speed of 1.7 mm/s with a pre- and post-test speed of 2 mm/s and 5 mm/s, respectively. Six mechanical parameters were obtained by the PA test. From the force–strain profile, the maximal force (FM), maximal force strain (MFS), force linear distance (FLD), final force (FF) and force area (FA) were calculated, and the gradient (socalled elastic modulus) was determined from the plot of stress versus strain. FM had a similar physical meaning as firmness in previous studies (Rajkumar et al., 2012; Leiva-Valenzuela et al., 2014, 2013) according to definitions and test settings. If the Pearson correlation coefficient (R) between two mechanical parameters was beyond 0.8 (Table 1), only one parameter was subjectively chosen for further analysis, e.g., cohesiveness was highly correlated with gumminess (R = 0.9219) and the former was selected to be predicted by hyperspectral imaging. To some extent, the selected parameters viz. hardness, springiness, cohesiveness, resilience, FM, MFS, gradient and FF might be regarded as containing most mechanical information of the discarded parameters.
Fig. 3. Main steps of hyperspectral interactance cube processing. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Flowchart of the entire data processes for predicting blueberry comprehensive mechanical properties.
2.4. Image and spectral processing
Eq. (1) was applied for correcting the original images line by line.
The line reference images for interactance modes were obtained using a rectangular white standard (Spectral Imaging Ltd., Finland). The purpose of the line correction method is to avoid the instability of light source in spatial dimension. The following
Sr ¼
SR SD 104 RW RD
ð1Þ
where Sr is the relative sample image, SR is the sample image, SD is the dark image of operated exposure time, RW is the reference white image of corrected exposure time, and RD is the reference
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Table 2 Statistics of mechanical properties of blueberries. Mechanical properties
Mean
Standard deviation
Min
Max
Variability (%)a
Hardness (N) Springiness () Cohesiveness () Resilience () MaximumForce (N) Max Force Strain (%) Gradient (g/mm2/%) Final Force (N)
0.059 0.676 0.330 0.133 0.078 37.321 1.0175 0.059
0.011 0.035 0.041 0.020 0.018 7.464 0.338 0.012
0.026 0.456 0.239 0.089 0.024 21.053 0.099 0.055
0.112 0.778 0.505 0.226 0.154 79.998 2.112 0.073
18.1 5.2 12.4 15.0 23.7 20.0 33.2 19.9
a
Variability = the ratio between standard deviation and mean of the parameters.
dark image of corrected exposure time. Ten reference white and dark images were acquired each time, and then averaged for correction. A segmentation algorithm based region growing (Adams and Bischof, 1994) was utilized to acquire the mask images from the relative interactance images, and the steps for this process are summarized in Fig. 3. Since the shield cannot block the reflected light completely, the pixels below the centeroid of blueberry were discarded to obtain predominant interacted signals. Subsequently, the mask operation was used to form images without background for extracting the spectral data. In current study, Standard Normal Variate (SNV) and the Savitzky-Golay of the first derivative using a 9-point window and a second-order polynomial (Der) were applied for smoothing the spectra (Rinnan et al., 2009). 2.5. Wavelength elimination and prediction model An improved Kennard-Stone sampling method (Galvão et al., 2005) was applied for separating samples into calibration and prediction sets. For each mechanical parameter, the calibration set included 75% of samples and the remaining samples formed the prediction set. Partial least squares (PLS) regression is an approach to construct the relationship between two data matrices, and has become a standard tool in chemometrics with many applications in food (Wold et al., 2001; Leiva-Valenzuela et al., 2013). Least squares support vector machines (LS-SVM) are least squares versions of support
vector machines (SVM) which are a set of kernel based supervised learning methods for classification and regression analysis (Suykens et al., 2002; van Gestel et al., 2004). LS-SVM has proved to be feasible in many applications in the area of food quality detection (Wu et al., 2013). In this study, a radial basis function was chosen as the kernel function, and two tuning parameters, i.e., regularization and kernel function parameters, were initially set to 10 and 0.2, respectively. The optimal values of two tuning parameters were first searched by a coupled simulated annealing, and afterwards fine-tuned by a simplex method (De Brabanter et al., 2011). In the current work, PLS and SVM regressions were respectively applied for wavelength selection and modeling. Monte Carlo-uninformative variable elimination (MC-UVE) method is based on PLS using a Monte Carlo algorithm as stochastic technique for stability analysis of regression coefficients (Cai et al., 2008). In this study, the sub-calibration sets including 75% of calibration samples were randomly selected via the MC technique, and numerous PLS models were established using these sample sets. Using the PLS regression coefficients, the following equation defined by Centner et al. (1996) was utilized to calculate the stability of each wavelength. mean bj ð2Þ Sj ¼ std bj In Eq. (2), mean (bj) and std (bj) refer to the mean and standard deviation values of PLS regression coefficients for the jth wavelength, respectively.
Fig. 5. Typical interactance spectra of 10 blueberries in the 390.65–1113.54 nm wavelength range. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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In order to determine a suitable cutoff threshold, the following formula is defined according to the equation for conventional UVE (Cai et al., 2008): ð3Þ cutoff ¼ k max abs Sj where max (abs (Sj)) denotes to obtain the maximum value of the absolute stability, and k is an arbitrary value from 0.10 to 0.75 with interval of 0.05. According to the definition of UVE, the larger the absolute value of stability, the more important is the corresponding wavelength. Hence, the wavelengths with absolute stability above the cutoff thresholds will be retained and the remaining will be eliminated. Moreover, Monte Carlo cross-validation (MCCV) developed by Picard and Cook (1984) is used in MC-UVE to estimate the parameters of models replacing K-fold and leave-one-out method to decrease the risk of model overfitting (Xu et al., 2004). The procedure of wavelength elimination and modelling is described as follows: (1) Determine the latent variable (LV) number: 200 PLS models based on original spectra were constructed for each mechanical parameter using corresponding 200 sub-calibration sets, and this procedure was repeated 5 times. The appropriate LV number was confirmed by root mean square errors for crossvalidation (RMSECV). (2) Calculate cutoff threshold: 200 PLS models were established using the optimal LV number for every k in Eq. (3). The suitable cutoff threshold was determined by the minimum RMSECV value. This process was carried out for both SNV and Der spectra. (3) Establish prediction model: the informative wavelengths could be determined by MC-UVE with the optimal cutoff, and used for subsequent modelling with the application of partial-least support vector machine (LS-SVM). This run was repeated 5 times. In addition, the prediction models based on whole 406 wavelengths were also established. The performances of the resulting models were examined in terms of the Pearson correlation coefficients of calibration (Rc) and prediction (Rp), root mean square error of calibration (RMSEc) and prediction (RMSEp).
All the aforementioned operations inclusive of image and spectral processing as well as chemometric analysis were executed in Matlab R2009b software (The Math Work, Inc., Natick, MA, USA). A LS-SVMlab Toolbox version 1.8 (Suykens, Leuven, Belgium) was used for LS-SVM modelling. Fig. 4 summarizes the entire data analysis in this study. 3. Results and discussion 3.1. Mechanical properties and spectral features of blueberry A large variability in quality parameters will make the model robust, and the statistical data on eight blueberry quality parameters is presented in Table 2. Apart from springiness, the other parameters had a variability beyond 12.4% (Table 2). The variability of PA parameters was large compared to the TPA parameters. As can be seen from Fig. 5, considerably lower and more consistent interactance occurred in the visible region from 390.65 nm to 675.33 nm. The low interactance might be attributed to the strong absorption of deep dark pigments in the blueberry skin. This explanation is in agreement with the study of Leiva-Valenzuela et al. (2013) who investigated the firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Beyond 675.33 nm, the interactance increased dramatically to a peak around 860.52 nm, and a subsequent decline was observed from the first peak to a valley around 975.02 nm. The possible reason causing this spectral valley was the combined absorption of OH groups from carbohydrates and water. In the rest of spectral region, a small peak occurred. However, numerous noises existed between the spectral range from 1000.76 nm and 1113.54 nm, due in part to the low quantum efficiency of the EMCCD in this range. The pattern of blueberry interactance spectra was different with the reflectance and transmittance in the previous study of Leiva-Valenzuela et al. (2014). For the purpose of acquiring useful wavelengths for modelling, the spectral region from 675.33 nm to 1000.76 nm was selected for the following analysis.
Table 3 Prediction models for mechanical parameters of blueberries using 406 wavelengths from 675.33 nm to 1000.76 nm. Mechanical parameter
Pre-processing
Rp
RMSEp
Rc
RMSEc
Hardness (N)
SNV Der
0.6924 0.8072
0.0568 0.0583
0.9487 0.8461
0.0470 0.0717
Springiness ()
SNV Der
0.8461 0.7708
0.0207 0.0257
0.8425 0.8539
0.0348 0.0335
Cohesiveness ()
SNV Der
0.9128 0.8631
0.0325 0.0459
0.9124 0.9425
0.0482 0.0399
Resilience ()
SNV Der
0.8620 0.8108
0.0429 0.0517
0.8893 0.9550
0.0575 0.0404
Maximum Force (N)
SNV Der
0.7384 0.7837
0.0711 0.0774
0.7544 0.6012
0.1078 0.1222
Max Force Strain (%)
SNV Der
0.6639 0.5975
0.0660 0.0604
0.7690 0.8067
0.0807 0.0791
Gradient (g/mm2/%)
SNV Der
0.5757 0.6271
0.1145 0.1145
0.7259 0.6404
0.1478 0.1589
Final force (N)
SNV Der
0.6243 0.6404
0.0743 0.0764
0.7212 0.7492
0.1003 0.0984
Note: RMSE values were calculated from the normalized values of mechanical parameters; the units of RMSE are presented in the first column.
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Fig. 6. Plots of RMSECV for mechanical parameters of (a) TPA and (b) PA (red symbols indicate the minimization of RMSECV). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
hyperspectral interactance spectra from 675.33 nm to 1000.76 nm for estimating TPA parameters and FF of blueberry.
3.2. Prediction models using whole spectra Table 3 summarizes calibration and prediction results for blueberry mechanical parameters based on whole spectra. Models with SNV spectra were promising for springiness, cohesiveness and resilience, with the Rp (Rc) values of 0.85 (0.84), 0.91 (0.91) and 0.86 (0.89), respectively. The performance of Der spectra was better than that of SNV spectra for hardness prediction, with nearly 0.12 increments in Rp. On the aspect of PA parameters, the FM prediction model of Der spectra produced the Rp (Rc) value of 0.78 (0.60). However, both SNV and Der spectra did not yield desired results for predicting MFS and gradient as well as FF, with the Rp values below 0.66. The higher RMSE had been observed for gradient than the other parameters, indicating the hyperspectral interactance mode might be unsuitable for predicting gradient. Furthermore, examination of RMSE values in Table 3 demonstrated that TPA parameters had the lower RMSE than those of PA parameters. The above results show the possibility to use
3.3. Determination of LV number and cutoff The optimal LV number for each parameter was determined by the minimum RMSECV. In Fig. 6 the mean values of RMSECV are shown, which were calculated from 1000 PLS models. From Fig. 6, it was clear that the LV numbers of all mechanical parameters reached the smallest RMSECV in the range from 6 to 9. The LV number of 9 could be used for hardness and FM; 7 for springiness and resilience, and 6 for gradient and FF. In terms of cohesiveness, the optimal LV number was 8. Particularly, two optimal LV numbers were observed for MFS (i.e. 7 and 9). In order to make the following selection scheme simple, 7 was chosen as LV number for MFS. There are several cutoff criterions in traditional UVE (Moros et al., 2008; Ye et al., 2008). However, the cutoff criterion for MCUVE is still limited. In the current study, we used the method
Table 4 Prediction models for mechanical parameters of blueberries using MC-UVE-LS-SVM with 5 repeated runs. Mechanical parameter
Pre-processing
k
Wavelength number (5 results)
Rp (s )
RMSEp
Rc (s )
RMSEc
Hardness (N)
SNV Der
0.65 0.35
27; 29; 33; 37; 39 83; 92; 79; 87; 89
0.6849 (0.0121) 0.7653
0.0578 0.0638
0.9148 0.8640
0.0578 0.0676
Springiness ()
SNV Der
0.45 0.25
119; 119; 126; 110; 120 138; 127; 131; 128; 125
0.8437 0.7453
0.0212 0.0268
0.8517 0.8823
0.0340 0.0307
Cohesiveness ()
SNV Der
0.50 0.30
28; 45; 39; 31; 33 105; 101; 103; 110; 105
0.9072 0.8399
0.0334 0.0485
0.8955 0.9238
0.0518 0.0446
Resilience ()
SNV Der
0.45 0.35
64; 59; 57; 50; 73 132; 143; 131; 139; 136
0.8593 0.8051
0.0431 0.0541
0.8869 0.9453
0.0580 0.0434
Maximum force (N)
SNV Der
0.65 0.65
15; 23; 17; 25; 16 42; 46; 30; 46; 36
0.6739 (0.0248) 0.7106 (0.0263)
0.0754 0.0751
0.6855 (0.0317) 0.7297 (0.0398)
0.1146 0.1095
Max force Strain (N)
SNV Der
0.45 0.65
31; 36; 35; 45; 37 13; 12; 11; 14; 14
0.6537 0.5493 (0.0100)
0.0664 0.0638
0.7613 (0.0199) 0.7628 (0.0178)
0.0813 0.0825
Gradient (g/mm2/%)
SNV Der
0.70 0.60
57; 63; 48; 39; 53 25; 36; 29; 30; 33
0.5779 0.5821 (0.0168)
0.1143 0.1181
0.6751 0.6886
0.1534 0.1511
Final force (N)
SNV Der
0.35 0.50
145; 143; 145; 140; 129 52; 55; 55; 50; 51
0.6238 0.6148 (0.0101)
0.0747 0.0776
0.7219 (0.0138) 0.6148
0.1002 0.0934
Note: s is the standard deviation of 5 repeated runs; the absence of s means the value is lower than 0.0100; RMSE values were calculated from the normalized values of mechanical parameters; the units of RMSE are presented in the first column.
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Fig. 7. Stability distribution of SNV pre-processed spectra for hardness in calibration set (number of latent variables = 9, and MC procedure is repeated 200 times).
described in step (2) in Section 2.5 to confirm the cutoff. The optimal k value in Eq. (3) for each predicted parameter is shown in Table 4. The stability distribution of hardness is shown in Fig. 7. The two horizontal dot lines refer to the lower and upper cutoff thresholds. Based on the principle of MC-UVE, the wavelengths within the dot lines should be discarded. Therefore, in this MC-UVE run, total 33 wavelengths were selected for further hardness modelling. Using the same operation, the informative wavelengths could be determined for the other mechanical parameters. 3.4. Prediction models based on MC-UVE selected wavelengths Table 4 shows the calibration and prediction models of blueberry mechanical parameters based on MC-UVE selected wavelengths. In the case of hardness, the Der model performed better than SNV, whereas the former required nearly 50 more spectra than SNV based model for every run. This was also observed for FM in both calibration and prediction results. Previous study of Dai et al. (2014a) reported Rp = 0.85 for hardness in prawn using reflectance imaging, which was better than that in blueberry using interacance spectra in this investigation. The SNV preprocessing method resulted in superior models for predicting springiness, cohesiveness, resilience and MFS than Der, with Rp (Rc) values of 0.84 (0.85), 0.91 (0.91), 0.86 (0.89) and 0.65 (0.76), respectively. Moreover, in addition to MFS, the SNV models of springiness and cohesiveness as well as resilience required fewer wavelengths than those based on Der spectra. This was considerably obvious for cohesiveness prediction, indicating that the SNV spectra were useful for selecting wavelengths particularly informative with respect to cohesiveness. These two spectral pretreatment approaches yielded similar performances for estimating gradient and FF. The small standard deviation of 5 repeated runs revealed satisfactory repeatability of LS-SVM model based on the MC-UVE method (Table 4). Furthermore, comparison of statistical indicators in Tables 3 and 4 demonstrated the possibility to use MC-UVE selected interactance spectra for predicting blueberry mechanical properties. The prediction models reported in this study based on interactance data shows the feasibility of non-invasive sorting and grading of agricultural and horticultural products. For fresh
produce industry, an online multispectral interactance system could be developed for classifying blueberry. The interactance model can be used for the continuous and non-destructive acquisition of data on mechanical properties on the same intact berries in relation to quality during postharvest operations. 4. Conclusion In this study, hyperspectral interactance imaging was used to estimate the mechanical properties of intact blueberry. Based on MC-UVE selected wavelengths, a relatively high correlation was obtained between cohesiveness and SNV interactance spectra with Rp (Rc) of 0.91 (0.91). For the other fruit quality parameters, good prediction results based on SNV spectra were achieved for springiness, resilience, MFS and FF with Rp (Rc) of 0.84 (0.85), 0.86 (0.87), 0.65 (0.76) and 0.62 (0.72), respectively. Using Der spectra, hardness, FM and gradient could be predicted with Rp (Rc) of 0.77 (0.86), 0.71 (0.73) and 0.58 (0.69), respectively. Overall, the results were considered to be acceptable compared to the corresponding models obtained using entire wavelengths. These findings show that hyperspectral interactance spectra coupled with the MC-UVE method has potential for predicting mechanical properties of blueberry. Acknowledgements This study was supported by the National Natural Science Foundation of China (NSFC31271896), the Innovation Fund Project for Graduate Student of Shanghai (JWCXSL1401) and the Joint Science and Technology Research of Triangle Area of Science and Technology Commission of Shanghai Municipality (15395810900). U.L. Opara’s contribution was supported by the South African Research Chairs Initiative of the Department of Science and Technology. References Ariana, D.P., Lu, R., 2010. Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. J. Food Eng. 96 (4), 583–590. Ariana, D.P., Lu, R., 2008a. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype. Sens. Instrum. Food Qual. Saf. 2 (3), 152–160.
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Contents lists available at ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Prediction of mechanical properties of blueberry using hyperspectral interactance imaging Meng-Han Hua , Qing-Li Donga,* , Bao-Lin Liua,* , Umezuruike Linus Oparab,c a
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, 516 Jun Gong Rd., Shanghai 200093, PR China Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Horticultural Science, Stellenbosch University, Stellenbosch 7602, South Africa c Postharvest Technology Research Laboratory, South African Research Chair in Postharvest Technology, Department of Food Science, Stellenbosch University, Stellenbosch 7602, South Africa b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 April 2015 Received in revised form 22 November 2015 Accepted 23 November 2015 Available online xxx
The purpose of this investigation was to develop and validate a hyperspectral interactance imaging system to non-destructively estimate blueberry mechanical properties. Four texture profile analysis (TPA) and four puncture analysis (PA) parameters were predicted. A region growing based algorithm was used to segment the acquired interactance hypercubes and to assist in extracting mean spectra. Subsequently, the spectra were smoothed by Standard Normal Variate (SNV) and Savitzky-Golay first derivative (Der). Least squares support vector machines integrated with Monte Carlo uninformative variable elimination (MC-UVE) models were developed for mechanical parameters. Based on the MCUVE selected wavelengths, the SNV model performed best for cohesiveness with Rp (Rc) value of 0.91 (0.91). The SNV models of springiness, resilience, max force strain and final force resulted in Rp (Rc) values of 0.84 (0.85), 0.86 (0.87), 0.65 (0.76) and 0.62 (0.72), respectively. Using Der spectra, the Rp (Rc) values were found to be 0.77 (0.86), 0.71 (0.73) and 0.58 (0.69) for hardness, maximum force and gradient, respectively. Generally, the overall performances of MC-UVE based models were similar to those with full spectra. The above results showed the potential of hyperspectral interactance imaging coupled with MCUVE approach for predicting the mechanical properties of blueberry and the other small fruit. ã 2015 Elsevier B.V. All rights reserved.
Keywords: Interactance imaging Fruit quality Texture Monte Carlo Wavelength selection
1. Introduction Blueberry (Vaccinium corymbosum) is considered as a soft fruit with great economic value owing to its health-promoting properties and flavor (Gilbert et al., 2014; Flores et al., 2014). Blueberry mechanical properties have been found to be a significant factor associated with fruit quality and freshness, which in turn affect consumer preference and acceptability (Giongo et al., 2013; Saftner et al., 2008). Giongo et al. (2013) reported the application of puncture analysis (PA) to characterize the mechanical properties of commercial blueberry cultivars during fruit development, ripening and storage. Other studies have shown that mechanical properties influenced variety selection in breeding programs and transportability as well as postharvest life (Blaker et al., 2014; Li et al., 2011). For example, the texture profile analysis (TPA) parameters were successfully related to changes of blueberry quality attributes during postharvest
* Corresponding authors. Fax: +86 21 5527 1117. E-mail addresses: [email protected] (Q.-L. Dong), [email protected] (B.-L. Liu). http://dx.doi.org/10.1016/j.postharvbio.2015.11.021 0925-5214/ ã 2015 Elsevier B.V. All rights reserved.
storage (Chiabrando et al., 2009). Mechanical properties are usually detected using invasive and contact methods, such as puncture, penetrate and compression (Paniagua et al., 2014; Chen and Opara, 2013a,b; Retamales and Hancock, 2012; Chiabrando et al., 2009). However, such traditional approaches are laborious, time-consuming and a sampling examination. Considering the importance of mechanical properties as quality cues during consumption of blueberry and the desire for non-destructive measurements, it is necessary to develop accurate, rapid, efficient and non-contact techniques for determining their mechanical parameters. Hyperspectral imaging technique has been efficiently used to evaluate food quality. Compared with other non-destructive techniques, hyperspectral imaging requires minimal sample preparation and acquires both spatial and spectral information (Lorente et al., 2012). In contrast to traditional spectroscopy, hyperspectral imaging can capture more representative spectra and is thus more suitable for application in online detection and measurement. Furthermore, hyperspectral techniques can facilitate deeper understanding of the structure and quality of nonhomogeneous products (Wang et al., 2013; Schaare and Fraser,
M.-H. Hu et al. / Postharvest Biology and Technology 115 (2016) 122–131
Nomenclature
Texture profile analysis Puncture analysis Standard Normal Variate Savitzky-Golay of the first derivative Monte Carlo-uninformative variable elimination LV Latent variable LS-SVM Partial-least support vector machine Rc, Rp Pearson correlation coefficients of calibration and prediction RMSEC, RMSEp Root mean square error of calibration and prediction Sr Relative sample image SR Sample image SD Dark image of operated exposure time Rw Reference white image of corrected exposure time RD Reference dark image of corrected exposure time Sj Stability of jth wavelength bj PLS regression coefficients of jth wavelength k An arbitrary value in Eq. (3) TPA PA SNV Der MC-UVE
2000). Based on imaging architecture, this optical technique can be categorized into reflectance, transmittance, interactance and scattering sensing modes. Among these modes, the scattering imaging was found not feasible for small fruit such as blueberry in our preliminary experiments. The reflectance mode is widely used in food quality detection as it is easy to construct and perform (Wu and Sun, 2013a), and Leiva-Valenzuela et al. (2013) applied this mode to estimate the blueberry firmness and soluble solids content with correlation coefficients of prediction of 0.87 and 0.79, respectively. In contrast, the transmittance mode is considered to be better than the reflectance mode for internal quality evaluation because it allows the acquisition of information inside the food materials via analyzing the transmitted light (Magwaza et al., 2012). However, this mode is frequently used for qualitative
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analysis rather than quantitative analysis (Coelho et al., 2013; Huang et al., 2012). A hyperspectral imaging system combining reflectance and transmittance modes was reported by Ariana and Lu (2008a,b), and this integrated architecture led to satisfactory performance for quality evaluation of pickling cucumbers (Lu and Ariana, 2013; Ariana and Lu, 2010). Another group of investigators inspected embedded bone fragments in chicken fillets simultaneously using hyperspectral transmittance and reflectance modes (Seung et al., 2008). In terms of blueberry, a comparison of reflectance and transmittance and their integrated modes was reported by Leiva-Valenzuela et al. (2014), and the authors concluded that reflectance was superior to transmittance mode in predicting firmness and soluble solids content, and the combined mode did not result in improved predictions. Furthermore, our previous study assessed the blueberry comprehensive mechanical properties using similar imaging setup, and results showed that such system was acceptable for estimating most mechanical parameters (Hu et al., 2015). The interactance sensing is considered a compromise between reflectance and transmittance modes for near-infrared (NIR) spectroscopy (Magwaza et al., 2012). The difference among three sensing modes is the position of the light source and optical detector (Wu and Sun, 2013b). For interactance, the illumination and imaging unit are located on the same side of the sample and parallel to each other in such a way that specular reflection does not reach the detector (Fig. 1, left). In the case of interactance imaging, some literature had been published on the quality assessment of meat products (Gou et al., 2013; O’Farrell et al., 2010; ElMasry and Wold, 2008). In comparative studies using reflectance, transmittance and interactance modes, interactance has been reported to have more advantages than the others such as carrying deeper information about sample structure and composition (Wang et al., 2013; Schaare and Fraser, 2000) due in part to less effects of product surface and thickness on performance (Wu and Sun, 2013b). However, there are limited publications using interactance imaging for food quality estimation because of its relatively complicated structure. Results reported by Leiva-Valenzuela et al. (2014) suggested that the hyperspectral interactance measurement might be unsuitable for small fruits, especially for blueberry. In our preliminary experiments, images with good quality were successfully acquired by adjusting the imaging conditions, and this highlighted the scope for further study investigating the application of hyperspectral interactance imaging for evaluating blueberry quality.
Fig. 1. Photographs of hyperspectral reflectance/transmittance/interactance/scattering imaging system. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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(2014) on pear soluble solids content estimation using visible and NIR spectra. Nevertheless, the potential use of this method needs to be explored in hyperspectral data analysis. This study aims to investigate the feasibility of hyperspectral interactance imaging for predicting mechanical properties of blueberry measured by texture profile analysis (TPA) and puncture analysis (PA). The specific steps of the current study are as follows: (1) acquire hyperspectral interactance images of blueberry with good quality, (2) eliminate uninformative variables using MC-UVE algorithm, and (3) develop least squares support vector machines (LS-SVM) models of blueberry mechanical properties using the whole and selected wavelengths. 2. Materials and methods 2.1. Blueberry samples
Fig. 2. Distribution of incident light in hyperspectral interactance mode.
Hyperspectal images comprise a large number of datasets with redundant information, and, therefore, it is necessary to choose spectra that are particularly informative with respect to the relevant quality attributes of the product under study. Several investigators had reviewed research on wavelength selection methods in hyperspectral image analysis (Dai et al., 2014b; Liu et al., 2014; Firtha, 2007). These include interval partial least squares (PLS) (Leiva-Valenzuela et al., 2014), uninformative variable elimination (UVE) (Wang et al., 2012), competitive adaptive reweighted sampling (He et al., 2014) and artificial neural networks (ElMasry et al., 2009). Among these approaches, UVE can eliminate useless variables through assessing their stability (Centner et al., 1996) and has been shown to perform well in several hyperspectral imaging applications, such as estimating the chemical composition of mutton (Pu et al., 2014) and detecting gelatin adulteration in prawn (Wu et al., 2013). To overcome the problem of low efficiency encountered with standard UVE, Cai et al. (2008) proposed a procedure based on Monte Carlo random sampling, called MC-UVE, which was verified to outperform traditional PLS methods as well as standard UVE on NIR data. The feasibility of MC-UVE was further validated by Li et al.
A total of 429 and 383 blueberries (V. corymbosum) with size of 11–13 mm in height were used for texture profile analysis (TPA) and puncture analysis (PA), respectively. The first batch of blueberries was imported from Frutera San Fernando S.A., Chile and the latter batch from TAL S.A., Peru in October, 2014. Berries were stored at 4 C and the experiments were carried out within 6 days after transportation to the lab. To guarantee the model robustness, both blueberries with little visible physical damage and sound surface were analyzed. 2.2. Hyperspectral interactance image acquisition A pushbroom Vis–NIR hyperspectral interactance imaging system (Fig. 1, left) was constructed based on a hyperspectral reflectance and transmittance imaging system (Fig. 1, right) by Isuzu Optics Corp., Taiwan according to our design. This system mainly comprised of an imaging spectrograph (Imspector V10E, Spectral Imaging Ltd., Finland) attached to a 16-bit electronmagnifying charge-coupled detector (EMCCD) camera of 1004 1002 (spatial spectral) active pixels (Falcon EM285CL, Raptor Photonics Led., U.K.) and a C-mount lens (Xenoplan 1.4/17, Jos. Schneider Optische Werke GmbH, Germany), a halogen light source and control unit (3900-ER, 21 V/150 W, Illumination Technologies, Inc., USA) connected to line lights (9130-HT, Illumination Technologies, Inc., USA) assembling collector lenses (9560, Illumination Technologies, Inc., USA) by optic fibers (9145HT, Illumination Technologies, Inc., USA), and a mobile sample stage controlled by a linear travel translation stage controller (IRCP0076-1COMB, Isuzu Optics Corp., Taiwan).
Table 1 Pearson correlation matrices for TPA and PA parameters of blueberry.
Hardness Springiness Resilience Gumminess Cohesiveness Chewiness Hardness2
Maximum Force Max Force Strain Force Area Force Linear Distance Gradient Final Force
Hardness
Springiness
Resilience
Gumminess
Cohesiveness
Chewiness
Hardness2
1.0000 0.3523 0.5286 0.7613 0.5738 0.4080 0.9887
1.0000 0.3589 -0.0451 0.3413 0.2837 0.2961
1.0000 0.1165 0.2538 0.9659 0.4339
1.0000 0.9219 0.2230 0.8251
1.0000 0.3264 0.6556
1.0000 0.3263
1.0000
Force Max
Max Force Strain
Force Area
Force Linear Distance
Gradient
Final Force
1.0000 0.5557 0.9005 0.9048 0.3212 0.3995
1.0000 0.5369 0.4782 0.1572 0.3653
1.0000 0.8413 0.3184 0.5983
1.0000 0.3243 0.6232
1.0000 0.0018
1.0000
Note that the parameters in bold are selected for further analysis.
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In order to achieve interactance imaging, a new unit was suspended over a mobile sample stage, allowing the field of hypercube collection was parallel to the illuminated field. This unit mainly consisted of a line light with a collector lens, a focusing mirror and an adjustable light shield. The light shield, made of two black metal plates with adjustable height, could reduce unwanted reflected light from both the sample surface and light source (O’Farrell et al., 2010). Fig. 2 shows the distribution of incident light in interactance mode. For the interactance mode, the motor speed was set at 1.5 mm/ s and the exposure time was 100 ms. The actual light intensities at the surface of sample stage were 20 kLux. After the interactance with sample, it was difficult to detect the interacted light using the EMCCD camera, and the gain of EMCCD was, therefore, adjusted to obtain values close to 80% of the maximum pixel output of the EMCCD camera. The hyperspectral images of blueberries were captured with the stem scar facing vertically toward the lens. A total of 406 wavelengths between 675.33 nm and 1000.76 nm with a spectral resolution of 0.80 nm were selected for further analysis. 2.3. Mechanical parameters measurement A texture analyzer (TA.XTPlus, Stable Micro Systems, Inc., Surrey, U.K.) was used to obtain the mechanical parameters. The texture analyzer was equipped with a 490 N load cell and a blueberry was placed between a compression/puncture cylindrical probe and a cylindrical stainless flat platform with its stem scar facing vertically toward the probe. An auto force trigger of 5 g and a digital data acquisition resolution of 500 points per second were applied for both tests. The force and height measurements of the texture analyzer were calibrated prior to the tests. The calculation of mechanical parameters has been described in our previous study (Hu et al., 2015; Fig. S2 for review)
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See Supp Figure S1 as supplementary file. Supplementary material related to this article found, in the online version, at http://dx.doi.org/10.1016/j.postharvbio.2015.11.021. For TPA tests, the samples were compressed twice to 30% deformation by a cylindrical plate of 50 mm diameter at the test speed of 0.8 mm/s with a pre- and post-test speed of 1.6 mm/s and 2 mm/s, respectively. After the first compression, the probe returned to the trigger position and held at this position for 10 s. Seven TPA mechanical parameters viz. hardness, hardness2, resilience, cohesiveness, gumminess, chewiness and springiness were derived from the generated curve. Owing to the mechanical characters of blueberry and the setting of deformation degree, fracturability, adhesiveness and stringiness in the typical TPA curve presented by Bourne (2002) were not calculated. In the PA tests, samples were punctured to 80% of their initial height using a probe of 5 mm diameter at the test speed of 1.7 mm/s with a pre- and post-test speed of 2 mm/s and 5 mm/s, respectively. Six mechanical parameters were obtained by the PA test. From the force–strain profile, the maximal force (FM), maximal force strain (MFS), force linear distance (FLD), final force (FF) and force area (FA) were calculated, and the gradient (socalled elastic modulus) was determined from the plot of stress versus strain. FM had a similar physical meaning as firmness in previous studies (Rajkumar et al., 2012; Leiva-Valenzuela et al., 2014, 2013) according to definitions and test settings. If the Pearson correlation coefficient (R) between two mechanical parameters was beyond 0.8 (Table 1), only one parameter was subjectively chosen for further analysis, e.g., cohesiveness was highly correlated with gumminess (R = 0.9219) and the former was selected to be predicted by hyperspectral imaging. To some extent, the selected parameters viz. hardness, springiness, cohesiveness, resilience, FM, MFS, gradient and FF might be regarded as containing most mechanical information of the discarded parameters.
Fig. 3. Main steps of hyperspectral interactance cube processing. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 4. Flowchart of the entire data processes for predicting blueberry comprehensive mechanical properties.
2.4. Image and spectral processing
Eq. (1) was applied for correcting the original images line by line.
The line reference images for interactance modes were obtained using a rectangular white standard (Spectral Imaging Ltd., Finland). The purpose of the line correction method is to avoid the instability of light source in spatial dimension. The following
Sr ¼
SR SD 104 RW RD
ð1Þ
where Sr is the relative sample image, SR is the sample image, SD is the dark image of operated exposure time, RW is the reference white image of corrected exposure time, and RD is the reference
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Table 2 Statistics of mechanical properties of blueberries. Mechanical properties
Mean
Standard deviation
Min
Max
Variability (%)a
Hardness (N) Springiness () Cohesiveness () Resilience () MaximumForce (N) Max Force Strain (%) Gradient (g/mm2/%) Final Force (N)
0.059 0.676 0.330 0.133 0.078 37.321 1.0175 0.059
0.011 0.035 0.041 0.020 0.018 7.464 0.338 0.012
0.026 0.456 0.239 0.089 0.024 21.053 0.099 0.055
0.112 0.778 0.505 0.226 0.154 79.998 2.112 0.073
18.1 5.2 12.4 15.0 23.7 20.0 33.2 19.9
a
Variability = the ratio between standard deviation and mean of the parameters.
dark image of corrected exposure time. Ten reference white and dark images were acquired each time, and then averaged for correction. A segmentation algorithm based region growing (Adams and Bischof, 1994) was utilized to acquire the mask images from the relative interactance images, and the steps for this process are summarized in Fig. 3. Since the shield cannot block the reflected light completely, the pixels below the centeroid of blueberry were discarded to obtain predominant interacted signals. Subsequently, the mask operation was used to form images without background for extracting the spectral data. In current study, Standard Normal Variate (SNV) and the Savitzky-Golay of the first derivative using a 9-point window and a second-order polynomial (Der) were applied for smoothing the spectra (Rinnan et al., 2009). 2.5. Wavelength elimination and prediction model An improved Kennard-Stone sampling method (Galvão et al., 2005) was applied for separating samples into calibration and prediction sets. For each mechanical parameter, the calibration set included 75% of samples and the remaining samples formed the prediction set. Partial least squares (PLS) regression is an approach to construct the relationship between two data matrices, and has become a standard tool in chemometrics with many applications in food (Wold et al., 2001; Leiva-Valenzuela et al., 2013). Least squares support vector machines (LS-SVM) are least squares versions of support
vector machines (SVM) which are a set of kernel based supervised learning methods for classification and regression analysis (Suykens et al., 2002; van Gestel et al., 2004). LS-SVM has proved to be feasible in many applications in the area of food quality detection (Wu et al., 2013). In this study, a radial basis function was chosen as the kernel function, and two tuning parameters, i.e., regularization and kernel function parameters, were initially set to 10 and 0.2, respectively. The optimal values of two tuning parameters were first searched by a coupled simulated annealing, and afterwards fine-tuned by a simplex method (De Brabanter et al., 2011). In the current work, PLS and SVM regressions were respectively applied for wavelength selection and modeling. Monte Carlo-uninformative variable elimination (MC-UVE) method is based on PLS using a Monte Carlo algorithm as stochastic technique for stability analysis of regression coefficients (Cai et al., 2008). In this study, the sub-calibration sets including 75% of calibration samples were randomly selected via the MC technique, and numerous PLS models were established using these sample sets. Using the PLS regression coefficients, the following equation defined by Centner et al. (1996) was utilized to calculate the stability of each wavelength. mean bj ð2Þ Sj ¼ std bj In Eq. (2), mean (bj) and std (bj) refer to the mean and standard deviation values of PLS regression coefficients for the jth wavelength, respectively.
Fig. 5. Typical interactance spectra of 10 blueberries in the 390.65–1113.54 nm wavelength range. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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In order to determine a suitable cutoff threshold, the following formula is defined according to the equation for conventional UVE (Cai et al., 2008): ð3Þ cutoff ¼ k max abs Sj where max (abs (Sj)) denotes to obtain the maximum value of the absolute stability, and k is an arbitrary value from 0.10 to 0.75 with interval of 0.05. According to the definition of UVE, the larger the absolute value of stability, the more important is the corresponding wavelength. Hence, the wavelengths with absolute stability above the cutoff thresholds will be retained and the remaining will be eliminated. Moreover, Monte Carlo cross-validation (MCCV) developed by Picard and Cook (1984) is used in MC-UVE to estimate the parameters of models replacing K-fold and leave-one-out method to decrease the risk of model overfitting (Xu et al., 2004). The procedure of wavelength elimination and modelling is described as follows: (1) Determine the latent variable (LV) number: 200 PLS models based on original spectra were constructed for each mechanical parameter using corresponding 200 sub-calibration sets, and this procedure was repeated 5 times. The appropriate LV number was confirmed by root mean square errors for crossvalidation (RMSECV). (2) Calculate cutoff threshold: 200 PLS models were established using the optimal LV number for every k in Eq. (3). The suitable cutoff threshold was determined by the minimum RMSECV value. This process was carried out for both SNV and Der spectra. (3) Establish prediction model: the informative wavelengths could be determined by MC-UVE with the optimal cutoff, and used for subsequent modelling with the application of partial-least support vector machine (LS-SVM). This run was repeated 5 times. In addition, the prediction models based on whole 406 wavelengths were also established. The performances of the resulting models were examined in terms of the Pearson correlation coefficients of calibration (Rc) and prediction (Rp), root mean square error of calibration (RMSEc) and prediction (RMSEp).
All the aforementioned operations inclusive of image and spectral processing as well as chemometric analysis were executed in Matlab R2009b software (The Math Work, Inc., Natick, MA, USA). A LS-SVMlab Toolbox version 1.8 (Suykens, Leuven, Belgium) was used for LS-SVM modelling. Fig. 4 summarizes the entire data analysis in this study. 3. Results and discussion 3.1. Mechanical properties and spectral features of blueberry A large variability in quality parameters will make the model robust, and the statistical data on eight blueberry quality parameters is presented in Table 2. Apart from springiness, the other parameters had a variability beyond 12.4% (Table 2). The variability of PA parameters was large compared to the TPA parameters. As can be seen from Fig. 5, considerably lower and more consistent interactance occurred in the visible region from 390.65 nm to 675.33 nm. The low interactance might be attributed to the strong absorption of deep dark pigments in the blueberry skin. This explanation is in agreement with the study of Leiva-Valenzuela et al. (2013) who investigated the firmness and soluble solids content of blueberries using hyperspectral reflectance imaging. Beyond 675.33 nm, the interactance increased dramatically to a peak around 860.52 nm, and a subsequent decline was observed from the first peak to a valley around 975.02 nm. The possible reason causing this spectral valley was the combined absorption of OH groups from carbohydrates and water. In the rest of spectral region, a small peak occurred. However, numerous noises existed between the spectral range from 1000.76 nm and 1113.54 nm, due in part to the low quantum efficiency of the EMCCD in this range. The pattern of blueberry interactance spectra was different with the reflectance and transmittance in the previous study of Leiva-Valenzuela et al. (2014). For the purpose of acquiring useful wavelengths for modelling, the spectral region from 675.33 nm to 1000.76 nm was selected for the following analysis.
Table 3 Prediction models for mechanical parameters of blueberries using 406 wavelengths from 675.33 nm to 1000.76 nm. Mechanical parameter
Pre-processing
Rp
RMSEp
Rc
RMSEc
Hardness (N)
SNV Der
0.6924 0.8072
0.0568 0.0583
0.9487 0.8461
0.0470 0.0717
Springiness ()
SNV Der
0.8461 0.7708
0.0207 0.0257
0.8425 0.8539
0.0348 0.0335
Cohesiveness ()
SNV Der
0.9128 0.8631
0.0325 0.0459
0.9124 0.9425
0.0482 0.0399
Resilience ()
SNV Der
0.8620 0.8108
0.0429 0.0517
0.8893 0.9550
0.0575 0.0404
Maximum Force (N)
SNV Der
0.7384 0.7837
0.0711 0.0774
0.7544 0.6012
0.1078 0.1222
Max Force Strain (%)
SNV Der
0.6639 0.5975
0.0660 0.0604
0.7690 0.8067
0.0807 0.0791
Gradient (g/mm2/%)
SNV Der
0.5757 0.6271
0.1145 0.1145
0.7259 0.6404
0.1478 0.1589
Final force (N)
SNV Der
0.6243 0.6404
0.0743 0.0764
0.7212 0.7492
0.1003 0.0984
Note: RMSE values were calculated from the normalized values of mechanical parameters; the units of RMSE are presented in the first column.
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Fig. 6. Plots of RMSECV for mechanical parameters of (a) TPA and (b) PA (red symbols indicate the minimization of RMSECV). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
hyperspectral interactance spectra from 675.33 nm to 1000.76 nm for estimating TPA parameters and FF of blueberry.
3.2. Prediction models using whole spectra Table 3 summarizes calibration and prediction results for blueberry mechanical parameters based on whole spectra. Models with SNV spectra were promising for springiness, cohesiveness and resilience, with the Rp (Rc) values of 0.85 (0.84), 0.91 (0.91) and 0.86 (0.89), respectively. The performance of Der spectra was better than that of SNV spectra for hardness prediction, with nearly 0.12 increments in Rp. On the aspect of PA parameters, the FM prediction model of Der spectra produced the Rp (Rc) value of 0.78 (0.60). However, both SNV and Der spectra did not yield desired results for predicting MFS and gradient as well as FF, with the Rp values below 0.66. The higher RMSE had been observed for gradient than the other parameters, indicating the hyperspectral interactance mode might be unsuitable for predicting gradient. Furthermore, examination of RMSE values in Table 3 demonstrated that TPA parameters had the lower RMSE than those of PA parameters. The above results show the possibility to use
3.3. Determination of LV number and cutoff The optimal LV number for each parameter was determined by the minimum RMSECV. In Fig. 6 the mean values of RMSECV are shown, which were calculated from 1000 PLS models. From Fig. 6, it was clear that the LV numbers of all mechanical parameters reached the smallest RMSECV in the range from 6 to 9. The LV number of 9 could be used for hardness and FM; 7 for springiness and resilience, and 6 for gradient and FF. In terms of cohesiveness, the optimal LV number was 8. Particularly, two optimal LV numbers were observed for MFS (i.e. 7 and 9). In order to make the following selection scheme simple, 7 was chosen as LV number for MFS. There are several cutoff criterions in traditional UVE (Moros et al., 2008; Ye et al., 2008). However, the cutoff criterion for MCUVE is still limited. In the current study, we used the method
Table 4 Prediction models for mechanical parameters of blueberries using MC-UVE-LS-SVM with 5 repeated runs. Mechanical parameter
Pre-processing
k
Wavelength number (5 results)
Rp (s )
RMSEp
Rc (s )
RMSEc
Hardness (N)
SNV Der
0.65 0.35
27; 29; 33; 37; 39 83; 92; 79; 87; 89
0.6849 (0.0121) 0.7653
0.0578 0.0638
0.9148 0.8640
0.0578 0.0676
Springiness ()
SNV Der
0.45 0.25
119; 119; 126; 110; 120 138; 127; 131; 128; 125
0.8437 0.7453
0.0212 0.0268
0.8517 0.8823
0.0340 0.0307
Cohesiveness ()
SNV Der
0.50 0.30
28; 45; 39; 31; 33 105; 101; 103; 110; 105
0.9072 0.8399
0.0334 0.0485
0.8955 0.9238
0.0518 0.0446
Resilience ()
SNV Der
0.45 0.35
64; 59; 57; 50; 73 132; 143; 131; 139; 136
0.8593 0.8051
0.0431 0.0541
0.8869 0.9453
0.0580 0.0434
Maximum force (N)
SNV Der
0.65 0.65
15; 23; 17; 25; 16 42; 46; 30; 46; 36
0.6739 (0.0248) 0.7106 (0.0263)
0.0754 0.0751
0.6855 (0.0317) 0.7297 (0.0398)
0.1146 0.1095
Max force Strain (N)
SNV Der
0.45 0.65
31; 36; 35; 45; 37 13; 12; 11; 14; 14
0.6537 0.5493 (0.0100)
0.0664 0.0638
0.7613 (0.0199) 0.7628 (0.0178)
0.0813 0.0825
Gradient (g/mm2/%)
SNV Der
0.70 0.60
57; 63; 48; 39; 53 25; 36; 29; 30; 33
0.5779 0.5821 (0.0168)
0.1143 0.1181
0.6751 0.6886
0.1534 0.1511
Final force (N)
SNV Der
0.35 0.50
145; 143; 145; 140; 129 52; 55; 55; 50; 51
0.6238 0.6148 (0.0101)
0.0747 0.0776
0.7219 (0.0138) 0.6148
0.1002 0.0934
Note: s is the standard deviation of 5 repeated runs; the absence of s means the value is lower than 0.0100; RMSE values were calculated from the normalized values of mechanical parameters; the units of RMSE are presented in the first column.
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Fig. 7. Stability distribution of SNV pre-processed spectra for hardness in calibration set (number of latent variables = 9, and MC procedure is repeated 200 times).
described in step (2) in Section 2.5 to confirm the cutoff. The optimal k value in Eq. (3) for each predicted parameter is shown in Table 4. The stability distribution of hardness is shown in Fig. 7. The two horizontal dot lines refer to the lower and upper cutoff thresholds. Based on the principle of MC-UVE, the wavelengths within the dot lines should be discarded. Therefore, in this MC-UVE run, total 33 wavelengths were selected for further hardness modelling. Using the same operation, the informative wavelengths could be determined for the other mechanical parameters. 3.4. Prediction models based on MC-UVE selected wavelengths Table 4 shows the calibration and prediction models of blueberry mechanical parameters based on MC-UVE selected wavelengths. In the case of hardness, the Der model performed better than SNV, whereas the former required nearly 50 more spectra than SNV based model for every run. This was also observed for FM in both calibration and prediction results. Previous study of Dai et al. (2014a) reported Rp = 0.85 for hardness in prawn using reflectance imaging, which was better than that in blueberry using interacance spectra in this investigation. The SNV preprocessing method resulted in superior models for predicting springiness, cohesiveness, resilience and MFS than Der, with Rp (Rc) values of 0.84 (0.85), 0.91 (0.91), 0.86 (0.89) and 0.65 (0.76), respectively. Moreover, in addition to MFS, the SNV models of springiness and cohesiveness as well as resilience required fewer wavelengths than those based on Der spectra. This was considerably obvious for cohesiveness prediction, indicating that the SNV spectra were useful for selecting wavelengths particularly informative with respect to cohesiveness. These two spectral pretreatment approaches yielded similar performances for estimating gradient and FF. The small standard deviation of 5 repeated runs revealed satisfactory repeatability of LS-SVM model based on the MC-UVE method (Table 4). Furthermore, comparison of statistical indicators in Tables 3 and 4 demonstrated the possibility to use MC-UVE selected interactance spectra for predicting blueberry mechanical properties. The prediction models reported in this study based on interactance data shows the feasibility of non-invasive sorting and grading of agricultural and horticultural products. For fresh
produce industry, an online multispectral interactance system could be developed for classifying blueberry. The interactance model can be used for the continuous and non-destructive acquisition of data on mechanical properties on the same intact berries in relation to quality during postharvest operations. 4. Conclusion In this study, hyperspectral interactance imaging was used to estimate the mechanical properties of intact blueberry. Based on MC-UVE selected wavelengths, a relatively high correlation was obtained between cohesiveness and SNV interactance spectra with Rp (Rc) of 0.91 (0.91). For the other fruit quality parameters, good prediction results based on SNV spectra were achieved for springiness, resilience, MFS and FF with Rp (Rc) of 0.84 (0.85), 0.86 (0.87), 0.65 (0.76) and 0.62 (0.72), respectively. Using Der spectra, hardness, FM and gradient could be predicted with Rp (Rc) of 0.77 (0.86), 0.71 (0.73) and 0.58 (0.69), respectively. Overall, the results were considered to be acceptable compared to the corresponding models obtained using entire wavelengths. These findings show that hyperspectral interactance spectra coupled with the MC-UVE method has potential for predicting mechanical properties of blueberry. Acknowledgements This study was supported by the National Natural Science Foundation of China (NSFC31271896), the Innovation Fund Project for Graduate Student of Shanghai (JWCXSL1401) and the Joint Science and Technology Research of Triangle Area of Science and Technology Commission of Shanghai Municipality (15395810900). U.L. Opara’s contribution was supported by the South African Research Chairs Initiative of the Department of Science and Technology. References Ariana, D.P., Lu, R., 2010. Evaluation of internal defect and surface color of whole pickles using hyperspectral imaging. J. Food Eng. 96 (4), 583–590. Ariana, D.P., Lu, R., 2008a. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype. Sens. Instrum. Food Qual. Saf. 2 (3), 152–160.
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