Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy

Computers and Electronics in Agriculture 117 (2015) 226–233

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Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy Wenchuan Guo ⇑, Lijie Fang, Dayang Liu, Zhuanwei Wang College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China

a r t i c l e

i n f o

Article history: Received 1 February 2015 Received in revised form 26 July 2015 Accepted 14 August 2015 Available online 28 August 2015 Keywords: Pear Permittivities Dielectric spectra Soluble solids content Firmness

a b s t r a c t To investigate the feasibility of dielectric spectroscopy as a nondestructive technique in determining soluble solids content (SSC) and firmness of pears during ripening period, the dielectric constants and dielectric loss factors of 105 ‘‘Dangshansu” pears with different maturities were obtained at 201 discrete frequencies from 20 MHz to 4500 MHz with an open-ended coaxial-line probe and a vector network analyzer. The joint x–y distances sample set partitioning (SPXY) method was applied to divided all samples into calibration set (70 pears) and prediction set (35 pears). Nineteen and 13 characteristic variables were extracted for SSC and firmness, respectively, from the full dielectric spectra by using successive projection algorithm. The nonlinear model, i.e., least squares support vector machine, extreme learning machine (ELM) and generalized regression neural network, and linear models, i.e., multiple linear regression and partial least squares regression, were used to establish SSC and firmness determination models based on the full dielectric spectra and extracted characteristic variables by SPA. Results showed that SPA-ELM was the best model for SSC and firmness prediction, with the correlation coefficient and rootmean-square error of prediction set of 0.838 and 0.464 for SSC, and of 0.865 and 0.362 for firmness. The study indicates that dielectric spectroscopy combined with artificial neural network might be applied in developing portable SSC and firmness detectors of intact pears during on-tree ripening. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction Appropriate harvest is important for offering fruits with good quality. Too early harvest makes the fruits have lower sugar content and worse flavor, since sugars content and flavor increase while total acids decrease during late ripening (Etienne et al., 2002; Vizzotto et al., 1996). However, too late harvest makes the fruits have light flavor and soft flesh, leading to losses in the marketing chain. Although the skin color of many fruits can indicate the degree of ripeness, for pear, its skin color changes very little during on-tree ripening. It is very difficult to determine the pear maturity based on skin color. Therefore, evaluation of pear ripeness is an important issue in pear industry. The assessment of ripeness, a major part of quality evaluation, depends on several factors such as soluble solid content (SSC), firmness, acidity, sugars, organic acids, ethylene rate, and color (de Oliveira et al., 2014). For pear, SSC and firmness are the most important internal quality attributes (Nicolaї et al., 2008). The traditional method used to measure fruit SSC requires juice extracted from the fruit pulp, and is carried out by using digital refractometer or Abbe refractometer. The firmness ⇑ Corresponding author. Tel.: +86 29 87092391; fax: +86 29 87091737. E-mail address: [email protected] (W. Guo). http://dx.doi.org/10.1016/j.compag.2015.08.012 0168-1699/Ó 2015 Elsevier B.V. All rights reserved.

is usually measured by a penetrometer to penetrate fruit flesh to a depth. These traditional methods can offer precise measurement results, but they are destructive. There is a need for nondestructive techniques for the assessment of internal quality attributes to instruct pear planting and harvesting. The SSC and/or firmness have been determined on pears using near-infrared spectroscopy (Blanke, 2013; Cavaco et al., 2009; Fan et al., 2014; Li et al., 2013; Liu and Ying, 2007; Ying and Liu, 2008). However, all of these studies applied the postharvest pears as samples. Few reports have been noted on predicting internal qualities of pears during ripening before harvest. Dielectric properties of materials are those electrical properties that determine the interaction of the materials with electric fields. Dielectric spectroscopy is a rapid, easy, and nondestructive detection technique that can be a suitable substitute for traditional methods. It has been applied in determining some qualities of foods, such as protein content in milk (Zhu et al., 2015), fat content in meat (Ng et al., 2008), sucrose or sugar content in honey (Guo et al., 2011a, 2010b), water content in milk, legume, and cheese, etc. (Fagan et al., 2005; Guo et al., 2010a; Zhu et al., 2013). At present, no clear linear correlation between permittivities at a single frequency and an internal quality attribute has been noted in either external surface measurements or internal tissue measurements in apples

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(Guo et al., 2011b), watermelons (Guo et al., 2008; Nelson et al., 2007), peaches (Guo and Chen, 2010), honeydew melon (Nelson et al., 2006), and cantaloupes (Guo et al., 2008). Nelson et al. (1995) indicated that a permittivity-based maturity index, the ratio of the loss tangents at 10 GHz and 0.2 GHz, could be used to distinguish peach maturity (Nelson et al., 1995). Shang et al. (2013) applied dielectric spectroscopy to predict SSC of postharvest nectarines, and reported that SSC of nectarines could be predicted based on obtained dielectric spectra. However, to our knowledge, no attempt has been made to predict the SSC and firmness of pears during ripening using dielectric spectroscopy. In this study, pears during ripening were used for permittivity (dielectric constant and dielectric loss factor) measurements over the frequency range from 20 to 4500 MHz, along with the measurement of SSC and firmness of pears. Dielectric spectroscopy technique combined with modeling methods were used to predict the SSC and firmness. The specific aims of this research are (1) to select the characteristic dielectric variables from full spectra (FS) of permittivities by using successive projections algorithm (SPA); (2) to develop different models to quantitatively predict SSC and firmness; and (3) to assess the feasibility of dielectric spectroscopy technique in determining SSC and firmness of intact pears with different maturities. 2. Materials and methods 2.1. Pears Pears, variety ‘Dangshansu’, one of the most famous pear cultivars planted in China widely, were picked at about 15-day intervals from August 5 to September 22, 2013, from a local orchard, located at 34°210 north latitude, 108°100 east longitude, and at an elevation of 455 m in Yangling, Xi’an, Shaanxi Province, China. The sampling dates were August 5, August 22, September 6, and September 22, 2013. At each sampling time, more than 30 pears were randomly picked from the pear orchard in the afternoon the day before measurement. After the pears were washed with tap water to remove any foreign materials on surface, they were kept at room temperature (22 ± 2 °C) and allowed to equilibrate overnight. About 25 pears with regular shape, without damage or defects, were measured at each sampling time. Totally, 105 pears were used in the study. 2.2. Dielectric properties measurements The system used to acquire dielectric spectra consisted of an E5071C vector network analyzer, an 85070E open-ended coaxialline probe, 85070 dielectric probe kit software (Agilent Technologies, Penang, Malaysia), a computer and a laboratory jack. The schematic diagram of the dielectric properties measurement system is shown in Fig. 1. The dielectric constant e0 and dielectric loss factor e00 , real and imaginary parts, respectively, of the complex permittivity relative to free space e ¼ e0 þ je00 are the principal permittivities used in this study. They were calculated from the reflection coefficient of the material in contact with the active tip of the coaxial-line probe (Blackham and Pollard, 1997). Settings were made to provide measurements at 201 discrete frequencies on a logarithmic scale from 20 MHz to 4500 MHz. It means that there were two dielectric spectra, i.e., dielectric constant spectrum and dielectric loss factor spectrum, for each sample. Therefore, each sample had 402 values of permittivities, including 201 dielectric constant values and 201 dielectric loss factor values. In this study, the 201 dielectric constant values were numbered from 1 to 201, and the other 201 dielectric loss factors were numbered from 202 to 402 when the frequency increased from 20 to 4500 MHz.

Vector network analyzer

Computer

Coaxial-line probe Pear sample Laboratory jack Fig. 1. The schematic diagram of the dielectric properties measurement system.

The permittivity data for each sample was a 402-dimensional vector.

2.3. Determination of firmness and soluble solids content The pear pulp firmness was measured with a GY-3 fruit penetrometer (Sundoo Instruments, Zhejiang, China) with an 8-mm-diameter penetrometer tip. Before measuring the tissue firmness, a peeler was used to remove the peel in the equatorial region of pear to a depth of about 2 mm. An even force was applied to the penetrometer tip to penetrate the pear pulp. When the probe advanced into the tissue to the required scale, the force was removed, and the force gage reading, in kg/cm2, was recorded. Soluble solids content, being mostly sugars (i.e., 80–85%) in fruits, was used as a measure of sugar content, and was determined from measurements on juice, which was expressed from pear pulp in a garlic press with cheesecloth patches. A digital refractometer (Model PR101a, Atago Co. Ltd., Tokyo, Japan) was used to measure SSC. The refractometer readings are frequently referred to as °Brix readings, which are expressed in percent total soluble solids by weight (Nelson, 2003).

2.4. Procedures The E5071C network analyzer was warmed up for at least 1 h for stabilization, followed by calibrating with an open, short, and matched load in sequence at the port used for the dielectric properties measurement. Then the network analyzer and the 85070E open-ended coaxial-line probe were connected with a cable for the probe. The probe was calibrated using air, short-circuit, and 25 °C deionized water. A measurement was made on 25 °C deionized water to verify that proper permittivity values were being obtained. The permittivity measurements of intact pears were made with the probe in firm contact with the surface of pears, supported by a laboratory jack, in the equatorial region at four points about 90° apart around the perimeter of the fruit. Firm contact implies the elimination of any air gaps between the probe and the fruit measured without application of force to otherwise deform or bruise the fruit pulp. After completion of the permittivity measurements on intact pears, the firmness and SSC were measured on four points where the dielectric properties were measured. The means of repeated measurements for dielectric constant, dielectric loss factor, firmness and SSC values of each pear were calculated and used in the study.

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2.5. Spectral preprocessing To reduce or even eliminate the effects on spectra arising from instrument and measurement environment, the spectral preprocessing methods, such as first derivative (1-Der), second derivatives (2-Der) and standard normal variate (SNV) were used to preprocess dielectric spectra using Unscrambler 9.8 (CAMO, Trondheim, Norway). It was found that the full spectra (FS) could offer better SSC and firmness determination performance. For example, when partial least squares regression (PLSR) models were built using FS and the preprocessed spectra with 1-Der, 2-Der and SNV, respectively, it was found that 1-Der could give the best determination performance for SSC with correlation coefficient (R) of 0.738, but it was just a little better than FS (R = 0.737). Moreover, FS given the best determination performance for firmness with R of 0.679, followed by 1-Der (0.636), 2-Der (0.597) and SNV (0.557). Therefore, the full dielectric spectra without preprocessing were used in the study. 2.6. Sample division All samples were divided into two subsets, i.e., calibration set and prediction set according to the ratio of 2:1. The samples in the calibration set were used to build a model, and the samples in the prediction set were used to test the built model. The method employed for data partitioning was the sample sets partitioning method based on the joint x–y distances (SPXY), developed by Galvão et al. (2005) and extended from the classic Kennard-Stone (KS) algorithm. Contrasted with KS algorithm, SPXY considered spectra and reference values together. 2.7. Characteristic variables selection by SPA The acquired dielectric spectra usually suffer from the problem of multicollinearity. Some congruent variables are related to similar constituents, and consequently contain much same information (Liu and Guo, 2015). A lot of researches have indicted that selecting effective variables, which contain much information of the full spectra is important to generate more stable models with superior interpretability, to simplify established models, and to produce the lowest prediction error. SPA, a forward selection method, was proposed as a variable selection strategy for multivariate calibration by Araújo et al. (2001). It starts with one variable, then incorporates a new one at each iteration, until a specified number N of variables is reached. Its purpose is to select variables whose information content is minimally redundant, in order to solve collinearity problems. SPA shows the advantage of selecting much smaller representative set of variables with a minimum of collinearity than other characteristic variable selection methods, such as uninformative variable elimination algorithm (Ye et al., 2008). At present, SPA has been successfully applied to select variables in Vis/NIR spectra (Liu and Guo, 2015) and in dielectric spectra (Shang et al., 2013).

mation. The training problem in SVM is reducible to solving a convex quadratic programming problem. However, one problem of SVM is that the size of the matrix of the quadratic programming problem is directly proportional to the number of training points. It causes that the standard quadratic programming packages cannot be used even for a moderately large data set (Chua, 2003). To solve this problem, Suykens and Vandewalle (1999) proposed a modified version of SVM for classification called least squares SVM (LSSVM). The details of LSSVM algorithm could be found in the literature (Suykens et al., 2002). 2.8.2. Extreme learning machine Extreme learning machine (ELM), developed for single hidden layer feedforward networks (SLFN) and proposed by Huang et al. (2006), is a fast learning algorithm and an emergent nonlinear technique. ELM not only learns much faster with higher generalization performance than the traditional gradient-based learning algorithms, but also avoids many difficulties faced by gradientbased learning methods (Zhu et al., 2005). The SLFN contains three layers, i.e., an input layer, a hidden layer, and an output layer. Selecting the number of neurons in hidden layer is an essential problem to be solved in establishing ELM models. ELM randomly chooses and fixes the weights between input neurons and hidden neurons based on continuous probability density function, and then analytically determines the weights between hidden neurons and output neurons of the SLFN (Ouyang et al., 2013). More detailed theories of ELM can be found elsewhere (Huang et al., 2006). 2.8.3. Generalized regression neural network GRNN, developed by Specht (1991), is a one-pass learning algorithm with a highly parallel structure. There are 4 layers in GRNN, i.e., input, pattern, summation and output layers. In GRNN the esti^ mated concentration YðxÞ, which represents a weight term for all observed concentration Y i , can be calculated as (Specht, 1991):

  exp  2Dai2 ^   YðxÞ ¼ P N Di i¼1 exp  2a2 PN

i¼1 Y i

ð1Þ

where Y i expresses the i-th observed concentration, N is the sample size, a is the spread parameter of kernel function, also named as smoothing factor. The scalar function D2i is defined as:

D2i ¼ ðX  X i ÞT ðX  X i Þ

ð2Þ

where X is the observed input value, and Xi is the i-th neuron of the corresponding sample. T means transpose of matrix. The value of a is the primary parameter of the GRNN. The detailed information was reported elsewhere (Specht, 1991). Unlike conventional neural networks, GRNN does not need iterative training.

In this study, three nonlinear modeling methods, i.e., the least squares support vector machine (LSSVM), extreme learning machine (ELM) and generalized regression neural network (GRNN), and two linear modeling methods, i.e., multiple linear regression (MLR) and PLSR were employed to establish determination models for SSC and firmness of pears during ripening.

2.8.4. Multiple linear regression MLR is an approach that fits a linear equation between two or more explanatory variables and a response variable. MLR model is simple and easy to be interpreted, but it is usually affected by collinearity between variables (Næs and Mevik, 2001). Moreover, MLR just can be used when the response variables are much fewer than explanatory variables. In this study, it means that the used dielectric spectral variables should be much fewer than pear samples. Therefore, MLR was conducted to establish the linear relationship between each internal quality, i.e. SSC and firmness, and the selected characteristic variables by SPA here.

2.8.1. Least squares support vector machine Support vector machines (SVMs), put forwarded by Vapnik (1995), are powerful tools for data classification and function esti-

2.8.5. Partial least squares regression PLSR is a developed generalization of MLR. It is of particular interest because, unlike MLR, it can analyze data with strongly

2.8. Modeling methods

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80

Dielectric constant Dielectric loss factor

70 60

Perimittivities

collinear correlated, noisy, and numerous X-variables, and also simultaneously model several response variables, Y, i.e., profiles of performance (Wold et al., 2001). By using PLSR, the original independent information (spectral data) is projected onto a small number of latent variables (LVs). This is helpful to reduce the dimensionality, to compress the original spectra data, and to simplify the relationship between the spectral data and the target quality properties matrix.

50 40 30 20

2.9. Model assessment

10

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xnc Xnc ^i  yi Þ2 ^  yc Þ2 RC ¼ ð y ðy i¼1 i¼1 i qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xnp Xnp ^i  yi Þ2 ^i  yp Þ2 RP ¼ ð y ð y i¼1 i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xnc ^ i  y i Þ2 ðy RMSEC ¼ i¼1 nc

RMSEP ¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xnp ^  y i Þ2 ðy i¼1 i np

ð3Þ

ð4Þ

ð5Þ

ð6Þ

^i is the predicted value of an internal qualities attribute of where y the i-th sample. yi is the measured value of an internal qualities attribute of the i-th sample, yc and yp are the mean values of the calibration and prediction sets, respectively. nc is the number of samples in the calibration set and np is the number of samples in the prediction set. A good model should have high values of RC and RP, low values of RMSEC and RMSEP. Moreover, the number of input variables of the established model should be as low as possible (Ouyang et al., 2013). 3. Results and discussion

0

3.2. Linear correlation between permittivities and pear internal quality attributes Fig. 3 shows the linear correlation coefficient between the permittivities and SSC (Fig. 3a) and between permittivities and firm-

108

109

1010

Fig. 2. Typical results (mean values ± standard deviation) for dielectric constant and dielectric loss factor for permittivity measurements in four replicates of a single pear at 201 frequencies from 20 to 4500 MHz at room temperature.

ness (Fig. 3b) when their relationships were described by the linear model of y ¼ ax þ b, where y represents e0 or e00 , x represents SSC or firmness, a and b are regression constants. It was noted that the linear correlation coefficient was lower than 0.25 for SSC (Fig. 3a), and lower than 0.38 for firmness (Fig. 3b). The results express that it is impossible to select one permittivity value for accurate prediction of SSC and firmness of pears, and indicate the necessity to use more permittivity values or even entire dielectric spectra for SSC and firmness prediction. Therefore, chemometrics was used to select characteristic dielectric variables, and artificial neural network was used to build nonlinear determination models of SSC and firmness.

0.30

(a) Rε‘

0.25

Rε“

0.20 0.15 0.10

3.1. Frequency dependence of permittivities

0.05 0.00 107

108

109

1010

Frequency, Hz 0.40 0.35 0.30

(b) Rε‘ Rε‘’

0.25

R

Typical mean results showing the frequency dependence of permittivities (dielectric constant and dielectric loss factor) for the four measurements for a single pear are shown in Fig. 2, where the error bars indicate plus and minus one standard deviation. Fig. 2 shows that the dielectric constant decreased monotonically with increasing frequency. Especially in the low frequency regions, i.e., lower than 100 MHz, dielectric constant decreased sharply as frequency increased. For example, when the frequency increased from 20 MHz to 100 MHz, the dielectric constant decreased from 65.05 to 42.17, then decreased to 28.95 when the frequency increased from 100 MHz to 4500 MHz. The dielectric loss factor decreased to a broad minimum at about 1000–2000 MHz before increasing. The dielectric loss mechanisms might include those attributable to bound water, Maxwell–Wagner relaxations, and other ion-related phenomena (Nelson et al., 2007).

107

Frequency, Hz

R

The model performances were evaluated by correlation coefficient of calibration set (RC), correlation coefficient of prediction set (RP), root-mean-square error of calibration set (RMSEC), and root-mean-square error of prediction set (RMSEP). These indices are defined as follows:

0.20 0.15 0.10 0.05 0.00 107

108

109

1010

Frequency, Hz Fig. 3. Linear correlation between the permittivities and SSC (a) and between the permittivities and firmness (b) over the frequency range of 20–4500 MHz.

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Table 1 The statistics of SSC and firmness of pears in calibration and prediction sets. Sample set

Amount of samples

Calibration set Prediction set a

70 35

Firmness, kg/cm2

SSC, °Brix Min.

Max.

Mean ± std

6.9 7.7

12.8 10.6

9.0 ± 1.1 9.1 ± 0.7

a

Min.

Max.

Mean ± std

3.4 4.2

8.3 6.8

5.6 ± 2.5 5.3 ± 0.7

Std represents standard deviation.

3.3. Sample division and statistics of SSC and firmness One hundred and five pears were divided into calibration and prediction sets according to the ratio of 2:1 for SSC and firmness prediction using the SPXY algorithm. Seventy pears were classified as calibration set and other 35 pears were classified as prediction set. The statistics for the SSC and firmness values in calibration and prediction sets are shown in Table 1. The results show that the minimum values of SSC and firmness in calibration set were smaller than those in prediction set, and the maximum values of SSC and firmness in calibration set were larger than those in prediction set, indicating the sample divisions were reasonable. 3.4. Selection of characteristic variables by SPA The RMSEC was calculated at different variable numbers from 1 to 25 during characteristic variables selection process by SPA for SSC and firmness, respectively. The results are shown in Fig. 4. Fig. 4 tells that when the numbers of variables were 19 and 13, the minimum RMSEC values were found for SSC (RMSEC = 0.336) and firmness (RMSEC = 0.439), respectively. Therefore, 19 and 13 variables, shown in Table 2, were extracted as characteristic

0.9

SSC Firmness

0.8

RMSEC

0.7 0.6 0.5 0.4 0.3

0

5

10

15

20

25

Number of variables included in SPA Fig. 4. Changed RMSEC with the number of variables included in SPA. The solid square and triangle represent the points at which the final numbers of variables were selected for SSC and firmness.

Table 2 The selected characteristic variables for predicting SSC and firmness by using SPA. Quality attributes

No. of selected variables

Selected permittivities

The selected permittivities at some frequencies, MHz

SSC

19

e0 e00

20.0, 49.2, 131.0, 4500.0 21.4, 22.2, 22.8, 26.5, 54.1, 127.0, 181.0, 868.0, 1300.0, 2130.0, 2560.0, 3090.0, 3370.0, 4120.0, 4500.0 20.0, 88.9, 153.0 20.0, 24.3, 54.1, 193.0, 645.0, 868.0, 2130.0, 2400.0, 3370.0, 4120.0

Firmness

13

e0 e00

variables by SPA for SSC and firmness, respectively. For SSC, there were 4 variables of dielectric constant and 15 variables of dielectric loss factor. For firmness, there were 3 variables of dielectric constant and 10 variables of dielectric loss factor. The numbers of selected permittivity variables were 4.7% and 3.2% of the 402 variables in the full dielectric spectra for SSC and firmness, respectively. 3.5. Determination model for SSC and firmness 3.5.1. LSSVM determination models Before the application of LSSVM, two crucial problems should be solved firstly, i.e., proper kernel function and the optimal LSSVM parameters. The usually used kernel functions include linear kernel, polynomial kernel and radial basis function (RBF) kernel. Contrasted with linear and polynomial kernel functions, RBF kernel function is able to reduce the computational complexity of training procedure, to handle the nonlinear relationships between the spectra and target attributes, and to give a good performance (Suykens and Vandewalle, 1999). Therefore, RBF was used as the kernel function of LSSVM here. Regularization parameter c and RBF kernel parameter r2 are two important parameters should be determined. The two parameters determine the learning ability, prediction ability and generalization ability of LSSVM (Suykens et al., 2002). c is used to maximize model performance (on training) and minimize model complexity. Large c implies little regularization, and thus a more nonlinear model. r2 influences the number of neighbors in the model (Li et al., 2013). Large r2 means more neighbors in the model which leads to a more nonlinear model. Grid search method was used to find the optimal values of c and r2. The ranges of c and r2 were set as 20–220 with an increment of 20.5. For each combination of c and r2, LSSVM model was established and the RMSEC was calculated. The optimal values of c and r2 were determined by the smallest RMSEC in all combinations of c and r2. Table 3 lists the determined c and r2 of LSSVM models for SSC and firmness when the full spectra and selected characteristic variables were used as inputs of LSSVM. Table 4 lists the calibration and prediction performances of LSSVM models for determining SSC when full spectra and selected characteristic variables by SPA were used as inputs in modeling. Table 4 shows that the LSSVM model based on full spectra (FSLSSVM) had a little better calibration performance (RC = 0.906, RMSEC = 0.450) and prediction performance (RP = 0.805, RMSEP = 0.419) than that based on SPA (SPA-LSSVM) with RC = 0.902, RMSEC = 0.457, RP = 0.797 and RMSEP = 0.440. That means some useful information might not be included in selected

Table 3 The determined modeling parameters of LSSVM and GRNN models for SSC and firmness at different characteristic variables selection methods. Characteristic variables selection methods

LSSVM

GRNN SSC

Firmness

c

r2

c

r2

a

a

FS SPA

100028.7 63223.8

26767.0 863.1

29979.3 7016.5

12223.5 156.4

1 1

0.4 1

SSC

Firmness

231

W. Guo et al. / Computers and Electronics in Agriculture 117 (2015) 226–233 Table 4 Comparison of determination results for SSC by different models at different characteristic variables selection methods. Modeling methods

Variable selection methods

Calibration set RC

RMSEC

RP

RMSEP

LSSVM

FS SPA FS SPA FS SPA SPA FS SPA

0.906 0.902 0.817 0.896 0.891 0.801 0.875 0.733 0.785

0.450 0.457 0.609 0.468 0.516 0.682 0.459 0.640 0.523

0.805 0.797 0.758 0.838 0.731 0.800 0.832 0.717 0.742

0.419 0.440 0.656 0.464 0.474 0.433 0.436 0.670 0.547

ELM GRNN MLR PLS

Prediction set

characteristic variables by using SPA. However, the variables used in SPA was only 4.7% of the full spectra, and the FS-LSSVM did not presented outstanding predominance in improving pear SSC calibration and prediction performances. Therefore, SPA-LSSVM was regarded as the better SSC determination model than FS-LSSVM in predicting SSC. Table 5 lists the calibration and prediction performances of LSSVM models for determining firmness when full spectra and selected characteristic variables by SPA were used as inputs in modeling. It shows that the SPA-LSSVM had a little better calibration performance (RC = 0.891, RMSEC = 0.442) than FS-LSSVM (RC = 0.884, RMSEC = 0.457), while FS-LSSVM had better prediction performance (RP = 0.872, RMSEP = 0.348) than SPA-LSSVM (RP = 0.843, RMSEP = 0.389). It also indicates that some useful variables were not extracted by SPA.

3.5.2. ELM determination models In ELM modeling, the hidden node parameters, input weights, hidden biases, and impact factors of hidden nodes, were randomly generated. The output weights were determined using Moore– Penrose generalized inverse. The excitation function was set as ‘‘sig” function. The number of nodes in the hidden layer is the only parameter that needs to be determined for ELM. This parameter was obtained by trial and error method. The basic steps are summarized here. Firstly, the range of hidden nodes was set from 5 to 50. Then, the number of hidden nodes was gradually increased by an interval of 1. Average of 50 trials of simulations for each fixed size of single-hidden layer feedforward neural network was obtained, and the almost optimal number of nodes for ELM was determined based on the lowest RMSEC value. In this study, the ultimately determined numbers of hidden layer nodes under different input variables for SSC and firmness are listed in Table 6. Tables 4 and 5 list the calibration and prediction performances of ELM models when different variables were used for determining

Table 6 Modeling parameters of ELM models for SSC and firmness at different characteristic variables selection methods. Parameters

SSC

Input layer nodes Hidden layer nodes Output layer nodes

Firmness

FS

SPA

FS

SPA

70 30 1

19 24 1

70 25 1

13 27 1

SSC and firmness, respectively. Table 4 presents that SPA-ELM had much better SSC calibration and prediction performances (RC = 0.896, RMSEC = 0.468, RP = 0.838, and RMSEP = 0.464) than FS-ELM. Table 5 shows that SPA-ELM also had much better calibration and prediction performances (RC = 0.876, RMSEC = 0.467, RP = 0.865, and RMSEP = 0.362) than FS-ELM in determining pear firmness. These results indicate that SPA is helpful to improve SSC and firmness determination performance of ELM model. 3.5.3. GRNN determination models Given a training set, the adjustment of only one parameter, namely spread parameter a, is required to be determined in establishing GRNN model. To some extent, a describes the forecasting ability and the generalizability of the GRNN. The range of a was set from 0.1 to 1 with 0.1 step. The RMSEC at each spread value was calculated. The optimal value of a was decided according to the smallest RMSEC. Fig. 5 shows the values of RMSEC at each spread value at different characteristic variable selection for SSC and firmness. The determined optimal values of a are listed in Table 3. Tables 4 and 5 also list the calibration and prediction performances of GRNN models when different variables were used for determining SSC and firmness, respectively. Table 4 presents that FS-GRNN had much better calibration performance (RC = 0.891, RMSEC = 0.516) than SPA-GRNN (RC = 0.801, RMSEC = 0.682), but SPA-GRNN had better prediction performance (RP = 0.800, RMSEP = 0.433) than FS-GRNN (RP = 0.731, RMSEP = 0.474). Table 5 tells that FS-GRNN had a little better calibration performance (RC = 0.902, RMSEC = 0.419) than SPA-GRNN (RC = 0.883, RMSEC = 0.456), but SPA-GRNN had a little better prediction performance (RP = 0.817, RMSEP = 0.425) than FS-GRNN (RP = 0.810, RMSEP = 0.451). 3.5.4. MLR determination models Since the variables (402) in full spectra are much more than the samples in calibration set (70), the MLR models for internal quality

1.5 1.4

SSC-FS SSC-SPA Firmness-FS Firmness-SPA

1.3 1.2

RMSEC

Table 5 Comparison of determination results for firmness by different models at different characteristic variables selection methods.

1.1 1.0

Modeling methods

Variable selection methods

Calibration set

Prediction set

RC

RMSEC

RP

RMSEP

0.9

LSSVM

FS SPA FS SPA FS SPA SPA FS SPA

0.884 0.891 0.843 0.876 0.902 0.883 0.848 0.754 0.797

0.457 0.442 0.522 0.467 0.419 0.456 0.414 0.570 0.417

0.872 0.843 0.755 0.865 0.810 0.817 0.833 0.767 0.782

0.348 0.389 0.528 0.362 0.451 0.425 0.422 0.549 0.534

0.8

ELM GRNN MLR PLS

0.7 0.6 0.0

0.2

0.4

0.6

0.8

1.0

1.2

α Fig. 5. The calculated RMSEC at different spread values in GRNN models and at different characteristic variable selection methods for SSC and firmness.

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1.3

SSC-FS SSC-SPA

1.2

Firmness-FS Firmness-SPA

1.1

RMSEC

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3

0

2

4

6

8

10

12

14

16

18

20

Numbler of latent variables Fig. 6. Changed RMSEC with the number of latent variables in PLSR models for SSC and firmness prediction when FS and selected characteristic variables by SPA were used as input variables.

determination using full spectra were not established. The SSC and firmness determination results of MLR models established by using selected characteristic variables by SPA are shown in Tables 4 and 5, respectively. For SSC, the RC and RP were 0.875 and 0.832, and the RMSEC and RMSEP were 0.459 and 0.436, respectively (Table 4). For firmness, the RC and RP were 0.848 and 0.833, and the RMSEC and RMSEP were 0.414 and 0.422, respectively (Table 5). 3.5.5. PLSR determination models A critical step in building a robust PLSR model is choosing the optimal number of LVs, which can avoid establishing an overfitting or underfitting model. Generally, the optimal number of LVs corresponds to the lowest value of RMSEC (Zou et al., 2007). The calculated RMSEC values at different number of LVs in PLSR models for SSC and firmness determination when FS and selected characteristic variables by SPA were used as input variables are shown in Fig. 6. Fig. 6 indicates that for each internal quality, the RMSEC decreased with increasing number of LVs until the number of LVs reached a value, then it increased with the number of LVs. Therefore, the optimal number of LVs for predicting SSC using FS-PLSR and SPA-PLSR was 5, and the number was 12 and 7 for firmness using FS-PLSR and SPA-PLSR, respectively. The determination performances of PLSR for determining SSC and firmness of used pears are shown in Tables 4 and 5, respectively. For SSC determination, SPA-PLSR had higher RC (0.785) and RP (0.742), and had lower RMSEC (0.523) and RMSEP (0.547) than FS-PLSR (Table 4). Table 5 indicates that for firmness determination, SPA-PLSR also had higher RC (0.797) and RP (0.782), and had lower RMSEC (0.417) and RMSEP (0.534) than SPA-PLSR. That is SPA-PLSR had better SSC and firmness determination performances than FS-PLSR. 3.6. Comparison When compare the performance of different models in determining SSC and firmness, it was found that the PLS models had the worst, and SPA-MLR had moderate performance. Generally, the SSC and firmness determination performance of nonlinear models were better than linear models. For SSC determination, it was found that SPA-ELM had the highest RP, higher RC, lower RMSEC and RMSEP. Moreover, only 19 variables were used in the model. It was concluded that SPA-ELM was the best SSC prediction model. When the performances of different models for determining firmness were compared, it was found that FS-LSSVM had the highest RP, the lowest RMSEP, higher RC and lower RMSEP. However, all 402 dielectric variables in full spectra of dielectric constant and

dielectric loss factor were used in FS-LSSVM. The more variables used in model causes the model be more complicated, which is not beneficial to improving model calculation speed. The prediction performance of SPA-ELM is next to FS-LSSVM. Moreover, only 13 variables, which was 3.2% of 402 variables in full dielectric spectra, was used in SPA-ELM. Therefore, SPA-ELM was regarded as the best model in predicting pear firmness. Contrasted with reported data on SSC and firmness prediction for postharvest pears using Vis/NIR spectroscopy, it was found that although the highest RP for SSC obtained here (RP = 0.838) was lower than that observed by Fan et al. (2014) (RP = 0.956), by Jiang and Zhu (2013) (RP2 = 0.91), by Li et al. (2013) (RP = 0.9164) and by Xu et al. (2012) (RP2 = 0.880), the SSC prediction error in this study (RMSEP = 0.464) was similar to what reported by Xu et al. (2012) (RMSEP = 0.459) and by Nicolaї et al. (2008) (RMSEP = 0.44). Moreover, the SSC prediction performance obtained here was better than that presented by Paz et al. (2009) with standard error of cross-validation between 0.59 and 1.49, and with R2 between 0.39 and 0.76. With regard to firmness, the best RP obtained here (0.872) was a little lower than that obtained by Jiang and Zhu (2013) (RP = 0.90) and by Li et al. (2013) (RP = 0.8912), but better than that obtained by Paz et al. (2009) with R2 between 0.39 and 0.76. The reported RMSEP here (0.348) was lower than that reported by Li et al. (2013) (RMSEP = 0.6247), and was much lower than that reported by Machado et al. (2012) (RMSEP = 1.08). These indicate that dielectric spectroscopy technique could be applied to predict soluble solids content and firmness of intact pears with different maturities.

4. Conclusions Poor linear relationship between permittivity at a single frequency and SSC or firmness tells that it is impossible to select one permittivity value for accurate prediction of SSC and firmness of pears during ripening. Dielectric spectroscopy technique combined with nonlinear modeling methods of LSSVM, ELM and GRNN, and linear modeling methods of MLR and PLSR was successfully utilized for the determination of SSC and firmness of pears. Nineteen characteristic variables for SSC and 13 characteristic variables for firmness were selected by SPA from the spectra of dielectric constant and dielectric loss factor at 201 discrete frequencies from 20 to 4500 MHz. The established nonlinear models had better SSC and firmness determination performance than linear models. SPAELM was regarded as the best model for predicting SSC (RP = 0.838, RMSEP = 0.464) and firmness (RP = 0.865, RMSEP = 0.362) of pears with different maturities. Dielectric spectroscopy technique could get an ideal performance in the measurement of SSC and firmness of pears, and it will become an alterative nondestructive internal quality detection method of fruits. The study offers a method for developing portable fruit maturity detector to instruct fruit planting and harvesting. Acknowledgements The authors gratefully acknowledge the financial support provided by National Natural Science Foundation of China (Project No. 31171720). References Araújo, M.C.U., Saldanha, T.C.B., Galvao, R.K.H., Yoneyama, T., Chame, H.C., Visani, V., 2001. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst. 57 (2), 65–73.

W. Guo et al. / Computers and Electronics in Agriculture 117 (2015) 226–233 Blackham, D.V., Pollard, R.D., 1997. An improved technique for permittivity measurements using a coaxial probe. IEEE Trans. Instrum. Meas. 46 (5), 1093–1099. Blanke, M.M., 2013. Non-invasive assessment of firmness and nir sugar (tss) measurement in apple, pear and kiwi fruit. Erwerbs-Obstbau 55 (1), 19–24. Cavaco, A.M., Pinto, P., Antunes, M.D., da Silva, J.M., Guerra, R., 2009. ‘Rocha’ pear firmness predicted by a Vis/NIR segmented model. Postharvest Biol. Technol. 51 (3), 311–319. Chua, K.S., 2003. Efficient computations for large least square support vector machine classifiers. Pattern Recogn. Lett. 24, 75–80. de Oliveira, G.A., Bureau, S., Renard, C.M.-G.C., Pereira-Netto, A.B., de Castilhos, F., 2014. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 143, 223–230. Etienne, C., Moing, A., Dirlewanger, E., Raymond, P., Monet, R., Rothan, C., 2002. Isolation and characterization of six peach cDNAs encoding key proteins in organic acid metabolism and solute accumulation: involvement in regulating peach fruit acidity. Physiol. Plant. 114 (2), 259–270. Fagan, C.C., Everard, C., O’Donnell, C.P., Downey, G., O’Callaghan, D.J., 2005. Prediction of inorganic salt and moisture content of process cheese using dielectric spectroscopy. Int. J. Food Prop. 18 (3), 543–557. Fan, S.-x., Huang, W.-q., Li, J.-b., Zhao, C.-j., Zhang, B.-h., 2014. Characteristic wavelengths selection of soluble solids content of pear based on nir spectral and LS-SVM. Spectrosc. Spectral Anal. 34 (8), 2089–2093 (in Chinese). Galvão, R.K.H., Araujo, M.C.U., José, G.E., Pontes, M.J.C., Silva, E.C., Saldanha, T.C.B., 2005. A method for calibration and validation subset partitioning. Talanta. 67, 736–740. Guo, W., Chen, K., 2010. Relationship between dielectric properties from 10 to 4500 MHz and internal quality of peaches. Trans. Chin. Soc. Agric. Mach. 41 (3), 134–138 (in Chinese). Guo, W., Liu, Y., Zhu, X., Wang, S., 2011a. Dielectric properties of honey adulterated with sucrose syrup. J. Food Eng. 107 (1), 1–7. Guo, W., Nelson, S.O., Trabelsi, S., Kays, S.J., 2008. Radio frequency (RF) dielectric properties of honeydew melon and watermelon juice and correlations with sugar content. Trans. Chin. Soc. Agric. Eng. 24 (5), 289–292 (in Chinese). Guo, W., Zhu, X., Liu, H., Yue, R., Wang, S., 2010a. Effects of milk concentration and freshness on microwave dielectric properties. J. Food Eng. 99 (2), 344–350. Guo, W., Zhu, X., Liu, Y., Zhuang, H., 2010b. Sugar and water contents of honey with dielectric property sensing. J. Food Eng. 97 (2), 275–281. Guo, W., Zhu, X., Nelson, S.O., Yue, R., Liu, H., Liu, Y., 2011b. Maturity effects on dielectric properties of apples from 10 to 4500 MHz. LWT-Food Sci. Technol. 44 (1), 224–230. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1–3), 489–501. Jiang, H., Zhu, W., 2013. Determination of pear internal quality attributes by Fourier transform near infrared (FT-NIR) spectroscopy and multivariate analysis. Food Anal. Methods 6 (2), 569–577. Li, J., Huang, W., Zhao, C., Zhang, B., 2013. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J. Food Eng. 116 (2), 324–332. Liu, D., Guo, W., 2015. Identification of kiwifruits treated with exogenous plant growth regulator using near-infrared hyperspectral reflectance imaging. Food Anal. Methods 8 (1), 164–172. Liu, Y., Ying, Y., 2007. Noninvasive method for internal quality evaluation of pear fruit using fiber-optic FT-NIR spectrometry. Int. J. Food Prop. 10 (4), 877–886. Machado, N.P., Fachinello, J.C., Galarca, S.P., Betemps, D.L., Pasa, M.S., Schmitz, J.D., 2012. Pear quality characteristics by Vis/NIR spectroscopy. Anais Da Academia Brasileira De Ciencias 84 (3), 853–863.

233

Næs, T., Mevik, B.-H., 2001. Understanding the collinearity problem in regression and discriminant analysis. J. Chemom. 15 (4), 413–426. Nelson, S.O., 2003. Frequency- and temperature-dependent permittivities of fresh fruits and vegetables from 0.01 to 1.8 GHz. Trans. ASAE 46 (2), 567–574. Nelson, S.O., Forbus Jr., W.R., Lawrence, K.C., 1995. Assessment of microwave permittivity for sensing peach maturity. Trans. ASAE 38 (2), 579–585. Nelson, S.O., Guo, W., Trabelsi, S., Kays, S.J., 2007. Dielectric properties of watermelons for quality sensing. Meas. Sci. Technol. 18, 1887–1892. Nelson, S.O., Trabelsi, S., Kays, S.J., 2006. Dielectric spectroscopy of honeydew melons from 10 MHz to 1.8 GHz for quality sensing. Trans. ASABE 49 (6), 1977– 1981. Ng, S.K., Ainsworth, P., Plunkett, A., Haigh, A.D., Gibson, A.A.P., Parkinson, G., Jacobs, G., 2008. Determination of added fat in meat paste using microwave and millimetre wave techniques. Meat Sci. 79 (4), 748–756. Nicolaї, B.M., Verlinden, B.E., Desmet, M., Saevels, S., Saeys, W., Theron, K., Cubeddu, R., Pifferi, A., Torricelli, A., 2008. Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biol. Technol. 47 (1), 68–74. Ouyang, Q., Chen, Q., Zhao, J., Lin, H., 2013. Determination of amino acid nitrogen in soy sauce using near infrared spectroscopy combined with characteristic variables selection and extreme learning machine. Food Bioprocess Technol. 6 (9), 2486–2493. Paz, P., Sánchez, M.-T., Pérez-Marín, D., Guerrero, J.E., Garrido-Varo, A., 2009. Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Comput. Electron. Agric. 69 (1), 24–32. Shang, L., Gu, J., Guo, W., 2013. Non-destructively detecting sugar content of nectarines based on dielectric properties and ANN. Trans. Chin. Soc. Agric. Eng. 29 (17), 257–264 (in Chinese). Specht, D.F., 1991. A general regression neural network. IEEE Trans. Neural Networks 2 (6), 568–576. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., 2002. Least Squares Support Vector Machines. World Scientific, Singapore. Suykens, J.A.K., Vandewalle, J., 1999. Least squares support vector machine classifiers. Neural Process. Lett. 9 (3), 293–300. Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York. Vizzotto, G., Pinton, R., Varanini, Z., Costa, G., 1996. Sucrose accumulation in developing peach fruit. Physiol. Plant. 96 (2), 225–230. Wold, S., Sjostrom, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58 (2), 109–130. Xu, H., Qi, B., Sun, T., Fu, X., Ying, Y., 2012. Variable selection in visible and nearinfrared spectra: application to on-line determination of sugar content in pears. J. Food Eng. 109 (1), 142–147. Ye, S., Wang, D., Min, S., 2008. Successive projections algorithm combined with uninformative variable elimination for spectral variable selection. Chemom. Intell. Lab. Syst. 91 (2), 194–199. Ying, Y., Liu, Y., 2008. Nondestructive measurement of internal quality in pear using genetic algorithms and FT-NIR spectroscopy. J. Food Eng. 84 (2), 206–213. Zhu, Q.Y., Qin, A.K., Suganthan, P.N., Huang, G.B., 2005. Evolutionary extreme learning machine. Pattern Recogn. 38, 1759–1763. Zhu, X., Guo, W., Jia, Y., Kang, F., 2015. Dielectric properties of raw milk as functions of protein content and temperature. Food Bioprocess Technol. 8 (3), 670–680. Zhu, X., Guo, W., Wang, S., 2013. Sensing moisture content of buckwheat seed from dielectric properties. Trans. ASABE 56 (5), 1855–1862. Zou, X., Zhao, J., Li, Y., 2007. Selection of the efficient wavelength regions in FT-NIR spectroscopy for determination of SSC of ‘Fuji’ apple based on BiPLS and FiPLS models. Vib. Spectrosc. 44 (2), 220–227.