Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging

Postharvest Biology and Technology 112 (2016) 134–142

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Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging Longguo Wua , Jianguo Hea,b,* , Guishan Liub , Songlei Wangb , Xiaoguang Heb a b

Institute of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia 750021, China School of Agriculture, Ningxia University, Yinchuan, Ningxia 750021, China

A R T I C L E I N F O

A B S T R A C T

Article history: Received 6 December 2014 Received in revised form 4 September 2015 Accepted 4 September 2015 Available online 6 November 2015

A hyperspectral imaging technique was used for acquiring reflectance images to identify common defects (bruise, insect-infestetation and cracks) on jujube fruit. Hyperspectral images of jujubes were evaluated from the regions of interest through principal component analysis (PCA) to select five optimal wavelengths (420,521,636,670,679 nm) from 300 samples in the spectral region of 400–1000 nm and four important wavelength (1028,1118,1359,1466 nm) in the region of 978–1586 nm. Compared with support vector machine (SVM) models, the soft independent modeling of class analogy (SIMCA) models of intact, cracked, bruised, and insect-infested jujubes based on five wavelengths in NIR showed good performance with high classification rates of 96%, 96%, 93.9% and 95.6%, respectively. This research demonstrates the feasibility of implementing hyperspectral imaging for identifying common defects and enhancing the product quality and marketability. ã 2015 Published by Elsevier B.V.

Keywords: Hyperspectral imaging Non-destruction detection Jujubes Common defects

1. Introduction In recent years, jujube (Zizyphus jujuba Mill.) has been much admired for its delicious taste, elliptical shape and high nutritional value, so more and more farmers in China are planting jujube trees. Jujube is broadly used as a crude drug in traditional Chinese medicine as analeptic, palliative, and antibechic. Jujube has been a kind of food, food additive, and flavoring for thousands of years (Li et al., 2007). However, during the process of jujube’s picking and transportation, damage such as insect infestation, cracks and bruises occur frequently. Certain of these types of damage will cause cross-contamination, which later will ruin an entire batch of fruit during storage and distribution. Therefore, it is important to identify the damaged jujubes before shipping to market. Traditionally, visual inspection by humans is the only method for discriminating cracked and insect-infested jujubes, which is inefficient and unreliable for large processing enterprises. In order to meet the rapid development of the jujube industry, it is necessary to find a rapid, automated and non-destructive detecting method. In the past few decades, a number of different non-invasive techniques had been explored as possible instrumental methods

* Corresponding author at: Institute of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia 750021, China. Fax: +86 951 2061283. E-mail address: [email protected] (J. He). http://dx.doi.org/10.1016/j.postharvbio.2015.09.003 0925-5214/ ã 2015 Published by Elsevier B.V.

for evaluating various food quality attributes (Fathi et al., 2011 and Kumar and Mittal, 2010). These techniques included machine vision (Quevedo and Aguilera, 2010), X-ray imaging (Zou et al., 2010) and near infrared reflectance (NIR) spectroscopy (Kotwaliwale et al., 2007). It is generally known that almost all of these instrumental methods have drawbacks that more or less limit their applicability to certain specific cases. For instance, near-infrared reflection (NIR) spectroscopy has gained widespread acceptance for its rapid analysis of a wide variety of food products and parameters and it has been used to analyze multiple attributes simultaneously (Klaypradit et al., 2011). Unfortunately, NIR spectroscopic instruments are considered as point-based scanning instruments that provide one spectrum of the target sample without giving any spatial information. In fact, the spatial distribution of quality parameters is necessary in many cases. Hyperspectral imaging now has used a new generation of spectrum detection technology to identify defective fruit. The combined imaging and spectroscopy in a hyperspectral imaging enables this system to simultaneously provide physical and chemical characteristics of an object as well as their spatial distributions. Hundreds of contiguous discrete spectral bands consist of a hyperspectral images for each spatial position of the object. Consequently, each pixel in a hyperspectral image contains a spectrum (also called spectral signature or spectral fingerprint), representing the light absorbing or scattering properties of the pixel. In essence, spectral signatures can be used to identify and

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discriminate different classes of the given materials in the image. Since each hyperspectral image is represented by a threedimensional spectral data cube (3D hypercube), it is more efficient than conventional imaging or spectroscopic techniques in agriculture, environment, geology, pharmaceuticals, medicine, food quality, and food safety (ElMasry and Sun, 2010). Many studies about internal quality evaluation of fruits and vegetables have been reported, such as SSC of jujube (He et al., 2012), moisture, firmness and soluble solids content (SSC) of banana (Rajkumar et al., 2012), firmness of apple (Wang et al., 2012), SSC and pH of grape (Boiano et al., 2012), firmness and SSC of blueberries (Gabriel et al., 2013). These techniques are also applied to detect surface defects in apples (Nicolai et al., 2006) and mealiness detection in apple (Huang and Lu, 2010), cuticle defects in cherry tomatoes (Cho et al., 2013), internal defects in cucumbers Ariana and Lu (2010), and detection of insects in small fruits like jujube (Wang et al., 2011). These studies showed the feasibility of hyperspectral imaging for measuring the quality of samples through image processing and physicochemical properties. However, no study has been published on external common defect detection of jujube through hyperspectral imaging. This technique should be suitable for simultaneous inspection of defects of jujube by providing both spectral and spatial information, thereby identifying the presence of insects, bruises, cracks. The objectives of this study were: (1) To investigate the feasibility of hyperspectral imaging to detect insect-infestation, bruise and cuticle crack defects in jujubes; (2) To categorize spectral signatures for intactness, insect, bruise and cracks in jujubes; (3) To contrast the model of soft independent modeling of class analogy (SIMCA) among raw spectrum, pretreated spectrum and transformed spectrum parameters; (4) To determine optimal wavelengths and establish support vctor machine (SVM) and SIMCA models according to spectral information respectively; (5) To choose the optimal model of classification for intact, bruised, cuticle cracked and insect infested jujubes.

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2. Materials and methods 2.1. Sample preparation Four hundred “Lingwu”jujubes (Zizyphus jujube Mill.) were randomly and manually collected from three orchards in Lingwu, China during the harvest period of 2013. Among them three hundred sample jujubes were chosen from the picked jujubes, including a set of 100 defective jujubes with cracks; a set of 100 insect-infested jujubes with a hole greater than 0.1 mm in diameter on each of the selected jujube’s surfaces; a set of 50 bruised jujubes, which are normal jujubes dropped from 1 m and with the region of injury marked; a set of 50 normal jujubes. At the same time; the jujube samples were grouped into the calibration and validation sets, with the rate of 4:1, 240 samples were labeled as the calibration set and 60 samples were the validation set. All of the jujubes were selected from those of lightred and red color in their ripening stages to assure the consistency of the samples. In the laboratory, these jujubes were stored at 4
C. Before measuring, the samples were kept overnight at room temperature (23
C). The average diameter, average length and average weight of the jujube samples were 21.68  0.5 mm, 42.98  0.5 mm and 11.21 1 g. Representative images for each peel condition were shown in Fig. 1. 2.2. Hyperspectral imaging theory The imaging spectrometer system contains the target, the objective lens, the entrance slit, the inspector optics, and the matrix detector. The system used a push-broom configuration to record a whole line of an image as well as spectral information simultaneously corresponding to each spatial pixel in the line, and store the hyperspectral image in a band-interleaved-by-line (BIL) format. The process for capturing an image is: the matrix detector acquires a line image of the target via the entrance slit (X direction). At the same time, when the transportation plate is moving, a complete and vertical scan (Y direction) is obtained.

Fig. 1. Typical surface peel conditions of jujube samples(Marked areas are as defective regions.).

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Comprehensive x and y information is made up the three dimension hyperspectral image cube. A 3-D image (x,y, l), with the whole spectral dimension (l) and spatial dimension (x, y), is acquired at a one time. The acquired hyperspectral image consists of 256 congruent sub-images containing intensities at different wavelength bands spanning from 918 to 1678 nm. 2.3. Hyperspectral imaging system By using the the push-broom system in the range of Vis-NIR wavelengths, the main components of the laboratory-based hyperspectral imaging system include a spectrograph (Imspector N17E, Golden Way Scientific Co., Ltd., US), a CCD camera along with focusing lens (G4-232, Golden Way Scientific Co., Ltd., US), an illumination unit consisting of two line halogen lamps (90– 254 VAC, 47–63 Hz, Golden Way Scientific Co., Lab., EQUIP), a conveying stage operated by a stepper motor (VT-80, Headwall Photonics Instruments Co., Ltd., Beijing, China) and a computer supported with SpectraCube data acquisition software (Hyperspec-N for AndorLuca Rev A.3.1.4.vi, Headwall Photonics Instruments Co., Ltd., Beijing, China). For the push-broom system in the NIR wavelengths, the main components of the laboratory-based hyperspectral imaging system include a spectrograph (ImSpector N17E, Specim, Spectral Imaging Ltd., Oulu, Finland), a CCD camera along with focusing lens (Models XC-130 100 Hz, Ophir Optronics Solutions Ltd., Jerusalem, Israel), an illumination unit consisting of four 35 W halogen lamps (ViP V-light, Lowel Light Inc., NY, HSIA-LS-TDIF, Zolix instruments Co., Ltd., Beijing, China), a conveying stage operated by a stepper motor (PSA200-11-X, Zolix Instruments Co., Ltd., Beijing, China) and a computer supported with SpectraCube data acquisition software (SpectraSENS Zolix Instruments Co., Ltd., Beijing, China). 2.4. Image acquisition and pre-processing The CCD device in the hyperspectral imaging system records noise and useful information, due to its illumination distribution, the different shapes of fruit, and the existence of dark current. Hence, hyperspectral images must be corrected (Polder et al., 2003). The original hyperspectral images (R0) should be corrected based on black and white reference images, which will reduce the influence of illumination and the dark current of the camera. In this study, the acquired images from the hyperspectral imaging system were corrected using the following equation: R¼

Ro  D  100 WD

ð1Þ

R was the corrected hyperspectral image in a unit of relative reflectance (%), R0 was the original hyperspectral image, and D was the dark image for which there was almost no reflection, and W was the white reference image that was almost total reflection. The spectral profiles of the pixels in the retained jujubes of the image were transformed into absorbance (A) and K–M units using the following equations: A ¼ lgR

ð2Þ

2

KM¼

ð1  RÞ 2R

ð3Þ

The average of the spectra in the region of interest of each image was extracted as a representative spectrum for the different types of jujube samples. Accordingly, three spectral calibration sets with different units, i.e., R, A and K–M, were obtained and used.

2.5. Image acquisition parameters The image acquisition parameters controlled by the software include motor speed, exposure time, and wavelength range. Based on the system configuration for Vis-NIR hyperspectral imaging, the proper speed of the translation stage was adjusted to 160 mm/s. The exposure time was set at 15 ms, and the object distance was set at 385 mm. The CCD camera had a three-dimensional sensor array with 1004  501 125 (spatial  spectral) photodiodes (called pixels). This showed that the dimension of the acquired hyperspectral image was 1004 pixels in x-direction, 501 pixels in ydirection and 125 bands in l-direction. Based on the system configuration for NIR hyperspectral imaging, the proper speed of the translation stage was adjusted at 14 mm/s and the exposure time was set at 10 ms. In order to illuminate the target samples sufficiently for using the reflectance mode, the object distance was set at 40 cm to reduce shadowing effects. The spectrograph in this system had a thermoelectrically cooled indium gallium arsenide (InGaAs) detector to disperse incident broadband light into the spectral range from 918 to 1678 nm with a spectral increment of about 2.8 nm between the contiguous bands, thus producing a total of 256 bands. The CCD camera had a three-dimensional sensor array with 320  300  256 (spatial spectral) photodiodes (called pixels). This meant that the dimension of the acquired hyperspectral image was 320 pixels in x-direction, 300 pixels in y-direction, and 256 bands in l-direction. 2.6. Hyperspectral imaging acquirement The samples were laid in a defined queue at the plate to be scanned by the hyperspectral imaging system. To conduct spectral data extraction from each subsample in the hyperspectral image, the region of interest (ROI) function of ENVI v4.6 software was used to isolate the subsample from different types of jujubes. The ‘regions of interest’ were manually set around defective areas, and then the average spectra was calculated within the ROI. The Multivariate data analysis procedure was conducted using Unscrambler software (Version 10.2, CAMO, Oslo, Norway). 2.7. Experimental methodology 2.7.1. SVM The Support Vector Machines (SVMs, also support vector networks) method is based on statistical learning of Vapnik– Chervonenkis (VC) dimension theory and the structure risk minimum principle, according to the limited sample information relative to the complexity of the model and learning ability to seek the best generalization. The SVMs regression model is constructed by using a nonlinear function (kernel function). In this paper, the four types of kernel function (linear, polynomial, radial basis function, sigmoid) are applied, and the kernel can be regarded as a nonlinear data to a high-dimensional feature space, and provides a fast calculation by allowing a linear algorithm to deal with high dimensional feature space. 2.7.2. SIMCA Soft independent modeling of class analogy (SIMCA) is based on making a PCA model for each class in a defined training data set, consisting of samples with a set of attributes and their class membership(Andrea et al., 2015). The SIMCA model requires building a principal component analysis (PCA) model for each class which describes the structure of that class as well as possible. The optimal number of principal components should be chosen for

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each model separately, according to a suitable validation procedure.

3.2. The SIMCA model between raw spectral and spectral preprocessing

3. Results

The spectra not only contained useful information, but also contained useless information such as noise, color of background, dark current, and so on (ElMasry et al., 2013). Although image correction could decrease the effect of dark current, it could not remove the phenomena of noise and baseline drift. To reduce the effect of the phenomena, data pretreatment was applied, such as Savitzky–Golay (S–G) smoothing (three polynomial and five point of smoothing), First derivative (FD), Multiple scattering corrections (MSC) and Standard Normal Variable transformation (SNV). The results of SIMCA methods presented in Table 1 enabled comparison of the classification accuracies of the five studied classifiers for raw and transformed data. The percentages of correct classification of the original spectrum was superior to that of the pretreated spectrum (S-G, MSC, SNV and FD). The percentage of correct classification of intact jujubes, cracked jujubes, bruised jujubes, and insect-infested jujubes was above 95% and non of the factors was less.

3.1. Spectral characteristics In this study, the pixels of intact jujubes and defects of cracked, bruised and insect-infested jujubes were extracted by Envi v4.6 software working on the regions of interest (ROI) (Anna et al., 2010). The typical raw spectra extracted from the examined four types of jujube samples in the wavelength range of 400–1000 nm and 900–1700 nm are shown in Fig. 2 and Fig. 3. Fig. 2 which shows that significant differences existed in the spectral characteristics of intact, bruised, insect-infested, and cracked regions of jujubes in the Vis-NIR ranges. Considerably lower and more consistent reflectance was observed from all jujubes samples for the visible region of 500–675 nm. Beyond 675 nm, reflectance for all samples started to increase dramatically and reached the peak when occurring at 900 nm for most of the samples. There was obvious baseline drift. Fig. 3 shows the original spectra of four types of jujubes samples, from which it could be seen that the NIR spectrogram for each sample varied according to the sample’s variety. Considerably higher and more consistent reflectance was observed from all jujubes samples for the near-NIR region of 990–1185 nm. Beyond 1320 nm, reflectance for all samples started to decrease dramatically and reached the valley that occurred at 1455 nm for most of the samples because of O H’s absorption associated with the absorption of water. Some bands appeared obviously to be noise below 978 nm and above 1586 nm. And all spectra of samples had the phenomena of baseline drift, because peak values of curve had a huge difference. The SIMCA models in region of 978–1586 nm and 400–1000 nm were chosen to study further.

3.3. The SIMCA model of different spectrum parameter Three types of SIMCA models were established based on reflectance, absorbance and K–M spectral profiles in the full wavelength range of 400–1000 nm and 978–1586 nm. These models were denoted as R-SIMCA, A-SIMCA and KM-SIMCA models, respectively. The performance of the best models of each type was shown in Table 2. These results demonstrate that the best R-SIMCA model had better performance than those of the other two types, with the highest percentage of correct classification over 95% as well as the lowest number of of factors of 2, respectively. However, the SIMCA models of absorbance and K–M spectrum were not obviously able to classify the intact jujube, cracked jujubes, bruised jujubes, insect-infested jujubes.

Fig. 2. Raw spectral profiles of jujubes in 400–1000 nm. (a) is the plot of intact jujubes; (b) is the plot of cracked jujubes; (c) is the plot of bruised jujubes; (d) is the plot of insect infested jujubes.

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Fig. 3. Raw spectral profiles of jujubes in 918–1678 nm. (a) is the plot of intact jujubes; (b) is the plot of cracked jujubes; (c) is the plot of bruised jujubes; (d) is the plot of insect infested jujubes.

Table 1 The SIMCA model using original spectra and pre-treated spectra in the wavelengths of 400–1000 nm and 978–1586 nm. Jujube types

Wavelength/ Raw nm No. of factors

Savitzk–Golay smoothing

MSC

SNV

1st Derivative

Percentage of correctly classified

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

Intact

400–1000 978–1586

5 4

98.0 100

5 5

98 100

5 7

94.0 100

5 7

94.0 100

8 16

0 0

Cracked

400–1000 978–1586

3 3

95.0 99

3 3

94 99

5 6

96.0 95

5 7

96.0 96

9 15

76 0

Bruised

400–1000 978–1586

2 4

98.0 100

2 4

98 98

5 7

98.0 100

5 7

98.0 100

8 13

0 0

3

96.7

3

95.5

5

95.6

6

4

99

7

94

4

94.7

11

infested

Insect-

400– 1000

93.3

11

93.3 978– 1586

99

100

4

3.4. Optimal wavelength extraction Using the full spectral range could imply the risk of over fitting, noise and nonlinearities resulting in less accurate models. Some irrelevant information might exist in hyperspectral images, which could weaken the performance of model calibration. Therefore, most useful information should be selected from the hyperspectral image to reduce and even eliminate redundancy, thus speeding up data processing and improving the efficiency of data analysis. To meet the need for online sensing, one feasible solution would be multispectral imaging systems where a great number of wavelengths (>200) in the hyperspectral imaging systems can be reduced to less than 10, thus resulting in substantially reduced image sizes that help alleviate hardware requirements and computing loads. In this study, PCA was employed to allocate

important wavelengths. The reflectance spectrum of intact, cracked, bruised, and insect-infested jujubes in the regions of 400–1000 nm and 978–1586 nm were analyzed by PCA in the Unscrambler X 10.2 software. Fig. 4 shows the score plot and loading plot of different types of jujubes in Vis-NIR range and NIR range. The four types of jujubes were represented with different colors and icons, and it was easy to know that the jujubes of the same class were clustered and those of different classes were distributed. In Fig. 4a, the contribution rates of the first three principal components in 400–1000 nm were 86%, 10%, 2%, respectively. In Fig. 4b, the contribution rates of the first three principal components in 978–158 6 nm were 80%, 19%, 1%, respectively. It was easy to distinguish the insect jujubes from the intact or bruised jujubes. However, it was not easy to distinguish the four types of jujubes.

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Table 2 The SIMCA model of using different spectrum parameters in 400–1000 nm and 978–1586 nm. Jujube types

Wavelength/ nm

R

A

K–M

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

Intact

400–1000 978–1586

5 4

98.0 100

4 5

0 0

4 5

0 0

Cracked

400–1000 978–1586

3 3

95.0 99

3 3

0 0

3 3

77 0

Bruised

400–1000 978–1586

2 4

98.0 100

2 4

0 0

4 5

0 0

Insectinfested

400–1000 978–1586

3 4

96.7 99

3 4

0 0

3 4

91 12

Based on the weighted coefficients of PCA, the wavelengths of upward peaks and downward peaks were assumed to be the optimal wavelengths Wu et al., 2013. The important wavelengths of PC1-PC3 were determined for different types of jujubes as shown in Fig. 5. In Fig. 5a, the loading curve of PC1 was quite smooth and the optimal wavelengths were not apparent. The curve of PC2 and PC3 had obvious peak values and the optimal wavelengths (420, 521, 636, 670, 679 nm) were chosen. In Fig. 5b, the loading plots for the NIR wavelength was similar to that of the Vis-NIR wavelength. The curves of PC1 and PC3 were quite smooth and the optimal wavelengths could not be determined. The curve of PC2 had obvious peak values and the optimal wavelengths (1028, 1118, 1359, 1466 nm) were chosen. 3.5. The SVM model between optimal wavelengths and full wavelength The SVM method was applied to build the predictive model for the defective jujubes using full spectral data and optimal spectral data, covering the wavelength range of 400–1000 nm and 978– 1586 nm. In order to predict instrumental attributes, new SVM models were built in 400–1000 nm range using the selected

optimal wavelengths and the full spectral range and the performances of these models are compared in Table 3. Similar to the SVM regression models with full spectra, the percentage of correct classification using the optimal wavelength was poor. Compared with the models of Nu-SVM, the models of C-SVM were better because of the higher percentage of correct classification. At the same time, the percentage of correct classification of different core functions had different results. The classified SVM models based on line core function were better than that of the other three kinds of kernel function model and the sigmoid core function of the SVM model was the worst. The selected optimal wavelengths and the full wavelengths of the SVM models in 978–1586 nm range were shown in Table 4. All core function of the C-SVM models was better than that of the NuSVM models yielding a higher percentage of correct classification. Compared with different core function of C-SVM models, the percentage of correctly classified of the polynomial was better than that of linear and radial basis function and the correct classification rate of the sigmoid was the worst. Simply stated, the SVM model built from the full wavelength was superior to that of optimal wavelength.

Fig. 4. The PCA loading score plot of four type of damaged jujubes. (a) and (b) are plots of score in the spectral range of 400–1000 nm and 978–1586 nm, respectively.

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Table 5. As shown in Table 5, the SIMCA models in full wavelength had good classification rates, above 95%. Similarly, the optimal classification models were established by SIMCA based on these selected wavelengths from raw spectral, and similar good results of all classification models were obtained when comparing with the models using full spectra that were able to classify 93% above. Compared with the region of Vis-NIR wavelength, the SIMCA model using full spectra and optimal spectra showed a lower identification rate for different type of jujubes in the region of NIR. For example, the classification rate of intact, cracked, bruised and insect-infested jujubes in Vis-NIR spectra was 100%, 99%, 100%, 99%, respectively. In the other hand, the number of principal components was very similar in Vis-NIR and NIR wavelength. 4. Discussion

Fig. 5. The loading plot of four types jujubes. (a) and (b) are plots of loading in the spectral range of 400–1000 nm and 978–1586 nm, respectively.

3.6. Comparing the SIMCA model between optimal wavelengths and full wavelength In order to choose a good method, the SIMCA models were built using the selected optimal wavelengths instead of the full spectral range and the performance of these models were compared in

Table 1 summarizes the results of SIMCA models of original spectrum and pretreatment spectrum in 400–1000 nm and 978– 1586 nm. The percentages of correct classification of multiple scattering correctsion (MSC) was equal to the rate of correct classification of SNV. However, pre-processing of FD had lower percentages of correctly classified. The major reason was the first derivative which enlarged the useful information and meanwhile brought about much noise. The percentages of correct classification using the Savitzky–Golay smoothing spectrum were equal to that of original spectrum. It also suggests that noise did not contribute to the original spectra. The original spectral data could directly be used for modeling in future. The SIMCA models in region of 978–1586 nm were better than that of in region of 400– 1000 nm. So, the original spectrum was chosen to reduce data preprocessing work. Table 2 summarizes the results for three spectral parameter modes (i.e., A, K–M) for differentiating normal jujubes from three defected ones. The results showed that the SIMCA model of absorbance and K–M spectrum were not the obvious choice to classify the intact, cracked, bruised, and insect-infested jujubes. The emergence of this phenomenon might eliminate the effective information differences between different types of long jujubes. Therefore, the reflectance spectrum data were considered an optimal spectrum.

Table 3 Comparison of the optimal wavelengths and full wavelength of SVM model in 400–1000 nm. Jujube types

Wavelength forms

The percentage of correctly classified (%) Linear

Radial basis function

Sigmoid

Calibration set

Validation set

Polynomial Calibration set

Validation set

Calibration set

Validation set

Calibration set

Validation set

Nu-SVM

Full wavelength Optimal wavelength

85.47 77.51

82 72.66

83.04 43.25

76.12 53.29

80.97 77.16

77.51 71.97

44.64 38.41

41.86 40.83

C-SVM

Full wavelength Optimal wavelength

95.5 74.05

91 72.66

90.66 77.16

83.04 69.55

79.93 75.43

73.01 69.55

43.6 40.83

38.75 36.33

Table 4 Comparison of the optimal wavelengths and full wavelength of SVM model in 978–1586 nm. Jujube types

Wavelength forms

The percentage of correctly classified (%) Linear

Radial basis function

Sigmoid

Calibration set

Validation set

Polynomial Calibration set

Validation set

Calibration set

Validation set

Calibration set

Validation set

Nu-SVM

Full wavelength Optimal wavelength

91.05 80.85

89.14 79.56

85.94 78.59

84.34 77.96

92.65 80.83

89.14 78.27

38.34 79.87

41.85 79.23

C-SVM

Full wavelength Optimal wavelength

98.72 79.23

93.29 78.59

99.68 84.03

92.65 80.19

87.86 78.91

86.26 77.00

36.1 47.92

33.87 46.00

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Table 5 The SIMCA model of different types of defective jujubes using optimal wavelengths and full wavelength. Jujube type

Wavelength (nm)

No. of factors

Percentage of correctly classified

No. of factors

Percentage of correctly classified

Intact

400–1000 978-1586

5 4

98.0 100

4 4

98 96

Cracked

400–1000 978–1586

3 3

95.0 99

3 2

96 96

Bruised

400–1000 978–1586

2 4

98.0 100

2 4

96 93.9

Insect-infested

400–1000 978–1586

3 4

96.7 99

3 3

94.4 95.6

Full wavelength

Optimal wavelength

Fig. 4 shows the score plot of four-type jujubes in region of 400– 1000 nm and 978–1586 nm. The chemical bonds of biological materials absorbed light energy at specific wavelengths. In the region of Vis-NIR, the research found the optimal wavelength related to chemical composition. The wavelength of 420 nm could be related to carotenoids; the wavelength of 521 nm could be related to anthocyanins; the wavelength of 630–690 nm could be related to chlorophylls, which depended on the ripening degree through cuticle color of jujubes, so the optimal wavelengths (420, 521, 636, 670, 679 nm) were employed in identification of the defective jujubes. In the region of NIR, the optimal wavelengths for different types of jujubes as shown in Fig. 5b were found to be 1028, 1118, 1359, 1466 nm in the region of 978–1586 nm. The wavelength 1120 nm could be related to the moisture content of the agricultural food products (Senthilkumar et al., 2012). The near peaks at 1050 nm and 1400 nm were related to the combination bands of O H in water (Collect et al., 2010; Iqbal et al., 2013). The wavelength 1359 nm might be related to the C H bond representing fiber and starch content such as was reported for soybean seeds (Wang et al., 2003). Similarly, wavelengths such as 1300 and 1347 nm were considered the most significant to classify fungal contaminated canola and wheat kernels, respectively (Senthilkumar et al., 2012; Singh et al., 2007). Hence, variation at wavelength 1028, 1118, 1359, 1466 nm in this study can be used to classify different red jujubes. Tables 3 and 4 show that the model of C-SVM was better than the models of Nu-SVM, because the range of the parameter C was from 1 to infinity, which increased the difference among the different samples. What is more, there was the linear relationship between samples. Hence, the classified SVM models based on line core function had the higher correctly classified rate and the sigmoid core function was the worst. Compared with the method of the SVM and the SIMCA, the results showed the method of SIMCA was better than the method of SVM. This result may be an artifact of the small sample size. This study demonstrated spectra variation with different types of jujubes by analyzing both pretreated spectra and transformed spectra, which mainly appeared at the wavelengths range of 400–1000 nm and 978– 1586 nm. PCA was used to reduce high spectral dimensionality to a few optimal wavelengths centered at 420, 521, 636,670, and 679 nm in region of 400–1000 nm and at 1028, 1118, 1359, and1466 nm in the region of 978–1586vnm. The SIMCA method proved to be effective for the purpose of examining different types of jujubes for classification and was more robust than the SVM algorithm for classification. The SIMCA model of intact, cracked, bruised and insect-infested jujubes based on the raw spectra and by using only five wavelengths in NIR showed good performance with a high classification rate of 96%, 96%, 93.9% and 95.6%, respectively. The results obtained in this study would encourage

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