Classification of internally damaged almond nuts using hyperspectral imagery

Classification of internally damaged almond nuts using hyperspectral imagery

Journal of Food Engineering 103 (2011) 62–67 Contents lists available at ScienceDirect Journal of Food Engineering journal homepage: www.elsevier.co...

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Journal of Food Engineering 103 (2011) 62–67

Contents lists available at ScienceDirect

Journal of Food Engineering journal homepage: www.elsevier.com/locate/jfoodeng

Classification of internally damaged almond nuts using hyperspectral imagery Songyot Nakariyakul a,⇑, David P. Casasent b a b

Department of Electrical and Computer Engineering, Thammasat University, 99 Moo 18 Phaholyothin Rd., Khlongluang, Pathumthani 12120, Thailand Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

a r t i c l e

i n f o

Article history: Received 20 January 2010 Received in revised form 19 June 2010 Accepted 24 September 2010 Available online 1 October 2010 Keywords: Almond nuts Feature selection Hyperspectral data Product inspection Ratio features

a b s t r a c t Hyperspectral transmission spectra of almond nuts are studied for discriminating internally damaged almond nuts from normal ones. We introduce a novel internally damaged almond detection method that requires only two sets of ratio features (the ratio of the responses at two different spectral bands) for classification. Our proposed method avoids exhaustively searching the whole feature space by first ordering the set of ratio features and then choosing the best ratio features based on the ordered set. Use of two sets of ratio features for classification is attractive, since it can be used in real-time practical multispectral sensor systems. Experimental results demonstrate that our method gives a higher classification rate than does use of the best feature selection subset of separate wavebands or than does use of feature extraction algorithms using all wavelength data. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Hyperspectral (HS) data are high-dimensional data that contain more than a hundred responses of the object in narrowly spaced k spectral bands. Use of an HS system has been investigated for food and agricultural product inspection, since it provides a noninvasive, accurate inspection system. This use occurs, since the spectral information in HS data uniquely characterizes and identifies the chemical and/or physical properties of the constituent parts of an agricultural product (Lu and Chen, 1999). HS applications in agricultural products include anomaly detection such as detecting skin tumors on chicken carcasses (Kong et al., 2004; Nakariyakul and Casasent, 2007, 2009), surface defects on apples (Kleynen et al., 2005), and aflatoxin levels in corn kernels (Pearson et al., 2001). Furthermore, HS data have also been used for food quality measurement such as determining wheat grain quality (Dowell et al., 1999), pork quality and marbling level (Qiao et al., 2007), moisture in soybean seeds (Lamb and Hurburgh, 1991), and sugar content in potatoes (Mehrubeoglu and Cote, 1997). In this present work, we consider classification of almond nuts, Prunus dulcis, as having concealed internal damage or not using HS feature selection techniques. Internal damage in almonds is defined as a browning of the kernel interior after cooking (Reil et al., 1996). Internally damaged almonds are not easily distinguished from normal ones by their external appearances. They cause lower consumer consumption because of their reddish⇑ Corresponding author. Tel.: +66 2564 3001 9x3148; fax: +66 2564 3010. E-mail addresses: [email protected] (S. Nakariyakul), [email protected] edu (D.P. Casasent). 0260-8774/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2010.09.020

brown internal appearance, bitter flavor, and lower nutrition due to degraded amino acids after roasting and are thus not acceptable by the almond industry. Fig. 1 shows HS images of a normal almond and an internally damaged almond after cooking (Pearson, 1999). Pearson (1999) showed that, by using the whole spectrum of HS data from 700 to 1400 nm, he could distinguish internally damaged nuts from normal ones before roasting at an error rates as low as 12.4%. However, the HS system that measures the full transmission spectrum of whole almonds is slow and cannot achieve an inspection rate of 40 nuts/s required by almond processing plants. Thus, a new feature selection algorithm is needed that uses only a small subset of HS wavebands (use of only several k spectral features) from HS data for use in a real-time multispectral camera (such cameras can rapidly acquire 4–6 waveband response data). We consider the use of ratio features (the ratio of the responses at two different spectral bands) because they are invariant to multiplicative scaling (Keshava, 2004) that often occurs in HS data. Ratio features have been successfully used in many product inspection applications. Prior work (Pearson et al., 2001) on ratio feature selection for aflatoxin detection in an HS corn kernel database considered all ratio feature combinations for only every third spectral response; this significantly reduced the number of possible feature sets that had to be evaluated, but the classification performance was also limited. In another HS inspection application (Delwiche et al., 2000), the best 22 spectral bands were first chosen by the sequential forward selection algorithm (Whitney, 1971), and an exhaustive search of all 231 single ratio feature combinations of these 22 spectral bands was then performed to select the best single ratio feature. However, the best ratio features are not necessarily the

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Fig. 1. HS images of normal and internally damaged almonds (Pearson, 1999).

ratios of the best individual spectral bands. We propose a new detection method for internally damaged almonds that requires use of only two sets of ratio features for classification; an inexpensive and fast inspection system can be fabricated using filters at only a maximum of four spectral bands out of all bands. We compare the classification results of our proposed method to those obtained using the four and six best individual wavebands and extracted features chosen by other feature selection and feature extraction algorithms. 2. Materials and methods 2.1. Almond nuts and hyperspectral imaging system Internal damage in almonds is often developed when the nuts are exposed to a warm and moist environment (Kader and Thompson, 1992; Reil et al., 1996), which happens when a heavy rain occurs during harvest. There are many cultivars of almonds grown in the United States, but Mission variety almonds are more prone to internal damage than other almond varieties. This is possibly due to a late harvest time in September when it usually rains heavily. Thus, Mission almonds were used in this work. Pearson (1999) found that almond nuts treated with different moisture exposure and drying temperature had different percentage of developing internal damage. For example, 44.4% of nuts that were exposed to long moisture (soaked in water for 60 min and then stored in a 95% relative humidity environment for 60 h) and then dried in an air convection dehydrator at 110 °C were found to have internal damage. Pearson (1998) also reported that the storage of almonds for a long period of time (7 and 12 months) did not significantly affect the severity of internal damage in almonds. The Mission variety almond database used in this work was provided by Dr. Tom Pearson from the Agricultural Research Service in Kansas, United States. 454 almond nuts were treated with long moisture and different convection drying. The transmission spectra of these almond nuts from 700 to 1400 nm were then measured. The central region of each almond was illuminated by a 100 W quartz tungsten halogen lamp (Oriel, Stratford, CT, USA). Two different fiber optic transmission spectrometers were used to collect HS spectra; a silicon photodiode array sensor based spectrometer (Ocean Optics, Dunedin, FL, USA) was used to measure the spectrum from k = 700–1000 nm in 0.48 nm intervals, and an InGaAs photodiode array spectrometer (Control Development,

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South Bend, IN, USA) was used to obtain the spectrum from k = 950–1390 nm in 3.2 nm intervals. For each almond, ten complete transmission spectra were obtained and the average spectra from each spectrometer were used. Each spectrum was then smoothed by a 19 point Savitzky–Golay second-order filter (Hruschka, 1987), sampled at Dk = 5 nm increments and combined to produce an HS spectra with 137 spectral samples from k = 710– 1390 nm. For each almond sample, its spectral response was normalized by dividing the spectral response at each waveband by the average spectral response for that almond sample; this corrects for variations in skin quality, nut thickness, and nut shape (Pearson, 1999). After the spectra were obtained, the nuts were cooked at 135 °C for 90 min in a gravity convection oven (Lab-Line Instrument, Inc., Melros Park, IL, USA) to induce browning of the kernel interior. After cooking, the almond nuts were split at the suture and then visually inspected to determine whether they were internally damaged. 109 out of 454 Mission almonds were found to have internal damage. The database used contains the spectral responses for 454 almonds. The 454 nuts are divided into a training set of 183 nuts (139 normal and 44 internally damaged), a validation set of 45 nuts (34 normal and 11 internally damaged), and a test set of 226 nuts (172 normal and 54 internally damaged). The validation set is used to select algorithm parameters and sets of ratio features with the best generalization. 2.2. Ratio feature selection method Feature selection is an N–P complete problem (Cover and Campenhout, 1977), i.e., only an exhaustive search can locate the features that give the best classification rate PC. Using an exhaustive search to find the best single spectral band ratio feature requires (for our 137-feature database) that we evaluate performance of the classifier used for all training set samples for   137 a total of = 9316 band ratios or sets of ratio features. To se2 lect the best two pairs of ratio features requires a search of classi  9316 fication performance on the training set for = 43,389,270 2 combinations of two ratio features; this is clearly excessive. Our method reduces the number of combinations necessary and greatly improves the time required for training. Fig. 2 shows the block diagram of our proposed method; it includes spectral band reduction, band ratio feature formation, ratio feature ordering and selection, and ratio feature classification. 2.2.1. Spectral band reduction The first step in the selection method is to remove a number of feature (spectral) bands with similar information. This should not significantly affect classification performance, since the discarded k features do not give much new information expected to be useful for classification. We discard similar features before computing all possible ratio feature combinations to reduce the search time needed in training. Mean square error (MSE) is used to measure the similarity among all bands, and those bands with the highest degree of similarity are discarded. MSE is defined as

MSEðka ; kb Þ ¼

NT 1 X ðka ðiÞ  kb ðiÞÞ2 N T i¼1

ð1Þ

where ka(i) is the spectral response in band a for the i-th training sample. This measure computes the mean over all NT training samples of the square of the difference of two spectral responses. The smaller the MSE, the more similar the two spectra are. The MSE values are entered as the elements of an Nk  Nk symmetric MSE matrix M (i.e., MSE(ka, kb) = MSE(kb, ka)), where Nk is the total

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2.2.3. Ratio feature ordering and selection In this step, we order the set of ratio features and choose the final two sets of ratio features based on the ordered set. To order the ratio feature set, we apply the sequential forward selection (SFS) algorithm (Whitney, 1971) to the set of 4950 possible ratio features to select the best subset of 50 ratio features. The SFS method first selects the best single ratio feature and then adds one ratio feature at a time, which in combination with the first ratio feature maximizes some criterion function J. We use the Mahalanobis distance as the criterion function,

J ¼ ðl1  l2 ÞT C1 ðl1  l2 Þ;

Fig. 2. Block diagram of the ratio feature selection method.

number of wavebands. The M(a, b) element is the MSE between the responses of the a-th and b-th wavebands over the training set, and all diagonal elements are zeros. To reduce the number of bands used, the non-zero minimum element of M is first determined; this defines the two wavebands with the most similarity. Assume bands a and b are most similar. To decide which band (a or b) to be discarded, we examine row a and row b of M (ignoring elements M(a, b) and M(b, a)), and the row with the remaining smallest non-zero element is the band removed. For our example, assume that band a is to be omitted, we thus set row a and column a of M to be zero. We repeat this procedure until the number of bands to be discarded is met. For our almond database, MSE values are cal  137 culated for all = 9316 possible pairs of two out of all 137 2 original k bands, and 37 spectral bands are omitted after this first step. 2.2.2. Band ratio feature formation There are now 100 remaining spectral bands. All band ratios or ratio features of them are formed. In the ratio feature, we place the lowest numbered waveband in the numerator of the ratio feature; we do not consider both ratios of the same two wavebands, as they are both expected to have similar information. Thus, there are   100 = 4950 combinations of two of those 100 selected bands 2 or 4950 sets of ratio features to consider. Our goal is to select the best two pairs of ratio features to use for classification. An exhaustive search requires evaluating the performance of the classifier used for all training set samples for a total of   4950 = 12,248,775 combinations of two ratio features; this is 2 clearly excessive. The next step in our ratio feature selection method reduces this number and thus further improves training time.

ð2Þ

where l1 and l2 are the mean vectors for the training samples for the two classes in the problem (in our case, good-almond class and internally-damaged-almond class, respectively), and C is the covariance matrix for the training samples. The Mahalanobis distance is large if the mean difference between two classes is large. Prior work (Pearson et al., 2001) also used the Mahalanobis distance. To select the best subset of m ratio features out of n original ratio features, the SFS algorithm evaluates J for (2n  m + 1)m/2 subsets of ratio features. For our example, to select the best subset of 50 ratio features out of 4950 total ratio features, the SFS algorithm requires evaluating J for (2  4950  50 + 1)  50/ 2 = 246,275 subsets of ratio features. After this, we obtain an SFSordered set of 50 ratio features and the other set of the remaining 4900 ratio features. We choose the SFS algorithm since it considers discrimination. Applying SFS to the set of ratio features versus waveband features is new. Other feature ordering methods can also be used. To select the final two best sets of ratio features, we make the assumption that at least one of the two best ratio features is one of the 50 SFS selected ones; this seems very likely. To select the second ratio feature set (recall, two sets of ratio features are desired), we could choose it from either the remaining first set of 50 SFS-ordered ratio features or from the other set of 4900 ratio      50 50 4900 features. To address both choices, þ = 2 1 1 1225 + 50  4900 = 246,225 combinations of two sets of ratio features must be examined. This is a reduction by a factor of approximately 60 compared to all 12 million combinations of two sets of ratio features needed to be exhaustively searched. Section 2.2.4 details how we examine these 246,225 combinations. We note that since our method does not consider all possible combinations of two sets of ratio features, our result is sub-optimal, i.e., there is no guarantee that the result will yield the optimal two sets of ratio features chosen by an exhaustive search. 2.2.4. Ratio feature classification In selecting the best two sets of ratio features out of 246,225 combinations, we proceed as follows. A support vector machine (SVM) classifier with a Gaussian kernel is used as the classifier. The two parameters, C for error penalization in the SVM and the Gaussian kernel parameter r are set to 50 and 1, respectively; they are chosen ad hoc. For each set of two ratio features, the SVM is trained on the training set samples. The error rate for this SVM is then measured on the validation set for the same two sets of ratio features. These procedures are repeated for all 246,225 combinations of two sets of ratio features; and, the two sets of ratio features that yield the best classification rate on the validation set are selected as the final ratio features. 3. Results and discussion As noted earlier, almond nuts that are exposed to a warm and moist environment are more likely to develop internal damage.

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Almonds exposed to moisture for a long period of time produce reducing sugars (Kader and Thompson, 1992), and lipids may oxidize (Pearson, 1998), which initiates the internal browning in almonds. The drying process at a lower temperature can reduce the incidence of concealed internal damage (Pearson, 1999). Experimental results on the chemical and color measurements of the almond kernels after the moisture treatment and drying process were reported in (Pearson, 1998). The average transmittance spectra of the training set of normal and internally damaged almond nuts obtained after drying but before cooking are shown in Fig. 3. As seen, they are noticeably different, especially around 710 nm and 930 nm. The internally damaged nuts have less absorbance at the 930 nm waveband (attributed to oil (Tkachuk, 1987)), possibly due to oxidization during moisture exposure (Pearson, 1998). We note that the moisture treatment and drying process affect the chemical composition of almond nuts, which subsequently produces the internal damage in some, but not all, almonds. Our proposed ratio feature selection method is designed to select two sets of ratio features based on the spectral responses of the normal and internally damaged nuts. Thus, the moisture treatment and drying process affect the percentage of internally damaged almonds but do not influence the ratio features selected by our method. We now discuss the results of our proposed method. The first step in our method addresses the similarity among the responses in different wavebands over the training set samples. For the almond database, we found that the responses for many bands are essentially identical. For example, the MSE between the wavebands 1090 and 1095 nm is very small (7.22  106). Thus, waveband 1095 nm could be removed without much loss of information. The 37 such bands are removed in this first step; all

1.6

Normal nuts Internally damaged nuts

Transmittance response

1.4 1.2 1 0.8 0.6 0.4 700

800

900

1000

1100

1200

1300

1400

Wavelength (nm) Fig. 3. Average HS spectral responses of the training set of normal and internally damaged almond nuts.

similar responses found and omitted were adjacent bands. All 4950 combinations of ratio features of two bands for the reduced set of 100 bands are then formed in step 2. The SFS algorithm is used to rank the first 50 ratio features, and our 246,225 combinations of two sets of ratio features are fed to the SVM classifier after step 3. Table 1 shows the total correct classification PC rates (percentage of normal and internally damaged almonds correctly classified) for the training, validation, and test set obtained by our proposed method. The false positive (normal nuts misclassified as internally damaged) and false negative (internally damaged nuts misclassified as normal) error rates for the test set are also included. To find the single best ratio feature to use (band ratio 925/945 nm) involves measuring the classification rate for all 4950 ratio features on the validation set and selection of the best classification rate. We note that this single best ratio feature (using PC score) also occurs to be the best SFS-ordered ratio feature (using the criteria function J). From Table 1, the PC scores for the training, validation and test set seem to be comparable whether one or two ratio features chosen from the set of SFS features are used; thus, generalization is good. Note that the best two sets of ratio features chosen (by our algorithm) from the 50 SFS-ordered set (925/945 nm and 810/ 815 nm) are not the two best features ranked by J in the SFS algorithm; rather, they are ranked first and thirty-fourth in the SFSordered set of 50. Although using two sets of ratio features from the set of 50 SFS-ordered features gives a larger PC score on the training set than using the best single ratio feature, they yield the same PC score on the validation and test sets. As seen, the best PC score is obtained when one ratio feature (850/1210 nm) was chosen from the 50 SFS-ordered set and the other (1160/ 1335 nm) from the remaining 4900 ratio features. Figs. 4a and b show plots of the training and test set samples using these two best sets of ratio features, respectively. Most of the normal nuts are seen to be separated in distance from the internally damaged nuts. The best ratio feature (850/1210 nm) from the set of 50 SFS-ordered ratio features is ranked thirty-third in the 50 SFS ratio features. The test set PC score (91.15%) for this case is noticeably higher than that (86.73%) obtained by choosing both sets of ratio features from the 50 SFS-ordered set. For these best two ratio features, only 1.74% (3 out of 172) of the normal almond nuts in the test set are misclassified as internally damaged and 31.48% (17 out of 54) of the internally damaged nuts are misclassified as normal. A high false negative error rate is obtained due to the small size of the training set for internally damaged nuts. The false negative error rate should be lower if more almond nuts with internal damage are available for training. The false positive error rate (1.74%) obtained by our method is excellent for economic reasons, since only a few normal nuts would be misclassified as internally damaged and discarded from almond nut processors. We also compare our proposed method with other feature selection algorithms on the same almond database. Casasent and Chen (2003) used the best subset of four and six separate wavebands chosen from all 137 original wavebands. To select the best sets of four and six individual wavebands, they first used the

Table 1 Classification results on our internally damaged almond database. Model

Best single ratio feature Best 2 from 50 SFS ratio features 1 From 50 SFS ratio features and 1 from 4900 ratio features

Band ratios (nm)

925/945 925/945 and 810/815 850/1210 and 1160/1335

Training set PC score (%)

Validation set PC score (%)

Test set PC score (%)

False positive error rate (%)

False negative error rate (%)

85.25 87.43 90.16

88.89 88.89 93.33

86.73 86.73 91.15

1.16 0.58 1.74

51.85 53.70 31.48

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0.75

0.75 normal nuts (training) damaged nuts (training)

normal nuts (test) damaged nuts (test) 0.7 Band ratio 850 nm/1210 nm

Band ratio 850 nm/1210 nm

0.7

0.65

0.6

0.55

0.5

0.45

0.65

0.6

0.55

0.5

1

1.2 1.4 1.6 Band ratio 1160 nm/1335 nm

1.8

0.45

1

1.2 1.4 1.6 Band ratio 1160 nm/1335 nm

1.8

Fig. 4. Plot of the training (a) and test (b) set samples for the two best sets of ratio features chosen by our method.

Table 2 Test set PC comparison for different feature reduction algorithms on the almond database. Method

KLD-MBB feature selection Principle component regression HDGD feature extraction Our proposed method

Features

Test set PC score (%)

710, 820, 905, 1095 nm 765, 880, 905, 935, 975, 1220 nm 8 principle components

83.2 85.9

4 HDGD extracted features 850/1210 nm, 1160/1335 nm

90.7 91.2

87.6

Kullback–Leibler distance (KLD) to reduce the number of original wavebands from 137 to 30; they then applied the modified branch and bound (MBB) feature selection algorithm (Casasent and Chen, 2003) to select the optimal subset of four and six wavebands from the reduced 30-dimensional feature space. A nearest neighbor classifier was used as the classifier. We refer to this as the KLD–MBB feature selection algorithm. We also consider the performance of two feature extraction algorithms that use all available wavebands. Feature extraction results are useful as they show what performance one can obtain if all wavelength data are used. Pearson (1999) used the principle components of the absorbance, the first derivative and second derivative spectra between 1000 and 1300 nm. In another feature extraction method, Casasent and Chen (2003) used the high-dimensional generalization discriminant (HDGD) feature extraction algorithm, since it was shown to give better PC performance than other well-known feature extraction algorithms such as principle component analysis and linear discriminant analysis in many applications. Table 2 shows test data results on the Mission almond database using our proposed method and the four other algorithms noted above. As seen, our method using two sets of ratio features gives a higher classification rate by 8% and 5% compared to use of the subset of the best four and six separate wavebands chosen by the KLD-MBB feature selection algorithm, respectively. None of the best four or six separate wavebands is used in our selected two sets of ratio features. This clearly shows that the best individual wavebands do not necessarily give the best ratio features. As Table 2 shows, our proposed ratio feature selection method, using only two sets of ratio features, gives a higher classification rate than the principle component and HDGD

feature extraction algorithms, which use all wavelength data. Thus, use of ratio features for classification is encouraging and should be considered for various applications. 4. Conclusion Our method to select two sets of ratio features for classification was described. On an internally damaged Mission almond HS database, it gave good generalization and performed much faster than an exhaustive search. Initial results showed that using only two sets of ratio features gave a higher classification rate than did use of the best feature selection subset of separate wavebands or than did use of feature extraction algorithms using all spectral responses. None of the best four or six separate wavebands in feature selection was present in our selected ratio features; thus, an algorithm such as ours is necessary. Our classification results may be acceptable for commercial use, since it minimizes the number of normal nuts misclassified as internally damaged and keeps the false negative error rate as low as possible. Our proposed method is suitable for online processing because the two sets of ratio features (or the responses at only four different wavebands) can be recorded by many fast multispectral sensor systems. The method could be easily applied to other agricultural product inspection applications and other HS applications. We tested our algorithm on Mission variety almonds, since they are more prone to internal damage than other almond varieties. Future work should consider other commercial varieties of almonds such as Nonpareil and Carmel. We expect that the spectral responses of the normal and internally damaged nuts of different varieties should be significantly different. Thus, other two sets of ratio features would be chosen by our ratio feature selection algorithm. Acknowledgement The authors would like to thank Dr. Tom Pearson of the Agricultural Research Service in Kansas, United States for providing the HS almond database used in this work. References Casasent, D., Chen, X.-W., 2003. Waveband selection for hyperspectral data: optimal feature selection. Proceedings of SPIE 5106, 259–270.

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