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Detecting internal insect infestation in tart cherry using transmittance spectroscopy
Postharvest Biology and Technology 49 (2008) 411–416
Contents lists available at ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Detecting internal insect infestation in tart cherry using transmittance spectroscopy Juan Xing ∗ , Daniel Guyer Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
a r t i c l e
i n f o
Article history: Received 28 September 2007 Accepted 23 March 2008 Keywords: Cherries Transmittance Insect infestation Damage detection
a b s t r a c t This paper introduces an application of using transmittance spectroscopy to identify the infestation in tart cherry resulting from past or present insect activities. The spectra were recorded within a wavelength region between 550 and 980 nm with a FieldSpec spectroradiometer. The fresh tart cherries were hand harvested from different orchards in Michigan in 2004–2007. The samples included intact as well as infested cherries with different damage levels. The spectral analysis indicates that the maturity of tart cherry has effects on the classification accuracy. The intact cherries harvested late in the season (overripened) have similar spectral characteristics as the infested tissues. The classification accuracy for the samples harvested at normal time is better than that for the late harvested samples. Depending on the arrangement of the samples into non-infested or infested classes, the total classification accuracy varies from 82% to 87%. These findings and results demonstrate that transmittance spectroscopy has strong potential to detect the internal insect infestation within a tart cherry fruit. © 2008 Elsevier B.V. All rights reserved.
1. Introduction There are about 22.3 thousand hectares of red tart cherries in the USA, with Michigan accounting for nearly 16.2 thousand hectares and producing about 76.9% of the crop with a cash receipt value about 47.5 million dollars (NASS, 2005). Tart cherry is susceptible to several key pests including insects, diseases, nematodes, and weeds. Plum curculio is one of the key pests to cause damage in tart cherries and is considered a difficult pest to control and requires a full dosage of an effective pesticide (Howitt, 2005). Over time, a tart cherry production system that relies on applications of broad-spectrum pesticides to control these pests has evolved. With the spread of organic practice and concern of environment protection, less chemical pesticides are used, which increases the potential of insect presence in the fruits. Additional potential factors influencing the occurrence of plum curculio in tart cherry are increased by over-winter survival of the insects due to several consecutive mild winters; check-off and diversion marketing programs that lead to more fruit being left at the orchard that could serve as hosts for the pests (Guyer et al., 2006). The zero tolerance regulation for the occurrence of plum curculio in tart cherry imposes high strains on the cherry industries. Thus, there exists a growing need to identify and eliminate any kind of pest or insect infested
∗ Corresponding author. Tel.: +1 517 3534517. E-mail addresses: [email protected] (J. Xing), [email protected] (D. Guyer). 0925-5214/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.postharvbio.2008.03.018
fruit during postharvest handling. To date, research on the plum curculio focused on control strategies including monitoring, sanitation, and biological controls. No research has yet been conducted for potential methods of detection and removal of insect damaged cherry fruits from the processing stream. Relating to the insects or insect infestation detection in agricultural materials, although not commercialized as yet, X-ray has been a topic of considerable research, particularly in grain kernels (Schatzki and Fine, 1988; Keagy and Schatzki, 1993; Karunakaran et al., 2003). X-ray has also been reported for defects detection in apples and pears (Tollner et al., 1992; Schatzki et al., 1997; Lammertyn et al., 2003; Hansen et al., 2005). Little attention has been given for the detection of insect infestations on fruits (Hansen et al., 2005). Although the radiography techniques provide promising results for internal quality/insect detection in food/fruit products, it is not very practical for being implemented on line, especially in the consideration of economic cost. Generally, fruit infested by insects has a small hole on the surface of a fruit at the point of exit, suggesting some sort of machine vision may be an effective means of detection. Ridgeway and Chamber (1998) found that the wheat kernels infested internally with larvae had markedly different appearance from uninfested kernels when imaged at certain wavelengths in NIR. However, it has been noted that machine vision inspection for determining the presence of insects depending on the surface feature is unreliable, because other kinds of surface damage may leave similar marks as holes (Jackson and Haff, 2006). Several studies have been carried out at
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J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416
the Department of Biosystems and Agricultural Engineering, Michigan State University, to detect the occurrence of plum curculio larvae in tart cherry using optical properties, including machine vision and spectroscopy approaches (Shrestha et al., 2003; Guyer et al., 2006). Machine vision technique, especially the UV fluorescence images, has proved to be encouraging to differentiate the larvae from background tissue (sound or infested) when larvae were exposed on the surface. Naturally, it is impractical in an online inspection system as such. Because normally the insects reside inside of fruits and near the pit of tart cherry; and UV light possess weak penetrating capability, it is impossible to detect the internal larvae by the fluorescence image that is taken from outside for an entire cherry. Meanwhile, plentiful research that uses near infrared spectroscopy with a transmittance mode has been reported for internal quality prediction in fruits. Due to the high moisture content of fruits, it is difficult for the light in the long wavelength near infrared range of 1100–2500 nm to penetrate through an entire
fruit (Krivoshiev et al., 2000). Therefore, the short wavelength near infrared (SWNIR) spectroscopy is normally employed in such studies. Upchurch and Thai (2002) have concluded that the spectral data captured with a spectrophotometer exhibited differences that could be used to distinguish pecan nutmeat from weevil larvae. Maghirang et al. (2002) studied detecting single wheat kernels containing live or dead insects using near infrared reflectance spectroscopy. In the infested tart cherry tissue, the symptoms of tissue damages resulting from the burrow of the larvae and moisture variation compared to the healthy tissue are expected to be revealed from their spectral curves. Thus, in the later stages of the project, more efforts were focused on the spectroscopic studies accomplished with whole fruit. Because a plum curculio larva hides deep inside of the cherry tissue and mostly resides right next to the pit, it is not likely that vis/NIR can detect the insect directly. However, the presence of insect would likely affect other chemical and optical properties of whole cherry that can be detected with vis/NIR spectroscopy. Thus,
Fig. 1. Example images of cherries at different damage levels.
J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416
413
Table 1 Number of tart cherry samples over each year of measurement Intact 2004 2005 2006 2007
43 77 51 19
Level 1 17 31 23 5
Level 2
Level 3
2 14 11 12
3 14 3 10
Level 4 2 11 3 4
Level 5 6 9 5 2
Total 73 156 96 52
the objective of this paper is to evaluate the potential of using transmittance spectroscopy to detect internal insect infestation on tart cherry. 2. Materials and methods 2.1. Tart cherry Tart cherries (cv. Montomorency) were collected from Northport, Fennville, Traverse City, Frankfort and Hart orchards located in Michigan between June and August of 2004–2007. The detailed sampling is shown in Table 1. A number of tart cherry samples were selected on each day of measurement representing a wide range of infestation levels based on visual observation. After the spectral measurement on a whole cherry, the cherry samples were cut into halves for a visual grading from 0 (no damage) through 5 (severe damage) based on the insect presence, discoloration and condition of the flesh. In Level 0 samples, no larvae were present and the pits were tightly surrounded by the tissue; and the tissues as well as the surface of the pits were clean. In Level 1 samples, no larvae were observed; the pits were normally held tightly, but the tissue or the surface of the pits had slight contamination, which indicated a possible presence of larvae. In the samples from Level 2 through Level 5, larvae were observed with increasing size; the tissues were obviously infested showing a color from brown to dark; the pits became separated from the surrounding tissues due to the activities of the larvae. Example images of cherries at different infestation levels are given in Fig. 1. 2.2. Spectral acquisition A spectroradiometer (model: FSFR FieldSpec, Analytical Spectral Devices, Boulder, CO) was used to measure spectral characteristics of cherries in transmittance mode. An illumination source from a 150-W dc regulated tungsten halogen light (model: FO-150, Fostec Inc., Auburn, NY) was used for the spectral measurement. The spectroradiometer was operated under VNIR mode which has the spectral range of 350–1050 nm with 1 nm increment. Considering the signal to noise ratio, only the wavelength region between 550 and 980 nm was considered in the later analysis. A stage was erected on an optical table to hold the cherry sample and fiber optic bundles (Fig. 2). The light was delivered from a fiber bundle positioned underneath the sample and captured by a fiber optic probe positioned above the sample (0 degree to the normal line) that was connected to the spectroradiometer. The whole cherry sample being measured was positioned under a 12-mm diameter hole on the stage and pressed against a soft compression spring to maintain consistent distance to the fiber optic probe connected to the spectroradiometer. The transmittance of Teflon material was taken at the beginning of each 12-sample measurement. The integration time for transmittance of whole cherry was set to 272 ms. Four measurements were taken on each cherry’s cheek by rotating the sample 90◦ each time. The average of these four measurements was used for representing the spectral profile of one cherry.
Fig. 2. Sketch of the measurement setup.
2.3. Data analysis Prior to doing data analysis, the number of variables (wavelengths) was reduced by using averaging method. The spectral resolution used for further data analysis was 10 nm. Due to the large variation in the transmittance spectra among different measurement dates as well as among different fruits, it is necessary to preprocess the spectral data prior to further analysis in order to remove some influential effects that are not very related to the class information. For this purpose, maximum normalization was performed. With this method, each spectrum is divided by its maximum absolute value. The equation is as follows: MN Ri,k =
Ri,k max(|Ri,∗ |)
MN is the maximum-normalized reflectance; R where Ri,k i,k is the reflectance at kth waveband of the ith measurement and Ri,* is the reflectance of the ith measurement. Afterwards, the normalized spectra were further analyzed using different chemometric tools, like Principal Components Analysis (PCA) and Canonical Discriminant Analysis (CDA). Principal Components Analysis is a technique normally used as the first step to explore spectral data. It is a dimensional reduction method by concentrating most of the variations among the raw data into several uncorrelated principal components. Each principal component is a linear combination of all the original variables (wavelengths in the case of spectroscopy). PCA is a helpful multivariate data analysis method to reveal the underlying spectral patterns (i.e., clustering and outliers identification) in the data and to identify the important factors related to the interested features. Data analysis involved in this experiment also includes discriminant analysis based on the measured features. Canonical Discriminant Analysis procedure is employed in this study. It is a technique related to principal components analysis and canonical correlation. CDA derives canonical components that summarize between-class variation in much the same way that principal components summarize total variation among data. Although the damaged cherries were evaluated into six levels according to the infestation degree (from 0 (intact) to 5 (severe damage)), the ultimate target is to distinguish the non-infested cherries from the infested ones. Therefore, the final class variables entered in the classification model are only non-infested or infested. The grading for different levels of infestation is used for determining which
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J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416
Fig. 3. PCA scores scatter plot based on the maximum normalized spectral data.
level/degree of infestation a classification model can discern with a satisfactory accuracy. Different arrangement for the non-infested class was made by including only the original intact samples or the original intact sample joined with slight infested cherries, i.e., Level 1 and/or Level 2. The data collected from 2004 to 2006 were explored for calibrating classification models. In 2007, similar transmittance spectral measurements were repeated for the purpose of external validation of the calibrated models. The same grading criteria were used to determine the infestation degree. The spectral normalization and PCA analysis were performed with a statistical software package for multivariate calibration called ‘The Unscrambler’ V8 (CAMO AS, Trondheim, Norway); and the CDA procedures were carried out with SAS 8.2 (SAS Institute, Cary, NC). 3. Results and discussion 3.1. Overview of the data PCA was performed to see the data variations and possible influence factors for the data distribution. Fig. 3 shows the scatter plot of
the PCA scores values based on the maximum normalized transmittance spectral data in a broad wavelength region (550–980 nm). It can be seen that the data distribution in the first two PC spaces displays a V-shape and it seems difficult to have clearly separated clusters corresponding to the damage levels. In general, the intact samples scattered along the entire curve with a major part in the positive direction of the PC1 axis. The Levels 1 and 2 samples scattered around the base of the V-shape. The Level 3 infested samples mostly fall nearby the origin region. The Levels 4 and 5 damaged samples can mainly be observed in the negative part of PC1 axis. After a closer examination of the intact samples falling into the negative direction of PC1 axis, it was found that these intact samples were all from late harvest date (over-ripened). It indicates that the over-ripened intact cherries display similar spectral characteristics as the infested ones in the wavelength range between 550 and 980 nm. The similarity of these two groups may be partially explained by the fact that the damage of the tissue caused by the presence of the larvae accelerated the ripeness of the cherry. Fig. 4a and b shows the average transmittance spectra of cherries in different damage levels collected at normal and late harvest time, respectively. It can be observed that the spectral characteristics of the infested tissue (Levels 2–5) are similar for the normal and late harvested cherries. However, a substantial difference can be observed for the intact and Level 1 infested cherries at different maturity stages. The over-ripened intact cherries (late harvested) indeed have more similar spectral response as the damaged ones (Fig. 4b). It implies that the late harvested intact samples may very likely be misclassified as infested cherry and therefore deteriorate the classification accuracy. It can also be seen from Fig. 4 that the spectra maxima position of infested cherry tissue is shifted to the longer wavelength region compared to that of sound cherry tissue. This trend is especially clear for the cherries harvested at normal time. The shift of spectral maxima indicates that the visible light dissipated more than the NIR light did in the infested cherry tissues. This could be due to the coherent contribution of absorption and scattering of the light propagation in the cherry tissue. When the tissue was damaged by insects, the light has more chance to interfere with the cellular material and therefore be more absorbed and scattered. Normally, plant tissue absorbs more visible light than NIR light (especially the SNIR light). The NIR therefore extinguished in the cherry tissue relatively less than the visible light.
Fig. 4. Average plot of normalized transmittance of cherries harvested at (a) normal time and (b) late time; in the late harvested samples, no Level 4 damages were found.
J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416 Table 2 Classification results for different arrangements of the class variables (based on the data collected in 2004–2006) Harvest time
True classes
Classified into Infested (%)
Non-infested:{0}; infested: {1, 2, 3, 4, 5} Infested (n = 121) 76.86 Normal Intact (n = 84) Infested (n = 34) 55.88 Late Intact (n = 86) Infested (n = 155) 72.90 Combined Intact (n = 170) Non-infested:{0, 1}; infested: {2, 3, 4, 5} Infested (n = 71) 80.28 Normal Intact (n = 134) Infested (n = 13) 38.46 Late Intact (n = 107) Infested (n = 84) 72.62 Combined Intact (n = 241) Non-infested:{0–2}; infested {3, 4, 5} Infested (n = 49) 81.63 Normal Intact (n = 156) Infested (n = 8) 37.50 Late Intact (n = 112) Infested (n = 57) 68.42 Combined Intact (n = 268)
Table 3 External validation for the classification models using measurements of 2007 True classes
Total (%) Non-infested (%)
88.10 79.07 79.41
91.79 89.72 89.21
93.59 95.54 93.66
82.48 67.48
415
Classified into Infested: {2, 3, 4, 5} (%)
Total (%) Non-infested: {0, 1} (%)
(a) Models calibrated with the combined dataset Infested (n = 28) 85.71 Intact (n = 24) 79.17 (b) Models calibrated with the normal dataset Infested (n = 28) 78.57 Intact (n = 24) 95.83
82.44
87.20
76.16
86.04 64.09 80.92
87.61 66.52 81.04
with a satisfactory accuracy. This represents a practical grouping as Level 1 cherries have very minimal and grade-acceptable damage. The classification results using an external validation dataset (data collected in 2007) are given in Table 3(a) and (b) corresponding to the model built with the combined or normal dataset, respectively. As can be seen, the classification accuracy was above 82% when the Level 1 samples are considered as ‘non-infested’, which is an encouraging result. Specially, the model calibrated with the normal dataset gives 5% higher accuracy than that calibrated with the combined dataset. It confirms again that the presence of the over-ripened cherries affects the performance of classification models. 4. Conclusion
3.2. Discriminant analysis Since it has been noticed that the over-ripened intact cherries may cause misclassifications between the sound and infested samples, the discriminant analysis was performed for different datasets: Combined (all the data are together), normal (only the data collected from the samples harvested at normal time) and late (only the data collected from the samples harvested at late time). The leave-one-out crossvalidation results are given in Table 2. The classification analysis indicated better results for the normal harvested samples than the late harvested ones. The similarity between the over-ripened intact and infested tissue hinders the effort to derive a proper decision boundary between the two classes. The poorer results could be a consequence of the small number of infested samples and the confusion between the over-ripened intact cherry tissues and damaged tissues. The classification accuracy for the entire dataset is moderate. By including Level 1 or Levels 1 and 2 infested samples into the non-infested class, the classification accuracy is increased compared to without. When only the original intact samples were considered as non-infested, about 82% of accuracy can be obtained for the normal harvested cherries and only 67% of accuracy for the overripened samples. As can be seen from Fig. 4a, the Level 1 sample has quite similar spectral response as the intact samples. If these samples were also considered as non-infested in the model, the classification accuracy for the normal harvested samples rises up to about 86% total accuracy (about 80% and 92% correct recognition for the infested and non-infested samples, respectively). Much worse classification accuracy for the late harvested samples was obtained. By including both Levels 1 and 2 infested cherries into the non-infested class in the model, the total classification accuracy is improved a little bit. Although detection of all damages would be ideal, we are predominantly interested in the severe damage caused by infestation, i.e., Levels 3–5. Therefore, no trial was done for including the Levels 3 and 4 into the non-infested samples. As the improvement by including both Levels 1 and 2 samples into noninfested class is not significant compared to only joining original intact and Level 1 samples, it may be concluded that the classification model can identify the infested cherries from Level 2 and above
Transmittance spectra (550–980 nm) were collected from tart cherries harvested from different orchards and in different years in Michigan, USA. After applying the maximum normalization on the raw spectral data, some spectral characteristics of intact and insect infested tissue were extracted. First, it was observed that the spectral apex of infested cherry tissue shifted to the longer wavelength region compared to that of sound cherry tissue. Second, the over-ripened intact cherries demonstrated similar spectral features as the infested ones. According to the canonical discriminant analysis, about 80–86% of total classification accuracy can be obtained depending on the various arrangements of the samples into non-infested and infested classes. Future work will concentrate on increasing classification accuracy, which presumably can be accomplished by training with a larger number of samples so that a more precise decision boundary can be achieved. Additionally, TSS and firmness measurements can be carried out as complementary factors for explaining the difference between the insect infested and sound tart cherries and eventually lead to better classification models. Acknowledgements The authors would like to thank the financial support of USDACSREES, Integrated Research, Education and Extension Competitive Grants Program. Grant # 2004-51100-02212 “Development and optimization of pre and postharvest pest control strategies in cherries: A multi-tactic approach”. References Guyer, D.E., Ariana, D., Shrestha, B., Lu, R.F., 2006. Opto-electonic determination of insect presence in fruit. In: Portland, OR, Proceedings of the ASAE, Paper number 066061. Hansen, J., Schlaman, D.W., Haff, R.P., Yee, W.L., 2005. Potential postharvest used of radiograph to detect internal pests in deciduous tree fruits. J. Entomol. Sci. 40, 255–262. Howitt, A.H., 2005. Plum curculio. Fruit IPM fact sheet. http://web1.msue.msu.edu/ vanburen/plumcurc.htm, accessed in June 2007. Jackson, E.S., Haff, R.P., 2006. X-ray detection and sorting of olives damaged by fruit fly. In: Proceedings of the ASAE, Portland, OR, Paper number 066062.
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Karunakaran, C., Jayas, D.S., White, N.D.G., 2003. Soft X-ray inspection of wheat kernels infested by Sitophylus oryzae. Trans. ASAE 46, 739–745. Keagy, P.M., Schatzki, T.F., 1993. Machine recognition of weevil damage in wheat radiograms. Cereal Chem. 70, 696–700. Krivoshiev, G.P., Chalucova, R.P., Moukarev, M.I., 2000. A possibility for elimination of the interference from the peel in nondestructive determination of the internal quality of fruit and vegetables by VI-NIR spectroscopy. Lebensm. -Wiss. U. -Technol. 33, 344–353. Lammertyn, J., Dresselaers, T., Van Hecke, P., Jancsok, P., Eevers, M., Nicolai, B.M., 2003. MRI and x-ray CT study of spatial distribution of core breakdown in ‘Conference’ pears. Magn. Reson. Imaging 21, 805–815. Maghirang, E.B., Dowell, F.E., Baker, J.E., Thorne, J.E., 2002. Detecting single wheat kernels containing live or dead insects using near-infrared reflectance spectroscopy. In: Proceedings of the ASAE, Chicago, IL, Paper number 023067.
Ridgeway, C., Chamber, J., 1998. Detection of insects inside wheat kernels by NIR imaging. J. Near Infr. Spectrosc. 6, 115–119. Schatzki, T.F., Fine, T.A., 1988. Analysis of radiograms of wheat kernels for quality control. Cereal Chem. 65, 233–239. Schatzki, T.F., Haff, R.P., Young, R., Can, I., Lee, L.C., Toyofuku, N., 1997. Defect detection in apples by means of X-ray imaging. Trans. ASAE 40, 1407–1415. Shrestha, B.P., Guyer, D.E., Ariana, D.P., 2003. Opto-electronic determination of insect presence in fruit. Proceedings of SPIE. Vol 5271. Tollner, E.W., Hung, Y.C., Upchurch, B.L., Prussia, S.E., 1992. Relating X-ray absorption to density and water content in apples. Trans. ASAE 35, 1921– 1928. Upchurch, B.L., Thai, C.N., 2002. Spectral characterization of pecan weevil and pecan nutmeat using multispectral imaging. In: Proceedings of the ASAE, Milwaukee, WI, Paper number 006119.
Contents lists available at ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
Detecting internal insect infestation in tart cherry using transmittance spectroscopy Juan Xing ∗ , Daniel Guyer Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
a r t i c l e
i n f o
Article history: Received 28 September 2007 Accepted 23 March 2008 Keywords: Cherries Transmittance Insect infestation Damage detection
a b s t r a c t This paper introduces an application of using transmittance spectroscopy to identify the infestation in tart cherry resulting from past or present insect activities. The spectra were recorded within a wavelength region between 550 and 980 nm with a FieldSpec spectroradiometer. The fresh tart cherries were hand harvested from different orchards in Michigan in 2004–2007. The samples included intact as well as infested cherries with different damage levels. The spectral analysis indicates that the maturity of tart cherry has effects on the classification accuracy. The intact cherries harvested late in the season (overripened) have similar spectral characteristics as the infested tissues. The classification accuracy for the samples harvested at normal time is better than that for the late harvested samples. Depending on the arrangement of the samples into non-infested or infested classes, the total classification accuracy varies from 82% to 87%. These findings and results demonstrate that transmittance spectroscopy has strong potential to detect the internal insect infestation within a tart cherry fruit. © 2008 Elsevier B.V. All rights reserved.
1. Introduction There are about 22.3 thousand hectares of red tart cherries in the USA, with Michigan accounting for nearly 16.2 thousand hectares and producing about 76.9% of the crop with a cash receipt value about 47.5 million dollars (NASS, 2005). Tart cherry is susceptible to several key pests including insects, diseases, nematodes, and weeds. Plum curculio is one of the key pests to cause damage in tart cherries and is considered a difficult pest to control and requires a full dosage of an effective pesticide (Howitt, 2005). Over time, a tart cherry production system that relies on applications of broad-spectrum pesticides to control these pests has evolved. With the spread of organic practice and concern of environment protection, less chemical pesticides are used, which increases the potential of insect presence in the fruits. Additional potential factors influencing the occurrence of plum curculio in tart cherry are increased by over-winter survival of the insects due to several consecutive mild winters; check-off and diversion marketing programs that lead to more fruit being left at the orchard that could serve as hosts for the pests (Guyer et al., 2006). The zero tolerance regulation for the occurrence of plum curculio in tart cherry imposes high strains on the cherry industries. Thus, there exists a growing need to identify and eliminate any kind of pest or insect infested
∗ Corresponding author. Tel.: +1 517 3534517. E-mail addresses: [email protected] (J. Xing), [email protected] (D. Guyer). 0925-5214/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.postharvbio.2008.03.018
fruit during postharvest handling. To date, research on the plum curculio focused on control strategies including monitoring, sanitation, and biological controls. No research has yet been conducted for potential methods of detection and removal of insect damaged cherry fruits from the processing stream. Relating to the insects or insect infestation detection in agricultural materials, although not commercialized as yet, X-ray has been a topic of considerable research, particularly in grain kernels (Schatzki and Fine, 1988; Keagy and Schatzki, 1993; Karunakaran et al., 2003). X-ray has also been reported for defects detection in apples and pears (Tollner et al., 1992; Schatzki et al., 1997; Lammertyn et al., 2003; Hansen et al., 2005). Little attention has been given for the detection of insect infestations on fruits (Hansen et al., 2005). Although the radiography techniques provide promising results for internal quality/insect detection in food/fruit products, it is not very practical for being implemented on line, especially in the consideration of economic cost. Generally, fruit infested by insects has a small hole on the surface of a fruit at the point of exit, suggesting some sort of machine vision may be an effective means of detection. Ridgeway and Chamber (1998) found that the wheat kernels infested internally with larvae had markedly different appearance from uninfested kernels when imaged at certain wavelengths in NIR. However, it has been noted that machine vision inspection for determining the presence of insects depending on the surface feature is unreliable, because other kinds of surface damage may leave similar marks as holes (Jackson and Haff, 2006). Several studies have been carried out at
412
J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416
the Department of Biosystems and Agricultural Engineering, Michigan State University, to detect the occurrence of plum curculio larvae in tart cherry using optical properties, including machine vision and spectroscopy approaches (Shrestha et al., 2003; Guyer et al., 2006). Machine vision technique, especially the UV fluorescence images, has proved to be encouraging to differentiate the larvae from background tissue (sound or infested) when larvae were exposed on the surface. Naturally, it is impractical in an online inspection system as such. Because normally the insects reside inside of fruits and near the pit of tart cherry; and UV light possess weak penetrating capability, it is impossible to detect the internal larvae by the fluorescence image that is taken from outside for an entire cherry. Meanwhile, plentiful research that uses near infrared spectroscopy with a transmittance mode has been reported for internal quality prediction in fruits. Due to the high moisture content of fruits, it is difficult for the light in the long wavelength near infrared range of 1100–2500 nm to penetrate through an entire
fruit (Krivoshiev et al., 2000). Therefore, the short wavelength near infrared (SWNIR) spectroscopy is normally employed in such studies. Upchurch and Thai (2002) have concluded that the spectral data captured with a spectrophotometer exhibited differences that could be used to distinguish pecan nutmeat from weevil larvae. Maghirang et al. (2002) studied detecting single wheat kernels containing live or dead insects using near infrared reflectance spectroscopy. In the infested tart cherry tissue, the symptoms of tissue damages resulting from the burrow of the larvae and moisture variation compared to the healthy tissue are expected to be revealed from their spectral curves. Thus, in the later stages of the project, more efforts were focused on the spectroscopic studies accomplished with whole fruit. Because a plum curculio larva hides deep inside of the cherry tissue and mostly resides right next to the pit, it is not likely that vis/NIR can detect the insect directly. However, the presence of insect would likely affect other chemical and optical properties of whole cherry that can be detected with vis/NIR spectroscopy. Thus,
Fig. 1. Example images of cherries at different damage levels.
J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416
413
Table 1 Number of tart cherry samples over each year of measurement Intact 2004 2005 2006 2007
43 77 51 19
Level 1 17 31 23 5
Level 2
Level 3
2 14 11 12
3 14 3 10
Level 4 2 11 3 4
Level 5 6 9 5 2
Total 73 156 96 52
the objective of this paper is to evaluate the potential of using transmittance spectroscopy to detect internal insect infestation on tart cherry. 2. Materials and methods 2.1. Tart cherry Tart cherries (cv. Montomorency) were collected from Northport, Fennville, Traverse City, Frankfort and Hart orchards located in Michigan between June and August of 2004–2007. The detailed sampling is shown in Table 1. A number of tart cherry samples were selected on each day of measurement representing a wide range of infestation levels based on visual observation. After the spectral measurement on a whole cherry, the cherry samples were cut into halves for a visual grading from 0 (no damage) through 5 (severe damage) based on the insect presence, discoloration and condition of the flesh. In Level 0 samples, no larvae were present and the pits were tightly surrounded by the tissue; and the tissues as well as the surface of the pits were clean. In Level 1 samples, no larvae were observed; the pits were normally held tightly, but the tissue or the surface of the pits had slight contamination, which indicated a possible presence of larvae. In the samples from Level 2 through Level 5, larvae were observed with increasing size; the tissues were obviously infested showing a color from brown to dark; the pits became separated from the surrounding tissues due to the activities of the larvae. Example images of cherries at different infestation levels are given in Fig. 1. 2.2. Spectral acquisition A spectroradiometer (model: FSFR FieldSpec, Analytical Spectral Devices, Boulder, CO) was used to measure spectral characteristics of cherries in transmittance mode. An illumination source from a 150-W dc regulated tungsten halogen light (model: FO-150, Fostec Inc., Auburn, NY) was used for the spectral measurement. The spectroradiometer was operated under VNIR mode which has the spectral range of 350–1050 nm with 1 nm increment. Considering the signal to noise ratio, only the wavelength region between 550 and 980 nm was considered in the later analysis. A stage was erected on an optical table to hold the cherry sample and fiber optic bundles (Fig. 2). The light was delivered from a fiber bundle positioned underneath the sample and captured by a fiber optic probe positioned above the sample (0 degree to the normal line) that was connected to the spectroradiometer. The whole cherry sample being measured was positioned under a 12-mm diameter hole on the stage and pressed against a soft compression spring to maintain consistent distance to the fiber optic probe connected to the spectroradiometer. The transmittance of Teflon material was taken at the beginning of each 12-sample measurement. The integration time for transmittance of whole cherry was set to 272 ms. Four measurements were taken on each cherry’s cheek by rotating the sample 90◦ each time. The average of these four measurements was used for representing the spectral profile of one cherry.
Fig. 2. Sketch of the measurement setup.
2.3. Data analysis Prior to doing data analysis, the number of variables (wavelengths) was reduced by using averaging method. The spectral resolution used for further data analysis was 10 nm. Due to the large variation in the transmittance spectra among different measurement dates as well as among different fruits, it is necessary to preprocess the spectral data prior to further analysis in order to remove some influential effects that are not very related to the class information. For this purpose, maximum normalization was performed. With this method, each spectrum is divided by its maximum absolute value. The equation is as follows: MN Ri,k =
Ri,k max(|Ri,∗ |)
MN is the maximum-normalized reflectance; R where Ri,k i,k is the reflectance at kth waveband of the ith measurement and Ri,* is the reflectance of the ith measurement. Afterwards, the normalized spectra were further analyzed using different chemometric tools, like Principal Components Analysis (PCA) and Canonical Discriminant Analysis (CDA). Principal Components Analysis is a technique normally used as the first step to explore spectral data. It is a dimensional reduction method by concentrating most of the variations among the raw data into several uncorrelated principal components. Each principal component is a linear combination of all the original variables (wavelengths in the case of spectroscopy). PCA is a helpful multivariate data analysis method to reveal the underlying spectral patterns (i.e., clustering and outliers identification) in the data and to identify the important factors related to the interested features. Data analysis involved in this experiment also includes discriminant analysis based on the measured features. Canonical Discriminant Analysis procedure is employed in this study. It is a technique related to principal components analysis and canonical correlation. CDA derives canonical components that summarize between-class variation in much the same way that principal components summarize total variation among data. Although the damaged cherries were evaluated into six levels according to the infestation degree (from 0 (intact) to 5 (severe damage)), the ultimate target is to distinguish the non-infested cherries from the infested ones. Therefore, the final class variables entered in the classification model are only non-infested or infested. The grading for different levels of infestation is used for determining which
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Fig. 3. PCA scores scatter plot based on the maximum normalized spectral data.
level/degree of infestation a classification model can discern with a satisfactory accuracy. Different arrangement for the non-infested class was made by including only the original intact samples or the original intact sample joined with slight infested cherries, i.e., Level 1 and/or Level 2. The data collected from 2004 to 2006 were explored for calibrating classification models. In 2007, similar transmittance spectral measurements were repeated for the purpose of external validation of the calibrated models. The same grading criteria were used to determine the infestation degree. The spectral normalization and PCA analysis were performed with a statistical software package for multivariate calibration called ‘The Unscrambler’ V8 (CAMO AS, Trondheim, Norway); and the CDA procedures were carried out with SAS 8.2 (SAS Institute, Cary, NC). 3. Results and discussion 3.1. Overview of the data PCA was performed to see the data variations and possible influence factors for the data distribution. Fig. 3 shows the scatter plot of
the PCA scores values based on the maximum normalized transmittance spectral data in a broad wavelength region (550–980 nm). It can be seen that the data distribution in the first two PC spaces displays a V-shape and it seems difficult to have clearly separated clusters corresponding to the damage levels. In general, the intact samples scattered along the entire curve with a major part in the positive direction of the PC1 axis. The Levels 1 and 2 samples scattered around the base of the V-shape. The Level 3 infested samples mostly fall nearby the origin region. The Levels 4 and 5 damaged samples can mainly be observed in the negative part of PC1 axis. After a closer examination of the intact samples falling into the negative direction of PC1 axis, it was found that these intact samples were all from late harvest date (over-ripened). It indicates that the over-ripened intact cherries display similar spectral characteristics as the infested ones in the wavelength range between 550 and 980 nm. The similarity of these two groups may be partially explained by the fact that the damage of the tissue caused by the presence of the larvae accelerated the ripeness of the cherry. Fig. 4a and b shows the average transmittance spectra of cherries in different damage levels collected at normal and late harvest time, respectively. It can be observed that the spectral characteristics of the infested tissue (Levels 2–5) are similar for the normal and late harvested cherries. However, a substantial difference can be observed for the intact and Level 1 infested cherries at different maturity stages. The over-ripened intact cherries (late harvested) indeed have more similar spectral response as the damaged ones (Fig. 4b). It implies that the late harvested intact samples may very likely be misclassified as infested cherry and therefore deteriorate the classification accuracy. It can also be seen from Fig. 4 that the spectra maxima position of infested cherry tissue is shifted to the longer wavelength region compared to that of sound cherry tissue. This trend is especially clear for the cherries harvested at normal time. The shift of spectral maxima indicates that the visible light dissipated more than the NIR light did in the infested cherry tissues. This could be due to the coherent contribution of absorption and scattering of the light propagation in the cherry tissue. When the tissue was damaged by insects, the light has more chance to interfere with the cellular material and therefore be more absorbed and scattered. Normally, plant tissue absorbs more visible light than NIR light (especially the SNIR light). The NIR therefore extinguished in the cherry tissue relatively less than the visible light.
Fig. 4. Average plot of normalized transmittance of cherries harvested at (a) normal time and (b) late time; in the late harvested samples, no Level 4 damages were found.
J. Xing, D. Guyer / Postharvest Biology and Technology 49 (2008) 411–416 Table 2 Classification results for different arrangements of the class variables (based on the data collected in 2004–2006) Harvest time
True classes
Classified into Infested (%)
Non-infested:{0}; infested: {1, 2, 3, 4, 5} Infested (n = 121) 76.86 Normal Intact (n = 84) Infested (n = 34) 55.88 Late Intact (n = 86) Infested (n = 155) 72.90 Combined Intact (n = 170) Non-infested:{0, 1}; infested: {2, 3, 4, 5} Infested (n = 71) 80.28 Normal Intact (n = 134) Infested (n = 13) 38.46 Late Intact (n = 107) Infested (n = 84) 72.62 Combined Intact (n = 241) Non-infested:{0–2}; infested {3, 4, 5} Infested (n = 49) 81.63 Normal Intact (n = 156) Infested (n = 8) 37.50 Late Intact (n = 112) Infested (n = 57) 68.42 Combined Intact (n = 268)
Table 3 External validation for the classification models using measurements of 2007 True classes
Total (%) Non-infested (%)
88.10 79.07 79.41
91.79 89.72 89.21
93.59 95.54 93.66
82.48 67.48
415
Classified into Infested: {2, 3, 4, 5} (%)
Total (%) Non-infested: {0, 1} (%)
(a) Models calibrated with the combined dataset Infested (n = 28) 85.71 Intact (n = 24) 79.17 (b) Models calibrated with the normal dataset Infested (n = 28) 78.57 Intact (n = 24) 95.83
82.44
87.20
76.16
86.04 64.09 80.92
87.61 66.52 81.04
with a satisfactory accuracy. This represents a practical grouping as Level 1 cherries have very minimal and grade-acceptable damage. The classification results using an external validation dataset (data collected in 2007) are given in Table 3(a) and (b) corresponding to the model built with the combined or normal dataset, respectively. As can be seen, the classification accuracy was above 82% when the Level 1 samples are considered as ‘non-infested’, which is an encouraging result. Specially, the model calibrated with the normal dataset gives 5% higher accuracy than that calibrated with the combined dataset. It confirms again that the presence of the over-ripened cherries affects the performance of classification models. 4. Conclusion
3.2. Discriminant analysis Since it has been noticed that the over-ripened intact cherries may cause misclassifications between the sound and infested samples, the discriminant analysis was performed for different datasets: Combined (all the data are together), normal (only the data collected from the samples harvested at normal time) and late (only the data collected from the samples harvested at late time). The leave-one-out crossvalidation results are given in Table 2. The classification analysis indicated better results for the normal harvested samples than the late harvested ones. The similarity between the over-ripened intact and infested tissue hinders the effort to derive a proper decision boundary between the two classes. The poorer results could be a consequence of the small number of infested samples and the confusion between the over-ripened intact cherry tissues and damaged tissues. The classification accuracy for the entire dataset is moderate. By including Level 1 or Levels 1 and 2 infested samples into the non-infested class, the classification accuracy is increased compared to without. When only the original intact samples were considered as non-infested, about 82% of accuracy can be obtained for the normal harvested cherries and only 67% of accuracy for the overripened samples. As can be seen from Fig. 4a, the Level 1 sample has quite similar spectral response as the intact samples. If these samples were also considered as non-infested in the model, the classification accuracy for the normal harvested samples rises up to about 86% total accuracy (about 80% and 92% correct recognition for the infested and non-infested samples, respectively). Much worse classification accuracy for the late harvested samples was obtained. By including both Levels 1 and 2 infested cherries into the non-infested class in the model, the total classification accuracy is improved a little bit. Although detection of all damages would be ideal, we are predominantly interested in the severe damage caused by infestation, i.e., Levels 3–5. Therefore, no trial was done for including the Levels 3 and 4 into the non-infested samples. As the improvement by including both Levels 1 and 2 samples into noninfested class is not significant compared to only joining original intact and Level 1 samples, it may be concluded that the classification model can identify the infested cherries from Level 2 and above
Transmittance spectra (550–980 nm) were collected from tart cherries harvested from different orchards and in different years in Michigan, USA. After applying the maximum normalization on the raw spectral data, some spectral characteristics of intact and insect infested tissue were extracted. First, it was observed that the spectral apex of infested cherry tissue shifted to the longer wavelength region compared to that of sound cherry tissue. Second, the over-ripened intact cherries demonstrated similar spectral features as the infested ones. According to the canonical discriminant analysis, about 80–86% of total classification accuracy can be obtained depending on the various arrangements of the samples into non-infested and infested classes. Future work will concentrate on increasing classification accuracy, which presumably can be accomplished by training with a larger number of samples so that a more precise decision boundary can be achieved. Additionally, TSS and firmness measurements can be carried out as complementary factors for explaining the difference between the insect infested and sound tart cherries and eventually lead to better classification models. Acknowledgements The authors would like to thank the financial support of USDACSREES, Integrated Research, Education and Extension Competitive Grants Program. Grant # 2004-51100-02212 “Development and optimization of pre and postharvest pest control strategies in cherries: A multi-tactic approach”. References Guyer, D.E., Ariana, D., Shrestha, B., Lu, R.F., 2006. Opto-electonic determination of insect presence in fruit. In: Portland, OR, Proceedings of the ASAE, Paper number 066061. Hansen, J., Schlaman, D.W., Haff, R.P., Yee, W.L., 2005. Potential postharvest used of radiograph to detect internal pests in deciduous tree fruits. J. Entomol. Sci. 40, 255–262. Howitt, A.H., 2005. Plum curculio. Fruit IPM fact sheet. http://web1.msue.msu.edu/ vanburen/plumcurc.htm, accessed in June 2007. Jackson, E.S., Haff, R.P., 2006. X-ray detection and sorting of olives damaged by fruit fly. In: Proceedings of the ASAE, Portland, OR, Paper number 066062.
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