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Nondestructive detection of brown core in the Chinese pear ‘Yali’ by transmission visible–NIR spectroscopy
Food Control 17 (2006) 604–608 www.elsevier.com/locate/foodcont
Nondestructive detection of brown core in the Chinese pear ÔYaliÕ by transmission visible–NIR spectroscopy q Donghai Han *, Runlin Tu, Chao Lu, Xinxin Liu, Zhaohui Wen College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China Received 4 November 2004; received in revised form 10 March 2005; accepted 14 March 2005
Abstract Brown core is an internal disorder sometimes seen in pears under controlled atmosphere (CA) storage. The symptoms are not externally recognizable and visible only after cutting the fruit. Development of nondestructive measurements of brown core of pears would benefit producers, processors, and packers. A technique using body transmittance in the 651–1282 nm region was evaluated as a nondestructive technique for identifying the Chinese pear ÔYaliÕ with brown core. Based on the 651–1282 nm region, discriminant analysis with Mahalanobis Distance (MD) analysis can discriminate between brown core and normal pears and grade the brown core pears to three classes—slight, moderate, and severe. None of the good pears was misclassified as brown core, and none of pears with brown core was misclassified as normal pears. Exhaustive searches of the best combination of individual chrematistic wavelengths were performed on a set of 66 pears from two categories–good and brown core. The best modeling classification occurred for precisely aligned pears using the difference at two wavelengths, 713 and 743 nm. When optical density (OD) difference between 713 and 743 nm was applied to a test set, the classification model correctly identified the good or brown core with 95.4% accuracy. Only 5.3% of the good pears were incorrectly classified, 4.3% error of pear with brown core were classified as good fruit. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Chinese pear ÔYaliÕ; Grading; Visible–NIR spectroscopy; Nondestructive detection; Brown core
1. Introduction The Chinese pear ÔYaliÕ is a variety in the ÔWhite pear systemÕ. It is delicious and is popular with consumers in china. During storage under controlled atmosphere (CA), the Chinese pear ÔYaliÕ can develop internal disorders such as brown core. The symptoms of the disorder consist of browning of pulp tissue, sometimes with the subsequent development of cavities. The frequency and severity of the symptoms are higher in late harvested fruit, and can be due to different climate (Wang & Wu, 1997). It is difficult to recognize externally the q
The abstract of this paper had been received by 2004 CIGR International Conference on page II-158. * Corresponding author. Tel.: +86 10 62737503; fax: +86 10 82388508. E-mail address: [email protected] (D. Han). 0956-7135/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodcont.2005.03.006
symptoms if they are confined to the inner part of the cortical parenchyma. The surface color of pear can become dark when the symptom is severe. Given the possible impact of brown core on pear quality, it became important to identify the factors that may influence brown core incidence in the Chinese pear ÔYaliÕ. A wide range of environmental and physiological factors has been implicated in the development of this disorder. Of these, the influence of the storage temperature and high atmosphere CO2 condition were identified as critical, and brown core incidence and severity in pears is associated with harvest data and fruit maturity (Wang & Wu, 1997). Browning disorders affect not only the Chinese pear ÔYaliÕ but also other ÔWhiteÕ pears including ÔLaiyangshiliÕ, ÔPingguoliÕ and ÔJingfengliÕ (Zhang & Wang, 1991). Consequently, a reliable, nondestructive method for detecting and segregating such fruit would be readily accepted
D. Han et al. / Food Control 17 (2006) 604–608
by industry. And development of nondestructive measurements of brown core of pears would benefit producers, processors, and packers. Brown core of apples can be detected nondestructively by X-ray imaging (Schatzki et al., 1997), NIR spectroscopy (Choi & Lee, 2001; Clark, McGlone, & Jordan, 2003; Upchurch, Biasi, & Aneshansley, 1997), and MRI (Clark & Burmeister, 1999; Gonzalez et al., 2001). Brown core of pear can be detected nondestructively by time-resolved reflectance spectroscopy (Paola, Maurizio, Rinaldo, Antonio, & Alessandro, 2002). As a commercial technology, NIR spectroscopy has been widely used in agricultural industry (Armstrong, 2000), and then generally operating in a ÔreflectanceÕ mode, i.e., spectral data is derived primarily from superficial regions of the sample. It is anticipated that future generations of instruments will operate more commonly in a ÔtransmissionÕ mode where light must pass through the fruit and is more likely to detect hidden, internal defects. An NIR range below 1100 nm is more generally useful for intact food as the light can penetrate much deeper (Osborne, Fearn, & Hindle, 1993). Chen and Nattuvetty (1980) evaluated experimentally a technique that utilizes fiber optics to measure light transmittance through a region of an intact fruit. Sumio, Takayuki, and Mutsuo (1993) studied the nondestructive detection of sugar content in satsuma mandarin using NIR transmittance. Slaughter (1995) determined that visible and NIR spectroscopy could be used to measure nondestructively the internal quality of peaches and nectarines as characterized by their soluble solid contents (SSC), sorbitol and chlorophyll. Lammertyn (1998) established a relationship between NIR spectra and apple quality parameters such as acidity, pH, sugar content and firmness. Clark et al. (2003) established the model of detection of brown heart in ÔBraeburnÕ apple by transmission NIR spectroscopy. Reports about nondestructive detection of in the Chinese pear ÔYaliÕ by transmission spectroscopy were not found in the scientific literature. The objective of this study was to investigate the visible–NIR spectroscopy techniques as a nondestructive method to allow discrimination between brown core pears and normal ones. The specific objectives were to: (1) develop discriminant analysis (DA) with Mahalanobis Distance (MD) models to qualitatively classify Chinese pears ÔYaliÕ of different quality, and (2) develop the method of optical density difference for classifying brown core pears and normal ones.
2. Materials and methods 2.1. Materials Chinese pears ÔYaliÕ used for the experiments were harvested in 2003 from commercial orchards in Daxing,
605
Beijing. The pears were packed in cartons directly after harvest, and stored at 0 °C until the experiments were conducted. Defective pears, such as those having wormholes, bruises, rots or chilling injuries, were eliminated. A total of 66 Chinese pears ÔYaliÕ were used for the experiments. For developing the model, samples were divided randomly into calibration and validation sets. 2.2. Transmission measurements Visible–NIR spectra of intact pears were acquired with a custom-designed laboratory system consisting of a wide-band light source (DC tungsten halogen lamp), a fruit holder, detector and PC. Transmission spectra were collected in wavelength ranges of 640– 1295 nm. Only the wavelengths from 651 nm to 1282 nm was used by the selection of soft TQ analysis of Nicolet Ltd. Transmission spectra were collected from three different locations on each pear. The signals accumulated over 10 repetitive scans were averaged and then transformed to absorption. One spectrum was created per measurement with wavelength increments of 0.32 nm (2044 points per spectrum). 2.3. Brown core assessment Following Visible–NIR measurements all fruits were dissected to determine the extent of brown. Each pear was cut in half through the equator. The cut surfaces of each fruit were photographed with a digital camera (Olympus C-300 zoom, made in Korea). The level of the brown core was assessed using a brown core index with four grades (1: no brown core; 2: slight; 3: moderate; 4: severe, Fig. 1). 2.4. Visible–NIR spectroscopy analysis For the qualitative analysis, a DA (it was derived from procedure TQ analysis of Nicolet Ltd.) was applied for pears NIR data. The DA classification technique can be used to determine the class or classes of known materials which are most similar to an unknown material by computing the unknown distance from each class center in MD units. DA methods typically specify at least two classes of known materials, but the method also works with only one class. In the research, Chinese pears ÔYaliÕ were classified into normal pears and brown core ones and the brown core pears were graded to three classes—slight, moderate, and severe. Multiple standards may be used to describe each class (at least one class must contain two or more standards). Multiple regions of the spectrum may be used for the analysis. A DA method applies the spectral information in the specified region or regions of an unknown sample
606
D. Han et al. / Food Control 17 (2006) 604–608
Fig. 1. Photographs show the sequential development of brown core symptoms.
spectrum to a stored calibration model to determine which class of standards is most similar to the unknown. During calibration, the software computes a mean spectrum and then generates a distribution model by estimating the variance at each frequency in the analysis range. Because one model was selected for using all classes, the software subtracted the class average from each standard and then created a single variance spectrum using information from all of the classes. In order to calculate the statistics properly, the single model requires at least one class that contains two or more standards and every class must contain at least two standards. When the method is used to analyze an unknown sample, the software performs a principal component analysis (PCA) on the spectra of the standards and uses those results to determine score values for the unknown sample spectrum. The score plots are used to produce MD values, which in turn are used to rank the classes. 2.5. The optical density (OD) difference analysis The OD difference discriminance is a method of nondestructive detection on fruit quality (Lite, 1998). In general, a difference measurement of OD is preferred because many variables such as size, shape, and positioning of the sample, as well as instrument factors such as lamp stability and photometer stability affect an individual measurement of OD. The effects of these variables are largely cancelled in a difference measurement.
3. Results and discussion Browning of the tissue within a pear affected the spectral content of the light transmitted through the fruit. The average transmission spectrum for each class of the calibration set is shown in Fig. 2. Generally, absorption at wavelengths between 640 and 860 nm was greater for pears with brown core than pears without the defect. The difference between brown core pear and good one was significant at 710 and 750 nm. The degree of absorption by the pear was dependent on the level of browning present in the tissue.
Fig. 2. Average spectral composition of the light transmitted through pears with and without brown core.
3.1. DA method for brown core classes A set of 46 samples (11, 21, 9, 5 samples for grade 1 to 4, respectively) was selected for calibration according to the degree of brown core, and a set of 20 samples for the validation. Fig. 3 showed the MD value between every two classes in four classes. The closer the MD value is to zero, the better is the match. The results indicated that none of good pears were misclassified to brown core ones, and none of pears with brown core were misclassified to normal pears. But two pears were misclassified between second grade and third grade, and two pears were misclassified between third grade and fourth grade. And the total accuracy rate was 94.7%. Although there were a few sample in the calibration model, the result indicated that the DA method had the possibility to separate the good pears from brown core ones. 3.2. DOD analysis for brown core classes From Fig. 2, the amplitude of the transmittance at several wavelengths was dependent on the internal
D. Han et al. / Food Control 17 (2006) 604–608
607
Fig. 3. Distribution of distance value between four classes.
condition of the pear. To minimize the influence of path length, a difference between the transmittance at 713 and 743 nm was selected as the feature for discriminating between defective and good pears. Since any level of browning would result in rejection of the pear, all of the pears with brown core were combined into a single class. According to Fig. 4, 0.231 was selected as a threshold of discriminating defective and good pears. A pear with DOD(713–743 nm) more than 0.231 was classified as a brown core pear, while any pear with DOD(713–743 nm) equal to or less than 0.231 was classified as a good pear. No effort was made to distinguish between the classes of browning. Only 1 out of the 19 good pears (5.3%) was misclassified as having browning, while 2 out of the 46 brown core pears (4.3%) were misclassified as good fruit. Only one outlier was found in 66 pears. Different factors contributed to the errors of classification. Very slight brown core is very difficult to detect because that two pears misclassified into good fruit fell to slight brown core. Compared with the discrimination results of two methods, It could be found that DA on the 651– 1282 nm region was more accurate than discrimination of OD difference between 713 and 743 nm. However,
Fig. 4. Histogram chart for of good and defective pears.
the calculation time for the latter method was shorter than DA, and it appear, therefore, to have more potential for exploitation as a portable sorter for the Chinese pear ÔYaliÕ.
608
D. Han et al. / Food Control 17 (2006) 604–608
4. Conclusion A technique using body transmittance in the (651– 1282) nm region was evaluated as a nondestructive technique for identifying the Chinese pear ÔYaliÕ with brown core. Pears with brown core were distinguished by using two methods: DA with MD on 651–1282 nm region and the method of OD difference between 713 and 743 nm. Based on 651–1282 nm region, the DA with MD analysis can discriminate between brown core and normal pears and grade the brown core pears to three classes—slight, moderate, and severe. None of good pears was misclassified to brown core, and none of pears with brown core was misclassified to normal pears. When DOD (713–743 nm) was applied to a test set, the classification model correctly identified the good or brown core with 95.4% accuracy. Only 5.3% of the good pears were incorrectly classified, 4.3% error of pear with brown core were classified as good fruit. Acknowledgement The authors acknowledge the equipmentÕs support of professor Yanlu Yan. References Armstrong, R. (2000). ÔSweetness guaranteedÕ fruit arrives in Europe. Eurofruit Magazine, 8, 44. Chen, P., & Nattuvetty, V. R. (1980). Light transmittance through a region of an intact fruit. Transactions of the ASAE, 23, 519–522. Choi, S. T., & Lee, C. S. (2001). Non-destructive evaluation of internal browning in ÔFujiÕ apples. Journal of the Korean Society for Horticultural Science, 42, 83–86.
Clark, C. J., & Burmeister, D. M. (1999). Magnetic resonance imaging of browning development in ÔBraeburnÕ apple during CA storage under high CO2. HortScience, 34, 915–919. Clark, C. J., McGlone, V. A., & Jordan, R. B. (2003). Detection of brown heart in ÔBraeburnÕ apple by transmission NIR spectroscopy. Postharvest Biology and Technology, 28, 87–96. Gonzalez, J. J., Valle, R. C., Bobroff, S., Biasi, W. V., Mitcham, E. J., & McCarthy, M. J. (2001). Detection and monitoring of internal browning development in ÔFujiÕ apples using MRI. Postharvest Biology and Technology, 22, 179–188. Lammertyn, J. (1998). Nondestructive measurement of acidity, soluble solids, and firmness of Jonagold apples using NIR-spectroscopy. Transactions of the ASAE, 41, 1089–1094. Lite, L. (1998). Physical properties of food (pp. 340–344). Beijing: China Agricultural Publishing Company. Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy. London: Longman Scientific and Technical. Paola, E. Z., Maurizio, G., Rinaldo, C., Antonio, P., & Alessandro, T. (2002). Nondestructive detection of brown heart in pears by timeresolved reflectance spectroscopy. Postharvest Biology and Technology, 25, 87–97. Schatzki, T. F., Haff, R. P., Young, R., Can, I., Le, L. C., & Toyofuku, N. (1997). Defect detection in apples by means of X-ray imaging. Transactions of the ASAE, 40, 1407–1415. Slaughter, D. C. (1995). Nondestructive determination of internal quality in peaches and nectarines. Transactions of the ASAE, 38, 617–623. Sumio, K., Takayuki, F., & Mutsuo, I. (1993). Nondestructive detection of sugar content in satsuma mandarin using near infrared (NIR) transmittance. Journal of the Japanese Society for Horticultural Science, 62, 465–470. Upchurch, B. L., Biasi, W. V., & Aneshansley, D. J. (1997). Detecting internal breakdown in apples using interactance measurements. Postharvest Biology and Technology, 10, 15–19. Wang, J., & Wu, J. W. (1997). Studies about the influence of controlled atmosphere (CA) on browning core of Chinese pear ÔYaliÕ. China Fruit Research, 2, 7–9. Zhang, H. Y., & Wang, S. G. (1991). Studies on the relationship between the storage property and tissue structure of several pear varieties fruit. Journal of Laiyang Agricultural College, 8, 276– 279.
Nondestructive detection of brown core in the Chinese pear ÔYaliÕ by transmission visible–NIR spectroscopy q Donghai Han *, Runlin Tu, Chao Lu, Xinxin Liu, Zhaohui Wen College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China Received 4 November 2004; received in revised form 10 March 2005; accepted 14 March 2005
Abstract Brown core is an internal disorder sometimes seen in pears under controlled atmosphere (CA) storage. The symptoms are not externally recognizable and visible only after cutting the fruit. Development of nondestructive measurements of brown core of pears would benefit producers, processors, and packers. A technique using body transmittance in the 651–1282 nm region was evaluated as a nondestructive technique for identifying the Chinese pear ÔYaliÕ with brown core. Based on the 651–1282 nm region, discriminant analysis with Mahalanobis Distance (MD) analysis can discriminate between brown core and normal pears and grade the brown core pears to three classes—slight, moderate, and severe. None of the good pears was misclassified as brown core, and none of pears with brown core was misclassified as normal pears. Exhaustive searches of the best combination of individual chrematistic wavelengths were performed on a set of 66 pears from two categories–good and brown core. The best modeling classification occurred for precisely aligned pears using the difference at two wavelengths, 713 and 743 nm. When optical density (OD) difference between 713 and 743 nm was applied to a test set, the classification model correctly identified the good or brown core with 95.4% accuracy. Only 5.3% of the good pears were incorrectly classified, 4.3% error of pear with brown core were classified as good fruit. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Chinese pear ÔYaliÕ; Grading; Visible–NIR spectroscopy; Nondestructive detection; Brown core
1. Introduction The Chinese pear ÔYaliÕ is a variety in the ÔWhite pear systemÕ. It is delicious and is popular with consumers in china. During storage under controlled atmosphere (CA), the Chinese pear ÔYaliÕ can develop internal disorders such as brown core. The symptoms of the disorder consist of browning of pulp tissue, sometimes with the subsequent development of cavities. The frequency and severity of the symptoms are higher in late harvested fruit, and can be due to different climate (Wang & Wu, 1997). It is difficult to recognize externally the q
The abstract of this paper had been received by 2004 CIGR International Conference on page II-158. * Corresponding author. Tel.: +86 10 62737503; fax: +86 10 82388508. E-mail address: [email protected] (D. Han). 0956-7135/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodcont.2005.03.006
symptoms if they are confined to the inner part of the cortical parenchyma. The surface color of pear can become dark when the symptom is severe. Given the possible impact of brown core on pear quality, it became important to identify the factors that may influence brown core incidence in the Chinese pear ÔYaliÕ. A wide range of environmental and physiological factors has been implicated in the development of this disorder. Of these, the influence of the storage temperature and high atmosphere CO2 condition were identified as critical, and brown core incidence and severity in pears is associated with harvest data and fruit maturity (Wang & Wu, 1997). Browning disorders affect not only the Chinese pear ÔYaliÕ but also other ÔWhiteÕ pears including ÔLaiyangshiliÕ, ÔPingguoliÕ and ÔJingfengliÕ (Zhang & Wang, 1991). Consequently, a reliable, nondestructive method for detecting and segregating such fruit would be readily accepted
D. Han et al. / Food Control 17 (2006) 604–608
by industry. And development of nondestructive measurements of brown core of pears would benefit producers, processors, and packers. Brown core of apples can be detected nondestructively by X-ray imaging (Schatzki et al., 1997), NIR spectroscopy (Choi & Lee, 2001; Clark, McGlone, & Jordan, 2003; Upchurch, Biasi, & Aneshansley, 1997), and MRI (Clark & Burmeister, 1999; Gonzalez et al., 2001). Brown core of pear can be detected nondestructively by time-resolved reflectance spectroscopy (Paola, Maurizio, Rinaldo, Antonio, & Alessandro, 2002). As a commercial technology, NIR spectroscopy has been widely used in agricultural industry (Armstrong, 2000), and then generally operating in a ÔreflectanceÕ mode, i.e., spectral data is derived primarily from superficial regions of the sample. It is anticipated that future generations of instruments will operate more commonly in a ÔtransmissionÕ mode where light must pass through the fruit and is more likely to detect hidden, internal defects. An NIR range below 1100 nm is more generally useful for intact food as the light can penetrate much deeper (Osborne, Fearn, & Hindle, 1993). Chen and Nattuvetty (1980) evaluated experimentally a technique that utilizes fiber optics to measure light transmittance through a region of an intact fruit. Sumio, Takayuki, and Mutsuo (1993) studied the nondestructive detection of sugar content in satsuma mandarin using NIR transmittance. Slaughter (1995) determined that visible and NIR spectroscopy could be used to measure nondestructively the internal quality of peaches and nectarines as characterized by their soluble solid contents (SSC), sorbitol and chlorophyll. Lammertyn (1998) established a relationship between NIR spectra and apple quality parameters such as acidity, pH, sugar content and firmness. Clark et al. (2003) established the model of detection of brown heart in ÔBraeburnÕ apple by transmission NIR spectroscopy. Reports about nondestructive detection of in the Chinese pear ÔYaliÕ by transmission spectroscopy were not found in the scientific literature. The objective of this study was to investigate the visible–NIR spectroscopy techniques as a nondestructive method to allow discrimination between brown core pears and normal ones. The specific objectives were to: (1) develop discriminant analysis (DA) with Mahalanobis Distance (MD) models to qualitatively classify Chinese pears ÔYaliÕ of different quality, and (2) develop the method of optical density difference for classifying brown core pears and normal ones.
2. Materials and methods 2.1. Materials Chinese pears ÔYaliÕ used for the experiments were harvested in 2003 from commercial orchards in Daxing,
605
Beijing. The pears were packed in cartons directly after harvest, and stored at 0 °C until the experiments were conducted. Defective pears, such as those having wormholes, bruises, rots or chilling injuries, were eliminated. A total of 66 Chinese pears ÔYaliÕ were used for the experiments. For developing the model, samples were divided randomly into calibration and validation sets. 2.2. Transmission measurements Visible–NIR spectra of intact pears were acquired with a custom-designed laboratory system consisting of a wide-band light source (DC tungsten halogen lamp), a fruit holder, detector and PC. Transmission spectra were collected in wavelength ranges of 640– 1295 nm. Only the wavelengths from 651 nm to 1282 nm was used by the selection of soft TQ analysis of Nicolet Ltd. Transmission spectra were collected from three different locations on each pear. The signals accumulated over 10 repetitive scans were averaged and then transformed to absorption. One spectrum was created per measurement with wavelength increments of 0.32 nm (2044 points per spectrum). 2.3. Brown core assessment Following Visible–NIR measurements all fruits were dissected to determine the extent of brown. Each pear was cut in half through the equator. The cut surfaces of each fruit were photographed with a digital camera (Olympus C-300 zoom, made in Korea). The level of the brown core was assessed using a brown core index with four grades (1: no brown core; 2: slight; 3: moderate; 4: severe, Fig. 1). 2.4. Visible–NIR spectroscopy analysis For the qualitative analysis, a DA (it was derived from procedure TQ analysis of Nicolet Ltd.) was applied for pears NIR data. The DA classification technique can be used to determine the class or classes of known materials which are most similar to an unknown material by computing the unknown distance from each class center in MD units. DA methods typically specify at least two classes of known materials, but the method also works with only one class. In the research, Chinese pears ÔYaliÕ were classified into normal pears and brown core ones and the brown core pears were graded to three classes—slight, moderate, and severe. Multiple standards may be used to describe each class (at least one class must contain two or more standards). Multiple regions of the spectrum may be used for the analysis. A DA method applies the spectral information in the specified region or regions of an unknown sample
606
D. Han et al. / Food Control 17 (2006) 604–608
Fig. 1. Photographs show the sequential development of brown core symptoms.
spectrum to a stored calibration model to determine which class of standards is most similar to the unknown. During calibration, the software computes a mean spectrum and then generates a distribution model by estimating the variance at each frequency in the analysis range. Because one model was selected for using all classes, the software subtracted the class average from each standard and then created a single variance spectrum using information from all of the classes. In order to calculate the statistics properly, the single model requires at least one class that contains two or more standards and every class must contain at least two standards. When the method is used to analyze an unknown sample, the software performs a principal component analysis (PCA) on the spectra of the standards and uses those results to determine score values for the unknown sample spectrum. The score plots are used to produce MD values, which in turn are used to rank the classes. 2.5. The optical density (OD) difference analysis The OD difference discriminance is a method of nondestructive detection on fruit quality (Lite, 1998). In general, a difference measurement of OD is preferred because many variables such as size, shape, and positioning of the sample, as well as instrument factors such as lamp stability and photometer stability affect an individual measurement of OD. The effects of these variables are largely cancelled in a difference measurement.
3. Results and discussion Browning of the tissue within a pear affected the spectral content of the light transmitted through the fruit. The average transmission spectrum for each class of the calibration set is shown in Fig. 2. Generally, absorption at wavelengths between 640 and 860 nm was greater for pears with brown core than pears without the defect. The difference between brown core pear and good one was significant at 710 and 750 nm. The degree of absorption by the pear was dependent on the level of browning present in the tissue.
Fig. 2. Average spectral composition of the light transmitted through pears with and without brown core.
3.1. DA method for brown core classes A set of 46 samples (11, 21, 9, 5 samples for grade 1 to 4, respectively) was selected for calibration according to the degree of brown core, and a set of 20 samples for the validation. Fig. 3 showed the MD value between every two classes in four classes. The closer the MD value is to zero, the better is the match. The results indicated that none of good pears were misclassified to brown core ones, and none of pears with brown core were misclassified to normal pears. But two pears were misclassified between second grade and third grade, and two pears were misclassified between third grade and fourth grade. And the total accuracy rate was 94.7%. Although there were a few sample in the calibration model, the result indicated that the DA method had the possibility to separate the good pears from brown core ones. 3.2. DOD analysis for brown core classes From Fig. 2, the amplitude of the transmittance at several wavelengths was dependent on the internal
D. Han et al. / Food Control 17 (2006) 604–608
607
Fig. 3. Distribution of distance value between four classes.
condition of the pear. To minimize the influence of path length, a difference between the transmittance at 713 and 743 nm was selected as the feature for discriminating between defective and good pears. Since any level of browning would result in rejection of the pear, all of the pears with brown core were combined into a single class. According to Fig. 4, 0.231 was selected as a threshold of discriminating defective and good pears. A pear with DOD(713–743 nm) more than 0.231 was classified as a brown core pear, while any pear with DOD(713–743 nm) equal to or less than 0.231 was classified as a good pear. No effort was made to distinguish between the classes of browning. Only 1 out of the 19 good pears (5.3%) was misclassified as having browning, while 2 out of the 46 brown core pears (4.3%) were misclassified as good fruit. Only one outlier was found in 66 pears. Different factors contributed to the errors of classification. Very slight brown core is very difficult to detect because that two pears misclassified into good fruit fell to slight brown core. Compared with the discrimination results of two methods, It could be found that DA on the 651– 1282 nm region was more accurate than discrimination of OD difference between 713 and 743 nm. However,
Fig. 4. Histogram chart for of good and defective pears.
the calculation time for the latter method was shorter than DA, and it appear, therefore, to have more potential for exploitation as a portable sorter for the Chinese pear ÔYaliÕ.
608
D. Han et al. / Food Control 17 (2006) 604–608
4. Conclusion A technique using body transmittance in the (651– 1282) nm region was evaluated as a nondestructive technique for identifying the Chinese pear ÔYaliÕ with brown core. Pears with brown core were distinguished by using two methods: DA with MD on 651–1282 nm region and the method of OD difference between 713 and 743 nm. Based on 651–1282 nm region, the DA with MD analysis can discriminate between brown core and normal pears and grade the brown core pears to three classes—slight, moderate, and severe. None of good pears was misclassified to brown core, and none of pears with brown core was misclassified to normal pears. When DOD (713–743 nm) was applied to a test set, the classification model correctly identified the good or brown core with 95.4% accuracy. Only 5.3% of the good pears were incorrectly classified, 4.3% error of pear with brown core were classified as good fruit. Acknowledgement The authors acknowledge the equipmentÕs support of professor Yanlu Yan. References Armstrong, R. (2000). ÔSweetness guaranteedÕ fruit arrives in Europe. Eurofruit Magazine, 8, 44. Chen, P., & Nattuvetty, V. R. (1980). Light transmittance through a region of an intact fruit. Transactions of the ASAE, 23, 519–522. Choi, S. T., & Lee, C. S. (2001). Non-destructive evaluation of internal browning in ÔFujiÕ apples. Journal of the Korean Society for Horticultural Science, 42, 83–86.
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