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An application of image analysis to dehydration of apple discs
Journal of Food Engineering 67 (2005) 185–193 www.elsevier.com/locate/jfoodeng
An application of image analysis to dehydration of apple discs L. Ferna´ndez a
a,b
, C. Castillero a, J.M. Aguilera
a,*
Department of Chemical and Bioprocess Engineering, Pontificia Universidad Cato´lica de Chile, P.O. Box 306, Santiago, Chile b Department of Food Technology, Universidad Polite´cnica de Valencia, Camino de Vera s/n, 46071 Valencia, Spain Received 10 October 2003; accepted 1 May 2004
Abstract This paper presents a method based on computer vision to analyze the effect of drying on shrinkage, color and image texture of apple discs. A standardized image acquisition system consisting of a digital camera, illumination, computer hardware and software was developed to capture and process the images. All parameters related to shape (area, perimeter, Fourier energy, etc.) decreased with drying time. With regard to sample color, lightness (L*) remained almost constant while the chromatic co-ordinates (a* and b*) increased steadily as drying proceeded. Parameters related to the texture of the image and calculated from the color co-ordinates represented well the complexity and non-homogeneity of the visual appearance of samples. Apple discs were classified into classes depending on external image features at different stages of drying by an Euclidean distance classifier with an accuracy of 95%. This approach has the advantages over conventional methods of inspection of being versatile, quantitative and non-intrusive. 2004 Elsevier Ltd. All rights reserved. Keywords: Drying; Image processing; Apple; Shrinkage; Color; Image texture
1. Introduction Consumers select their foods in the supermarket primarily based on visual perception and often this is the only direct information received from the product. The visual sensation is a mix of the color, shape and size of the product (Paulus & Scherevens, 1999b). In recent years, much attention has been paid to the quality of dried foods which has led to the study of their microstructure as major responsible for physical and textural properties (Aguilera, 2003; Mate´, Quartaert, Meerdink, & vanÕt Riet, 1998; McMinn & Magee, 1997). Drying of cellular tissues produces several chemical (browning and other reactions) and physical (color, texture, shape, porosity, etc.) changes that are not independent but related in some complex ways (Del Valle, Cuadros, & Aguilera, 1998; Krokida & Maroulis, 1997; Krokida,
*
Corresponding author. Tel.: +56 2 586 4254; fax: +56 2 686 5803. E-mail address: [email protected] (J.M. Aguilera).
0260-8774/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2004.05.070
Maroulis, & Saravacos, 2001; Mattea, Urbicain, & Rotstein, 1989). The most commonly examined properties of dried products can be classified into two major categories, engineering and quality properties (Karathanos, Anglea, & Karel, 1996). For the chemical engineer the critical parameters derived from the drying process are the drying rate and the apparent moisture diffusivity of the product. However, for the food technologist properties such as color, shape (shrinkage) and rehydration capacity are determinant for the quality of the dried product. Shrinkage during dehydration of fruits and vegetables occurs when the viscoelastic matrix contracts into the space previously occupied by the water removed from the cells (Aguilera, 2003). Shrinkage has been studied by direct measurements with a caliper or micrometer or by changes in related parameters such as porosity and density. Porosity and density have been correlated as a function of water content (Krokida & Maroulis, 1997; Madamba, Driscol, & Buckle, 1994; Moreira, Figueiredo, & Sereno, 2000; Sjoeholm & Gekas, 1995;
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Sobral, Lebert, & Bimbenet, 2001; Wang & Brennan, 1995). Mulet, Garcı´a-Reverter, Bon, and Berna (2000) investigated the shape changes along the drying process of potato and cauliflower by image analysis and directly with a caliper. Ramos, Silva, Sereno, and Aguilera (2004) studied shrinkage in grape tissue at microscopic level by image analysis, quantifying several parameters directly related to cellular dimensions. Global shape descriptors are often inaccurate representations so they are usually combined with more precise Fourier descriptors to discriminate shapes (Zhang & Lu, 2003), as is the case presented for apples by Leemans, Magein, and Destain (1997). Ghazanfari, Irudayaraj, Kusalik, and Romaniuk (1997) used a subset of Fourier descriptors of the boundary to classify pistachio nuts. Paulus and Scherevens (1999a) analyzed the shape of apples using a Fourier expansion. Color is an important quality attribute resulting from the interaction between light, the object and the observer. Plant tissues such as apple exhibit extensive browning during drying but these color changes are not homogeneous. The effect of temperature and relative humidity on the drying process of fruit and vegetables is relatively well understood but not the kinetics of color changes. Within the trade, color is routinely measured with a colorimeter whose viewing area is normally 2–5 cm2, thus inappropriate to discriminate the overall color change of whole apple slices (Hatcher, Symons, & Manivannan, 2004). Often whole products are ground and extracted with a solvent to release the pigments supposedly responsible for color and the spectrophotometric reading of the filtered solution is associated to an average color value. Lopez et al. (1997) studied the influence of temperature (30–80 C) on browning of hazelnut (Corylus avellana L.) by measuring color of ground samples spectrophotometrically and using a Macbeth ColorEye 3000 colorimeter. Shishehgarha, Makhlouf, and Ratti (2002) investigated the drying kinetics as well as color (Minolta CM 300 colorimeter) and volume variation of whole and sliced strawberries after freeze-drying at various temperatures. Krokida, Tsami, and Maroulis (1998) studied the effect of temperature and air relative humidity on color changes during drying of apple, banana, carrot and potato. Krokida, Kiranoudis, Maroulis, and Marinos-Kouris (2000a, 2000b) investigated the effect of pre-treatments on color of dehydrated products and the properties of dried apple for various drying methods. Krokida et al. (2001) investigated the effect of the method of drying (conventional, vacuum, microwave, freeze-drying and osmotic drying) on the color of dehydrated products. The use of computer vision for the quality inspection of fruits and vegetables has increased during recent years (Blasco, Aleixos, & Molto´, 2003). A computer vision system (CVS) provides an alternative to the manual inspection of biological products by integrating an
image acquisition device and a computer (Jayas, Paliwal, & Visen, 2000). It has several advantages over convectional methods of inspection and be made compatible with other on-line processing tasks. Dimensional measurements by CVS are made more accurately and consistently than those performed by human beings, giving an objective measure of color and morphology of the object which an inspector may only assess subjectively (Batchelor, Hill, & Hodgson, 1985). Image texture is a commonly used term in computer vision. We all recognize the texture of an image when we see it, but it is very difficult to define it precisely. Bevk and Kononenko (2002) define image texture as a function of spatial variation in pixel values while Jayas et al. (2000) define it as the distribution of color in an image with respect to the spatial co-ordinates. It can be qualitatively evaluated as having one or more of the properties of fineness, coarseness, smoothness, granulation or randomness. Two objects in their digital image form, may be composed of the same number of pixels and exactly the same color histograms, but, if the distribution of color is dissimilar, they can have totally different appearance (or texture). The analysis of texture in images is a very important area of research as new algorithms are continuously being sought that can improve our ability to characterize different objects with unique feature signatures. Image texture analysis provides also numerical data that can be used in engineering analysis. This technique is better developed in the case of grey-scale image analysis as opposed to color image analysis. However, new algorithms for color texture analysis are increasingly being explored as color analysis becomes cheap and feasible (Singh, Markou, & Singh, 2002). Image texture analysis has been used for agricultural and food product quality and safety evaluation, particularly in grading and inspection. The co-occurrence matrix, which is the statistical relationship of a pixelÕs intensity to that of its neighboring pixels, has been used for image texture analysis (Park & Chen, 2001). One limitation of texture features, especially of those derived from the co-occurrence matrix, is the difficulty to relate them to visually perceived changes (Rodenacker & Bengtsson, 2003).
2. Image analysis Image processing relies heavily on computer technology and mathematical algorithms to recognize, differentiate and quantify images, and consists of several steps: 2.1. Image acquisition An image acquisition system consists of four basic components: illumination, camera, hardware and software. Vision systems require the use of a proper light source in order to avoid glitter and obtain sharp
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contrasts at the border of the sample image. The set-up of the digital camera and its illumination viewing environment is critical for image acquisition and to obtain meaningful and reproducible data (Hong, Ronnier Luo, & Rhodes, 2001). 2.2. Segmentation Through image segmentation the object of interest is separated from the background and other secondary entities (Da Fontoura & Marcondes, 2001). The segmentation process involved the following steps: (1) conversion of color image to grey-scale values; (2) application of a threshold at grey level 50 and background subtraction to obtain the binary image; (3) closing the small noisy holes within the object of interest; (4) removing all objects surrounding the apple contour; (5) overlapping the contour of the binary image to the original color image. 2.3. Image feature extraction The extraction of quantitative feature information from images is the objective of image analysis. Features are then inputs to the algorithms used for classifying objects into different categories. The goal is to use the fewest necessary measurements to characterize an object so that it may be unambiguously classified. Pavlidis (1980) distinguished two main categories of features, namely, external and internal features. External features describe the boundary information. Once the objects are separated from the background, their boundary co-ordinates can be used to extract morphological features, such as, Fourier descriptors, boundary chain codes, etc. (Jayas et al., 2000). The most common measurements performed on objects are those that describe shape. Shape is one of the primary low-level image features in content-based image retrieval (Zhang & Lu, 2003). Shape descriptors describe specific characteristics regarding the geometry of a particular feature. In general, they are some set of numbers that are produced to describe a given shape. The shape may not be entirely reconstructable from the descriptors, but the descriptors for different shapes should be different enough so that the shapes can be discriminated. 2.3.1. External image features 2.3.1.1. Morphological features. Morphological features like roundness, elongation, compactness, etc., are widely used in automated grading, sorting and detection of objects in industry (Jayas et al., 2000). Some specialized software such as Image Pro Plus gives over 30 geometrical parameters of identified objects. 2.3.1.2. Fourier descriptors. Among spectral descriptors, Fourier descriptors (FD) are one of the most popular
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shape representation methods for vision and pattern recognition having the advantage of simple computation and normalization (matching). The basic idea underlying this approach consists in representing the shape of interest in terms of a 1-D or 2-D signal (Da Fontoura & Marcondes, 2001). Features derived from the Fourier series expansion of a periodic function describing the boundary of an object, and hence encode that boundary (Ghazanfari et al., 1997). In FD-based method, a shape is first represented by a feature function called shape signature. Consider an object with an N point digital boundary in the xy plane. Starting at an arbitrary point (x0, y0), k co-ordinate pairs (x0, y0), (x1, y1), (x2, y2), . . ., (xN1, yN1) are encountered in traversing the boundary counter-clockwise. The centroid distance function is expressed by the distance of the boundary points from the centroid (xc, yc) of the shape. These co-ordinates can be expressed in the form of x(k) = (xkx0) and y(k) = (yky0). Each co-ordinate pair can be treated as a complex number so that r(k) = x(k) + jy(k) where j2 = 1 and k = 0, 1, 2, . . ., N 1, i.e., the x-axis is treated as the real axis and the y-axis as the imaginary axis of a sequence of complex numbers (Jayas et al., 2000). A discrete Fourier transform is applied to the signature to obtain the FD of the shape (Lu & Sajjanhar, 1999). Starting from this discrete Fourier transform, harmonic amplitudes are calculated and referred to as FDs. The algorithm chosen to compute FDs was the fast Fourier transform (FFT). The FFT algorithm requires the number of points N defining the shape to be a power of two. 2.3.2. Internal image features The features extracted from the properties of pixels inside the object boundary are called internal image features. These features combine information across an entire object, however, they do not emphasize shape boundary features which are equally important as interior features (Zhang & Lu, 2003). The most important internal features are color and image texture. To define and display color it is necessary to select a color space which is a mathematical representation of a set of colors. The three most common color spaces are: RGB (used for television, computer screens, scanners and digital cameras), CMYK (used by the printing industry) and the L*a*b* space (used in food research studies). The L*a*b* color space is device independent, providing consistent color regardless of the input or output device such as digital camera, scanner, monitor and printer (Yam & Papadakis, 2004). In image analysis, there are a number of techniques for calculation of image texture properties. These are usually categorized into statistical, spectral and structural methods. The statistical approach tries to characterize the texture of an image region using statistical measures. They have been proved to be more powerful
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and useful than structural features. Among all statistical methods, the most popular one is based on the estimation of the second order statistics (Haralick, Shammugam, & Dinstein, 1973). Each textural feature is computed from a set of co-occurrence matrix (COM), which is the statistical relationship of a pixelÕs intensity to the intensity of its neighboring pixels. A co-occurrence matrix is a square matrix whose elements correspond to the relative frequency of occurrence p(i, j) of two pixel values (one with intensity i and the other with intensity j), separated by a certain distance d in a given direction (Latif-Amet, Ertuzun, & Erc¸il, 1999; Unay & Gosselin, 2002). The co-occurrence matrix is, therefore, a square matrix, that has the size of the largest pixel value in the image. Haralick et al. (1973) proposed 14 measures of textural features which are derived from the co-occurrence matrices, each one representing specific image properties.
ter and 10 mm thickness) cut parallel to the main axis of the fruit using a cork borer and sliced by two parallel knives. 3.2. Air-drying Drying was performed in a laboratory dryer with accurate temperature control (model U-30, Memmert, Schwabach, Germany). Forty-five apples slices were placed over ceramic tiles coated with heat-resistant black paint and dried at 50 ± 1C during a period of 14 h. Sampling was performed every hour for the first 10 h and later every 2 h. A single tile containing six slices was removed at each time just to acquire images and placed back in the drier. Three samples from the remaining slices were randomly retrieved at each time, photographed and used for moisture content analysis. Moisture was gravimetrically determined by drying samples at 105 C until constant weight.
2.4. Recognition, classification and interpretation 3.3. Image acquisition and analysis Image processing algorithms are often used to extract a set of features or pattern, from the image. On the basis of the pattern, the object can then be classified into one of several pre-defined classes using a classification algorithm, called a pattern classifier. The classification criterion is usually derived from the observation of the known classes, called the training set. The derived classification criterion can then be applied to classify new observations, called the test set (Jayas et al., 2000). The most critical step in any classification procedure is selecting an appropriate set of features to represent an object (Ghazanfari et al., 1997). The overall objective of this work was to apply CVS to study color changes and shrinkage during drying of apple slices. Specific objectives were: • implement and apply a new method of capturing images and image analysis that is versatile, fast and non-destructive; • quantify and relate a set of color and geometrical features in apple discs during the drying process; • classify dried apple slices as a function of the drying time.
The CVS is shown in Fig. 1. Four fluorescent lamps of 60 cm (Philips, TLD series 90, 18W/965), with a color temperature of 6500 C (D65, daylight) and a color reproduction index near to 95% were used for illumination. The angle between the axis of the camera lens and the lightning source axis was 45 to capture the diffuse reflection responsible for the color, which occurs at that angle from the incident light (Papadakis, Abdul-Malek, Kandem, & Yam, 2000). Image of apples slices were captured directly from the black painted tiles as background using a CCD digital camera (Power Shot A70, Canon, USA). Parameters of the digital camera were 1/8 second shutter speed, macro focusing mode, F 8.0 aperture stop and ISO 50 sensitivity. Images consisted of 1024 · 768 pixels with a scaling factor of one centimeter equals 165 pixels and were saved in. jpeg mode. The axis of the digital camera formed an angle of 90 with
3. Materials and methods 3.1. Raw material Granny Smith apples (13.1 ± 0.2 Brix and 87.3 ± 0.2% moisture, w.b.) purchased from a local supermarket stored at ambient temperature (20 C) until the moment of the experiment. Samples for drying experiments were discs of apple tissue (22 mm in diame-
Fig. 1. Computer vision system for image acquisition.
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the plane of the sample and the lens was 15.7 cm above the sample. The digital data generated were directly transferred to a PC (Pentium II, 30 GB, 300 MHz) via a USB interface. Image processing and analysis were performed using macros written in Matlab 6.5 Release 13 (Mathworks, Inc.). The CVS perceived color as RGB signals that are device-dependent, therefore, to ensure correct color reproduction, they were converted into XYZ tristimulus values and later to CIE Lab color co-ordinates using a macro written in Matlab 6.5. In this system, L* value indicates the lightness, which ranges from 0 (black) to 100 (white), a* indicates greenness to redness, and b* indicates the blueness to yellowness (these chromatic components range from 120 to +120). For comparison, color of apple slices was also measured with a HunterLab MiniscanTM XE colorimeter model 45/0 LAV (Hunter Associates Inc., Reston, VA) at three random points. The following morphological parameters were determined on the segmented image: Average radius: Average distance from the centroid to the boundary points of the object. Area (A): Number of pixels within the boundary. Perimeter (P): Number of pixels in the boundary of the object. Feret diameter (DF): Diameter of a circle having the same area (A) as the object rffiffiffiffiffiffi 4A DF ¼ ð1Þ p Roundness (R): Ratio of the area (A) of the object and that of a circle with the same perimeter (P) 4pA ð2Þ P2 Fourier descriptors used in this work were: Energy: Sum of the squared magnitudes of the samples. For discrete time signals, it is the ‘‘size’’ of the signal
R¼
E¼
N X
2
jFDj
ð3Þ
189
the co-occurrence matrix. It reaches its highest value when color (or grey) level distribution has either a constant or a periodic form (Partio, Cramariuc, Gabbouj, & Visa, 2002): XX fpði; jÞg2 ð5Þ Energy ¼ i
j
where p(i, j) is the relative frequency of occurrence of two pixel values (one with intensity i and the other with intensity j). Entropy: Measures of the disorder or the randomness of an image. Complex textures tend to have higher entropy. Therefore, entropy is inversely proportional to energy XX Entropy ¼ pði; jÞ log pði; jÞ ð6Þ i
j
Contrast: Is a measure of the amount of local variations present in an image. A high contrast value indicates a high degree of local variation (Park & Chen, 2001) XX 2 Contrast ¼ ði jÞ pði; jÞ ð7Þ i
j
Inverse difference moment: Relates to image homogeneity and is the opposite of the contrast XX 1 Idm ¼ pði; jÞ ð8Þ 2 i j 1 þ ði jÞ A statistical classifier together with the feature vector formed by the Fourier and morphological features were used in order to discriminate between images of each class. The ‘‘minimum distance to the prototype’’ statistical classifier (Euclidean distance classifier) was implemented. In this classifier one prototype per class is defined and each prototype is the average of all samples in the training set. This classifier makes use of the correspondence between similarity and distance; it assigns an unknown pattern (input pattern) to the class of its nearest neighbor pattern. The advantage of this classifier is its computationally efficiency (Emı´dio, Schimidt, & Marcondes, 2000).
i¼0
Variance descriptors: V ¼
of
the
N 1 X ðFDi FDÞ2 N i¼0
distribution
of
the
Fourier 4. Results and discussion ð4Þ
where N is the number of boundary points and FD is the average of the Fourier descriptors. To reduce the computational complexity, only 4 of 14 textural features that are widely used in literature (Gonzalez & Woods, 1992; Haralick, 1979) were selected. Energy: Measures the textural uniformity of an image. It is the sum of the squares of all elements in
Fig. 2 depicts a gallery of images of the same apple disc as drying proceeded. There are evident visual changes in the size of the piece (shrinkage), form (leading to a progressively irregular shape) and color (browning and emergence of non-uniform color patterns). Fig. 3 presents a typical drying curve of moisture content versus drying time. Major changes in moisture content occurred in the time interval from 3 to 7 h. Under conditions of the experiment samples achieved a final moisture of about 12% (w.b.) at the end of drying.
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Fig. 2. Gallery of images of a single apple slice as a function of drying time (time in hours).
8
8 Area Perimeter
60 40 20
7
6
6
5
5
4
4
3
Perimeter (cm)
7
80 Area (cm2)
3
(A)
0
2
2
4
6 8 10 Drying time (h)
12
Fig. 3. Variation of moisture content (wet basis) of apple slices with drying time.
Variations in area, perimeter, average radius, roundness and Feret diameter of apple discs as a function of drying time are shown in Fig. 4. All morphological features decreased smoothly with drying time, a trend similar to that found in grape tissue by Ramos et al. (2004). The values of most parameters (except roundness) changed rapidly in the first 6 h of drying and remained almost constant thereafter. Interestingly, Bolin and Huxsoll (1987) (cited by Ramos et al. (2004)) reported that the roundness of cells in apple rings decreased with drying time. As observed in Fig. 5 the energy and the variance of Fourier descriptors also decreased with the drying time a consequence that the area contained by the curve of Fourier descriptors decreased (data not shown). Apple tissue exhibited extensive and non-homogeneous (spatially) browning during drying (e.g., Fig. 2). Color changes measured by image analysis and by the hand-held colorimeter gave high average correlation coefficients for L, a* and b* (R2 = 0.91, 0.94 and 0.95,
2 0
14
2
4
6
8
10
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14
Drying time (h) 2.5 Average Radius and Feret Diameter (cm)
0
2.5 Feret D. Average radio Roundness
2
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1.5
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Roundness
% Moisture content (w.b)
100
1
(B) 0.5
0.5 0
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6
8
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Drying time (h)
Fig. 4. Variation of morphological features of apple slices with drying time. (A) Area and perimeter; and (B) average radius, roundness and Feret diameter.
respectively). Values of lightness (L*) varied erratically between 62 and 75 (Fig. 6A). This unclear behavior, also reported by Krokida et al. (2001) was attributed to experimental error. The variation of experimental values for the chromatic co-ordinates a* and b* with drying time is presented in Fig. 6B. Redness (a* value) and
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191
80
Lightness
60
40
20
(A) 0 0
2
4
6 8 10 Drying time (h)
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75
(B)
Fig. 5. Energy and variance of Fourier variation with drying time.
65
1
55
-7 35 25
-15
15 0
2
4
6 8 10 Drying time (h)
12
14
Fig. 6. Colour variation with drying time. (A) Lightness; and (B) chromatic co-ordinates.
3
1 0.8 0.6 0.4
Cont L Cont a Cont b
2.5
Contrast normalized
Ener L Ener a Ener b
(A) Energy normalized
a b
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2 1.5 1
(C)
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Inverse Difference Moment normalized
1.3
Entropy normalized
45
a*
yellowness (b* value) increased as drying proceeded. These results were similar to those reported by Krokida et al. (2001) for color of apples measured with a colorimeter during conventional air drying. For image texture analysis, each textural feature (e.g., energy, entropy, etc.) was computed separately for L*, a* and b* from a set of co-occurrence matrices (COMs), not as usually done for gray values. COMs were calculated for a distance between pixels d = 1 and an angle = 0. In order to represent the textural features
b*
-3
Idm L Idm a Idm b
(D) 1 0.95 0.9 0.85 0.8 0
2
4
6
8
10
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14
Drying time (h)
Fig. 7. Variation in image texture with drying time. (A) Energy; (B) entropy; (C) contrast; and (D) inverse difference moment.
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calculated from the co-occurrence matrix for L, a* and b*, values for each drying time were normalized (i.e., divided by their initial value, at zero time) and results are presented in Fig. 7. Standard deviation bars were not placed pffiffiffi in the figure for clarity but the standard error ðs= nÞ was in all cases less than 0.05. Energy increased when the spatial distribution of the intensity is almost constant in an image. As shown in Fig. 7A, the energy decreased for all three color co-ordinates with increasing drying time, particularly in the first 8 h, meaning that samples became less uniform in color during that period. Entropy increased for the first 10 h of drying due to the higher image textural complexity (Fig. 7B). High values of entropy were to be expected if the frequencies of occurrence in the co-occurrence matrix were equally scattered over the matrix. This uniform scattering occurs when the largest spread of different pixel intensity occurs in the spectral image (Park & Chen, 2001). Contrast increased during the drying process (Fig. 7C) indicating a higher degree of local variation. On the contrary, inverse difference moment decreased since apple slices lost textural homogeneity (Fig. 7D). Classification model were developed for identifying samples from different time of drying using Euclidean distance. The feature vector was constructed considering the morphological (area, perimeter, etc.) and Fourier features. Although the program was designed to manage up to thirteen classes, one for each drying time, it was difficult to discern between classes when the drying time exceeded 6 h. Figs. 4 and 5 show that these external image features remain almost constant after 6 h of drying. Accordingly, classes 1–7 were defined for drying times 0, 1, 2, 3, 4, 5, and >6 h, hence, samples from drying times over 6 h were considered as belonging to the same class. The ‘‘minimum distance to the prototype’’ statistical classifier was used to process data and the program for pattern recognition was developed in Matlab 6.5. The program calculates all the patterns of the image, and it decides which class the input pattern belongs. The training set consisted of 72 images of different samples (six samples per drying time) and 39 random samples (test set) were used to evaluate the classifier. Ninety-five percent of the test samples were correctly classified into their respective classes.
5. Conclusions Under the experimental conditions of this work drying produces drastic changes in the shape, color and image texture of the apple slices that were quantified by means of CVS and image analysis. All morphological features decreased smoothly during the first 6–8 h of drying. Methods presented here have the advantage that represent quality factors perceived by consumers rather than process parameters (i.e., rate of drying, moisture
content) which have limited significance from the standpoint of product properties.
Acknowledgment This research was supported by the ALFA Networkii: Food and Bioprocess Engineering for Sustainability and Quality, and project FONDECYT 1030339.
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L. Ferna´ndez et al. / Journal of Food Engineering 67 (2005) 185–193 Krokida, M. K., Tsami, E., & Maroulis, Z. B. (1998). Kinetics on color changes during drying of some fruits and vegetables. Drying Technology, 16(3–5), 667–685. Krokida, M. K., Kiranoudis, C. T., Maroulis, Z. B., & MarinosKouris, D. (2000a). Effect of pretreatment on color of dehydrated products. Drying Technology, 18(6), 1239–1250. Krokida, M. K., Kiranoudis, C. T., Maroulis, Z. B., & MarinosKouris, D. (2000b). Drying related properties of apple. Drying Technology, 18(6), 1251–1267. Krokida, M. K., Maroulis, Z. B., & Saravacos, G. D. (2001). The effect of the method of drying on the colour of dehydrated products. International Journal of Food Science and Technology, 36(1), 53–59. Latif-Amet, A., Ertuzun, A., & Erc¸il, A. (1999). An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image and Vision Computing, 18(6–7), 543–553. Leemans, V., Magein, H., & Destain, M. F. (1997). Vision artificielle et quantification de la forme de pommes. Cahiers Agricultures, 6(6), 597–603. Lopez, A., Pique´, M. T., Boatella, J., Romero, A., Ferra´n, A., & Garcı´a, J. (1997). Influence of drying conditions on the hazelnut quality: III. Browning. Drying Technology, 15(3–4), 984–1002. Lu, G., & Sajjanhar, A. (1999). Region-based shape representation and similarity measure suitable for content-based image retrieval. Multimedia Systems, 7(2), 165–174. Madamba, P. S., Driscol, R. H., & Buckle, A. (1994). Shrinkage density and porosity of garlic during drying. Journal of Food Engineering, 23(3), 309–319. Mate´, J. I., Quartaert, C., Meerdink, G., & vanÕt Riet, K. (1998). Effect of blanching on structural quality of dried potato slices. Journal of Agriculture and Food Chemistry, 46(2), 676–681. Mattea, M., Urbicain, M. J., & Rotstein (1989). Computer model of shrinkage and deformation of cellular tissue during dehydration. Chemical Engineering Science, 44(12), 2853–2859. McMinn, W. A. M., & Magee, T. R. A. (1997). Quality and physical structure of a dehydrated starch-based system. Drying Technology, 15(6/8), 1961–1971. Moreira, R., Figueiredo, A., & Sereno, A. (2000). Shrinkage of apple disks during drying by warm air convection and freeze drying. Drying Technology, 18(1&2), 279–294. Mulet, A., Garcı´a-Reverter, J., Bon, J., & Berna, A. (2000). Effect of shape on potato and cauliflower shrinkage during drying. Drying Technology, 18(6), 1201–1219. Papadakis, S. E., Abdul-Malek, S., Kandem, R. E, & Yam, K. L. (2000). A versatile and inexpensive technique for measuring colour of foods. Food Technology, 54(12), 48–51.
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Park, B., & Chen, Y. R. (2001). Co-occurrence matrix texture features of multi-spectral images on poultry carcasses. Journal of Agricultural Engineering Research, 78(2), 127–139. Partio, M., Cramariuc, B., Gabbouj, M., & Visa, A. (2002). Rock texture retrieval using gray level co-occurrence matrix. In 5th Nordic signal processing symposium on board Hurtigruten. Norway. Paulus, I., & Scherevens, E. (1999a). Shape characterization of new apple cultivars by Fourier expansion of digitized images. Journal of Agricultural Engineering Research, 72(2), 113–118. Paulus, I., & Scherevens, E. (1999b). Evaluating and modelling the size classification of apples. Journal of Agricultural Engineering Research, 74(4), 411–419. Pavlidis, T. (1980). Algorithms for shape analysis of contours and waveforms. IEEE Transactions Pattern Analysis and Machine Intelligence, 2(4), 301–312. Ramos, I. N, Silva, C. L. M, Sereno, A. M., & Aguilera, J. M. (2004). Quantification of microstructural changes during first stage air drying of grape tissue. Journal of Food Engineering, 62(2), 159– 164. Rodenacker, K., & Bengtsson, E. (2003). A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology, 25(1), 1–36. Shishehgarha, F., Makhlouf, J., & Ratti, C. (2002). Freeze-drying characteristics of strawberries. Drying Technology, 20(1), 131–145. Singh, M., Markou, M., & Singh, S. (2002). Colour image texture analysis: dependence on colour spaces. In Proc. 15th international conference on pattern recognition (ICPRÕ02). Quebec. Sjoeholm, I., & Gekas, V. (1995). Apple shrinkage upon drying. Journal of Food Engineering, 25(1), 123–130. Sobral, P. J., Lebert, A., & Bimbenet, J. J. (2001). Effet du se´chage sur le re´tre´cissement de cubes de pomme de terre. Science des Aliments, 21, 231–242. Unay, D., & Gosselin, B. (2002). Apple defect detection and quality classification with MLP-Neural networks. In Proc. of PRORISC 2002, Eindhoven, The Netherlands. Wang, N., & Brennan, J. G. (1995). Changes in structure density and porosity of potato during dehydration. Journal of Food Engineering, 24(1), 61–76. Yam, K. L., & Papadakis, S. E. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61(1), 137–142. Zhang, D., & Lu, G. (2003). A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval. Journal of Visual Communication and Image Representation, 14, 41–60.
An application of image analysis to dehydration of apple discs L. Ferna´ndez a
a,b
, C. Castillero a, J.M. Aguilera
a,*
Department of Chemical and Bioprocess Engineering, Pontificia Universidad Cato´lica de Chile, P.O. Box 306, Santiago, Chile b Department of Food Technology, Universidad Polite´cnica de Valencia, Camino de Vera s/n, 46071 Valencia, Spain Received 10 October 2003; accepted 1 May 2004
Abstract This paper presents a method based on computer vision to analyze the effect of drying on shrinkage, color and image texture of apple discs. A standardized image acquisition system consisting of a digital camera, illumination, computer hardware and software was developed to capture and process the images. All parameters related to shape (area, perimeter, Fourier energy, etc.) decreased with drying time. With regard to sample color, lightness (L*) remained almost constant while the chromatic co-ordinates (a* and b*) increased steadily as drying proceeded. Parameters related to the texture of the image and calculated from the color co-ordinates represented well the complexity and non-homogeneity of the visual appearance of samples. Apple discs were classified into classes depending on external image features at different stages of drying by an Euclidean distance classifier with an accuracy of 95%. This approach has the advantages over conventional methods of inspection of being versatile, quantitative and non-intrusive. 2004 Elsevier Ltd. All rights reserved. Keywords: Drying; Image processing; Apple; Shrinkage; Color; Image texture
1. Introduction Consumers select their foods in the supermarket primarily based on visual perception and often this is the only direct information received from the product. The visual sensation is a mix of the color, shape and size of the product (Paulus & Scherevens, 1999b). In recent years, much attention has been paid to the quality of dried foods which has led to the study of their microstructure as major responsible for physical and textural properties (Aguilera, 2003; Mate´, Quartaert, Meerdink, & vanÕt Riet, 1998; McMinn & Magee, 1997). Drying of cellular tissues produces several chemical (browning and other reactions) and physical (color, texture, shape, porosity, etc.) changes that are not independent but related in some complex ways (Del Valle, Cuadros, & Aguilera, 1998; Krokida & Maroulis, 1997; Krokida,
*
Corresponding author. Tel.: +56 2 586 4254; fax: +56 2 686 5803. E-mail address: [email protected] (J.M. Aguilera).
0260-8774/$ - see front matter 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jfoodeng.2004.05.070
Maroulis, & Saravacos, 2001; Mattea, Urbicain, & Rotstein, 1989). The most commonly examined properties of dried products can be classified into two major categories, engineering and quality properties (Karathanos, Anglea, & Karel, 1996). For the chemical engineer the critical parameters derived from the drying process are the drying rate and the apparent moisture diffusivity of the product. However, for the food technologist properties such as color, shape (shrinkage) and rehydration capacity are determinant for the quality of the dried product. Shrinkage during dehydration of fruits and vegetables occurs when the viscoelastic matrix contracts into the space previously occupied by the water removed from the cells (Aguilera, 2003). Shrinkage has been studied by direct measurements with a caliper or micrometer or by changes in related parameters such as porosity and density. Porosity and density have been correlated as a function of water content (Krokida & Maroulis, 1997; Madamba, Driscol, & Buckle, 1994; Moreira, Figueiredo, & Sereno, 2000; Sjoeholm & Gekas, 1995;
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Sobral, Lebert, & Bimbenet, 2001; Wang & Brennan, 1995). Mulet, Garcı´a-Reverter, Bon, and Berna (2000) investigated the shape changes along the drying process of potato and cauliflower by image analysis and directly with a caliper. Ramos, Silva, Sereno, and Aguilera (2004) studied shrinkage in grape tissue at microscopic level by image analysis, quantifying several parameters directly related to cellular dimensions. Global shape descriptors are often inaccurate representations so they are usually combined with more precise Fourier descriptors to discriminate shapes (Zhang & Lu, 2003), as is the case presented for apples by Leemans, Magein, and Destain (1997). Ghazanfari, Irudayaraj, Kusalik, and Romaniuk (1997) used a subset of Fourier descriptors of the boundary to classify pistachio nuts. Paulus and Scherevens (1999a) analyzed the shape of apples using a Fourier expansion. Color is an important quality attribute resulting from the interaction between light, the object and the observer. Plant tissues such as apple exhibit extensive browning during drying but these color changes are not homogeneous. The effect of temperature and relative humidity on the drying process of fruit and vegetables is relatively well understood but not the kinetics of color changes. Within the trade, color is routinely measured with a colorimeter whose viewing area is normally 2–5 cm2, thus inappropriate to discriminate the overall color change of whole apple slices (Hatcher, Symons, & Manivannan, 2004). Often whole products are ground and extracted with a solvent to release the pigments supposedly responsible for color and the spectrophotometric reading of the filtered solution is associated to an average color value. Lopez et al. (1997) studied the influence of temperature (30–80 C) on browning of hazelnut (Corylus avellana L.) by measuring color of ground samples spectrophotometrically and using a Macbeth ColorEye 3000 colorimeter. Shishehgarha, Makhlouf, and Ratti (2002) investigated the drying kinetics as well as color (Minolta CM 300 colorimeter) and volume variation of whole and sliced strawberries after freeze-drying at various temperatures. Krokida, Tsami, and Maroulis (1998) studied the effect of temperature and air relative humidity on color changes during drying of apple, banana, carrot and potato. Krokida, Kiranoudis, Maroulis, and Marinos-Kouris (2000a, 2000b) investigated the effect of pre-treatments on color of dehydrated products and the properties of dried apple for various drying methods. Krokida et al. (2001) investigated the effect of the method of drying (conventional, vacuum, microwave, freeze-drying and osmotic drying) on the color of dehydrated products. The use of computer vision for the quality inspection of fruits and vegetables has increased during recent years (Blasco, Aleixos, & Molto´, 2003). A computer vision system (CVS) provides an alternative to the manual inspection of biological products by integrating an
image acquisition device and a computer (Jayas, Paliwal, & Visen, 2000). It has several advantages over convectional methods of inspection and be made compatible with other on-line processing tasks. Dimensional measurements by CVS are made more accurately and consistently than those performed by human beings, giving an objective measure of color and morphology of the object which an inspector may only assess subjectively (Batchelor, Hill, & Hodgson, 1985). Image texture is a commonly used term in computer vision. We all recognize the texture of an image when we see it, but it is very difficult to define it precisely. Bevk and Kononenko (2002) define image texture as a function of spatial variation in pixel values while Jayas et al. (2000) define it as the distribution of color in an image with respect to the spatial co-ordinates. It can be qualitatively evaluated as having one or more of the properties of fineness, coarseness, smoothness, granulation or randomness. Two objects in their digital image form, may be composed of the same number of pixels and exactly the same color histograms, but, if the distribution of color is dissimilar, they can have totally different appearance (or texture). The analysis of texture in images is a very important area of research as new algorithms are continuously being sought that can improve our ability to characterize different objects with unique feature signatures. Image texture analysis provides also numerical data that can be used in engineering analysis. This technique is better developed in the case of grey-scale image analysis as opposed to color image analysis. However, new algorithms for color texture analysis are increasingly being explored as color analysis becomes cheap and feasible (Singh, Markou, & Singh, 2002). Image texture analysis has been used for agricultural and food product quality and safety evaluation, particularly in grading and inspection. The co-occurrence matrix, which is the statistical relationship of a pixelÕs intensity to that of its neighboring pixels, has been used for image texture analysis (Park & Chen, 2001). One limitation of texture features, especially of those derived from the co-occurrence matrix, is the difficulty to relate them to visually perceived changes (Rodenacker & Bengtsson, 2003).
2. Image analysis Image processing relies heavily on computer technology and mathematical algorithms to recognize, differentiate and quantify images, and consists of several steps: 2.1. Image acquisition An image acquisition system consists of four basic components: illumination, camera, hardware and software. Vision systems require the use of a proper light source in order to avoid glitter and obtain sharp
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contrasts at the border of the sample image. The set-up of the digital camera and its illumination viewing environment is critical for image acquisition and to obtain meaningful and reproducible data (Hong, Ronnier Luo, & Rhodes, 2001). 2.2. Segmentation Through image segmentation the object of interest is separated from the background and other secondary entities (Da Fontoura & Marcondes, 2001). The segmentation process involved the following steps: (1) conversion of color image to grey-scale values; (2) application of a threshold at grey level 50 and background subtraction to obtain the binary image; (3) closing the small noisy holes within the object of interest; (4) removing all objects surrounding the apple contour; (5) overlapping the contour of the binary image to the original color image. 2.3. Image feature extraction The extraction of quantitative feature information from images is the objective of image analysis. Features are then inputs to the algorithms used for classifying objects into different categories. The goal is to use the fewest necessary measurements to characterize an object so that it may be unambiguously classified. Pavlidis (1980) distinguished two main categories of features, namely, external and internal features. External features describe the boundary information. Once the objects are separated from the background, their boundary co-ordinates can be used to extract morphological features, such as, Fourier descriptors, boundary chain codes, etc. (Jayas et al., 2000). The most common measurements performed on objects are those that describe shape. Shape is one of the primary low-level image features in content-based image retrieval (Zhang & Lu, 2003). Shape descriptors describe specific characteristics regarding the geometry of a particular feature. In general, they are some set of numbers that are produced to describe a given shape. The shape may not be entirely reconstructable from the descriptors, but the descriptors for different shapes should be different enough so that the shapes can be discriminated. 2.3.1. External image features 2.3.1.1. Morphological features. Morphological features like roundness, elongation, compactness, etc., are widely used in automated grading, sorting and detection of objects in industry (Jayas et al., 2000). Some specialized software such as Image Pro Plus gives over 30 geometrical parameters of identified objects. 2.3.1.2. Fourier descriptors. Among spectral descriptors, Fourier descriptors (FD) are one of the most popular
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shape representation methods for vision and pattern recognition having the advantage of simple computation and normalization (matching). The basic idea underlying this approach consists in representing the shape of interest in terms of a 1-D or 2-D signal (Da Fontoura & Marcondes, 2001). Features derived from the Fourier series expansion of a periodic function describing the boundary of an object, and hence encode that boundary (Ghazanfari et al., 1997). In FD-based method, a shape is first represented by a feature function called shape signature. Consider an object with an N point digital boundary in the xy plane. Starting at an arbitrary point (x0, y0), k co-ordinate pairs (x0, y0), (x1, y1), (x2, y2), . . ., (xN1, yN1) are encountered in traversing the boundary counter-clockwise. The centroid distance function is expressed by the distance of the boundary points from the centroid (xc, yc) of the shape. These co-ordinates can be expressed in the form of x(k) = (xkx0) and y(k) = (yky0). Each co-ordinate pair can be treated as a complex number so that r(k) = x(k) + jy(k) where j2 = 1 and k = 0, 1, 2, . . ., N 1, i.e., the x-axis is treated as the real axis and the y-axis as the imaginary axis of a sequence of complex numbers (Jayas et al., 2000). A discrete Fourier transform is applied to the signature to obtain the FD of the shape (Lu & Sajjanhar, 1999). Starting from this discrete Fourier transform, harmonic amplitudes are calculated and referred to as FDs. The algorithm chosen to compute FDs was the fast Fourier transform (FFT). The FFT algorithm requires the number of points N defining the shape to be a power of two. 2.3.2. Internal image features The features extracted from the properties of pixels inside the object boundary are called internal image features. These features combine information across an entire object, however, they do not emphasize shape boundary features which are equally important as interior features (Zhang & Lu, 2003). The most important internal features are color and image texture. To define and display color it is necessary to select a color space which is a mathematical representation of a set of colors. The three most common color spaces are: RGB (used for television, computer screens, scanners and digital cameras), CMYK (used by the printing industry) and the L*a*b* space (used in food research studies). The L*a*b* color space is device independent, providing consistent color regardless of the input or output device such as digital camera, scanner, monitor and printer (Yam & Papadakis, 2004). In image analysis, there are a number of techniques for calculation of image texture properties. These are usually categorized into statistical, spectral and structural methods. The statistical approach tries to characterize the texture of an image region using statistical measures. They have been proved to be more powerful
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and useful than structural features. Among all statistical methods, the most popular one is based on the estimation of the second order statistics (Haralick, Shammugam, & Dinstein, 1973). Each textural feature is computed from a set of co-occurrence matrix (COM), which is the statistical relationship of a pixelÕs intensity to the intensity of its neighboring pixels. A co-occurrence matrix is a square matrix whose elements correspond to the relative frequency of occurrence p(i, j) of two pixel values (one with intensity i and the other with intensity j), separated by a certain distance d in a given direction (Latif-Amet, Ertuzun, & Erc¸il, 1999; Unay & Gosselin, 2002). The co-occurrence matrix is, therefore, a square matrix, that has the size of the largest pixel value in the image. Haralick et al. (1973) proposed 14 measures of textural features which are derived from the co-occurrence matrices, each one representing specific image properties.
ter and 10 mm thickness) cut parallel to the main axis of the fruit using a cork borer and sliced by two parallel knives. 3.2. Air-drying Drying was performed in a laboratory dryer with accurate temperature control (model U-30, Memmert, Schwabach, Germany). Forty-five apples slices were placed over ceramic tiles coated with heat-resistant black paint and dried at 50 ± 1C during a period of 14 h. Sampling was performed every hour for the first 10 h and later every 2 h. A single tile containing six slices was removed at each time just to acquire images and placed back in the drier. Three samples from the remaining slices were randomly retrieved at each time, photographed and used for moisture content analysis. Moisture was gravimetrically determined by drying samples at 105 C until constant weight.
2.4. Recognition, classification and interpretation 3.3. Image acquisition and analysis Image processing algorithms are often used to extract a set of features or pattern, from the image. On the basis of the pattern, the object can then be classified into one of several pre-defined classes using a classification algorithm, called a pattern classifier. The classification criterion is usually derived from the observation of the known classes, called the training set. The derived classification criterion can then be applied to classify new observations, called the test set (Jayas et al., 2000). The most critical step in any classification procedure is selecting an appropriate set of features to represent an object (Ghazanfari et al., 1997). The overall objective of this work was to apply CVS to study color changes and shrinkage during drying of apple slices. Specific objectives were: • implement and apply a new method of capturing images and image analysis that is versatile, fast and non-destructive; • quantify and relate a set of color and geometrical features in apple discs during the drying process; • classify dried apple slices as a function of the drying time.
The CVS is shown in Fig. 1. Four fluorescent lamps of 60 cm (Philips, TLD series 90, 18W/965), with a color temperature of 6500 C (D65, daylight) and a color reproduction index near to 95% were used for illumination. The angle between the axis of the camera lens and the lightning source axis was 45 to capture the diffuse reflection responsible for the color, which occurs at that angle from the incident light (Papadakis, Abdul-Malek, Kandem, & Yam, 2000). Image of apples slices were captured directly from the black painted tiles as background using a CCD digital camera (Power Shot A70, Canon, USA). Parameters of the digital camera were 1/8 second shutter speed, macro focusing mode, F 8.0 aperture stop and ISO 50 sensitivity. Images consisted of 1024 · 768 pixels with a scaling factor of one centimeter equals 165 pixels and were saved in. jpeg mode. The axis of the digital camera formed an angle of 90 with
3. Materials and methods 3.1. Raw material Granny Smith apples (13.1 ± 0.2 Brix and 87.3 ± 0.2% moisture, w.b.) purchased from a local supermarket stored at ambient temperature (20 C) until the moment of the experiment. Samples for drying experiments were discs of apple tissue (22 mm in diame-
Fig. 1. Computer vision system for image acquisition.
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the plane of the sample and the lens was 15.7 cm above the sample. The digital data generated were directly transferred to a PC (Pentium II, 30 GB, 300 MHz) via a USB interface. Image processing and analysis were performed using macros written in Matlab 6.5 Release 13 (Mathworks, Inc.). The CVS perceived color as RGB signals that are device-dependent, therefore, to ensure correct color reproduction, they were converted into XYZ tristimulus values and later to CIE Lab color co-ordinates using a macro written in Matlab 6.5. In this system, L* value indicates the lightness, which ranges from 0 (black) to 100 (white), a* indicates greenness to redness, and b* indicates the blueness to yellowness (these chromatic components range from 120 to +120). For comparison, color of apple slices was also measured with a HunterLab MiniscanTM XE colorimeter model 45/0 LAV (Hunter Associates Inc., Reston, VA) at three random points. The following morphological parameters were determined on the segmented image: Average radius: Average distance from the centroid to the boundary points of the object. Area (A): Number of pixels within the boundary. Perimeter (P): Number of pixels in the boundary of the object. Feret diameter (DF): Diameter of a circle having the same area (A) as the object rffiffiffiffiffiffi 4A DF ¼ ð1Þ p Roundness (R): Ratio of the area (A) of the object and that of a circle with the same perimeter (P) 4pA ð2Þ P2 Fourier descriptors used in this work were: Energy: Sum of the squared magnitudes of the samples. For discrete time signals, it is the ‘‘size’’ of the signal
R¼
E¼
N X
2
jFDj
ð3Þ
189
the co-occurrence matrix. It reaches its highest value when color (or grey) level distribution has either a constant or a periodic form (Partio, Cramariuc, Gabbouj, & Visa, 2002): XX fpði; jÞg2 ð5Þ Energy ¼ i
j
where p(i, j) is the relative frequency of occurrence of two pixel values (one with intensity i and the other with intensity j). Entropy: Measures of the disorder or the randomness of an image. Complex textures tend to have higher entropy. Therefore, entropy is inversely proportional to energy XX Entropy ¼ pði; jÞ log pði; jÞ ð6Þ i
j
Contrast: Is a measure of the amount of local variations present in an image. A high contrast value indicates a high degree of local variation (Park & Chen, 2001) XX 2 Contrast ¼ ði jÞ pði; jÞ ð7Þ i
j
Inverse difference moment: Relates to image homogeneity and is the opposite of the contrast XX 1 Idm ¼ pði; jÞ ð8Þ 2 i j 1 þ ði jÞ A statistical classifier together with the feature vector formed by the Fourier and morphological features were used in order to discriminate between images of each class. The ‘‘minimum distance to the prototype’’ statistical classifier (Euclidean distance classifier) was implemented. In this classifier one prototype per class is defined and each prototype is the average of all samples in the training set. This classifier makes use of the correspondence between similarity and distance; it assigns an unknown pattern (input pattern) to the class of its nearest neighbor pattern. The advantage of this classifier is its computationally efficiency (Emı´dio, Schimidt, & Marcondes, 2000).
i¼0
Variance descriptors: V ¼
of
the
N 1 X ðFDi FDÞ2 N i¼0
distribution
of
the
Fourier 4. Results and discussion ð4Þ
where N is the number of boundary points and FD is the average of the Fourier descriptors. To reduce the computational complexity, only 4 of 14 textural features that are widely used in literature (Gonzalez & Woods, 1992; Haralick, 1979) were selected. Energy: Measures the textural uniformity of an image. It is the sum of the squares of all elements in
Fig. 2 depicts a gallery of images of the same apple disc as drying proceeded. There are evident visual changes in the size of the piece (shrinkage), form (leading to a progressively irregular shape) and color (browning and emergence of non-uniform color patterns). Fig. 3 presents a typical drying curve of moisture content versus drying time. Major changes in moisture content occurred in the time interval from 3 to 7 h. Under conditions of the experiment samples achieved a final moisture of about 12% (w.b.) at the end of drying.
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Fig. 2. Gallery of images of a single apple slice as a function of drying time (time in hours).
8
8 Area Perimeter
60 40 20
7
6
6
5
5
4
4
3
Perimeter (cm)
7
80 Area (cm2)
3
(A)
0
2
2
4
6 8 10 Drying time (h)
12
Fig. 3. Variation of moisture content (wet basis) of apple slices with drying time.
Variations in area, perimeter, average radius, roundness and Feret diameter of apple discs as a function of drying time are shown in Fig. 4. All morphological features decreased smoothly with drying time, a trend similar to that found in grape tissue by Ramos et al. (2004). The values of most parameters (except roundness) changed rapidly in the first 6 h of drying and remained almost constant thereafter. Interestingly, Bolin and Huxsoll (1987) (cited by Ramos et al. (2004)) reported that the roundness of cells in apple rings decreased with drying time. As observed in Fig. 5 the energy and the variance of Fourier descriptors also decreased with the drying time a consequence that the area contained by the curve of Fourier descriptors decreased (data not shown). Apple tissue exhibited extensive and non-homogeneous (spatially) browning during drying (e.g., Fig. 2). Color changes measured by image analysis and by the hand-held colorimeter gave high average correlation coefficients for L, a* and b* (R2 = 0.91, 0.94 and 0.95,
2 0
14
2
4
6
8
10
12
14
Drying time (h) 2.5 Average Radius and Feret Diameter (cm)
0
2.5 Feret D. Average radio Roundness
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Fig. 4. Variation of morphological features of apple slices with drying time. (A) Area and perimeter; and (B) average radius, roundness and Feret diameter.
respectively). Values of lightness (L*) varied erratically between 62 and 75 (Fig. 6A). This unclear behavior, also reported by Krokida et al. (2001) was attributed to experimental error. The variation of experimental values for the chromatic co-ordinates a* and b* with drying time is presented in Fig. 6B. Redness (a* value) and
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Fig. 5. Energy and variance of Fourier variation with drying time.
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Fig. 6. Colour variation with drying time. (A) Lightness; and (B) chromatic co-ordinates.
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yellowness (b* value) increased as drying proceeded. These results were similar to those reported by Krokida et al. (2001) for color of apples measured with a colorimeter during conventional air drying. For image texture analysis, each textural feature (e.g., energy, entropy, etc.) was computed separately for L*, a* and b* from a set of co-occurrence matrices (COMs), not as usually done for gray values. COMs were calculated for a distance between pixels d = 1 and an angle = 0. In order to represent the textural features
b*
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Fig. 7. Variation in image texture with drying time. (A) Energy; (B) entropy; (C) contrast; and (D) inverse difference moment.
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calculated from the co-occurrence matrix for L, a* and b*, values for each drying time were normalized (i.e., divided by their initial value, at zero time) and results are presented in Fig. 7. Standard deviation bars were not placed pffiffiffi in the figure for clarity but the standard error ðs= nÞ was in all cases less than 0.05. Energy increased when the spatial distribution of the intensity is almost constant in an image. As shown in Fig. 7A, the energy decreased for all three color co-ordinates with increasing drying time, particularly in the first 8 h, meaning that samples became less uniform in color during that period. Entropy increased for the first 10 h of drying due to the higher image textural complexity (Fig. 7B). High values of entropy were to be expected if the frequencies of occurrence in the co-occurrence matrix were equally scattered over the matrix. This uniform scattering occurs when the largest spread of different pixel intensity occurs in the spectral image (Park & Chen, 2001). Contrast increased during the drying process (Fig. 7C) indicating a higher degree of local variation. On the contrary, inverse difference moment decreased since apple slices lost textural homogeneity (Fig. 7D). Classification model were developed for identifying samples from different time of drying using Euclidean distance. The feature vector was constructed considering the morphological (area, perimeter, etc.) and Fourier features. Although the program was designed to manage up to thirteen classes, one for each drying time, it was difficult to discern between classes when the drying time exceeded 6 h. Figs. 4 and 5 show that these external image features remain almost constant after 6 h of drying. Accordingly, classes 1–7 were defined for drying times 0, 1, 2, 3, 4, 5, and >6 h, hence, samples from drying times over 6 h were considered as belonging to the same class. The ‘‘minimum distance to the prototype’’ statistical classifier was used to process data and the program for pattern recognition was developed in Matlab 6.5. The program calculates all the patterns of the image, and it decides which class the input pattern belongs. The training set consisted of 72 images of different samples (six samples per drying time) and 39 random samples (test set) were used to evaluate the classifier. Ninety-five percent of the test samples were correctly classified into their respective classes.
5. Conclusions Under the experimental conditions of this work drying produces drastic changes in the shape, color and image texture of the apple slices that were quantified by means of CVS and image analysis. All morphological features decreased smoothly during the first 6–8 h of drying. Methods presented here have the advantage that represent quality factors perceived by consumers rather than process parameters (i.e., rate of drying, moisture
content) which have limited significance from the standpoint of product properties.
Acknowledgment This research was supported by the ALFA Networkii: Food and Bioprocess Engineering for Sustainability and Quality, and project FONDECYT 1030339.
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