TY - JOUR
AU1 - Bora, Ganesh C
AU2 - Pathak, Rohit
AU3 - Ahmadi, Mojtaba
AU4 - Mistry, Purbasha
AB - Abstract Colour and moisture content are two most important attributes of the commercial food product. Estimation of moisture content is very important to know the storability of the food product. It also relates to the process of drying in a fruit or vegetable. Extra drying and shrinkage deteriorates the quality of food product. The goal of the experiment was to examine the changes in RGB values of an apple during drying at different temperatures. In this study, emphasis was given on how the colour changes when there is a significant change in the moisture content of the apple. Three randomly chosen varieties of apples were sliced to 8 mm thickness and dried in vacuum oven at 60°C, 70°C, and 80°C. The loss of moisture was recorded for every 30 min interval and corresponding digital images were taken to determine the change in RGB value. The images that were captured during the study was analysed in MATLAB image analysis computer software. The analysis of moisture content and average colour share with respect to time showed that average colour share value decreases with time at all three temperatures. More than 50 per cent of variation in moisture content was explained by average colour share. There is a significant linear relationship between moisture content and colour changes in RGB and can be used to predict the moisture content of apple during drying process. image processing, colour, moisture content, apple drying, temperature Introduction Image processing can be used to study the colour of fruits and vegetable and hence helps us to describe the quality of the material. It is tough for naked human eyes to find out minute colour variations and identify the quality of an item. Computer imaging could be the best alternative for effective acquisition of information from colour images. Computer imaging involves digital colour camera, colour monitor, and a simple software for analysis (Bora et al., 2015). Drying is a classical method of food preservation and an area where developments can be done via research (Velic et al., 2004; Sacilik and Elicin, 2006). Fruits can be dried using different methods and instruments (oven and dryer). However, it is difficult to achieve the good quality of dried food products during the processing. The quality and physicochemical characteristics of the dried food products are influenced by the methods of drying, and the variables used (Krokida and Maroulis, 1997). Seiiedlou et al., (2010) assessed the effects of drying on quality characteristics like shrinkage and colour of dried apple which were dried using a hot-air tray dryer. The changes in colour are the result of drying along with certain enzymatic and non-enzymatic reactions leading to browning (Vadivambal and Jayas, 2007). The changes in colour during drying are affected by different ongoing procedures: use of colour protection agent, temperature deviation, and intermittent drying (Krokida et. al., 2007). Lozano and Ibarz, (1997) studied the colour change in various fruits after heat treatments. Texture reveals the mechanical and microstructural properties of any fruit, which plays an important role in defining the quality of any dried food (Martynenkoa and Janaszek, 2014). It would be of great importance to observe and control the texture development during drying. Apple is a low calorie fruit with high fibre and vitamin C. It might help us to reduce the effects of asthma, maintain the weight, and the nutrients present can reduce cholesterol levels. So, after processing it is also necessary to retain the quality for nutritional values and health benefits. There are several studies done previously on apples using image processing to comment on the quality. For example, Shahin et al. (2002) investigated two varieties of apples for separating damaged apples from the others. Vesali et al. (2011) estimated the moisture content of apple with the help of image processing and simple weighing machine. Later, they also incorporated the neural network for further analysis. Veraverbeke et al. (2003) evaluated the specific effect of the cuticle structure and cracks in the modelling of moisture loss during long-term storage of apples. With long-term storage, there is reduction in moisture content of apples. The rate at which the moisture loss happens varies to different temperatures the apples are subjected. Furthermore, recently researchers are finding the image processing method that is very efficient in determining other characteristics of various fruits and crops. As reduction in moisture content also causes reduction in weight, in turn reducing the economic value of apples. A study done by Mustafa et al. (2008) considered the quality of the banana by determining the ripeness and size of the banana using the image processing toolbox in MATLAB. McDonald and Chen (1990) illustrated application of morphological image processing with demonstration of three examples involving corn kernel size discrimination, plant leaf identification, and texture analysis of marbling in beef longissimus dorsi muscle. Ribeiro et al. (2005) developed a computer-based image processing system to estimate the weed pressure. Mateos et al. (2014) used image processing in irrigation management applications. Usually colour images are displayed in three primary colour combinations Red–Green–Blue, which is based on additive colour theory. Additive theory explains after effects of light mixing rather than when pigments are mixed as in subtractive theory (Jensen, 2005). Information can be depicted in terms of chromaticity coordinates, which is used to specify colours. The coordinates here represent the relative amount of each primary colour as given in Equations (1) through (3). The sum of primary colour is always one as shown in the following equation: x=RR+G+B, (1) y=GR+G+B, (2) z=BR+G+B, (3) x + y + z = 1, (4) where R is red, B is blue, and G is green and they represent the amount of red, green, and blue needed to form any definite colour, and x, y, and z are normalized colour components known as trichromatic coefficients (Jensen, 2005). Another colour coordinate is Hue-Saturation-Intensity (H-I-S) which is based on hypothetical colour sphere. Hue is the attribute of colour perception through which one can identify any specific colour. The value of hue begins with 0 and increases counter-clockwise before finishing to 255. Intensity does not associate with any colour, it is just the relative of darkness and varies from dark (0) to white (255). Saturation is simply the purity of colour, the value also ranges from 0 to 255, and the value close to 0 represents completely impure colour, whereas 255 represents completely pure colour (Jensen, 2005). Image processing has various applications in different fields. Recently, it is being used in different aspects of agriculture mainly because it is a non-destructive method. Image processing involves taking multiple photographs to measure different set of characteristics. It usually treats images as two-dimensional signals which generally involves three steps. First step is to take the digital image; then manipulating, processing, and analysing the image; and finally results based on the image analysis which may be an altered image. Image processing is an effective method used to identify the quality, moisture content, volume, stages of ripening, any diseased condition, yield, canopy, etc., of agricultural food products. Image processing technology has been found to be an effective technique and shown improved accuracies in determining the vegetation indices, canopy measurement, and irrigated land mapping (Vibhute and Bodhe, 2012). With the advancement of the technologies in the agricultural area, image processing has become an easy, ecological friendly and cost effective method to study important parameters of agricultural products when compared with the conventional method. Changes in moisture content often induce changes in the colour content of the food product. According to Jokic et al., rehydration rates and colour characteristic of apple samples are dependent on differential drying conditions. The change in shape and volume and extra hardness in the produce cause bad impression on the customer. Therefore, it causes changes in economic value of the food product and shifts the consumer demand of the product. Air drying tends to increase the degree shrinkage and destroy the cellular structure (Sturm et al., 2012). Change in moisture content also causes alteration in the weight of the food product. Dehydration and evaporation of surface water bring changes in the colour of the material. Colour change such as browning or yellowing in fruits refers to represent the deteriorating quality of that fruit. While measuring the quality of any commercial fruit, colour and moisture content are two significant parameters. Apple, scientifically known as Malus domestica, is one of the delicious and popular fruit containing some essential nutrients good for health. There are lots of health benefits of apple fruit. Apple contains different phytochemicals like quercetin, epicatechin, and procyanidin that are beneficial for human health. Apple also contains soluble and insoluble fibre that helps in digestion process. Food drying is one of the traditional and oldest methods of food preservation. For commercial purpose, it is necessary that it can be stored for few months or so. Preserving apple is essential because apples are not only eaten raw but used to make desserts, jams, candies, cakes, etc. Dried fruit products like apple is good for widening product availability and to diversify markets (Contreras et al., 2008). Drying removes the moisture content which in turn reduces the bacterial and yeast activity and preserves the food for a long time. Also reduction in weight ease handling and storage. Fruits can be dried in sun and oven both with necessary combination of time and temperature. Moisture is evaporated during drying in warm temperatures. Low humidity enhances the movement of moisture to external ambient, whereas air current speeds up the process by replacing the surrounding moist air. Dried fruits are supposed to be appealing, long lasting, tasty, and nutritious. Therefore, it is important to study the changes of apple textures and colour at different temperatures. The main objective of the study is to investigate the drying of apple at different stages and temperatures to evaluate the changes in colour and correlate it to the moisture content. Material and Methods Three varieties of apples, Red delicious, Granny smith, and Gold delicious cultivar used in the study, were obtained from local store at Fargo. The three varieties were named as A, B, and C, respectively, for simplicity of analysis. Samples were bought and stored in a lab refrigerator. Three uniform size apples for each variety were selected and washed with tap water and wiped with tissue paper. Each apple was cut vertical to the axis into 8 slices of 8 mm thickness with the help of a locally available slicer shown in Figure 1. Only three wholesome slices from the middle of almost equal sizes were selected for drying. First three and last two slices were discarded for further processing. Then the seeds were manually removed with a knife. Figure 1. View largeDownload slide Eight millimetre thick apple slicer. Figure 1. View largeDownload slide Eight millimetre thick apple slicer. The experimental apparatus, vacuum oven (Model LBB1-69A-1, Despatch, Minneapolis, MN, USA), capable of maximum temperature of 240°C (400 F) with 2.4 KW heater, was used for drying the apple slices. Vacuum oven is generally used to measure the amount of water present in a material. Vacuum drying reduces the moisture content in an object with air drier. Vacuum drying involves reduced pressure environment which decreases the heat needed for speedy drying. This process requires less energy and is less damaging. Vacuum oven consists of two chambers with meshed tray–like permanent structures, allowing unrestricted air circulation. The air flow and temperature can be adjusted. Apple slices were uniformly distributed in thin layer in both the trays. The samples were shuffled at each repetition to maintain the uniform drying condition for all samples. Stopwatch available in IPhone was used for recording the drying time. D3100 SLR 14.2 MP Nikon digital camera was used for taking digital images of the sample. Digital weighing machine was used for the measurement of sample weights. A pink board with two colour circles, green and yellow as shown in Figure 2, was used for taking images so that it can be calibrated for the colour values with circles. Figure 2. View largeDownload slide Pink board with two colour circles: green and yellow. Figure 2. View largeDownload slide Pink board with two colour circles: green and yellow. Drying of apple slices was conducted in three different temperatures of 60°C, 70°C, and 80°C. Apple slices were put on the mesh tray sparsely ensuring adequate air circulation. The oven temperature was set to 60°C. Samples were taken out of the oven and moisture loss was recorded for every 30 min interval for each sample, so as to get the drying curves. Also digital images were taken for each corresponding record of moisture loss to determine the change in RGB values. Drying was continued until there was no change observed in the sample weight. The experiment was replicated for 70°C and 80°C. Dry basis moisture content was determined using the following equation (Wilhelm et al., 2004): M=mwmd, (5) where M = decimal moisture content dry basis (db), md = mass of dry matter in the product, and mw = mass of water in the product. Percent moisture content is found by multiplying the decimal moisture content by 100. Dry basis is generally used to measure the moisture content of any material during drying process. Dry basis moisture content can be defined as the amount of water per unit mass of dry solids in the sample. The moisture content for high moisture materials like fruits and vegetables can go up to 900 per cent on a dry basis. Digital images were processed by MATLAB software in RGB colour model to evaluate colour changes in each different stage of drying. Program calculated the average percentage of red (R), green (G), and blue (B) colours on sample area. Average RGB values obtained from MATLAB were exported directly to excel sheet for ease of data analysis. For decisive analysis of colour changes in RGB model, the following formula was used: Average share of each colour, ΔERGB=[(ΔR)2+(ΔG)2+(ΔB)2], (6) where ∆R, ∆G, and ∆B are differences between colour values of fresh samples and dried samples, and ΔERGB are the colour changes in RGB model. SAS software was used for statistical analysis. PROC REG procedure was employed for model statements. Analysis of variance (ANOVA) table, Root MSE, and R-square values were obtained. Data were analysed at 95% confidence level. Results and Analysis The drying of three different varieties of apple slices of thickness 8 mm at three temperatures of 60°C, 70°C, and 80°C and their colour changes were studied. For the simplicity, interpretability, and common acceptance of linear model, fit for linear model was explored. Figures 3, 4, and 5 represent the variation in moisture content as a function of drying time for the three different varieties (A, B, and C). Table 1 shows the percent moisture content, colour changes in RGB model with respect to time and temperature. Figure 3. View largeDownload slide Relationship between dry basis moisture content and drying time at 60°C. Figure 3. View largeDownload slide Relationship between dry basis moisture content and drying time at 60°C. Figure 4. View largeDownload slide Relationship between dry basis moisture content and drying time at 70°C. Figure 4. View largeDownload slide Relationship between dry basis moisture content and drying time at 70°C. Figure 5. View largeDownload slide Relationship between dry basis moisture content and drying time at 70°C. Figure 5. View largeDownload slide Relationship between dry basis moisture content and drying time at 70°C. Table 1. Time, temperature, percent moisture content, and colour changes in RGB for three varieties of apple. Time (min) Temperature 60°C 70°C 80°C A B C A B C A B C %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB 0 416.42 80.75 396.52 65.25 467.36 71.28 480.65 35.58 399.31 38.25 479.35 32.32 453.51 59.89 487.68 27.16 471.01 30.01 30 362.71 25.92 350.30 22.86 407.54 35.14 410.37 12.05 344.13 14.99 418.26 11.55 367.82 40.73 397.21 16.41 384.02 12.49 60 308.70 27.47 291.94 20.34 338.89 33.30 343.67 18.45 284.02 12.69 359.78 16.12 295.08 46.63 314.44 26.13 311.54 24.94 90 267.46 26.87 246.18 19.71 285.80 34.07 285.68 17.52 230.54 12.98 288.29 15.49 228.69 49.65 239.25 28.88 242.40 22.04 120 225.34 26.85 211.43 24.28 236.03 31.77 227.35 21.69 185.59 15.87 222.92 19.35 170.79 31.09 170.47 33.39 183.55 17.37 150 187.53 24.39 176.92 16.79 198.91 19.10 176.07 25.45 142.65 23.57 164.22 22.45 118.46 17.25 112.53 14.66 128.10 4.15 180 153.97 20.85 143.60 24.02 157.92 23.75 123.93 21.32 99.56 15.09 112.90 11.76 71.23 17.00 60.07 25.99 74.81 14.22 210 122.45 17.89 110.76 15.98 120.75 19.49 83.57 14.18 64.94 14.04 69.89 9.12 32.88 13.35 38.45 19.10 19.81 8.20 240 95.06 15.75 84.14 13.85 88.03 8.75 51.41 11.48 39.55 13.17 35.22 4.15 10.96 8.15 9.28 20.95 6.63 4.48 270 66.56 12.80 59.03 9.21 55.92 7.90 26.61 10.33 20.96 7.09 13.56 9.30 2.39 10.19 1.94 15.89 1.97 2.74 300 43.56 12.72 40.52 5.83 32.50 7.07 12.67 7.43 9.54 9.46 4.75 3.24 0.34 10.46 0.18 9.75 0.37 4.41 330 26.97 13.42 25.30 14.46 15.31 9.09 4.01 7.27 3.50 4.34 1.82 4.05 0.00 0.00 0.00 0.00 0.00 0.00 360 14.07 24.92 13.92 17.46 6.16 15.16 0.65 4.31 0.95 5.32 0.57 4.88 390 7.20 7.23 7.44 6.24 3.43 12.29 0.00 0.00 0.00 0.00 0.00 0.00 420 2.96 4.78 3.07 10.43 1.71 9.41 450 1.50 7.18 1.49 7.00 0.89 13.14 480 0.05 6.17 0.29 9.79 0.36 13.59 510 0.00 0.00 0.00 0.00 0.00 0.00 Time (min) Temperature 60°C 70°C 80°C A B C A B C A B C %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB 0 416.42 80.75 396.52 65.25 467.36 71.28 480.65 35.58 399.31 38.25 479.35 32.32 453.51 59.89 487.68 27.16 471.01 30.01 30 362.71 25.92 350.30 22.86 407.54 35.14 410.37 12.05 344.13 14.99 418.26 11.55 367.82 40.73 397.21 16.41 384.02 12.49 60 308.70 27.47 291.94 20.34 338.89 33.30 343.67 18.45 284.02 12.69 359.78 16.12 295.08 46.63 314.44 26.13 311.54 24.94 90 267.46 26.87 246.18 19.71 285.80 34.07 285.68 17.52 230.54 12.98 288.29 15.49 228.69 49.65 239.25 28.88 242.40 22.04 120 225.34 26.85 211.43 24.28 236.03 31.77 227.35 21.69 185.59 15.87 222.92 19.35 170.79 31.09 170.47 33.39 183.55 17.37 150 187.53 24.39 176.92 16.79 198.91 19.10 176.07 25.45 142.65 23.57 164.22 22.45 118.46 17.25 112.53 14.66 128.10 4.15 180 153.97 20.85 143.60 24.02 157.92 23.75 123.93 21.32 99.56 15.09 112.90 11.76 71.23 17.00 60.07 25.99 74.81 14.22 210 122.45 17.89 110.76 15.98 120.75 19.49 83.57 14.18 64.94 14.04 69.89 9.12 32.88 13.35 38.45 19.10 19.81 8.20 240 95.06 15.75 84.14 13.85 88.03 8.75 51.41 11.48 39.55 13.17 35.22 4.15 10.96 8.15 9.28 20.95 6.63 4.48 270 66.56 12.80 59.03 9.21 55.92 7.90 26.61 10.33 20.96 7.09 13.56 9.30 2.39 10.19 1.94 15.89 1.97 2.74 300 43.56 12.72 40.52 5.83 32.50 7.07 12.67 7.43 9.54 9.46 4.75 3.24 0.34 10.46 0.18 9.75 0.37 4.41 330 26.97 13.42 25.30 14.46 15.31 9.09 4.01 7.27 3.50 4.34 1.82 4.05 0.00 0.00 0.00 0.00 0.00 0.00 360 14.07 24.92 13.92 17.46 6.16 15.16 0.65 4.31 0.95 5.32 0.57 4.88 390 7.20 7.23 7.44 6.24 3.43 12.29 0.00 0.00 0.00 0.00 0.00 0.00 420 2.96 4.78 3.07 10.43 1.71 9.41 450 1.50 7.18 1.49 7.00 0.89 13.14 480 0.05 6.17 0.29 9.79 0.36 13.59 510 0.00 0.00 0.00 0.00 0.00 0.00 View Large Table 1. Time, temperature, percent moisture content, and colour changes in RGB for three varieties of apple. Time (min) Temperature 60°C 70°C 80°C A B C A B C A B C %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB 0 416.42 80.75 396.52 65.25 467.36 71.28 480.65 35.58 399.31 38.25 479.35 32.32 453.51 59.89 487.68 27.16 471.01 30.01 30 362.71 25.92 350.30 22.86 407.54 35.14 410.37 12.05 344.13 14.99 418.26 11.55 367.82 40.73 397.21 16.41 384.02 12.49 60 308.70 27.47 291.94 20.34 338.89 33.30 343.67 18.45 284.02 12.69 359.78 16.12 295.08 46.63 314.44 26.13 311.54 24.94 90 267.46 26.87 246.18 19.71 285.80 34.07 285.68 17.52 230.54 12.98 288.29 15.49 228.69 49.65 239.25 28.88 242.40 22.04 120 225.34 26.85 211.43 24.28 236.03 31.77 227.35 21.69 185.59 15.87 222.92 19.35 170.79 31.09 170.47 33.39 183.55 17.37 150 187.53 24.39 176.92 16.79 198.91 19.10 176.07 25.45 142.65 23.57 164.22 22.45 118.46 17.25 112.53 14.66 128.10 4.15 180 153.97 20.85 143.60 24.02 157.92 23.75 123.93 21.32 99.56 15.09 112.90 11.76 71.23 17.00 60.07 25.99 74.81 14.22 210 122.45 17.89 110.76 15.98 120.75 19.49 83.57 14.18 64.94 14.04 69.89 9.12 32.88 13.35 38.45 19.10 19.81 8.20 240 95.06 15.75 84.14 13.85 88.03 8.75 51.41 11.48 39.55 13.17 35.22 4.15 10.96 8.15 9.28 20.95 6.63 4.48 270 66.56 12.80 59.03 9.21 55.92 7.90 26.61 10.33 20.96 7.09 13.56 9.30 2.39 10.19 1.94 15.89 1.97 2.74 300 43.56 12.72 40.52 5.83 32.50 7.07 12.67 7.43 9.54 9.46 4.75 3.24 0.34 10.46 0.18 9.75 0.37 4.41 330 26.97 13.42 25.30 14.46 15.31 9.09 4.01 7.27 3.50 4.34 1.82 4.05 0.00 0.00 0.00 0.00 0.00 0.00 360 14.07 24.92 13.92 17.46 6.16 15.16 0.65 4.31 0.95 5.32 0.57 4.88 390 7.20 7.23 7.44 6.24 3.43 12.29 0.00 0.00 0.00 0.00 0.00 0.00 420 2.96 4.78 3.07 10.43 1.71 9.41 450 1.50 7.18 1.49 7.00 0.89 13.14 480 0.05 6.17 0.29 9.79 0.36 13.59 510 0.00 0.00 0.00 0.00 0.00 0.00 Time (min) Temperature 60°C 70°C 80°C A B C A B C A B C %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB %MC ∆ERGB 0 416.42 80.75 396.52 65.25 467.36 71.28 480.65 35.58 399.31 38.25 479.35 32.32 453.51 59.89 487.68 27.16 471.01 30.01 30 362.71 25.92 350.30 22.86 407.54 35.14 410.37 12.05 344.13 14.99 418.26 11.55 367.82 40.73 397.21 16.41 384.02 12.49 60 308.70 27.47 291.94 20.34 338.89 33.30 343.67 18.45 284.02 12.69 359.78 16.12 295.08 46.63 314.44 26.13 311.54 24.94 90 267.46 26.87 246.18 19.71 285.80 34.07 285.68 17.52 230.54 12.98 288.29 15.49 228.69 49.65 239.25 28.88 242.40 22.04 120 225.34 26.85 211.43 24.28 236.03 31.77 227.35 21.69 185.59 15.87 222.92 19.35 170.79 31.09 170.47 33.39 183.55 17.37 150 187.53 24.39 176.92 16.79 198.91 19.10 176.07 25.45 142.65 23.57 164.22 22.45 118.46 17.25 112.53 14.66 128.10 4.15 180 153.97 20.85 143.60 24.02 157.92 23.75 123.93 21.32 99.56 15.09 112.90 11.76 71.23 17.00 60.07 25.99 74.81 14.22 210 122.45 17.89 110.76 15.98 120.75 19.49 83.57 14.18 64.94 14.04 69.89 9.12 32.88 13.35 38.45 19.10 19.81 8.20 240 95.06 15.75 84.14 13.85 88.03 8.75 51.41 11.48 39.55 13.17 35.22 4.15 10.96 8.15 9.28 20.95 6.63 4.48 270 66.56 12.80 59.03 9.21 55.92 7.90 26.61 10.33 20.96 7.09 13.56 9.30 2.39 10.19 1.94 15.89 1.97 2.74 300 43.56 12.72 40.52 5.83 32.50 7.07 12.67 7.43 9.54 9.46 4.75 3.24 0.34 10.46 0.18 9.75 0.37 4.41 330 26.97 13.42 25.30 14.46 15.31 9.09 4.01 7.27 3.50 4.34 1.82 4.05 0.00 0.00 0.00 0.00 0.00 0.00 360 14.07 24.92 13.92 17.46 6.16 15.16 0.65 4.31 0.95 5.32 0.57 4.88 390 7.20 7.23 7.44 6.24 3.43 12.29 0.00 0.00 0.00 0.00 0.00 0.00 420 2.96 4.78 3.07 10.43 1.71 9.41 450 1.50 7.18 1.49 7.00 0.89 13.14 480 0.05 6.17 0.29 9.79 0.36 13.59 510 0.00 0.00 0.00 0.00 0.00 0.00 View Large From the graphs in Figures 3–5, for all three varieties, the dry basis moisture content has negative linear relationship with time of drying, that is, the moisture content decreases continuously by the time, which is a normal phenomenon. There is also direct relationship between complete drying time and the temperature; with increase in temperature, there is reduction in drying time. It showed that the time taken to completely dry the apple was the same in all the three varieties: 330 min for 80°C, 390 min for 70°C, and 510 min for 60°C. SAS Proc Reg model was used for the linear regression analysis of the moisture content response to colour changes. From the graphs in Figures 6–8, we can see the change in colour (Average colour share, ∆ERGB) for all three varieties with respect to time. We can see that average colour share value decreases with increase in time in all the three temperatures, though the changes are not smooth and there are some fluctuations. It is interesting to note that at all three temperatures, there is a rapid drop in the RGB colour share value at the first point of data collection, which is at 30 min. There is a sharp increase in the value at around 350 min for 60°C in all three varieties. The sharp rise is 150 min for 70°C and 100 min for 80°C, respectively. Figures 9–11 illustrate the correlation between the moisture content and average colour share for all treatment combinations of temperatures and varieties. Figure 6. View largeDownload slide Relationship between average colour share and drying time at 60°C. Figure 6. View largeDownload slide Relationship between average colour share and drying time at 60°C. Figure 7. View largeDownload slide Relationship between average colour share and drying time at 70°C. Figure 7. View largeDownload slide Relationship between average colour share and drying time at 70°C. Figure 8. View largeDownload slide Relationship between average colour share and drying time at 80°C. Figure 8. View largeDownload slide Relationship between average colour share and drying time at 80°C. Figure 9. View largeDownload slide The relationship between average colour share and moisture content for variety A at different temperatures (a through b). Figure 9. View largeDownload slide The relationship between average colour share and moisture content for variety A at different temperatures (a through b). Figure 10. View largeDownload slide The relationship between average colour share and moisture content for variety B at different temperatures (a through b). Figure 10. View largeDownload slide The relationship between average colour share and moisture content for variety B at different temperatures (a through b). Figure 11. View largeDownload slide The relationship between average colour share and moisture content for variety C at different temperatures (a through b). Figure 11. View largeDownload slide The relationship between average colour share and moisture content for variety C at different temperatures (a through b). The linear relationship between moisture content and average colour share is statistically significant at 5 per cent with adjusted coefficient of determination: R-square value of 69 per cent for 60°C, 54 per cent for 70°C, and 45 per cent for 80°C temperatures, respectively. For natural products like fruits and vegetables, it is likely to have a lower coefficient of determination. The linear models, derived from statistical analysis using SAS Proc Reg, are given from the following equations: For 60°C Color Share = 0.0953 Moisture Content + 6.812, (7) For 70°C Color Share = 0.0432 Moisture Content + 6.881, (8) For 80°C Color Share = 0.0611 Moisture Content + 9.924, (9) Conclusion The project was undertaken to study the changes in colour and moisture content of three different varieties of apple at multiple temperatures with time. The moisture content and average colour share value decreased with increase in time. For higher temperature, there is a lower drying time, since the rate of evaporation of the product increases with the temperature. It was also found that in the drying process of apple slices that the colour share value dropped significantly within 30 min, but the moisture content decreased gradually for all the varieties in all temperatures. Good coefficient of determination was established with significant linear relationship at lower temperature values of 60°C in colour share values. With increase in temperature, decrease in the coefficient of determination was noted. The adjusted coefficient of determination was found to be around 69 per cent for 60°C, 54 per cent for 70°C, and 45 per cent for 80°C temperatures, respectively, for the linear model of moisture content and the colour share value, as more than 50 per cent of change in moisture content was validated by average colour share, which is reasonable for natural products. 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TI - Image processing analysis to track colour changes on apple and correlate to moisture content in drying stages
JF - Food Quality and Safety
DO - 10.1093/fqsafe/fyy003
DA - 2018-05-12
UR - https://www.deepdyve.com/lp/oxford-university-press/image-processing-analysis-to-track-colour-changes-on-apple-and-eHo7G8qq0O
SP - 1
EP - 110
VL - Advance Article
IS - 2
DP - DeepDyve
ER -