The aim of this study was to examine the drying kinetics of pears (Pyrus communis L.) with and without vacuum impregnation and under the different temperature by using tray dryer. Vacuum impregnation were applied to the the pears (15 mm thickness, 65 mm outer and 20 mm inner dimensions respectively) with the conditions of 50⁰ Brix impregnation solution concentration, 225 mbar vacuum pressure and 45 min vacuum time. Drying process was carried out at temperatures of 55, 65 and 75°C. Drying time of non-vacuum impregnated pears was determined 640, 500 and 340 min and vacuum impregnated pears was determined 700, 540 and 560 min respectively. Page, Exponential, Henderson and Pabis, Diffusion Approach were examined for testing the drying kinetics. Experimental values are in accordance with the expected values resulted Page and Difussion models of with and without vacuum impregnated pears. Effective diffusion coefficient (Deff) was varying 2.74×10-11 to 7.31×10-11 m2/s. m2/s with respect to the drying temperatures. The activation energy for the non-vacuum impregnated and vacuum impregnated pears was 32.93 kJ / mol and 24.25 kJ / mol, respectively.

Colour is an essential parameter at product quality control stages, and finally, it is necessary for the consumer marketing decision. It is possible to damage the products during the process from collection to storage. Also, it is a well-known condition, cold environmental conditions protect fruits from deformations negative effects, but most of the time, most of the consumers keep the fruits at room temperature in open packs during the consumption process. Also, this condition affects the product storage time. In this study, it is aimed that to determine the behaviours of the fruits in room temperature and humidity conditions. For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. For this purpose, 100 “Akça” pear and 100 “Deveci” local pear cultivar used for experiments. Fruits were equally damaged by using a pendulum mechanism. The damaged fruits were kept at room temperature. Colour change areas on fruits were evaluated with X-rite Ci60 spectrophotometer, and the hardness of fruits was measured by using a fruit penetrometer. The colour (L, a, b) and ΔE values were analysed for the fruit cultivars. The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. The best prediction were found at the Majority Voting method (MAVL) 98.458 % success given with 70% partitioning.

Colour is an essential parameter at product quality control stages, and finally, it is necessary for the consumer marketing decision. It is possible to damage the products during the process from collection to storage. Also, it is a well-known condition, cold environmental conditions protect fruits from deformations negative effects, but most of the time, most of the consumers keep the fruits at room temperature in open packs during the consumption process. Also, this condition affects the product storage time. In this study, it is aimed that to determine the behaviours of the fruits in room temperature and humidity conditions. For this aim the colour change of the damaged pears were determined, in another term, colour change value from red to green and yellow to blue at the damaged pears were determined with lightness values by using image analysis technique and analysed with data mining methods. For this purpose, 100 “Akça” pear and 100 “Deveci” local pear cultivar used for experiments. Fruits were equally damaged by using a pendulum mechanism. The damaged fruits were kept at room temperature. Colour change areas on fruits were evaluated with X-rite Ci60 spectrophotometer, and the hardness of fruits was measured by using a fruit penetrometer. The colour (L, a, b) and ΔE values were analysed for the fruit cultivars. The relationship between fruit hardness and colour change were also demonstrated. The predictions were done supervised machine learning algorithms (Decision Tree and Neural Networks with Meta-Learning Techniques; Majority Voting and Random Forest) by using KNIME Analytics software. The classifier performance (accuracy, error, F-Measure, Cohen's Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values were given at the conclusion section of the research. The best prediction were found at the Majority Voting method (MAVL) 98.458 % success given with 70% partitioning.

Probiotic yoghurt with fruit was produced to enrich the intestinal flora of infants and to prevent various ailments in infants when the flora is inadequate. Peach, apple and pear purees (10% and 20% each), cow milk, milk powder, starter culture (combination of Streptococcus thermophilus, Lactobacillus delbrueckii ssp. bulgaricus, Bifidobacterium infantis, Bifidobacterium bifidum, Bifidobacterium longum and Lactobacillus paracasei) were used in the production of probiotic yogurt for babies. Some properties of yoghurt samples were investigated during fermentation and on the 1st, 7th, 14th and 21st days of storage. After ten hours of fermentation, the lowest pH was observed in samples with apple puree. It has been determined that syneresis increases with increasing concentrations of fruit purees. The water holding capacity was less in yoghurts containing fruit puree compared to control yoghurt and in 20% fruit puree compared to yoghurts containing 10% fruit puree. The number of L. bulgaricus generally increased in all samples during storage. It was determined that the number of S. thermophilus in control sample was higher than other samples during storage. The number of L. paracasei and Bifidobacterium spp. decreased during storage. While the control sample remained probiotic until the 14th day of storage, other samples lost its probiotic properties before the 7th day of storage. Considering that the number of probiotic microorganisms in a probiotic product should be at least 106-107 CFU/g according to FAO, it has been decided that the most suitable fruits for probiotic yogurt with fruit puree are peach and apple, respectively. Considering the structural features, it is more appropriate to use 10% fruit puree, and considering the probiotic feature, it is more appropriate to use 20% fruit puree. Choosing the appropriate packaging and fixing suitable storage conditions will help probiotic microorganisms to preserve their vitality for a long time.

Probiotic yoghurt with fruit was produced to enrich the intestinal flora of infants and to prevent various ailments in infants when the flora is inadequate. Peach, apple and pear purees (10% and 20% each), cow milk, milk powder, starter culture (combination of Streptococcus thermophilus, Lactobacillus delbrueckii ssp. bulgaricus, Bifidobacterium infantis, Bifidobacterium bifidum, Bifidobacterium longum and Lactobacillus paracasei) were used in the production of probiotic yogurt for babies. Some properties of yoghurt samples were investigated during fermentation and on the 1st, 7th, 14th and 21st days of storage. After ten hours of fermentation, the lowest pH was observed in samples with apple puree. It has been determined that syneresis increases with increasing concentrations of fruit purees. The water holding capacity was less in yoghurts containing fruit puree compared to control yoghurt and in 20% fruit puree compared to yoghurts containing 10% fruit puree. The number of L. bulgaricus generally increased in all samples during storage. It was determined that the number of S. thermophilus in control sample was higher than other samples during storage. The number of L. paracasei and Bifidobacterium spp. decreased during storage. While the control sample remained probiotic until the 14th day of storage, other samples lost its probiotic properties before the 7th day of storage. Considering that the number of probiotic microorganisms in a probiotic product should be at least 106-107 CFU/g according to FAO, it has been decided that the most suitable fruits for probiotic yogurt with fruit puree are peach and apple, respectively. Considering the structural features, it is more appropriate to use 10% fruit puree, and considering the probiotic feature, it is more appropriate to use 20% fruit puree. Choosing the appropriate packaging and fixing suitable storage conditions will help probiotic microorganisms to preserve their vitality for a long time.