Food Technology
Masoumeh Shokri; Mostafa Rahmati-Joneidabad; Mokhtar Heidari; Mousa Rasouli; Ahmad Zareh
Abstract
Introduction
Chitosan, as a bio-polymer, has many applications in agriculture. Coating fruits and vegetables with chitosan plays a positive role in increasing their shelf-life, since the chitosan coating reduce growth of fungi and preserves the quality of the fruits longer.
Materials and Methods
This ...
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Introduction
Chitosan, as a bio-polymer, has many applications in agriculture. Coating fruits and vegetables with chitosan plays a positive role in increasing their shelf-life, since the chitosan coating reduce growth of fungi and preserves the quality of the fruits longer.
Materials and Methods
This study was conducted to evaluate the effect of chitosan treatments (0, 0.25, 0.5 and 1%) and storage time (0, 20, 40 and 60 days) on maintaining quantitative and qualitative parameters and shelf life of grape fruit of Fakhri cultivar. The experiments were factorial based on a completely randomized design with three replications. The fruits were stored for 2 months. Some characteristics of fruits including percentage of weight loss, percentage of berries abscission, percentage of decay of berries, browning of berries and biochemical characteristics including titratable acidity, ascorbic acid content, total phenol, enzymes activity including peroxidase (POD), phenylalanine ammonia-lyase (PAL) and polyphenol oxidase (PPO) were measured in order to investigate the best treatment.
Results and Discussion
The results showed that the traits under study were affected by different concentrations of chitosan, with the lowest percentage of weight loss associated with the concentration of 0.5% chitosan. Chitosan, by forming a semi-permeable membrane, regulates gases,and reduces the transfer of water from fruit tissues. The lowest amount of browning of berries was observed in the concentration of 0.5% chitosan. Chitosan is partly prevented from increasing the activity of brown-peroxidase in chitosan-treated fruits. There was no significant difference in concentration of 0.5% chitosan with 1% concentration. The lowest percentage of contamination and percentage of berries abscission was observed in 1% chitosan concentration. It seems that these treatments prevent the effects of ethylene levels and the formation of a swab layer at the site of fruit attachment to the cluster. The slightest increase in the titratable acidity and the lowest decrease of ascorbic acid was observed in the concentration of 1% chitosan. Higher levels of ascorbic acid in fruits that are coated with chitosan may be due to decreased oxygen levels and respiration inhibition. The highest total phenol was related to the control treatment, which may be due to the loss of chlorophyll and the onset of synthesis of phenolic compounds. The highest level of activity of PAL enzyme was observed in the concentration of 0.5% chitosan and the control. This enzyme is stimulated by various live and non-living stresses. In general, the highest activity of peroxidase enzyme was observed in the concentration of 0.5% chitosan and the highest activity of polyphenol oxidase in 1% concentration of chitosan.
Conclusion
It seems that the concentration of 1% chitosan can improve the quality of fruits for a longer time while increasing the shelf life of fruit.
Shima Nasiri; Saman Abdanan; Mokhtar Heidari
Abstract
Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, ...
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Introduction: Texture represents one of the four principal factors defining food/fruit quality, together with appearance, flavour and nutritional properties (Bourne, 2002), and plays a key role in consumer acceptability and recognition of quince. Textural characteristics of quinces defined by “crispness”, “juiciness”, “hardness”,“firmness” and “mealiness” are often key drivers of consumer preference. Many non-destructive methods, including image analysis, spectroscopy, ultrasound and sound techniques, have been developed to diagnose internal and external defects in fruits and vegetables. Cheng and Haugh (1994) used a frequency of 250-kHz, rather than 1-MHz, to detect hollow heart. They were not able to transmit successfully the ultrasound wave through the whole tuber using 1-MHz transducers but found the 250-kHz transducers to be practical for a transmission path length of up to 89.7 mm. In a research an acoustic setup was developed to simultaneously detect the resonant frequencies from equator and from calyx shoulder of pear. The researchers proposed index based on these two frequencies was used for firmness evaluation of non-spherical pear; Compared with two types of single frequency-based indices, the firmness sensitivity of the dual-frequency index is mostly close to that of MT penetration test. The firmness index can classify pears with a high total accuracy (93.4%), making it suitable for nondestructive detection of firmness of differently shaped pears (Zhang et al., 2018). The goal of this study was to develop a nondestructive method based on acoustic impulse response of quince fruit using genetic programming and artificial neural network during storage. Materials and Methods: In the experiment 120 quince fruits (Cydonia oblonga) were harvested from a field near Isfahan 181 days after full flowering of the trees. For each cultivar, only samples of similar size and without visible external damage were chosen. The samples were packed in sterile nylon bags and stored at 4°C. Non-destructive test (acoustic response) as well as destructive test (chemical measurement and penetration test) were performed every 15 days for 4 months (Akbari Bisheh et al., 2014). Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan) and expressed as °Brix. Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al. (1973) procedure. Titratable acidity was measured according to the AOAC method. To determine the total phenol content of juice, the Waterhouse method (2000) was used. Determination of the pH of the fruit extract using a pH meter (Portable Model P-755, Japan). Physical attributes of the samples including volume as well as major, minor, intermittent diameters and mass were calculated using the relations proposed by Stroshine and Hammand (1994). Penetration test was conducted by the material test machine (SANTAM, STM-20 model, Iran).In order to analyze the response sound signal of quince in time and frequency domain, a system equipped with a sample holder with foam rubber covered surface, an impact mechanism, a microphone and an electronic circuit was utilized. To record impact sound features a microphone was positioned next to the fruit and was hit at three speed level (0.3, 0.9 and 1.5 m/s). After recoding sound, five features (acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency) were extracted and used as inputs for models. In order to predict the stiffness, four methods of genetic programming, neural network and existing mathematical models (FI and SIQ-FT) were used. In order to carry out statistical analysis, analysis of variance (ANOVA) and Duncan's multiple range test at 5% probability level were performed according to the completely randomized design (CRD). Results and discussion: In this study, Duncan's multiple range comparison test was used to investigate the significant difference between destructive and non-destructive parameters at 5% probability level. According to the results, acoustic peak, maximum acoustic pressure, mean acoustic pressure and natural frequency were decreased by increasing storage time. Statistical analysis of the destructive tests also showed a decreasing trend at the 5% level. In several papers, two mathematical equations have been used to obtain the relationship between the mass resonance frequency and the sound of impact. In this study, genetic programming and neural network modeling were used to compare the results of these relationships. The regression coefficients between the actual and the predicted values for the resonance-mass relation and the effect of the sound from the collision were R2= 0.601 and R2= 0.754, respectively. Also, the regression values obtained from genetic programming and neural network modeling were R2= 0.9567 and R2 = 0.933, respectively. In a research, the overall R2 value amounts for stiffness prediction was reported to be 0.79 (Schotte et al., 1999). Abbaszadeh et al. (2013) evaluated watermelons texture using their vibration responses. They declared their proposed method could predict textural acceptability of watermelons with determination coefficients 0.99. According to the obtained values, the best methods for stiffness prediction were genetic programming and f neural network methods, respectively.
Maryam Soltani Kazemi; Saman Abdanan; Mokhtar Heidari; Seyed Mojtaba Faregh
Abstract
Introduction: Blackberry is a perennial woody plant native to warm, temperate, and subtropical regions of Asia, Africa, North America, and southern Europe. Blackberry fruit (Morus Alba Varnigra L.) is a rich source of anthocyanins. Furthermore, it has great many medicinal properties such as an antidiabetic ...
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Introduction: Blackberry is a perennial woody plant native to warm, temperate, and subtropical regions of Asia, Africa, North America, and southern Europe. Blackberry fruit (Morus Alba Varnigra L.) is a rich source of anthocyanins. Furthermore, it has great many medicinal properties such as an antidiabetic (Asano et al., 2001), antihyperglycemic (Andallu &Varadacharyulu, 2003), antiviral (Du et al., 2003), antioxidative (Kim et al., 1998), hypolipidemic (El-Beshbishy et al., 2006), and neuroprotective (Kang et al., 2006). However, measuring some qualitative and nutrient parameters in this fruit such as anthocyanins, vitamin C and phenol directly has become a major issue (Pace et al, 2013). Therefore, researchers try to predict aforementioned parameters by mathematical models. One of these models is the fractal model which is widely used to study the properties of the images/objects (Welstead, 1999; Zhang, 2007). Recently, many researchers try to develop different methods to classify or predict the agricultural products quality (Langner, 2001). In a research Seng and Mirisaee (2009) designed a machine vision algorithm for classification of fruits (apple, lemon, strawberry and banana) based on color, shape and size. Li and He investigated the application of visible/near infrared spectroscopy (Vis/NIRS) for measuring the acidity of Chinese bayberry. The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138 showed an excellent prediction performance. Therefore, the aim of this study was to predict biochemical parameters (TSS, anthocyanins, browning compounds, total phenols, Ascorbic Acid, pH) of blackberry juice, nondestructively, during maturity process using machine vision and fractal analysis. To develop predictive models and data classification, artificial neural networks (ANN) and k-nearest neighbor (k-NN) were used.
Materials and methods: Eighty blackberry fruits from four maturity stages were selected. The fruit samples were placed in airtight polyethylene bags, stored in an ice-filled cooler and transported to the laboratory to keep at cold temperature (4±1◦C).
Fresh fruits were squeezed by a household juicer, and immediately transported to the laboratory. Then, juice images were taken with a digital camera CASIO (Model Exilim EX-ZR700; 16 megapixels, Japan) and stored to the computer.
There are several ways to measure the fractal dimension. In this study, the proposed method by Addison (2005) was used to calculate the fractal dimension.
Feature selection is one of the issues that have been raised in the context of machine learning. In this study, floating search method feature selection was used (Pudil et al., 1994).
k-Nearest Neighbor (k-NN) is one of the simplest methods for information classification. In this study, the Euclidean distance between two points was used to determine the distance between the input data with the training patterns (Mucherino et al., 2009).
To train the neural network, Levenberg–Marquardt training algorithm was used. In this regard, the data were divided randomly into two parts (two-thirds for training (60) and one-third (20) for testing the network). Input parameters were Xa, Xb, X, Y and S and output parameters were TSS, ascorbic acide, acidity, polyphenols, anthocyanins, brown-causing substances and pH. Moreover, in this study, the number of neurons in the hidden layer was selected by trial and error method.
After selecting the best features extracted from the image processing with the highest correlation with chemical parameters (TSS, anthocyanins, total phenols, ascorbic acid, and pH), a machine vision system was designed and built to be able to determine the internal properties of black mulberry juice.
Total soluble solids (TSS) were determined by a hand refractometer device (model: MT03 Japan). The anthocyanin content was estimated following the procedure of Holecraft et al., (1998). Ascorbic acid of the juice was measured by titration with copper sulfate and potassium iodide based on the Barakat et al., (1973) procedure. Titratable acidity was measured according to the Eksi and Turkman, (2011) method. Waterhouse (2002) method was used for measuring the total phenol of juice.
Results and discussion: Artificial neural network (ANN) and (k-NN) models were used to predict the changes of anthocyanin (AC), browning compounds, ascorbic acid (AA), total phenols (TP), acidity, TSS and pH in mulberry juice during ripening based on fractal analysis. Two features namely: maximum fractal and fractal curve area were selected from five extracted features and used for training neural network and k-NN classifier
