Eggs are a widely consumed material that can endanger people's health due to their high perishability. Therefore, detecting its Freshness is very important for the food industry. Several nondestructive methods have been proposed to measure the internal quality of eggs, and in this research, an acoustic system was used to detect egg Freshness. The samples were kept at room temperature for 16 days. A 45 cm long inclined surface with a 4 mm high step in the middle was used in the mentioned system. The sound resulting from the rolling of the samples and the impact caused by passing the step were recorded by a microphone. Then, the fractal dimension of sound signals was calculated using Higuchi and Katz methods. A destructive test was also done considering the Haugh unit and the eggs were divided into two groups AA and A+B in terms of Freshness. The results of the three classification methods of artificial neural network (ANN), support vector machine (SVM) and linear diagnostic analysis (LDA) showed that the SVM method has better efficiency compared to the other two methods. The average classification accuracy of test data in the SVM method for Katz fractal dimension and window length of 25 milliseconds was obtained as 78.07%.

Introduction: Every year about 600 million tons of fruits and vegetables are produced in Asia and around 35% out of it is wasted during production, postharvest, processing, distribution and consumption (FAO, 2011).In most cases, the sale rate of agricultural products is affected by their internal quality. Although consumers are unable to detect product’s internal quality and Freshness while buying, their negative perceptioncan be formed against their next buy if the internal quality of what they bought does not meet their satisfaction (Leemans et al., 2002).

Introduction: Nowadays, with the development of imaging systems and image processing algorithms, a new branch of agriculture and food industry quality control has emerged. Meat and related products have high commercial value and they are one of the most important items of household food basket (Jackman et al., 2011). The apparent color of the meat is one of the most important ranking factors which determines the quality and marketing value (Ramirez and Cava, 2007; Shiranita et al., 2000). …

Milk and dairy products as an animal protein source with a high nutritional value has a particular importance in human diet. Development of non-destructive and quick methods for assessing the quality and Freshness of milk is of particular importance in ensuring product safety before consumption. The aim of this study was to measure the dielectric power with the use of a parallel-plate sensor to develop a new method for rapid assessment of milk Freshness. Three milks with commercial names of Pak, Kaleh and Damdaran and a milk from dairy farm were dielectrically measured during seven storage days. Sinusoidal voltage wad applied to the milk samples and capacitor power within the frequency range of 0-150 MHz was measured using a spectrum analyzer. Results proved that the frequency range of 75-100 MHz varied considerably with storage time so that the frequency of the valley within this frequency range increased with storage time. The frequency of the valley as a spectrum characteristic predicted the storage day by a linear regression model with R2 and RMSE of 0.84 and 0.8, respectively. The correlation of milk pH and the frequency of the valley showed that during the first four days of storage with a minor change in pH, the frequency of valley varied significantly indicating further physico-chemical changes in milk with storage that affects the dielectric properties of milk. The dielectric power at the frequencies of 2.7 and 89.4 as the most effective frequencies, predicted the storage day and pH, respectively with RMSEs of 0.4 and 0.22. Multiple linear regression models decreased the errors of prediction to 0.26 and 0.11 for storage day and pH, respectively. Based on the findings of this study, dielectric power spectroscopy can be implemented as a non-destructive, simple and accurate method to milk pH and Freshness determination.

Garlic (Allium sativum L.) is a valuable medicinal plant that is widely used in pharmaceutical, food, cosmetic, and hygiene industries due to the various products it contains in the form of extracts and powders. Non-destructive and rapid methods for assessing food quality are of interest to industries and research. In the present study, garlic powder was determined by spectrometry in the range of 936 to 1660 nm and chemometric techniques. For this purpose, 120 spectra were recorded from the prepared samples of garlic powders during the storage period of 3, 90, and 360 days. The effects of SNV, MSC, D1+SG, and D2+SG preprocessing methods on the performance of ANN, SVM, and KNN classifiers were investigated. The principal component analysis (PCA) technique was used to reduce the spectral variables, and the first 4 principal components (PCs) were considered as the input of the models. The D1+SG preprocessing showed the greatest effect on the identification of the spectra of the garlic powder samples. Results showed that the highest classification accuracy of 96.88% was achieved in all the three classifiers. The results of the present study confirm the feasibility of using the near-infrared (NIR) technique and chemometrics for the rapid identification of the Freshness of garlic powder.

Garlic medicinal plant powder is significant from a commercial perspective and is widely used as an additive in the food and pharmaceutical industries. Spectroscopic techniques serve as non-destructive methods for assessing the quality of food products, medicinal plants, and related products. In the present study, the efficiency and potential of two widely used modes, reflectance and intractance, in near-infrared spectroscopy in the range of 936 to 1660 nm were compared to assess the feasibility of distinguishing fresh garlic powder. In each spectroscopy mode, 120 spectra in 2 replicates were obtained from garlic powder samples, resulting in a total of 240 spectra. Prior to modeling, 25% of the spectra were randomly selected for validation, while the remaining spectra were used to compile the models. To mitigate potential noise, the impact of common pre-processing methods on the performance of the artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) classifiers was investigated. The principle components analysis (PCA) technique was employed to reduce the dimensionality of the spectral variables, and the first four principal components were used as classifiers inputs. In the intractance spectroscopy condition, the SVM and KNN classifiers separated spectra obtained from powders at 3 days, 3 months, and 12 months with 100% accuracy. The ANN classifier achieved 100% accuracy in distinguishing the mentioned spectra in all preprocessing conditions under investigation, except for the raw spectra (without preprocessing). Near-infrared spectroscopy in the 1660-936 nm range, combined with chemometrics, is effective for quickly detecting the Freshness of garlic powder. Considering its ease of application in both industrial and laboratory settings, intractance spectroscopy mode is superior to reflectance mode.

Fresh eggs are an excellent source of nutrients and functional compounds for human health and they are in the attractive products category. Egg producers are always looking for optimal storage methods to provide safe and high-quality products with high sensory characteristics. The safety and durability of this food should be ensured through storage methods. Thermal processing is the most common method for processing of the products. However, increasing the product temperature causes destructive effects such as coagulation, as well as changes in the taste and texture of nutrients, including eggs. On the other hand, it needs high temperature to remove different bacteria such as Escherichia coli or Salmonella. Therefore, researchers should try to find a non-thermal and bactericidal processing technologies. Ultrasound is a fast, versatile, emerging, promising technology which can be replace with thermal processing. In general, ultrasound can be used in two step for egg: detection and disinfection. Assessing the Freshness of eggs, which is related to the level of proteins, minerals and vitamins in them, is highly requested for the food industry. Studies show that the speed of the ultrasonic phase depends on the physical and mechanical properties of the egg and ultrasound can be used as a non-destructive and efficient tool to analyze the quality of eggs. Ultrasound is also very effective and practical technology in terms of disinfection. Studies show that ultrasound alone or in combination with other pasteurization processes can be apply for this processing.

Ultrasound is a rapidly growing tool in the field of research, which shows an increasing use in the food industry for both analysis and modification of food products. Quality assessment of agricultural material has an important role in modern agriculture. This study demonstrates the possibility of non-destructive prediction of the main quality indices of the commercial eggs by processing a short ultrasound burst passing through the egg material and calculating the ultrasound phase velocity. For this purpose a set of three hundred samples of commercial eggs (Boris Brown, 33 weeks age) from the first day of egg lying were purchased from a farm and classified in two groups. The first group was kept in the room temperature (22-25°C) while the second group was kept within the refrigerator (4-5°C). 25 eggs were picked every week from each groups (room and refrigerator) were first subjected to the nondestructive ultrasound test at room temperature. Each day, the ultrasound signal is recorded from the eggs first. Then, immediately after that, the air cell, the thick albumen heights, the Haugh unit and the yolk index of the eggs were also determined destructively for comparison purposes. Significant differences at 5% level between the means of the destructive analysis at different days of storage of the eggs were found using ANOVA. Both the Haugh unit and yolk index decreased by time over 5 weeks in storage at room and refrigerator while the air cell height increased. The lower is the Haugh unit for the eggs in the refrigerator the lower is the phase velocity (1573 m/s at first day compared to 1540 m/s after 3 weeks). Similar changes of the phase velocity are found for the eggs in the room temperature (1571 m/s at first day compared to 1514 m/s after 3 weeks).