JOURNAL OF INNOVATIVE FOOD TECHNOLOGIES (JIFT)
Year:2023 | Volume:10 | Issue:4|Papers Count:6

Hyperspectral imaging, a technology that combines imaging and spectroscopy, provides extensive spatial and spectral information, simultaneously. It is currently being developed as a non-destructive and rapid diagnostic tool for assessing food quality and safety. In this study, hyperspectral imaging was utilized to investigate the process of bread staleness and its effect on the behavior of the bread crumb within the wavelength range of 950-400 nm and with a resolution of 0.795 nm. Principal components were extracted and three modeling methods - PCR, PLSR, and GRNN - were employed to predict texture characteristics during six days of storage. Based on the findings of this study, it was observed that the General Regression Neural Network (GRNN) method demonstrated superior performance in terms of R2 values for both springiness and stiffness, with values of 0.96 and 0.94, respectively. Furthermore, the GRNN method also exhibited the lowest Root Mean Square Error (RMSE) values for cohesiveness and stiffness, with values of 0.11 and 0.32, respectively. This demonstrates the capability of the generalized regression neural network model to predict the textural characteristics of bread.

Today, human’s specific attention to food quality has led to the development of fast, easy and non-destructive methods to assess the quality of foodstuffs. Meat is one of the most important foods and its freshness is considered as the most important qualitative feature. Therefore, checking its quality for consumption has great value worthwhile. The main objective of the present study is to investigate the possibility of using electronic nose and artificial neural network methods for detecting the freshness of chicken meat during storage in a refrigerator at 4 ºC. In the used neural network system, the input layer consists of 10 neurons based on the number of sensors and the output layer includes 3 neurons related to classes of different freshness classes of chicken meat. Different classifier networks were designed and after investigation of different network structures; the best structure of the network was obtained with a hidden layer and 6 neurons in that layer. Finally, the optimal network with a general structure of 10-6-3 was created to detect the freshness of chicken meat during different days of storage. The used statistical indices to assess the classifier to evaluate the freshness of chicken meat including accuracy, precision, sensitivity, specificity and area under the curve factors. The values of these indices for classification using selected characteristics are 95.77, 94.7, 92.18, 95.95, and 94.1 respectively. Therefore, given that the main objective of the present study was to develop and implementation of an intelligent diagnosis system of chicken meat freshness using an electronic nose system. The acceptable obtained results of the present study indicate that the proposed applied system based on the electronic nose system and artificial neural networks methods as a smart and reliable method can online classification of chicken meat as fast, easy, economical, non-destructive and with appropriate accuracy.

The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp<0.219, Sensitivity>0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice.

Camelina oil is highly unsaturated and is a rich source of omega-3 fatty acids. Camelina oil contains high amounts of tocopherol which confers a reasonable shelf life without the need for special storage conditions. Generally crud oils contain many unwanted matters which must be removed to yield a stable product with a bland or pleasant taste. Chemical refining includes degumming, neutralization, bleaching and deodorization as separate process. In this study, the qualitative (FFA and PV) and compositional characteristics of camelina oil obtained from four regions of Ilam, Kermanshah, Hamedan and Fars were investigated after chemical refining operations. Changes in composional specifications were also evaluated before and after the final stage of refining, deodorization.Oils were extracted from camelina seeds from above region by soxhlet method, The extracted oils were refined. Fatty acid methyl ester (FAME), tocopherols FFA and PV were determined. Collected data was subjected to a one- way analysis of variance and Duncan's posthoc at the P<0.05.The most important fatty acid in terms of quantity in all samples was linolenic acid (LAn-3) and then omega- 6 linoleic acid.The trans acid isomers content, not detected more than 0.06% in any of the samples. Total tocopherol content in the camelina oil samples before deodorization were measured from 893.1-1122mg/kg and after deodorization ranged from 626.68-727.53mg/kg. Significantly loss in tocopherol content (total and individual) was observed after deodorization in all samples (p<0.05). The predominantly measured tocopherol isomer was gamma tocopherol. The present study concluded that the chemical refining of camelina oil has no significant effect on the optimal composition of its fatty acids and no trans isomers was formed. FFA and PV after deodorization were in the range of Iran national standard organization. The loss of tocopherol can be seen both in total and individual.

هدف از این پژوهش بررسی اثر کیتوزان، تیمول و اتمسفر تغییر یافته بر ویژگی های فیزیکوشیمیایی، حسی و میکروبی خربزه تازه برش خورده بود. بدین منظور، برش های خربزه در محلول حاوی درصدهای مختلف کیتوزان (صفر، 1 و 2%) و تیمول (صفر، 25/0 و 5/0%) به مدت 2 دقیقه غوطه ور شدند. برش های تازه میوه پس از پوشش دهی و حذف محلول های اضافی و نمونه شاهد (پوشش داده نشده) در اتمسفر تغییریافته (10% دی اکسید کربن) و اتمسفر طبیعی بسته بندی و به مدت 8 روز در دمایC ͦͦ 4 نگهداری شدند. نتایج بیانگر افزایش میزان دی اکسید کربن و کاهش اکسیژن در بسته بندی تحت اتمسفر اصلاح شده و اتمسفر طبیعی همگام با افزایش دوره ماندگاری بود. اما در نمونه های پوشش دهی شده با کیتوزان و تیمول شدت کاهش اکسیژن و افزایش دی اکسیدکربن کمتر از نمونة شاهد بود. در پایان 8 روز نگهداری، افت وزنی در نمونه های پوشش دهی شده با کیتوزان و تیمول و نگهداری شده تحت اتمسفر تغییر یافته بسیار کمتراز نمونه شاهد بود (34/0% در مقایسه با 60/13%). افزایش غلظت کیتوزان و بسته بندی تحت اتمسفر تغییر یافته سبب افزایش حفظ میزان مواد جامد محلول، اسید قابل تیتراسیون، اسید اسکوربیک و سفتی بافت برش های خربزه شد، اما افزایش غلظت تیمول تاثیر معنی داری بر این ویژگی های کیفی نداشت. شاخص-های رنگی میزان سبزی- قرمزی (a*)، میزان آبی- زردی (b*)، میزان روشنایی (L*) و شاخص قهوه ای شدن (BI) نیز تحت تاثیر نوع بسته بندی و غلظت کیتوزان قرار گرفتند، اما، تیمار با تیمول بر شاخص های رنگی برش خربزه تاثیر معنی داری نداشت. اثر متقابل غلظت کیتوزان و غلظت اسانس نشان داد که برش های خربزه تیمار شده با غلظت 1% کیتوزان و غلظت های 25/0 و 5/0% تیمول دارای کمترین میزان بار میکروبی بودند. پوشش دهی با کیتوزان و تیمول و هم چنین نوع بسته بندی بر همه صفات حسی برش های خربزه (تاثیر معنی داری داشت (05/0 P<)؛ و ارزیابان در پذیرش کلی، غلظت 1%کیتوزان را بر غلظت 2% ترجیح دادند.

Introductio: Active anti-microbial packages, metal nanocomposites are a new generation of packages with nano structure, which are produced by direct combination of metal nanoparticles with base resin. This research was conducted with the aim of investigating the physicochemical, antioxidant and antimicrobial properties of whey protein concentrate smart film containing pomegranate and red grape anthocyanins and zinc oxide nanoparticles. Materials and methods: permeability to water vapor, antioxidant properties, antimicrobial properties by disk diffusion method and differential scanning calorimeter test were performed on the prepared films. Agar diffusion method was used to determine the antimicrobial effects of the film. Results and Discussion: By increasing the percentage of zinc oxide nanoparticles and anthocyanin in the film, the percentage of solids in the film increased. The antioxidant activity of active films increased significantly with the increase of anthocyanin content of the films. The highest antioxidant activity with a significant difference (p<0.05) was attributed to the film sample with 2.6 cc extract. According to the data obtained from the DSC test, with the addition of anthocyanins, the temperature of 290 and the glass transition have changed to some extent, and on the other hand, it can be concluded that the addition of nanoparticles to the film can reduce the glass transition temperature. Conclusion: In general, this study showed that anthocyanins and zinc oxide nanoparticles have the potential to be used to prepare films based on bioactive whey concentrate with improved physicochemical properties and biological properties such as antioxidant properties when used in appropriate concentrations.