The Changes in Color and Image Parameters and Sensory Attributes of Freeze-Dried Clones and a Cultivar of Red-Fleshed Apples

The target of breeding red-fleshed apples is to increase their potential health benefits related to red flesh coloration and consumer acceptance. The objective of this study was to determine the usefulness of four clones (90, 120, 156, and 158) of red-fleshed apples for freeze-drying compared to the cultivar 'Trinity'. Red-fleshed apples were dried in the form of slices using a laboratory freeze-dryer. The changes in color features and image texture parameters after drying and the sensory quality of freeze-dried samples were assessed. Trends of increase in the value of the L* parameter and decrease in the a* and b* parameters after freeze-drying were observed. Furthermore, freeze-drying caused statistically significant changes in analyzed image textures named XHMean, RHMean, SHMean, VHMean, LHMean, and UHMean. Machine learning models developed based on the color parameters L*, a*, and b* distinguished raw and freeze-dried red-fleshed apples with an average accuracy of 84% for clone 90 up to 99.0% for clone 156, and models based on twenty selected image textures exhibited an accuracy of 98.5% for clone 156 to 100% for clones 90 and 158 and the cultivar 'Trinity'. The very attractive external appearance, medium-intense fruity smell, crunchiness, and intense fruity taste of all the apple slices were revealed. The innovative aspect of this study included the comparison of the drying behavior and sensory quality of the new clones and a standard cultivar of red-fleshed apples. Moreover, innovative methods and results were used to determine the effect of freeze-drying on red-fleshed apple quality, considering novel models involving thousands of image textures and machine learning algorithms.

Integration of lanthanide MOFs/methylcellulose-based fluorescent sensor arrays and deep learning for fish freshness monitoring

Preserving fish meat poses a significant challenge due to its high protein and low fat content. This study introduces a novel approach that utilizes a common type of lanthanide metal-organic frameworks (Ln-MOFs), EuMOFs, in combination with 5-fluorescein isothiocyanate (FITC) and methylcellulose (MC) to develop fluorescent sensor arrays for real-time monitoring the freshness of fish meat. The EuMOF-FITC/MC fluorescence films were characterized with excellent fluorescence response, ideal morphology, good mechanical properties, and improved hydrophobicity. The efficacy of the fluorescence sensor array was evaluated by testing various concentrations of spoilage gases (such as ammonia, dimethylamine, and trimethylamine) within a 20-min timeframe using a smartphone-based camera obscura device. This sensor array enables the real-time monitoring of fish freshness, with the ability to preliminarily identify the freshness status of mackerel meat with the naked eye. Furthermore, the study employed four convolutional neural network (CNN) models to enhance the performance of freshness assessment, all of which achieved accuracy levels exceeding 93 %. Notably, the ResNext-101 model demonstrated a particularly high accuracy of 98.97 %. These results highlight the potential of the EuMOF-based fluorescence sensor array, in conjunction with the CNN model, as a reliable and accurate method for real-time monitoring the freshness of fish meat.

Shelf-life extension of Fragaria × ananassa Duch. using selenium nanoparticles synthesized from Cassia fistula Linn. leaves

Fragaria × ananassa Duch. (Strawberry) fruit is susceptible to postharvest diseases, thus decrease in quality attributes, such as physiological and biochemical properties leads to decrease in shelf life. The objective of the present study was to check the effect of Selenium NP's and packaging conditions on the shelf life of strawberry (Fragaria × ananassa Duch) fruits. The shelf life was observed with 4 days intervals and examined for characteristics such as physiological weight loss, moisture content, percentage decay loss, peroxidase, catalase, and DPPH radical scavenging. The quality change of postharvest Fragaria × ananassa Duch. was monitored by the application of selenium nanoparticles (T1 plant extract in 10 mM salt solution, T2 plant extract in 30 mM salt solution, T3 plant extract in 40 mM salt solution, T4 distilled water; control) in different packaging materials (plastic bags, cardboard, and brown paper) at different storage conditions (6°C and 25°C). 10 mM, 20 mM, and 30 mM solution of sodium selenite salt, prepared from 1 M stock solution. Selenium nanoparticles were synthesized using Cassia fistula L. extract and sodium selenite salt solution. Polyvinyl alcohol (PVA) was used as a stabilizer. The nanoparticles were characterized through UV-visible spectroscopy and X-Ray diffractometer (XRD). It was observed that the strawberry Fragaria × ananassa Duch. Treated with T1 (CFE and 10 mM salt solution) stored in plastic packaging at ±6°C showed the best physiological parameters and hence the treatment is recommended for storage without affecting the quality of strawberry fruit up to 16 days.

Machine Learning Integrated Multivariate Water Quality Control Framework for Prawn Harvesting from Fresh Water Ponds

Water contamination, temperature imbalance, feed, space, and cost are key issues that traditional fish farming encounters. The aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and promoting sustainability, all of which are open problems that need to be solved. The goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. The study described a proposed framework that collects data using sensors, analyses it using a machine learning framework, and provides results like a preferred list of water quality (QOW) variables that affect prawn development and yield, as well as pond categorization into low, medium, and high prawn-producing ponds. In this study, we use eight distinct machine-learning classifiers to discover the driving elements that influence the development and yield of aquatic food products in ponds in terms of QOW variables, as well as three feature selection approaches to identify the aspects that have the largest impact on the pond's total harvest performance. To validate and obtain satisfying results, the suggested system was installed and tested. The average F score and accuracy when yield is employed as a harvest parameter are determined to be 0.85 and 0.78, respectively. The average merit ratings of temperature, dissolved oxygen, and salinity are significantly higher than those of the other QOW components. The temperature variations are greatest during the second, fourth, and seventh weeks. Temperature, salinity, and dissolved oxygen are the three QOW variables that have the largest influence on overall pond harvest performance, according to the data. Additionally, it has been discovered that a key QOW factor in separating high-yielding ponds from low-yielding ponds is the temperature change following stocking.

Emerging trends in the agri-food sector: Digitalisation and shift to plant-based diets

Our planet is currently facing unprecedented interconnected environmental, societal, and economic dilemmas due to climate change, the outbreak of pandemics and wars, among others. These global challenges pose direct threats to food security and safety and clearly show the urgent need for innovative scientific solutions and technological approaches. Backed by the current alarming situation, many food-related trends have emerged in recent years in response to these global issues. This review looks at two megatrends in agriculture and the food industry; the shift to vegetable diets and the digital transformation in food production and consumption patterns. On one side, several innovative technologies and protein sources have been associated with more sustainable food systems and enhanced nutritional quality and safety. On the other side, many digital advanced technologies (e.g., artificial intelligence, big data, the Internet of Things, blockchain, and 3D printing) have been increasingly applied in smart farms and smart food factories to improve food system outcomes. Increasing adoption of vegetal innovations and harnessing Industry 4.0 technologies along the food supply chain have the potential to enable efficient digital and ecological transitions.

Understanding Gene Action, Combining Ability, and Heterosis to Identify Superior Aromatic Rice Hybrids Using Artificial Neural Network

The aromatic rice represents a smaller but independent rice collection, the quality of which is considered to be highly acceptable. Farmers are interested in growing aromatic rice due to high premium market price. The prime objective of this study was to enhance genetic improvement of aromatic rice. Combining ability analysis (GCA and SCA) and gene action are studied in a set of 7 × 7 half-diallel crosses. Twenty-one hybrids along with their seven parents were assessed in randomized complete block design. Different quantitative characters were used to estimate the magnitude of heterosis. GCA and SCA significance for all traits revealed the importance of both additive and nonadditive genetic components. Several genes determine quantitative traits, with each gene having very little impacts and being easily influenced by environmental factors. Pusa Basmati-1 and Govindobhog were the best combiners among the seven parents. In terms of per se performance, heterosis, and SCA effects on seed yield per plant and important yield qualities, the crosses BM-24 Deharadun Pahari, Baskota × Tulaipanji, and Pusa Basmati-1 × Tulaipanji may be of interest. Because of its interconnected processing properties, ANN can play a critical role in this experiment. As a result, the current study was carried out to collect data and validate it using an artificial neural network (ANN) on the combining ability, gene action, and heterosis involved in the expression of diverse fragrant rice features. Using ANN, the validation of the result was done and it was found that the overall efficiency was approximately 99%.