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Impact of Smoking Technology on the Quality of Food Products: Absorption of Polycyclic Aromatic Hydrocarbons (PAHs) by Food Products during Smoking
The food industry is striving for a sustainable development of thermal food processing. Smoking is an example of a process that has grown in popularity in recent years. There is a lack of systematic knowledge in the literature regarding this undervalued process, so the purpose of this review is to analyze the state of knowledge about the methods and technologies of smoking food products and their impact on changing the quality of essential food products. Therefore, a comprehensive review of the literature on smoking processes from the past two decades was conducted. The most essential components absorbed from smoke during smoking are polycyclic aromatic hydrocarbons (PAHs). In the present work, 24 PAHs are summarized, and the capability of 12 food products to absorb them is described. Analysis of the principal components of absorbed PAHs showed that some products from different groups exhibit a similar ability to absorb these compounds, mainly influenced by their physical properties. The pre-treatment practices of raw materials before smoking, the smoking raw materials used, and their quality parameters were characterized (along with the effects of smoking methods on selected product groups: fish, meats, and cheeses). In addition, the gap in research concerning the absorption of other components of smoke, e.g., phenols, alcohols, ketones, and aldehydes, which directly impact food quality, is indicated.
Uncovering rain-fed resilience power of grass pea in Iran using AMMI, BLUP, and multi-trait stability parameters
New Developments in Smart Farming Applied in Sustainable Agriculture
Sustainable agriculture aims to increase agricultural productivity while minimising negative environmental impacts [...]
Effect of Selenium Enriched Yeast Culture Saccharomyces Cerevisiae Supplementation in TMR of Pregnant Heifers and Cows on the Colostrum Quality
New Trends and Challenges in Precision and Digital Agriculture
Real change is needed in the agricultural sector to meet the challenges of the 21st century in terms of humanity’s food needs [...]
Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops
Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.
Genetic Diversity and Population Structure in Bread Wheat Germplasm from Türkiye Using iPBS-Retrotransposons-Based Markers
This study investigated the genetic diversity and population structure of 63 genotypes from Turkish bread wheat germplasm using iPBS-retrotransposons primers. The thirty-four iPBS primers produced a total of 1231 polymorphic bands, ranging from 8 (iPBS-2375) to 60 (iPBS-2381) alleles per marker, with an average number of 36.00 alleles. The polymorphism information content (PIC) per marker varied between 0.048 (iPBS 2087) and 0.303 (iPBS 2382), with an average of 0.175. The numbers of effective alleles (ne), genetic diversity of Nei (h), and Shannon’s information index (I) value were calculated as 1.157, 0.95, and 0.144, respectively. The greatest genetic distance (0.164) was between Eastern Anatolia Agricultural Research Institute genotypes and Çukurova Agricultural Research Institute genotypes. The unweighted pair-group method with arithmetic mean (UPGMA) dendrogram placed the 63 wheat genotypes into three clusters. The percentage of genetic diversity explained by each of the three main coordinates of the basic coordinate analysis was determined to be 44.58, 12.08, and 3.44, respectively. AMOVA (Analysis of Molecular Variance) showed that the variation within populations was 99% and that between populations was 1%. The result of genetic structure analysis suggests that the greatest value of K was calculated as 3. The F-statistic (Fst) value was determined as 0.4005, 0.2374, and 0.3773 in the first to third subpopulations, respectively. Likewise, the expected heterozygosity values (He) were determined as 0.2203, 0.2599, and 0.2155 in the first, second, and third subpopulations, respectively. According to the information obtained in the study, the most genetically distant genotypes were the G1 (Aksel 2000) and G63 (Karasu 90) genotypes. This study provided a deep insight into genetic variations in Turkish bread wheat germplasm using the iPBS-retrotransposons marker system.
Modeling Callus Induction and Regeneration in Hypocotyl Explant of Fodder Pea (Pisum sativum var. arvense L.) Using Machine Learning Algorithm Method
A comprehensive understanding of genetic diversity and the categorization of germplasm is important to effectively identify appropriate parental candidates for the goal of breeding. It is necessary to have a technique of tissue culture that is both effective and reproducible to perform genetic engineering on fodder pea genotypes (Pisum sativum var. arvense L.). In this investigation, the genetic diversity of forty-two fodder pea genotypes was assessed based on their ability of callus induction (CI), the percentage of embryogenic callus by explant number (ECNEP), the percentage of responding embryogenic calluses by explant number (RECNEP), the number of somatic embryogenesis (NSE), the number of responding somatic embryogenesis (RSE), the regeneration efficiency (RE), and the number of regenerated plantlets (NRP). The findings of the ANOVA showed that there were significant differences (p < 0.001) between the genotypes for all in vitro parameters. The method of principal component analysis (PCA) was used to study the correlations that exist between the factors associated with tissue culture. While RE and NRP variables were most strongly associated with Doğruyol, Ovaçevirme-4, Doşeli-1, Yolgeçmez, and Incili-3 genotypes, RECNEP, NSE, RDE, and RECNEP variables were strongly associated with Avcılar, Ovaçevirme-3, and Ardahan Merkez-2 genotypes. The in vitro process is a complex multivariate process and more robust analyses are needed for linear and nonlinear parameters. Within the scope of this study, artificial neural network (ANN), random forest (RF), and multivariate adaptive regression spline (MARS) algorithms were used for RE estimation, and these algorithms were also compared. The results that we acquired from our research led us to the conclusion that the employed ANN-multilayer perceptron (ANN-MLP) model (R2 = 0.941) performs better than the RF model (R2 = 0.754) and the MARS model (R2 = 0.214). Despite this, it has been shown that the RF model is capable of accurately predicting RE in the early stages of the in vitro process. The current work is an inquiry regarding the use of RF, MARS, and ANN models in plant tissue culture, and it indicates the possibilities of application in a variety of economically important fodder peas.
Genetic Characterization and Agronomic Evaluation of Drought Tolerance in Ten Egyptian Wheat (Triticum aestivum L.) Cultivars
This investigation was carried out for genetic characterization and determination of drought tolerance of ten Egyptian cultivars of wheat (Triticum aestivum L.), namely Misr 1, Misr 2, Gemmiza 9, Gemmiza 10, Gemmiza 11, Gemmiza 12, Shandawel 1, Giza 168, Giza 171, and Sids 14. These cultivars were grown in two winter seasons: 2018/2019 and 2019/2020 at the experimental farm Fac. of Agric., Suez Canal Univ., Ismailia, Egypt, under two watering regimes: normal (100%) and stress (50% FC) conditions. Six agronomic traits and five tolerance indices, namely stress tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), yield stability index (YSI), and drought susceptibility index (DSI), were used to evaluate the impact of drought stress. The results reflected Giza 171, Misr 2, and Giza 168 as precious germplasm for breeding of high-yielding drought-tolerant wheat. A highly significant positive correlation was recorded between yield under normal and stress conditions on the one hand and each of MP and GMP on the other hand. In addition, YSI appeared engaged in a highly significant positive correlation with yield under drought conditions only. TOL and DSI appeared insignificantly correlated with yield. Therefore, MP and GMP were reflected as the first runners among indices suitable to distinguish the high-yielding cultivars under drought conditions. At the molecular level, five primers of Start Codon Targeted (SCoT) markers were able to resolve and characterize the studied cultivars, which reflected SCoT as a potent gene-targeting molecular marker, able to characterize and resolve genetic diversity in wheat at the cultivar level using few primers. Therefore, SCoT is a time-efficient molecular marker, and it can efficiently replace indices in characterization of drought-tolerant genotypes with a high confidence level and reasonable cost.
Planting Geometry May Be Used to Optimize Plant Density and Yields without Changing Yield Potential per Plant in Sweet Corn
Planting geometry is one of the most important management practices that determine plant growth and yield of corn. The effects of eight planting geometries (35 × 23 cm, 40 × 21 cm, 45 × 19 cm, 50 × 18 cm, 55 × 17 cm, 60 × 16 cm, 65 × 15 cm, 70 × 15 cm) on plant growth and yields of three sweet corn hybrids (Argos F1, Challenger F1, Khan F1) were investigated under Erzurum, Türkiye conditions in 2022 and 2023 years. Variance analysis of the main factors shows a highly significant effect on whole traits but in two-way interactions some of the traits were significant and in the three-way interactions, it was insignificant. As an average of years, the number of plants per hectare at the harvest varied between 92,307 (35 × 23 cm) and 120,444 (70 × 15 cm) according to the planting geometries. The highest marketable ear number per hectare (107,456), marketable ear yield (24,887 kg ha−1), and fresh kernel yield (19,493 kg ha−1) were obtained from the 40 × 21 cm planting geometry. The results showed that the variety Khan F1 grown at 40 × 21 cm planting geometry obtained the highest marketable ear number (112,472), marketable ear yield (29,788 kg ha−1), and fresh kernel yield (22,432 kg ha−1). The plant density was positively correlated with marketable ear number (r = 0.904 **), marketable ear yield (r = 0.853 **), and fresh kernel yield (r = 0.801 **). The differences among the varieties were significant for the studied traits, except for plant density and kernel number per ear. In conclusion, the variety Khan F1 should be grown at the 40 × 21 cm planting geometry to maximize yields under study area conditions without water and nutrient limitations.
Can mineral resources be a blessing in disguise for green finance in G7 countries? Mineral resources for COP28 green financing goal
Absorpcja wielopierścieniowych węglowodorów aromatycznych (WWA) przez produkty spożywcze podczas wędzenia
Methodology for Assessing Tractor Traction Properties with Instability of Coupling Weight
The purpose of the study is to increase the efficiency of using the tractor hitch weight in traction mode by reducing the uneven distribution of vertical reactions between the wheels. This work is grounded on a methodology that involves summarizing and analyzing established scientific findings related to the theory of tractors operating in traction mode. The analytical method and comparative analysis were employed to establish a scientific problem, define research objectives, and achieve the goal. The key principles of probability theory were applied in developing the empirical models of the tractor. The main provisions of the methodology for evaluating the traction properties of the tractor with the instability of the coupling weight were formulated. The method of evaluating the vertical reactions on the wheels of the tractor is substantiated, which is based on the measurement of the vertical reaction on one of the four wheels. It was proven that tractors with a center of mass offset to the front or rear axles have the greatest probability of equal distribution of vertical reactions between the wheels of one axle, and tractors with a center of mass in the middle between the axles have the lowest probability. It is theoretically substantiated and experimentally confirmed that when the tractor performs plowing work with uneven distribution of loads on the sides, its operation with maximum traction efficiency is ensured by blocking the front and rear axle drivers.
Zinc Oxide Nanoparticles: An Influential Element in Alleviating Salt Stress in Quinoa (Chenopodium quinoa L. Cv Atlas)
Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g−1 leaf; CAT: EU g−1 leaf; POD: EU g−1 leaf), H2O2 (mmol kg−1), MDA (nmol g−1 DW), proline (µg g−1 FW), and sucrose (g L−1), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management.
Predictions and Estimations in Agricultural Production under a Changing Climate
In the 21st century, agriculture is facing numerous challenges [...]
Somaclonal Variation for Genetic Improvement of Starch Accumulation in Potato (Solanum tuberosum) Tubers
Starch content is one of the major quality criteria targeted by potato breeding programs. Traditional potato breeding is a laborious duty due to the tetraploid nature and immense heterozygosity of potato genomes. In addition, screening for functional genetic variations in wild relatives is slow and strenuous. Moreover, genetic diversity, which is the raw material for breeding programs, is limited due to vegetative propagation used in the potato industry. Somaclonal variation provides a time-efficient tool to breeders for obtaining genetic variability, which is essential for breeding programs, at a reasonable cost and independent of sophisticated technology. The present investigation aimed to create potato somaclones with an improved potential for starch accumulation. Based on the weight and starch content of tubers, the somaclonal variant Ros 119, among 105 callus-sourced clones, recorded a higher tuberization potential than the parent cv Lady Rosetta in a field experiment. Although this somaclone was similar to the parent in the number of tubers produced, it exhibited tubers with 42 and 61% higher fresh and dry weights, respectively. Additionally, this clone recorded 10 and 75% increases in starch content based on the dry weight and average content per plant, respectively. The enhanced starch accumulation was associated with the upregulation of six starch-synthesis-related genes, namely, the AGPase, GBSS I, SBE I, SBE II, SS II and SS III genes. AGPase affords the glycosyl moieties required for the synthesis of amylose and amylopectin. GBSS is required for amylose elongation, while SBE I, SBE II, SS II and SS III are responsible for amylopectin.
Interactive Effects of Nitrogen and Potassium Fertilizers on Quantitative-Qualitative Traits and Drought Tolerance Indices of Rainfed Wheat Cultivar
Increasing global food requirements and global warming are two challenges of future food security. Water availability and nutrient management are two important factors that affect high-yield and high-quality wheat production. The main and interactive effects of nitrogen and potassium fertilizers on quantitative-qualitative properties and drought tolerance of an Iranian rainfed cultivar of wheat, Azar-2, were evaluated. Four rates of nitrogen (N0, N30, N60, and N90 kg/ha), along with four concentrations of potassium (K0, K30, K60, and K90 kg/ha), were applied in rainfed (drought stress) and non-stress conditions. The interactive effect of N × K was significant on nitrogen and protein contents of grains at 5% and 1% probability levels, respectively. Different trends of SSI, STI, K1STI, and K2STI indexes were observed with the interactive levels of nitrogen and potassium. The lowest SSI index (0.67) was observed in N30K30, whereas the highest STI (1.07), K1STI (1.46), and K2STI (1.51) indexes were obtained by N90K60 and N90K90. The obtained results could be useful to increase yield and quality of winter rainfed wheat cultivars under drought stress with cool-rainfed areas. N60K30 and N90K60 can be recommended to increase the grain yield and protein content of rainfed wheat under drought stress and non-stress conditions, respectively.
Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning
The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g⋅100 g−1 to 1.42 g⋅100 g−1, and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression.
Prediction of Blueberry (Vaccinium corymbosum L.) Yield Based on Artificial Intelligence Methods
In this paper, we present a high-accuracy model for blueberry yield prediction, trained using structurally innovative data sets. Blueberries are blooming plants, valued for their antioxidant and anti-inflammatory properties. Yield on the plantations depends on several factors, both internal and external. Predicting the accurate amount of harvest is an important aspect in work planning and storage space selection. Machine learning algorithms are commonly used in such prediction tasks, since they are capable of finding correlations between various factors at play. Overall data were collected from years 2016–2021, and included agronomic, climatic and soil data as well satellite-imaging vegetation data. Additionally, growing periods according to BBCH scale and aggregates were taken into account. After extensive data preprocessing and obtaining cumulative features, a total of 11 models were trained and evaluated. Chosen classifiers were selected from state-of-the-art methods in similar applications. To evaluate the results, Mean Absolute Percentage Error was chosen. It is superior to alternatives, since it takes into account absolute values, negating the risk that opposite variables will cancel out, while the final result outlines percentage difference between the actual value and prediction. Regarding the research presented, the best performing solution proved to be Extreme Gradient Boosting algorithm, with MAPE value equal to 12.48%. This result meets the requirements of practical applications, with sufficient accuracy to improve the overall yield management process. Due to the nature of machine learning methodology, the presented solution can be further improved with annually collected data.
