Quality Control Of Agricultural Products Research Articles

Application of Advanced Deep Learning Models for Efficient Apple Defect Detection and Quality Grading in Agricultural Production

In this study, a deep learning-based system for apple defect detection and quality grading was developed, integrating various advanced image-processing technologies and machine learning algorithms to enhance the automation and accuracy of apple quality monitoring. Experimental validation demonstrated the superior performance of the proposed model in handling complex image tasks. In the defect-segmentation experiments, the method achieved a precision of 93%, a recall of 90%, an accuracy of 91% and a mean Intersection over Union (mIoU) of 92%, significantly surpassing traditional deep learning models such as U-Net, SegNet, PSPNet, UNet++, DeepLabv3+ and HRNet. Similarly, in the quality-grading experiments, the method exhibited high efficiency with a precision of 91%, and both recall and accuracy reaching 90%. Additionally, ablation experiments with different loss functions confirmed the significant advantages of the Jump Loss in enhancing model performance, particularly in addressing class imbalance and improving feature learning. These results not only validate the effectiveness and reliability of the system in practical applications but also highlight its potential in automating the detection and grading processes in the apple industry. This integration of advanced technologies provides a new automated solution for quality control of agricultural products like apples, facilitating the modernization of agricultural production.

Real-time grading of roasted tobacco using near infrared spectroscopy technology

Traditional tobacco quality grading methods have limitations such as high human resource consumption and poor consistency, resulting in the grading accuracy being affected. Near infrared spectroscopy (NIRS) technology, as a rapid and non-destructive analytical method, has been widely used for online inspection. This study aims to accomplish rapid, accurate and reliable real-time grading of tobacco quality by employing online near-infrared spectroscopy to detect key chemical indicators. A key indicators selection method based on information gain and Fisher criterion (IFRI) was proposed in this paper by associating tobacco chemical indicators with traditional quality grades. The key chemical indicators were selected from many indicators by two classical feature selection functions, and a chemical indicator grading table was designed based on a large number of statistical data on tobacco chemical indicators, and the table was applied to grade the tobacco quality of real data from different years. The results show that the IFRI algorithm is an effective method for selecting key indicators, and the selected reducing sugars-to-total nitrogen ratio and nicotine are the key indicators for tobacco quality grading. Moreover, we demonstrated that the designed grading table is reasonable and feasible. Compared with traditional grading, chemical indicator grading based on near infrared spectroscopy can not only be realized quickly by using an online near infrared instrument, but also the tobacco of the same grade is less discrete, better consistency and more representative after grading, and it is applicable to tobacco samples of different years, which is a convenient, rapid and promising grading method that can provide a reference for tobacco quality grading. Furthermore, the formulation design by utilizing different grades after grading can greatly improve the utilization rate of tobacco leaves. Notably, the method can also be applied to other fields, such as quality control of agricultural products, monitoring of food processing processes, etc.

Efficient residual network using hyperspectral images for corn variety identification.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

Ensuring sustainable development of agriculture: legal, managerial, digital approaches

The article discusses modern legal, managerial, digital approaches to ensuring the sustainable development of agriculture. The norms and standards of agricultural risk insurance, budget lending, organic agribusiness modeling and food safety developed by international institutions are evaluated. Methods of comparative, legal, informational analysis and scientific generalization of theoretical knowledge revealed organizational problems of budgeting and quality control of agricultural products, preventing food losses, and combating falsification of food products. It was noted that legal measures and management standards optimize agribusiness segments, reduce the number of intermediaries, and improve the trust of suppliers and consumers through direct links. The digitalization of agriculture contributes to the transparency of data exchange while reducing information imbalances and transaction costs for participants in agricultural markets. The connection of standards and norms of legal regulation of sustainable development of agriculture with food security in the EU and the EAEU is shown. The conclusions are indicated by the positive prospects for digital management of agribusiness subsidies and digital budgeting as the level increases in the EU and the EAEU. Unlike the EU, the EAEU is building a common space for organic and digital agriculture on the principles of Eurasian integration and cooperation.

Почвы залежного поля Ямальской опытной станции: эколого-аналитическая оценка

The agricultural use of soils is limited by their contamination with various compounds and low contents of nutrients. We aimed to study the unique soils of the Yamal Experimental Station to determine their contamination with heavy metals and assess their potential fertility. Established in 1932, the Yamal Experimental Station (Salekhard, Russia) has bred new varieties of vegetable crops in open and protected ground. In August 2021, we made a soil section and 40 pits in a 0–10 cm layer. X-ray fluorescence was used to determine 11 metals and oxides. The qualitative assessment was based on the total soil pollution, soil pollution, and geoaccumulation indexes. Finally, we determined the contents of nutrients. The metals and metal oxides showed regressive-accumulative distribution along the soil profile. The concentrations of all ecotoxicants (except for arsenic) were within the maximum/approximate permissible values. Since arsenic has a high regional background content, its elevated concentrations make the soil suitable for agricultural use if proper quality control is in place. The total soil pollution index classified the level of pollution as “acceptable”. The geoaccumulation index showed the soils as mostly “unpolluted” with metals. The soil pollution index had values below 1, which indicated the absence of pollution. The fallow soils of the Yamal Experimental Station have a high level of potential fertility and are suitable for agricultural reuse according to the soil quality indexes applied. They can also serve as a local geochemical standard that has a long history of agrogenic transformation in cryogenic ecosystems. Taking into account increased concentrations of arsenic, we recommend primary quality control of agricultural products to identify its possible migration in the soil-plant system.

The Chemical Training of Agrarian Specialists: From the Chemicalization of Agriculture to Green Technologies

The impact of agricultural production on the ecological condition of vast territories is of concern to the entire world community. Therefore, the concept of sustainable development should become the basis for the training of agricultural specialists. This article examines the transformation of the system of chemical training of agricultural students in Russia, taking into account the transition to the concept of sustainable development. The study of the history of the development of chemical training of agricultural specialists in Russia helped us to see the causes of the current crisis and highlights what should be preserved. The analysis of the modern needs of the agro-industrial complex made it possible to identify ways to modernize the system of teaching chemical disciplines in agricultural universities. As a result, the main trends of modernization of chemical training of future agricultural specialists in the context of the concept of sustainable development are identified. The chemical competencies of specialists of the agro-industrial sector working in the field of production, processing, and quality control of agricultural products are differentiated. The conditions of their formation are determined, and the effectiveness of the application of the principles of green chemistry for the formation of selected competencies is shown. The theoretical significance of the research is the development of the concept of ecological chemical education (green chemistry) for agricultural education. The practical significance of this work lies in the development of practical examples of the application of the principles of green chemistry in the educational process of an agrarian university. The originality of this research lies in determining the pedagogical conditions for the formation of chemical competencies of agricultural specialists in the context of orientation to the concept of sustainable development.

Identification of chemical components in Dianthus determined by widely targeted metabolomics

The chemical composition of the secondary metabolites is of great significance to the quality control of agricultural products. The genus Dianthus is famous for its beautiful flowers in the cut flower trade and also used in the traditional Chinese medicinal system and food market. However, the chemical composition in Dianthus is still unknown. The current study examined the levels of different metabolites of the flowers in Dianthus caryophyllus, Dianthus chinensis and Dianthus superbus via the use of the widely targeted metabolomic strategy. We obtained the structure and content of 423 metabolites in Dianthus, which included the primary and secondary metabolites. The principal component analysis was able to clearly seperate Dianthus caryophyllus, Dianthus chinensis and Dianthus superbus based on the flower metabolites. The differential metabolites can be categorised into 11 different classes, the majority were flavonoids, amino acids and their derivatives, phenolic acids and lipids. The results of this study provide important information for the effective use of Dianthus flowers in edible, medicinal and therapeutic aspects.

Electrochemical sensors for the determination of carbofuran in natural objects (a review)

The review is devoted to the analysis of literature data on the development of modern electrochemical sensors for the determination of carbofuran in natural objects (water, soil, food). Sensors for the determination of carbofuran can be conditionally divided into two groups according to the type of electrode materials used: carbon-containing and biosensors. Carbon-containing sensors manufactured using nanotechnologies based on 0D – 3D allotropic modifications of carbon (carbon black, graphene, carbon nanotubes, fullerene) exhibit unique properties such as structural polymorphism, high surface area, thermal and chemical stability, biocompatibility, and original catalytic properties. At the same time, biosensors are considered promising analytical systems that complement traditional analytical methods due to the possibility of rapid on-site monitoring and miniaturization. Currently, biosensors used for the determination of carbofuran are mainly divided (proceeding from the type of bio-recognition elements) into enzyme biosensors (acetylcholinesterase and other enzymes) and immunosensors (antibodies and aptamers). Two detailed tables present data on electrochemical sensors developed for the determination of carbofuran in natural objects, including their advantages and shortcomings. All the developed sensors for the determination of carbofuran are characterized by high sensitivity, selectivity, rapidity, and low manufacturing cost, which makes electroanalytical methods a worthy alternative to the methods of analysis traditionally used for the determination of pesticides (liquid and gas chromatography, spectrophotometry, capillary electrophoresis, etc.). Preparation of vegetable and fruit samples for analysis using sensors of various types is described: the main stage of sample preparation is the alkaline hydrolysis of carbofuran, which is electrochemically inactive, to carbofuran-phenol. This review may be of interest to laboratories for the quality control of agricultural products and foodstuffs.

Development and research of an information-measuring system for quality control of agricultural products

The article presents the result of an information-measuring system development for quality control of agricultural products based on the assessment of their moisture content, chemical composition, presence of organic and mineral impurities. It also substantiates the relevance of the use of such systems by the agro-industrial complex enterprises. The proposed information-measuring system is based on the author's dielcometric method for determining the electrophysical parameters of capacitive sensors, the principle of which is to decompose the measuring process into two stages. The study of this method was carried out using simulation modeling in the SimInTech environment, while custom operational amplifier blocks and sample-and-hold circuit were built. As a result of the operational amplifier model research, graphs of the dependency of the output voltage on the change in the integration time constant and amplification gain were plotted, on the basis of which the optimal values of these quantities were selected. Studies of the constructed models of the first and second measurement stages using the proposed method showed a high accuracy in determining the electrophysical parameters of capacitive sensors filled with agricultural products, with a relative error not exceeding ±0.2%.

DEVELOPMENT OF THE RECIPE OF WHEAT BREAD WITH THE ADDITION OF BEE POLLEN FROM PHACELIA

Introduction. The use of bee pollen and other biologically active additives in the process of food production makes it possible to enrich them with minerals, vitamins, increase nutrition, replace components with intolerance of the human body, improve taste. The goal of the work. To study the influence of phacelia bee pollen on indicators of quality of wheat bread and compliance of the finished product with national standards. Materials and methods of research. The research was conducted in the training laboratory of the Faculty of Food Technology and Quality Control of Agricultural Products of the National University of Life and Environmental Sciences of Ukraine, Kyiv, (Ukraine) and the Testing Laboratory of Methods of Quality and Safety Assessment of Beekeeping of the National Scientific Center “Institute of beekeeping named after P.I. Prokopovich”. The bee pollen was collected at a private apiary (Selezenivka, Kyiv Oblast, Ukraine). In theoretical studies, the methods of analysis and synthesis of scientific literature were applied using Scopus scientometric databases and the Google Scholar database. Experimental research was carried out in accordance with generally accepted, standard methods, as well as in accordance with the requirements of the national standards of Ukraine. The main stages in the production of bread with the addition of bee pollen, during which various studies of phacelia were carried out, were the preparation of raw materials and auxiliary materials, mixing and processing of dough, baking, cooling and storage. Results of research and discussion. The main stages of the bread production with the addition of phacelia bee pollen were the preparation of raw materials and auxiliary materials, mixing and processing of dough, baking, cooling and storage. Experimental studies were carried out on two specimens with different recipes and a method of treatment of bee pollen – powdery condition; the natural form that remains after the drying process. An organoleptic study using powdered milk, pumpkin seeds and powdered bee pollen in the first sample determined a number of advantages in terms of color, pulp state, taste, and smell. The results of the physico-chemical study of sample 2 revealed an imperfect recipe, and with minor deviations from the process or production modes, it may not meet the requirements for physico-chemical performance. It is believed that the high amount of carbohydrates is due to the large amount of bee pollen in the recipe. The recipe is estimated to have produced a finished product of potentially high biological value that can be used for functional nutrition, but requires further research. In addition, other scientists argue that adding these ingredients to food recipes significantly improves a number of their properties. Conclusions and prospects for further research. Influence of phacelia bee pollen in the composition of the wheat bread recipe on the indicators of quality and compliance of the finished product with national standards has been determined. As a result of the identification of the bee pollen by the number of pollen grains, monoflority was confirmed – 95.4% of pollen grains phacelia tanacetifolia. The main physico-chemical indicators of the finished product: moisture – 42.3%, acidity – 2.7%, porosity of pulp – 75.2%. It has been confirmed that the recipe of sample 1 has high organoleptic and physico-chemical characteristics. Based on the results of the obtained data, prospective studies were determined in revealing the influence on the human body from the consumption of food products with the components of the production or processing of beekeeping products.

Improving the quality control of agricultural products as a component of its quality management

Improving the quality control of agricultural products as a component of its quality management

Improving Students’ Learning Outcomes Using 5E Learning Cycle Model

Quality control of agricultural products is one of the study programs in the vocational school that applies online learning during the COVID-19 pandemic. The implementation of online learning in grade XI quality control has constraints, in which the average learning outcomes are still gaining at a low value of 58.06 in the analysis proximate material. At the time of observation, 70% of students did not understand the topic. Some students get difficulties in discovering or constructing a solution to a problem. Thus, an appropriate learning model is needed to improve student's learning outcomes. The purpose of this study was to determine the implementation of the learning cycle 5E model and discovery learning also to identify the differences in student learning outcomes between both models’ performance in the materials applying fresh milk quality standards. The research was conducted using the quasi-experimental (experiment classes used the learning cycle 5E model and control class used the discovery learning model). This study proved good analysis results. The application of the learning cycle 5E shows better results to the discovery learning model to improve student learning outcomes as evidenced by the fact that the average value of experiment classes is higher than that of the control class. The N-gain value of the experiment class is greater than that of the control class. Therefore, the learning cycle 5E model can improve students' learning outcomes.

Удосконалення аналітичного визначення залишкових кількостей діючих речовин класу триазолів в соках

Today, the most common among fungicides approved for use in Ukraine, as well as in other countries, are compounds of the triazole class. Juices, as a product of processing agricultural raw materials, can be consumed by a wide range of the population. The purpose of work was to develop analytical methods for compound of triazole (difenoconazole and propiconazole) in juices to ensure quality control of agricultural products grown with the use of these fungicides. Conditions for chromatography of difenoconazole and propiconazole using gas-liquid chromatography with capillary column SH-Rxi-5ms (30 m 0,25 mm) and electron capture detector were determined. Optimal conditions of difenoconazole chromatography were established: thermostat column temperature - 280 C, evaporator temperature - 290 C, detector thermostat temperature - 300 C. Retention time: isomer 1 - (6,91 +- 0,1) minutes, isomer 2 - (7,04 +- 0,1) minutes. The linear range of detection is 0,01-0,04 ug/cm3. The optimal conditions for chromatography of propiconazole were also established: the temperature of the column thermostat in the programming mode: initial T - 100 C with an exposure of 1 minute; from 90 C to 260 C - rate of temperature rise 40 C/min; exposure - 7,45 minutes; evaporator temperature - 270 C, detector temperature - 280 C. The retention time under these conditions was 3,49 +- 0,1 minutes for isomer 1 and 3,57 +- 0,1 minutes for isomer 2. The linear range of detection is 0,01-0,05 ug/cm3. The calibration dependence of the area of peaks of the investigated substances on their concentration was established and described by the linear regression equation.

Experimental Recognition System for Dirty Eggshell by Using Image Analysis Technique

The present study was focused on the design and implementation of an experimental recognition system for dirty chicken eggshell by using an image analysis technique. Image analysis based observation and evaluation techniques can be used efficiently and effectively for agricultural product quality control. Dirt stains on eggs are the result of mainly by feces (black to light brown stains), uric acid (white stains), yolk, and blood. The experimental system was used to obtain dark level images of dirty stains of chicken eggs owing to feces. For this aim, the dirty chicken eggs which have dirty parts were put under a webcam, and dirtiness degree was evaluated by using developed image analysis software at the LabVIEW platform. For the experiment, 100 clean and 100 dirty eggs were used to accurate the determination of dark stains. The results of the research showed that the designed experimental system pointed an accuracy of 99.8% at painted grade eggs. On the other hand, the accuracy of the differentiation of the dirt stains by feces was 98.5%. The developed system can be upgraded for developing egg sorting machines by presence-absence of dirty stains in eggshell.

Reviews on the Quality of China’s Agricultural Products Export to United States

The export of characteristic superior agricultural products plays an important role in realizing China’s agriculture going out and realizing the transformation from total expansion of agricultural products to quality expansion. This paper systematically combs the relevant literature at home and abroad from three aspects: the theory of agricultural trade, the gap between the quality of agricultural products between China and the United States and the factors affecting the quality of agricultural products. The progress and shortcomings of the research on quality control of agricultural products in China were compared and commented, based on the systematically combing of the relevant literature at home and abroad from two aspects: the gap between the quality of agricultural products between China and United States, and the factors affecting the quality of agricultural products. Then three research directions were put forward: promoting the quality upgrading of agricultural products, differentiating the export quality of different agricultural products, and then formulating national export standards for agricultural products, and realizing the transformation and upgrading of agricultural products in export competition. This research would be useful for the exploration and practice of the export quality of agricultural products in China.

Detección de pesticidas en el durazno (prunus pérsica) mediante una nariz electrónica

El presente trabajo tiene como objetivo el explorar las posibilidades de un sistema multisensorial, tipo nariz electrónica, para la detección de pesticidas organofosforados en el duraznero (Prunus pérsica), cultivado en el departamento de Norte de Santander, Colombia. Las pruebas se realizaron con una nariz electrónica conformada por 8 sensores químico-resistivos, y el método de procesamiento utilizado fue el análisis de componentes principales (PCA). Inicialmente se observó que los sensores reaccionan ante los compuestos volátiles presentes en el durazno, sin la presencia de pesticidas. Al agregar concentraciones conocidas de pesticidas al fruto, fue posible agrupar las muestras en cuatro grupos, así: sin pesticidas, 1, 2 y 3 ppm. Lo anterior indica que el sistema utilizado permite diferenciar las muestras puras de las que están contaminadas, además de agrupar los distintos rangos de concentración. Se concluye con la posibilidad de utilizar la nariz electrónica, en el control de calidad de productos agrícolas.

Detection of Odorant Molecules in the Gaseous Phase Using α-, β-, and γ-Cyclodextrin Films on a Quartz Crystal Microbalance

There is an interest in sensors for the detection of odorant molecules in the gaseous phase, especially those related to the fragrance of fruits, because odorant sensing is useful for on-site quality control of agricultural products. Previously, gas-chromatographic methods requiring bench-top devices were used for odorant-molecule detection. Herein, we report an odorant sensor based on cyclodextrins (CDs) as a stable odorant receptor, using a highly mass-sensitive and quantitative 27-MHz quartz crystal microbalance (QCM) device, which has the advantage of possible incorporation into portable devices. When ethyl butyrate (a model odorant molecule for fruit fragrances) was flowed onto a QCM plate modified with α-, β-, or γ-CD network films, a decrease in frequency was observed (corresponding to an increase in mass), owing to the capture of odorant molecules by CD molecules. The CD films were capable of capturing and releasing odorant molecules, depending on the type of CD (α-, β-, or γ-CD). Thus, these sensors are reusable for odorant-molecule sensing, and are applicable to pattern recognition of odorant molecules. Thus, sensors based on CD films combined with a QCM handheld device could be applied to monitoring the condition of fruits.

Non-iterative model based image reconstruction of diffuse optical tomography based on DE and RTE in quality control of agricultural product studies

In a previous study, we developed the non-iterative image reconstruction based on diffusion equation. Within this research, we applied the same non-iterative algorithm scheme using radiative transfer equation. Basically, the non-iterative image reconstruction is a development of model based image reconstruction that implemented truncated singular value decomposition and L-curve analysis to solve the ill-posed problem. This algorithm reduces the computation time to reconstruct the cross-sectional area. As part of continuous development on agricultural application, optical tomography based quality control is offered. An experiment was conducted on potato to evaluate these two non-iterative algorithms. The object was illuminated by the near infrared source from 8 positions on the object boundary. In this experiment, we vary the position and the amount of epoxy on the object then we analyze the residual value between measurement and reconstructed boundary data. The reconstructions were performed in continuous-wave domain. Furthermore, we compared the residual value from diffuse optical tomography using diffusion equation and radiative transfer equation. The result of this study indicates that these algorithms are promising to detect the presence of epoxy on potato which is useful for quality control of agricultural products.

Challenges and future prospects of agri-nanotechnology for sustainable agriculture in India

Abstract Nanotechnology presenting an inventive frontier in present day agricultural practices is expected to turn into a key force in future by contributing innovative applications. This new approach using nano-principles in agriculture has immense possibilities in handling the world wide challenges of environmental security and sustainability, food production, food safety, food security and global threats of climate change. Nanotechnology is very expansively used in contemporary fields of agriculture, food processing, and food protection, packing industry, dairy industry, packaging, transportation and quality control of agricultural products. It has enormous prospective in making agriculture more proficient and resourceful by using nanoparticles to improve the precision in delivering the nutrients to the specific part at a specific time. Use of nano- based agro-chemicals, ceramic devices, filters; lamination methods have great potential of making agriculture more organized and efficient by transforming the conventional agro-practices. Indian government is also supporting this by making plans to extend support for expansion and commercial applications and acceptance of nanotechnology by encouraging private sector investments and empowering partnerships in public and private sectors . Though nanostructures have enormous benefits in agri-sector, still their relevance and significance had not moved up- to practical field environment. The concerns related to the availability, synthesis, level of toxicity, health hazards, transportation challenges and incongruity of regulatory structure restrict the broad recognition and acceptance of adopting nanotechnology in agriculture. The present paper is an attempt to analyze and propose inputs in addressing the present and future possibilities, perspectives, applications and challenges of incorporating nanotechnology in agricultural sector with a focus on Indian perspective.

Identification of Nutritional Components in Black Sesame Determined by Widely Targeted Metabolomics and Traditional Chinese Medicines.

Chemical composition of secondary metabolites is of great importance for quality control of agricultural products. Black sesame seeds are significantly more expensive than white sesame seeds, because it is thought that black sesame seeds are more beneficial to human health than white sesame seeds. However, the differences in nutrient composition between black sesame seeds and white sesame seeds are still unknown. The current study examined the levels of different metabolites in black and white sesame seeds via the use of a novel metabolomics strategy. Using widely targeted metabolomics data, we obtained the structure and content of 557 metabolites, out of which 217 metabolites were identified, and discovered 30 metabolic pathways activated by the secondary metabolites in both black and white sesame seeds. Our results demonstrated that the main pathways that were differentially activated included: phenylpropanoid biosynthesis, tyrosine metabolism, and riboflavin metabolism. More importantly, the biomarkers that were significantly different between black seeds and white sesame seeds are highly related to the functions recorded in traditional Chinese medicine. The results of this study may serve as a new theoretical reference for breeding experts to promote the genetic improvement of sesame seeds, and therefore the cultivation of higher quality sesame varieties.