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The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help optimize processes and obtain more accurate results for decision-making regarding the processing and conservation of stored seeds. Therefore, the aim was to assess and predict the physical properties (moisture content, seed mass, length, thickness, width, volume, apparent specific mass, projected area, sphericity, mean diameter, circular area, circularity, drag coefficient), and physicochemical quality (crude protein, ash content, and acidity index) of Jatobá-do-Cerrado seeds under different processing conditions with pulp, without pulp (scarification), without pulp (fermented), and storage conditions at 10 and 23 °C over six months. Data were analyzed on Weka software (Waikato Environment for Knowledge Analysis) version 3.9.5. testing the following models: Pearson correlation, Artificial Neural Networks, decision tree algorithms RepTree and M5P, Random Forest, and Linear Regression. Processing cerrado jatobá seeds by fermentation and storage at 10 °C minimized physical changes and preserved the physicochemical quality of the seeds in polyethylene plastic, glass container, tetrapack, and polyethylene container, over six months. The combination of processing, temperature, and packaging variables for Artificial Neural Networks, RepTree, Random Forest, and M5P algorithms outperformed linear regression, providing higher accuracy rates. Artificial Neural Network and Random Forest models were the best predicting the effects of treatments on changes in physical properties and physicochemical quality of jatobá seed., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval: Experimental research and field studies on plants and plant material (either cultivated or wild), were comply with local and national regulations. The study complied with relevant institutional, national, and international guidelines and legislation., (© 2024. The Author(s).)

During storage, infestation by insect pests occurs, causing quantitative and qualitative losses in grains, which requires the control of these insects with phosphine gas. Rice husk has a high phosphine adsorption capacity, influencing the gas concentration during fumigation and potentially leading to inefficient fumigation. Additionally, the high sorption of rice husk results in a higher residue of phosphine in the grain. Therefore, the objective of this study was to evaluate the phosphine sorption and phosphine residue in rice husk, paddy rice, and brown rice, as well as the industrial quality (head rice yield, rehydration capacity, cooking time, colorimetric profile) of brown and white rice during storage. To achieve this, fumigation of paddy rice, brown rice, and rice husks with 3.0 g·m -3 of phosphine was carried out for 240 h (recommended duration in the industry). A high sorption rate was observed in the rice husk (94.77%), paddy rice (97.61%), and, lastly, brown rice (35.17%). Due to the high sorption rate, only brown rice maintained a concentration above the recommended level for effective pest control (400 ppm for 120 h). Higher phosphine residues than permitted were observed in the rice husk (0.25 ppm). Lower rice head yields were observed in non-fumigated rice samples when analyzing the brown rice samples (66.21% for paddy rice and 65.01% for brown rice). A greater rehydration capacity was observed in fumigated samples at the beginning of storage when analyzing the brown rice samples (1.21 for paddy rice, 1.23 for brown rice), reducing the cooking time (24.00 for paddy rice, 23.80 for brown rice). More studies should be carried out to evaluate the effectiveness of fumigation on paddy rice, considering the high sorption rate of the paddy.

Employing visible and near infrared sensors in high-throughput phenotyping provides insight into the relationship between the spectral characteristics of the leaf and the content of grain properties, helping soybean breeders to direct their program towards improving grain traits according to researchers' interests. Our research hypothesis is that the leaf reflectance of soybean genotypes can be directly related to industrial grain traits such as protein and fiber contents. Thus, the objectives of the study were: (i) to classify soybean genotypes according to the grain yield and industrial traits; (ii) to identify the algorithm(s) with the highest accuracy for classifying genotypes using leaf reflectance as model input; (iii) to identify the best input data for the algorithms to improve their performance. A field experiment was carried out in randomized block design with three replications and 32 soybean genotypes. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. A hyperspectral sensor was used to capture reflectance between the wavelengths from 450 to 824 nm. Representative spectral bands were selected and grouped into means. After harvest, grain yield was assessed and laboratory analyses of industrial traits were carried out. Spectral, industrial traits and yield data were subjected to statistical analysis. Data were analyzed by the following machine learning algorithms: J48 (J48) and REPTree (DT) decision trees, Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and conventional Logistic Regression (LR) analysis. The clusters formed were used as the output of the models, while two groups of input data were used for the input of the models: the spectral variables (WL) noise-free obtained by the sensor (450-828 nm) and the spectral means of the selected bands (SB) (450.0-720.6 nm). Soybean genotypes were grouped according to their grain yield and industrial traits, in which the SVM and J48 algorithms performed better at classifying them. Using the spectral bands selected in the study improved the classification accuracy of the algorithms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Black rice (Oryza sativa ) stands out for its high content of bioactive compounds with functional properties that play an important role in health benefits. The phytochemical level is affected by industrial processing due to its instability to the hydrothermal process. Studies about the influence of industrial processing on the phytochemical profile of black-rice-based foods are still scarce. This study carried out a comprehensive review of the influence of industrial applications on the bioactive compounds in food products based on black rice and their health-promoting effects. Most industrial processes such as drying, storage, cooking, and extrusion affect phytochemical content and antioxidant capacity. Alternatively, technologies such as fermentation, UV-C irradiation, and sprouting can maintain or improve the phytochemical content in black rice products.

In the context of grain storage, impurities and soybeans defects in soybeans can significantly impact the equilibrium moisture content. This, cause moisture migration and heating of the stored product, leading to increased respiratory activity. Furthermore, temperature measurements within stored grain mass do not provide sufficient information for effective grain quality monitoring, primarily due to the grains excellent thermal insulating properties. To address this issue, we propose a different approach: monitoring the equilibrium moisture content and CO 2 concentration as indicators of soybean respiration within the intergranular spaces of the stored grain mass. This study propose monitoring the CO 2 concentration in the intergranular air along with environmental variables for early detection of physicochemical and morphological changes in soybeans stored in vertical silos using near infrared spectroscopy, X-ray diffraction and scanning electron microscopy. Thermogravimetry and spectrometry analyses revealed that the interrelationships among variables had a direct impact on soybean quality attributes. Specifically, the presence of soybeans with 5.2 % impurities led to an increased in respiration rates, resulting in a CO 2 concentration of up to 5000 ppm and the consumption of up to 3.6 % of dry matter. Consequently, there were changes in the percentage of ash, proteins, fibers, and oils compositions. These findings highlight the potential for indirect assessments, enabling the prediction of physicochemical quality and contamination of soybeans stored in vertical silos through continuous monitoring of CO 2 concentration and equilibrium moisture content., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical-chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains., (© 2024. The Author(s).)

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein levels in the grains, and high grain yield is one of the major challenges for current breeding programs. To avoid the development of unpromising populations, selecting parents for crossbreeding is a crucial step in the breeding pipeline. Therefore, our objective was to estimate the combining ability of soybean cultivars based on the F 2 generation, aiming to identify superior segregating parents and populations for agronomic, nutritional and industrial traits. Field experiments were carried out in two locations in the 2020/2021 crop season. Leaf contents of the following nutrients were evaluated: phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, manganese, and zinc. Agronomic traits assessed were days to maturity (DM) and grain yield (GY), while the industrial traits protein, oil, fiber and ash contents were also measured in the populations studied. There was a significant genotype × environment (G × A) interaction for all nutritional traits, except for P content, DM and all industrial traits. The parent G3 and the segregating populations P20 and P27 can be used aiming to obtain higher nutritional efficiency in new soybean cultivars. The segregating populations P11 and P26 show higher potential for selecting soybean genotypes that combine earliness and higher grain yield. The parent G5 and segregant population P6 are promising for selection seeking improvement of industrial traits in soybean., (© 2023. Springer Nature Limited.)

The objective was to review the effects of the drying and storage conditions of corn on the physical-chemical quality in the processing of starch and flour, in the production of animal feed, and in the industrialization of ethanol. Initially, the review presented an overview of the post-harvest stages of corn grains, highlighting drying and storage. The main drying and storage methods used for corn grains were presented. Among the drying conditions, the air temperature was the main factor that affected the properties of starch, flour, feed, and ethanol produced from corn. It was verified that the corn grains submitted to drying at temperatures below 60 °C obtained better results in the industry. In storage, in addition to the storage time, factors such as temperature and moisture content of the grains affected the physical-chemical quality of the processed products. In this stage, the moisture content below 14% and the storage temperature below 25 °C conserved the physical-chemical quality of the grains and obtained better processing results. Further studies are needed to assess the effects of the drying and storage conditions of corn on the properties of flour, starch, animal feed, and, mainly, ethanol production., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© Association of Food Scientists & Technologists (India) 2022.)

Drying rice in a single layer in a silo-dryer-aerator allows uniform drying. The objective of this study was to evaluate the physical, physicochemical, and morphological quality of rice grain cultivars (IRGA 424, BRS Pampeira, and Guri INTA) in the lower (initial time) and upper (final time) layers in a silo-dryer-aerator, employing single-layer loading at low temperatures, using the methods of near-infrared spectroscopy, X-ray diffraction analysis, scanning electron microscopy, and multivariate statistical analysis. Drying rice in silo-dryer-aerator attenuated the moisture diffusivity in the grains, minimizing its effects on the physical, physicochemical, and morphological properties of the grains. However, the physicochemical constituents and morphology of starch were preserved by the low drying temperatures, mainly in the lower layers throughout the 2-month drying. The rice grains of the Guri INTA and BRS Pampeira cultivars were the most resistant to drying and showed greater uniformity on the final quality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Taking into account that the transport of grains can be carried out over long distances and that the mass of grains during transport often has high moisture content, there may be risks of heat and moisture transfer and heating of the grains mass, proving quanti-qualitative losses. Thus, this study aimed to validate a method with probe system for real-time monitoring of temperature, relative humidity and carbon dioxide in the grain mass of corn during transport and storage to detect early dry matter losses and predict possible changes on the grain physical quality. The equipment consisted of a microcontroller, system's hardware, digital sensors to detect air temperature and relative humidity, a non-destructive infrared sensor to detect CO 2 concentration. Real-time monitoring system determined early and satisfactorily in an indirect way the changes in the physical quality of the grains confirming by the physical analyses of electrical conductivity and germination. The equipment in real-time monitoring and the application of Machine Learning was effective to predict dry matter loss, due to the high equilibrium moisture content and respiration of the grain mass on the 2-h period. All machine learning models, except support vector machine, obtained satisfactory results, equaling the multiple linear regression analysis., (© 2023. The Author(s).)

The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optimize the processes and obtain accurate results for decision-making in the seed storage process. This study aimed to analyze the performance of ML algorithms from variables monitored during seed conditioning (temperature and packaging) and storage time to predict the physical and physiological quality of stored soybean seeds. Data analysis was performed using the Artificial Neural Networks, decision tree algorithms REPTree and M5P, Random Forest, and Linear Regression. In predicting seed quality, the combination of the input variables temperature and storage time for REPTree and Random Forest algorithms outperformed the linear regression, providing higher accuracy indices. Among the most important results, it was observed for apparent specific mass that T + P + ST, T + ST, P + ST, and ST had the highest r means and the lowest MAE means, however, Person's r coefficient for these inputs was 0.63 and the MAE between 9.59 to 10.47. The germination results for inputs T + P + ST and T + ST had the best results (r = 0.65 and r = 0.67, respectively) in the ANN, REPTree, M5P and RF models. Using computational intelligence algorithms is an excellent alternative to predict the quality of soybean seeds from the information of easy-to-measure variables., (© 2022. The Author(s).)

Introduction: Agriculture stands out in relation to the high number of occupational incidents and diseases. In this sense, grains postharvest operations, such as receiving, precleaning, drying, storage and shipping the grains, are highlighted in the number of injuries and fatalities., Aim: To identify and extract qualitative and quantitative data related to the main occupational hazards present in grains postharvest operations at preprocessing and storage facilities., Methods: A systematic review was carried out in the databases of Science Direct, Scopus and Web of Science for papers published between 1980 and 2019. The abstract should have described a study related to any occupational hazard (physical, chemical, biological, ergonomic and mechanical) and at least one of the occupational hazards should be related to any postharvest operations., Results and Discussions: In total, 42% of 38 analysed papers were published between 2015 and 2019. Three journals were responsible for 45% of publications related to occupational hazards present in grains postharvest operations. The most part of analysed publications related to confined spaces, grain entrapment, machine entanglement and falls hazards are related to Purdue University's Agricultural Safety and Health Program which applied research in occupational safety at grains postharvest., Conclusions: The creation of standardised internationals can collaborate to reduce occupational risks in grain storage units. It is suggested the development of monitoring technologies to obtain real-time information on noise, dust, gases and heat in postharvest operations and equipment. The use of intelligent algorithms can create prevention mechanisms for possible occupational risks and avoid injuries to employees., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

The study had as objective to evaluate occupational hazards on grain storage unit to define a conceptual model, implemented in an algorithm to manage the grains storage facilities safety standards compliance. Sampling points location were defined for static quantification of noise, dust and heat stress hazards in grains pre-processing operations to indicate the effectiveness of the control measures implemented. Safety standards applied to grain handling and storage facilities were identified and selected. Chart flows were elaborated to the algorithm logics and conceptual modeling. The highest level of noise was present in the grain cleaning operation (99.1 dB), while the expedition operation has the highest level of dust (20.27%). The heat stress was present in the grain drying operation (43.64 WBGT). Noise analysis did not show a difference between grains, only between operations. The flow of corn grain mass caused higher dust concentrations in the expedition operation. The method applied to characterize and quantify the hazards in grain storage units was satisfactory, and it is recommended as standard, for use in corn and soybean grains handling and storage units. The algorithm to manage occupational safety at storage facilities collaborates to monitor the safety compliance on postharvest operations., (© 2022. The Author(s).)

Anticipating the harvest period of soybean crops can impact on the post-harvest processes. This study aimed to evaluate early soybean harvest associated drying and storage conditions on the physicochemical soybean quality using of mathematical modeling and multivariate analysis. The soybeans were harvested with a moisture content of 18 and 23% (d.b.) and subjected to drying in a continuous dryer at 80, 100, and 120 °C. The drying kinetics and volumetric shrinkage modeling were evaluated. Posteriorly, the soybean was stored at different packages and temperatures for 8 months to evaluate the physicochemical properties. After standardizing the variables, the data were submitted to cluster analysis. For this, we use Euclidean distance and Ward's hierarchical method. Then defining the groups, we constructed a graph containing the dispersion of the values of the variables and their respective Pearson correlations for each group. The mathematical models proved suitable to describe the drying kinetics. Besides, the effective diffusivity obtained was 4.9 × 10 -10 m 2  s -1 promoting a volumetric shrinkage of the grains and influencing the reduction of physicochemical quality. It was observed that soybean harvested at 23% moisture, dried at 80 °C, and stored at a temperature below 23 °C maintained its oil content (25.89%), crude protein (35.69%), and lipid acidity (5.54 mL). In addition, it is to note that these correlations' magnitude was substantially more remarkable for the treatments allocated to the G2 group. Furthermore, the electrical conductivity was negatively correlated with all the physicochemical variables evaluated. Besides this, the correlation between crude protein and oil yield was positive and of high magnitude, regardless of the group formed. In conclusion, the early harvest of soybeans reduced losses in the field and increased the grain flow on the storage units. The low-temperature drying and the use of packaging technology close to environmental temperatures conserved the grain quality., (© 2021. The Author(s).)

Cultivars and fertilization levels influence rice productivity and can be associated with grain quality. Thus, it is possible to make decisions regarding the choice of cultivars and application of fertilizer levels based on the type of milling, a necessary post-harvest process that may minimize the nutrient load in the grains and result in loss in quality. This study relates the physicochemical composition and morphological quality of brown and polished milled rice grains, cultivar types, and different levels of soil fertilization using near-infrared spectroscopy analysis, X-ray diffraction and scanning electron microscopy. Statistical tools were used to test the various treatments and identify the relationship between factors and variables. A high fertilization level is related to increasing crude protein composition and starch for cultivar IRGA 431 CL associated with polished rice. However, the combination of cultivar IRGA 424 RI and brown rice demonstrated a higher grain resistance, and different percentages of whole, chalky, and damaged rice. The correlation between ash × crude protein and starch × crude fiber was found to be positive for brown rice and negative for the polished rice. Further, an increase in starch content was inversely proportional to the ash content, whereas an increase in crude protein was inversely proportional to the low-fat content in milled rice. The crystalline characteristics of rice starch were preserved at high fertilization levels associated with polished grains that demonstrated high starch content. Polished grains, however, showed more pores and cavities, and consequently greater permeabilities in the surface. It is recommended that batches of grains produced from cultivar IRGA 431 CL with high levels of fertilization be subjected to polished rice milling to achieve high protein and starch quality. However, grains from cultivar IRGA 424 RI with high levels of fertilization are recommended for brown rice milling owing to the high percentage of physical defects observed., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The final quality of pre-processed coffees is influenced by the applied drying technology. Thus, the aim of the study was to develop and validate a heated air flow diffusion system to optimize and reduce the drying time of rotary dryers and improve the final quality of coffee. Computational fluid dynamics was used for the simulation of the air fluid dynamics in the combustion chamber of the heat generator. It was observed that the energy losses in the upper and lower walls of the heat generator chamber were higher with an increase in the convective heat transfer coefficient. It was found that the rate of fluid flow presented a fully developed profile, in which the higher speed value was found in the central region of the outlet. The reduction in moisture content during coffee drying was directly proportional to the increase in temperature. The Midilli model shows the best fit to describe the drying curves of the coffee. The effective diffusion coefficient increases with increasing temperature of the drying air. It was observed that the adjustments of the fluid dynamics in the burning of gas and the adaptation of the diffuser system significantly influenced the drying time and final quality of naturally processed and pulped coffees. In conclusion, the adapted technological set, a rotary dryer with gas heating and diffusion of heated air, had a high performance in the final quality of the coffee, and for this reason it is recommended to producers and the industry., Competing Interests: The authors have declared that no competing interests exist.

Drying and storage methods are fundamental for maintaining the grain quality until processing. Therefore, the aim of this study was to evaluate the associations of the drying temperature with storage systems and conditions as a strategy for preserving the quality of maize grain postharvest on laboratory and field scales. An increase in temperature accelerated the reduction in grain moisture, but increased the deterioration. The wetting during the storage period reduced the grain quality. Hermetic and aerated storage systems maintained the chemical quality of the grains. The control with healthy and whole corn dried at 80 °C and stored in silos with natural aeration provided a satisfactory quality, equivalent to those of controlled drying and storage under airtight conditions and at low temperatures. Different conditions of drying and storage of corn on the laboratory and field scales were evaluated, which provides an appropriate management of these operations to maintain the grain quality.

Different regions have different environmental conditions, which may be unfavorable for the preservation of the quality of stored soybean seeds over time. Thus, it is necessary to adopt specific technologies to control the storage environment conditions. Big raffia bags are widely used for the storage of soybean seeds, however these consist of a porous, permeable material that allows the exchange of gases between the packaging and the storage environment. In an effort to find a solution to this problem, in this study we evaluated low cost big bag coating alternatives, in order to minimize the effects of temperature and intergranular humidity on stored seeds. Thus, the aim of this work was to evaluate the quality of soybean cultivars subjected to different temperature and storage duration conditions and stored in raffia bags with or without internal coating. We used a completely randomized, three-factor (10 × 6 × 5) experimental design. We assessed 10 soybean cultivars, six storage environments, and five evaluation periods. Our results showed that seeds of the M-SOY 8866, M7110 IPRO, CD 2737 RR, and BMX DESAFIO 8473 RSF soybean cultivars preserved their physiological quality better in different storage environments. The storage duration had a cumulative effect on the negative factors that favor the deterioration of the quality of the stored seeds. The storage temperature was the main factor that affected the physiological quality of the stored seeds. The use of coated packaging was beneficial in preserving the physiological quality of stored soybean seeds; however, its effect was greater at ambient temperature than in a cold environment. The best storage environment for the preservation of the quality of the seeds was characterized by 10°C temperature conditions and the use of coated packaging, while the worst storage environment was characterized by ambient temperature conditions without the use of coated packaging. Thus, it was concluded that the use of coatings in raffia big bags can be an alternative for maintaining the quality of seeds of different soybean cultivars during storage in seed processing units., Competing Interests: The authors have declared that no competing interests exist.

This study aimed to evaluate the quality of seeds of RR and RR2 PRO soybean cultivars stored in ambient air with raffia packaging (ANER), ambient air with laminated packaging (ANEL), modified atmosphere with polyethylene packaging (AMEP), refrigerated atmosphere (1 to 3°C) with raffia packaging (ARER), refrigerated atmosphere (1 to 3°C) with laminated packaging (AREL), and modified (-14 PSI) and refrigerated (1 to 3°C) atmosphere with polyethylene packaging (AMREP), over 6 months of storage. Results showed that the seeds of cultivar RR2 were preserved with better physiological quality. Raffia and polyethylene packaging under natural storage conditions, in a refrigerated and modified atmosphere, did not preserve the seed quality over the storage period. The conditions of storage in ambient air with laminated packaging (ANEL) and in a refrigerated atmosphere with laminated packaging (AREL) reduced the environmental effects of temperature and relative humidity, leading to better results of physiological quality of the seeds. Storage time negatively influenced the physiological quality of seeds, except for AREL and ANEL, which maintained the quality close to that of the initial conditions, over the 6 months of storage. The best alternatives for soybean seeds storage over 6 months are the laminated packaging in a natural environment, matching the refrigerated conditions. The technological laminated packaging can be used as a new alternative for conserving soybean seeds in processing and storage units., Competing Interests: The authors have declared that no competing interests exist.