Wheat Moisture Research Articles

Nondestructive detection of saline-alkali stress in wheat (Triticum aestivum L.) seedlings via fusion technology

BackgroundWheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management.ResultsThis research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.ConclusionsUnder the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.Graphical

Mm-Wheat: Noncontact Detection of Wheat Moisture Content With Millimeter-Wave Radar

mm-Wheat: Noncontact Detection of Wheat Moisture Content With Millimeter-Wave Radar

Terpinen-4-ol from tea tree oil prevents Aspergillus flavus growth in postharvest wheat grain

Plant volatile organic compounds (PVOCs) have gained increasing attention for their role in preventing fungal spoilage and insect contamination in postharvest agro-products owing to their effectiveness and sustainability. In this study, the essential oil was extracted from fresh M. alternifolia (tea tree) leaves, and the fumigation vapor of tea tree oil (TTO) completely inhibited the growth of Aspergillus flavus on agar plates at a concentration of 1.714 μL/mL. Terpinen-4-ol was identified as the major component (40.76 %) of TTO volatiles analyzed using headspace gas chromatography–mass spectrometry. Terpinen-4-ol vapor completely inhibited the A. flavus growth on agar plates and 20 % moisture wheat grain at 0.556 and 1.579 μL/mL, respectively, indicating that terpinen-4-ol serves as the main antifungal constituent in TTO volatiles. The minimum inhibitory concentration of terpinen-4-ol in liquid-contact culture was 1.6 μL/mL. Terpinen-4-ol treatment caused depressed, wrinkled, and punctured mycelial morphology and destroyed the plasma membrane integrity of A. flavus. Metabolomics analysis identified significant alterations in 93 metabolites, with 79 upregulated and 14 downregulated in A. flavus mycelia exposed to 1.6 μL/mL terpinen-4-ol for 6 h, involved in multiple cellular processes including cell membrane permeability and integrity, the ABC transport system, pentose phosphate pathway, and the tricarboxylic acid cycle. Biochemical analysis and 2,7-dichlorofluorescein diacetate staining showed that terpinen-4-ol induced oxidative stress and mitochondrial dysfunction in A. flavus mycelia. This study provides new insights into the antifungal effects of the main TTO volatile compounds terpinen-4-ol on the growth of A. flavus.

Design and Evaluation of Wheat Moisture Content Detection Device Based on a Stripline

The detection of the moisture content of wheat is an important index used to measure the quality and preservation of wheat. In order to rapidly and non-destructively detect the moisture content of wheat, in this study, we designed a stripline detection device that measures 151 frequency points in the 50–200 MHz frequency range with a vector network analyzer. Random forest (RF), extreme learning machine (ELM), and BP neural network prediction models were established, using the frequency, temperature, volume density and dielectric constant as input and the water content as output. It was shown that, in the frequency range 50–200 MHz, the permittivity of wheat decreases as the frequency increases, and that this is negatively correlated. The dielectric constant of wheat increases as the moisture content, temperature, and bulk density increase, and these are positively correlated. The random forest (RF) prediction model, which uses the frequency, temperature, effective dielectric constant εeff. and volume density as inputs and the wheat moisture content as the output, demonstrates the best performance. The determination coefficient (R2) = 0.99977, the mean absolute error (MAE) = 0.044368, the mean square error (MAE) = 0.0053011, and the root mean square error (RMSE) = 0.072809. This study provides a new device and method for the detection of the moisture content of wheat. The device is small and is not easily disturbed by the external environment. It can be measured in a variety of conditions and is important for the development of low-cost, high-precision, and portable devices for the detection of the moisture content of wheat.

Diffusivity and bias movement velocity of Cryptolestes ferrugineus (Coleoptera: Laemophloeidae) adults under different temperature and moisture differences

Understanding the movement of stored products insects is crucial to determine and predict their distribution in grain bins. The present work focuses on the determination of the diffusivity and bias movement velocity of Cryptolestes ferrugineus (Stephens) adults by using published data from our previous studies. The published data were collected under different storage conditions: different temperature differences (0 to 20 °C) and different moisture differences (1.0, 2.5, 3.0, or 5.0 percentage points (pp)) using 1 or 2 m columns for 24, 48, or 72 h movement periods. A transport equation and finite different methods were used to calculate the diffusivity and bias movement velocity of the adults. Results showed that the adult diffusivity was directly proportional to temperature difference while inversely related to moisture difference, column length, and movement period. The effect of temperature or moisture differences on diffusivity and bias movement velocity were significantly different for their synergistic or/and antagonistic interactions. Diffusivity of 1.6 × 10−3 m2/h was obtained for 5 pp moisture difference when high moisture wheat was placed at distance of 0.45 m on both sides of grain column. Similarly, for a 5 °C temperature difference, the diffusivity was 1.89 × 10−3 m2/h during 24 h in 2 m column. The bias movement velocity decreased significantly in 2 m columns compared to 1 m columns under all temperature differences in any movement periods (24, 48, or 72 h) except 20 °C temperature difference and 72 h. The synergism among the temperature and moisture was greater than their antagonistic effects during conditions of 5 °C temperature difference and 5 pp moisture difference when wet wheat was placed at 0.25 or 0.45 m. Overall, the calculated diffusivity was 3 to 3.7 × 10−3 m2/h in a heterogeneous environment. These findings are useful for three-dimensional modeling of insect movement, design and development of insect monitoring tools, and determination of sampling frequency and locations.

AdaBoost-SVR Model-Based Transmitted Microwave Sensing in Wheat Moisture Prediction

AdaBoost-SVR Model-Based Transmitted Microwave Sensing in Wheat Moisture Prediction

Quantitative determination of wheat moisture content based on microwave detection technique combined with multivariate data analysis

In this study, a new method is proposed for the quantitative detection of wheat moisture content based on double heterodyne mixing microwave transmission technique. First of all, the microwave detection device received the command from the computer and detected the wheat in the frequency domain of 2.5 GHz–11.5 GHz, while acquiring the transmission index corresponding to each frequency point. Then, competitive adaptive reweighted sampling (CARS) was used for coarse feature selection of the transmission index after least partial squares filtering, and Genetic Algorithm (GA) was used for secondary feature refinement of the coarse selected feature variables, and they were sorted and combined according to their weights. Finally, Support vector regression (SVR) was used to establish quantitative models with different combinations of feature variables to achieve rapid detection of wheat moisture content. The results showed that the CARS-GA-SVR model had better detection performance for wheat moisture content determination compared to the CARS-SVR model. Among them, the CARS-GA-SVR model had the best detection effect when the combination of feature variables is 6-dimensional, with coefficient of determination (RP2) of 0.9756 and relative prediction deviation (RPD) of 6.3234. Compared to the SVR model established by the full transmission index, the RPD improved from 3.7089 to 6.3234. The results reveal that rapid and high-precision detection of wheat moisture content can be achieved by using a self-made miniaturized microwave device combined with a suitable chemometric method.

Utilizing Modern Technology and Sustainable Methods for Storing Rice

Since the dawn of civilization, grain storage has been necessary to ensure food security. Grain storage has a more extended history than one might think and is connected to farming history. India is the next-largest producer of wheat, followed by China. In terms of food security, storage is primarily intended to keep grains for a longer time. It guards against degradation, exposure to the outside environment, and potential microbe and insect attacks. Silo, CAP storage, and Godown storage were used to test the wheat’s temperature and moisture content. To obtain the reading, a temperature sensor and a RH sensor were installed within the silo. Grain storage in underground pits was employed in the past and is still used in some regions today. The holes were excavated and measured at 1 m in diameter and 1 m in depth, with a mixture of 3:3:1 cow dung, straw, and sorghum, amounting to around two quintals each. A total of 10–20 wheat samples kept in the silo were examined for fungus and fungal development. The samples were placed inside a glass bottle filled with sterile water and closely watched to ensure that no fungal source entered the container used for collection. Each colony was isolated and purified separately once it emerged to obtain an isolated single fungus. The pure fungus was positioned and examined using imaging and organized on figurative plates under a microscope. The germination percentage in various storage arrangements was tested for seed viability using wet paper towels. For the following three months of storage, the temperature within the silo increased rapidly from 28°C to 35°C. The grain retained its quality during the first three months, and there was little to no infestation. The lower ambient temperature and measured relative humidity of 95–97% are to blame for this. Before drying, the moisture content in the silo increased from 11 to 17%. During the storage period from April 2006 to November 2006, the moisture content in Godown bag storage increased at the same pace as in the silo. Wheat moisture content rises in direct proportion to its storage time. This is thought to be the result of an increase in insect population brought on by the respiration of insects and wheat, which helped raise moisture content. All of the isolated fungi in the silo were discovered to produce a significant number of spores and conidia on their surfaces. Aspergillus is a harmful toxin-producing fungus well-known for damaging various seeds, grains, and nuts. It is also famous for its destructive properties.

Changes in quality characteristics and metabolites composition of wheat under different storage temperatures

The quality variation of wheat post harvesting is strongly affected by storage temperature. Low temperature storage can alleviate the quality deterioration in wheat after harvest. However, the mechanism regarding metabonomics analysis between different storage temperatures in postharvest wheat remains poorly understood. Here, we analyzed the changes in wet gluten content, fatty acid values, Catalase (CAT) activity, alpha-amylase activity, pasting properties changes, as well as the differentially expressed key metabolites in wheat, stored at 15, 20, and 30 °C for 180 days. In our study, wheat storage, processing, and edible quality remained steady at 15 °C and 20 °C storage temperatures, and wheat quality drastically degraded at 30 °C. Through non-targeted metabolomics analysis, carbohydrate metabolism, fatty acid metabolism, amino acid metabolism, and nucleotide metabolism pathways were proven to be involved in wheat storage. Specifically, gamma-glu-cys and 4-trimethylammoniobutanoic acid were up-regulated; adenine, l-gluconic acid, linoleic acid, 13(s)-HPODE, 9(s)-HPODE, 9-OxoODE, n-acetyl-l-glutamic acid, and trans-2,3-dihydroxycinnamate were down-regulated at 15 °C and 20 °C. These substances helped maintain the stability of cellular structures within the wheat kernel. The study's findings revealed the low-temperature impact mechanism on high moisture wheat from the perspective of metabolite changes. This may provide a scientific basis for the efficient preservation of wheat.

Response of Cryptolestes ferrugineus (Coleoptera: Laemophloeidae) adults to small temperature (0-6 °C) and moisture (1%) differences in wheat.

Insects respond to temperature and moisture and their differences or gradients in grain bulks, but how small these differences can be is unknown. Response of Cryptolestes ferrugineus (Stephens) adults to 0-6 °C temperature differences in 1 m wheat (12.5% moisture content, w.b.) columns was determined in 24 h. Similarly, the moisture response of the adults was determined in 1 m grain columns with a 1 percentage point difference in the wheat moisture content (12.5 and 13.5%) at 25 °C in 24 h. Adults were highly temperature and moisture sensitive and were able to respond to the lowest temperature difference of 1 °C and moisture difference of 1 percentage point within the wheat column. The temperature preference of the adults was confirmed with the recovery of about 78% of insects from the middle warmer sections of wheat at 25 or 30 °C when there was no temperature difference at other sections of the wheat columns. Irrespective of the temperature differences, on average of about 29% of adults moved towards the warmer end with the highest recovery of 47.3% observed at the temperature difference of 6 °C. The adult recovery from high-moisture locations decreased with an increase in distance away from the point of insect introduction (0.05-0.45 m). About 14% of adults moved to the furthest location of high-moisture ends (13.5% moisture content wheat) at 0.45 m. This study provided valuable insights for the development of mathematical models to predict 3D insect movement and distribution in storage grain bins.

Movement and distribution of Cryptolestes ferrugineus (Coleoptera: Laemophloeidae) adults in response to moisture in wheat filled in different lengths of columns

Major abiotic factors like the temperature and moisture and their differences or gradients significantly influence the insect movement and their distribution in grain bulks. The temperature response of adult Cryptolestes ferrugineus (Stephens) in stored wheat has been analyzed well in literature. However, the adult responses to nonlinear moisture differences or gradients are not yet studied in detail. The present work focused on determining the sensitivity of adults to moisture differences in grain bulks. The moisture response of the adults was determined in 1 and 2 m grain columns with 3 or 5 percentage points difference in wheat moisture content (12% and 15 or 17%) at 25 °C in different movement periods (12, 24, or 48 h). Adults could readily detect moist wheat in 24 h with positive hygrotactic response. More than 88% of adults remained in the high moisture wheat when introduced in the moist wheat. Adults demonstrated strong capability to respond to moist wheat irrespective of 3 or 5 percentage points of moisture differences. The adults tended to move away from dry wheat (12%) in search of high moisture wheat. The moisture response of the adults was significantly influenced by the movement distance and was effective until 0.25 m. Movement period significantly influenced adult recovery from the high moisture grain when movement distance was less than 0.5 m. There were many aggregations near and in the moist wheat, showing a uniform biased movement under bidirectional conditions in 1 and 2 m grain columns.

A decision support tool for the first stage of the tempering process of organic wheat grains in a mill

SummaryWheat tempering conditions grains before a milling process begins. Process adjustments must be made to reach a desired level of flour quality and yield, depending on multiple factors. This article aims to develop a decision support tool to help operators adjust the first‐stage tempering parameters. It is based on a regression model that predicts an increase in organic wheat moisture content according to the properties of the wheat (initial wheat moisture content, wheat protein content and wheat temperature), process parameters (targeted wheat moisture content, wheat flow rate, water flow rate, wheat quantity and resting time) and tempering conditions (water quantity, average day temperature and average day humidity). The increase in wheat moisture achieved during the first tempering stage varies between 0% and 5%. Five regression models were compared: OLS, LASSO, RIDGE, ElasticNet and XGBoost. The models have been developed and tested from a case study at an organic wheat mill. The results indicate that the LASSO model outperformed others, with an average prediction error of 0.428%. The model showed the importance of humidity and temperature factors during the tempering process. The flow of water and wheat were the most influential parameters for an increase in wheat moisture content.

The effect of volatile compounds of Syzygium aromaticum flower buds against Aspergillus flavus growth on wheat grain at postharvest stage

Reducing the postharvest loss of cereal grains caused by deleterious fungi is critically important for the grain supply chain. Exploring antifungal constituents of plant volatile organic compounds (PVOCs) could promote the development of natural gaseous fungicides in controlling the decay of postharvest grains. In this study, PVOCs were extracted from clove ( Syzygium aromaticum L.) buds and the primary constituents thereof were investigated for their effect on Aspergillus flavus growth in media postharvest grains. Fumigation with clove buds’ volatiles effectively inhibited A. flavus proliferation in 20% moisture wheat grains under simulated storage conditions. Gas chromatography-mass spectrometry analysis revealed that eugenol and caryophyllene were the two main constituents of clove bud volatiles (49.80% and 36.68%, respectively). An antifungal assay demonstrated that eugenol was responsible for the antifungal activity of clove bud volatiles against A. flavus. Eugenol can completely inhibit A. flavus growth at 0.12 and 0.40 μL/mL in vapor phase fumigation and liquid contact, respectively. The proliferation of A. flavus in 20% moisture wheat grains was completely controlled after fumigation with 600 μL/L eugenol vapor. Eugenol treatment can damage the membrane integrity of A. flavus mycelia, resulting in increased intracellular electrolyte leakage. The 38 metabolites in A. flavus hyphae exposed to 0.40 μL/mL eugenol were markedly differentially expressed and principally involved in the biosynthesis of glycerophospholipids, linoleic acid metabolism, fatty acid metabolism, sugar metabolism, and ATP-binding cassette transporters. Eugenol also causes phosphatidylserine eversion of the plasma membrane, increased reactive oxygen species, decreased ATP production, and damaged DNA in A. flavus . We propose that eugenol treatment can disrupt cell membrane integrity, fatty acid β -oxidation, and induce apoptosis in A. flavus . This study demonstrated the potential application of clove bud volatile organic compounds as biofumigants for postharvest grain management and provided new insights into the antifungal mechanism of the main constituents against A. flavus growth. • Clove volatiles from Syzygium aromaticum , L. could control Aspergillus flavus growth in wheat grains. • Eugenol and caryophyllene are the two main volatile components of clove determined by GC-MS. • Eugenol could inhibit the growth of A. flavus in vapor phase and liquid contact. • The antifungal actions of eugenol on A. flavus were studied by metabolomic and biochemical analyses. • Eugenol could be used as biofumigants for postharvest grain management.

Nondestructive Wheat Moisture Detection System With Metasurface Plane Wave Antenna and De-Embedding Algorithm

The precise measurement of wheat moisture content is a key issue to ensure the quality and safety of wheat. However, high-precision, rapid, and non-destructive detection of wheat moisture content remains challenging. In this paper, we proposed a system with single-layer low-frequency metasurface plane wave lens antenna for non-destructive wheat moisture detection in the frequency range of 5.8GHz - 5.9GHz. Based on the propagation characteristics of plane waves in multilayer dielectric, antenna and container error de-embedding algorithms are proposed to reduce the measurement errors introduced by the parameters of the antenna and container during the measurement process. According to the de-embedded scattering parameters of wheat, its dielectric constant and loss factor are obtained. The calculated dielectric constant and loss factor showed a good liner relationship with moisture content of wheat at 25°C(±0.5°C). Based on the relationship between the dielectric properties and moisture content of wheat, a linear regression equation is established to predict the moisture content of wheat. Experimental results show that the root mean square error, mean absolute error and maximum relative error of the predicted values were 0.19%, 0.16% and 4.0%.

Mechanisms underlying the inhibitory effects of linalool on Aspergillus flavus spore germination.

Biogenic volatile organic compounds hold remarkable potential for controlling fungal decay in agro- and food products. Recently, we reported that linalool, the major volatile component of the Zanthoxylum schinifolium pericarp, showed great potential as a biofumigant to control Aspergillus flavus growth in postharvest grains. In this study, the inhibitory effects of linalool on A. flavus growth in stored grains and its underlying mechanism were investigated through transcriptomic and biochemical analyses. Linalool vapor at 800 μL/L can effectively prevent A. flavus growth in 22% moisture wheat grains. Linalool at 2 μL/mL completely inhibited the germination of A. flavus spores, and 10 μL/mL caused spore death. Scanning electron microscopy revealed that linalool treatment caused wrinkling and spore breakage. Transcriptomics showed that 3806 genes were significantly differentially expressed in A. flavus spores exposed to 2 μL/mL linalool, predominantly showing enrichment regarding the ribosome, DNA replication, glutathione metabolism, peroxisome, and MAPK signaling pathways. Flow cytometry showed that linalool treatment caused hyperpolarization of mitochondrial membrane potential. 4,6-Diamidino-2-phenylindole staining indicated that linalool caused DNA fragmentation in A. flavus spores, and monodansylcadaverine staining confirmed that linalool induced autophagy in A. flavus spores. We thus propose that linalool can damage the plasma membrane, cause mitochondrial dysfunction and DNA damage, and induce autophagy in A. flavus spores. These findings considerably improve our understanding of the mechanisms underlying the inhibitory effects of linalool on A. flavus, which is crucial regarding the development of applications to prevent postharvest grain spoilage due to A. flavus infestations. KEY POINTS: • The inhibitory potency of linalool on A. flavus spore germination was determined. • Transcriptomic analyses were performed to identify differentially expressed genes of A. flavus exposed to linalool. • A functional mechanism underlying the inhibitory effects of linalool on A. flavus spore germination is proposed.

The possibility of planning winter wheat grain productivity in the southern part of the Rostov region

The current paper has presented the results of study conducted at the Agricultural Research Center “Donskoy” in 2019–2021. The winter bread wheat variety ‘Lidiya’ was taken as the object of study. The forecrop was sunflower. In the variants where it was planned to yield 5.0; 6.0 and 7.0 t/ha, there were used mineral fertilizers (ammonium nitrate, ammophos and potassium salt) in doses (mean through the years) of N66P66K43, N79P79K52 and N92P92K61, respectively. In the control variant, there were used on fertilizers. The purpose of the study was to find out the possibility obtaining the planned winter wheat grain productivity in the southern part of the Rostov region under the use of mineral fertilizers. The amount of precipitation during the agricultural year and a vegetation period of winter wheat was 520.3 and 431.6 mm, while the norm was 582.4 and 446.3 mm, respectively. Depending on the hydrothermal conditions, the sum of air humidity deficits from September to June varied from 1034.1 to 1278.2 mm over the years, and water need for plants ranged from 672.2 to 830.8 mm. The coefficients of precipitation and moisture availability for winter wheat were in the range of 0.53–0.67 over the years. The hydrothermal conditions during the years of study determined win­ter wheat moisture supply during a vegetation period and contributed to obtaining grain productivity of different levels. Under the prevailing hydrothermal conditions, it was not possible to obtain the planned productivity of the winter bread wheat variety ‘Lidiya’. However, all applied doses of mineral fertilizers were cost-effective and there were obtained a significant yield increase of 1.28–2.03 t/ha (the yield on the control was 3.52 t/ha). There has been recommended to take into account the conditions of moisture supply in the cultivation zone when planning productivity and effective use of mineral fertilizers.

Prediction method of wheat moisture content in the hot air drying process based on backpropagation neural network optimized by genetic algorithms

It is necessary to grasp the moisture content of wheat in the process of hot air drying in real time to save energy and improve quality. According to the result of the hot air drying experiment, a backpropagation (BP) neural network prediction model with a topology of 2-4-1 is established. The mean absolute error (MAE) and degree of fit (R2) of the predicted results are 0.216% and 0.9804. The BP neural network model is optimized with a genetic algorithm (GA-BP) to improve accuracy. The MAE and R2 are down to 0.069% and 0.9945. The generalization ability of the GA-BP prediction model is verified, when predicting the samples out of the training set, the MAE is 0.37% and R2 is 0.9993. It shows that the GA-BP prediction model is of good generalization ability. This study provides a new conception for the real-time control of moisture content in hot air drying wheat. Novelty impact statement The prediction model of wheat moisture content in the hot air drying process was established based on BP neural network algorithm and optimized by the genetic algorithm. The wheat moisture content can be accurately predicted at different hot air temperatures and drying times, which provides a new method for the real-time monitoring of moisture content in the drying process. According to the predicted results, the changes in the moisture content of wheat are grasped in advance, which solve the problem that the moisture content of wheat is difficult to obtain in real time during the hot air drying process and hard to adjust the hot air temperature in time.

Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ ​has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels.

Study on sampling scheme for detecting mycotoxin during wheat storage.

Mycotoxin produced by mould is one of the most serious contamination sources in food security. Safe storage of grain has become more important to control food security. Currently, there is no officially approved or standardized sampling scheme for detecting mycotoxin in grain storage worldwide. In this study, deoxynivalenol (DON) was taken as a typical mycotoxin in stored wheat to be detected. Population density of corn weevil could not significantly increase wheat moisture, but wheat moisture was highly significantly and positively correlated with DON content (P < 0.01). Corn weevil density significantly increased the DON content in wheat. DON contamination degree was mainly distributed in the region of 14-20 cm below the surface layer of wheat. In the process of ventilation and dehumidification during the storage period, moisture of wheat decreased slightly with the extension of ventilation, but the DON content in wheat increased significantly. Combined with the analysis of ventilation, DON content in the upper layer and H1 position, where the wind direction is not easy to reach, increased significantly. Areas with high insect population density (14-20 cm below the surface layer of stored wheat) and low ventilation and high humidification (H1 position in the upper layer) should be taken as the key cutting sample areas for detecting mycotoxin during the period of grain storage. This study provides for the first time a scientific basis for the standardization of the wheat sampling scheme to monitor mycotoxin contamination during wheat storage. © 2022 Society of Chemical Industry.

Predicting the moisture content of organic wheat in the first stage of tempering

The tempering process is a key process in wheat flour milling that requires proper adjustments to achieve a desired level of flour quality and yield. The present study aims to develop a tool to predict the moisture content of organic wheat at the end of the first stage of tempering. A study case was conducted at a mill located in the Quebec region to build and compare flour models: ordinary least squares (OLS), LASSO, RIDGE and ElasticNet. The models are based on wheat properties (initial wheat moisture content, wheat protein content and wheat temperature), process parameters (targeted wheat moisture content, wheat flow rate, water flow rate, wheat quantity and resting time) and tempering conditions (water quantity and day weather). The increase of wheat moisture achieved during the first tempering stage varies between 0% and 5%. The results indicated that ElasticNet model outperformed others in determining the final increase of wheat moisture with an average prediction errors of 0.21%.