Kernel Samples Research Articles

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data. Initially, the study automatically screening the best model from up to 504 algorithm combinations. Subsequently, multi-stage grid search is utilized for improving precision. We collected 270 kernel samples from different parts of the ear from 15 high oil and regular corn materials, with oil contents ranging from 1.4% to 13.1%. Experimental results show that the combinations SG + NONE+KS + PLSR(R2: 0.8570) and MA + LAR+Random+MLR(R2: 0.8523) performed optimally. After parameter optimization, their R2 values increased to 0.9045 and 0.8730, respectively. Additionally, the ACNNR model achieved an R2 of 0.8878 and an RMSE of 0.2243. The improved algorithm significantly outperforms traditional methods and ACNNR model in prediction accuracy and adaptability, offering an effective method for field applications.

Utilizing plant-based biogenic volatile organic compounds (bVOCs) to detect aflatoxin in peanut plants, pods, and kernels

Mycotoxins and particularly aflatoxin are a concern for peanut growers, processors, and consumers. Aflatoxins are responsible for approximately 25 % of liver cancer cases worldwide, while also costing millions of dollars in lost revenue on an annual basis. Current detection techniques based on high pressure liquid chromatography (HPLC) are time, labor and cost intensive. Peanut product can be held multiple days before testing is complete, costing processors additional revenue loss. Over the past few years, The Georgia Tech Research Institute (GTRI) has been investigating the use of plant-based biogenic volatile organic compounds (bVOCs) for monitoring the status of peanut plants. Initially, GTRI utilized these bVOCs to monitor for heat/drought stress in peanut plants. They quickly saw very promising indications of the ability of bVOCs to monitor other parameters in these plants. More recently, the team has started to investigate the of use of bVOCs to monitor aflatoxin development in plants, pods, and kernels pre- and post-harvest. A field trial for detection of aflatoxin using bVOCs was conducted in August–September of 2020 where three test groups were prepared: plants treated with Aspergillus fungus; plants treated with Afla-Guard (biocontrol agent); plants not treated – acting as a control group. Plant-based bVOCs were collected from the plants before treatment, and once a week post treatment using Stir Bar Sorptive Extraction (SBSE) devices or Twisters®. Each Twister® was then analyzed via gas chromatography–mass spectrometry (GC/MS). Pods from tested plants were harvested and sent to GTRI where bVOCs were collected and analyzed using GC/MS. Several statistical analysis and machine learning techniques were applied to all the collected GC/MS data. It was found using only bVOCs, that Random Forest classification performed well for the analysis of the pod and kernel samples with an F1 score of 0.80. On the other hand, Linear Discriminate Analysis (LDA) was only able to correctly classify 50 % of plant-based samples solely on bVOCs alone, which may be due to training models developed using original labels assuming no cross contamination at the field level. These results indicate the potential for bVOC screening for aflatoxin as an important way to lower impacts to growers, shellers, and consumers.

Fungal community and toxigenic taxa in chestnut fruits in postharvest conditioning process and storage.

Chestnut fruit quality is affected by fungal contamination. The study of the patterns of contamination in the postharvest is crucial to individuate the critical phases and propose solutions. To understand how fungal colonization varies on fruits, the composition of mycobiota was investigated in postharvest handling and in between tissues (shell and kernel). Fungal sequences were clustered into 308 operational taxonomic units (OTUs). Biodiversity was higher in shell than kernel tissues. Results evidenced the risk of new contamination in specific phases such as the 'cold bath' and storage. Genera known as mycotoxin producers were detected in all phases. Specifically, 47 OTUs belonging to Penicillium, eight to Fusarium and two to Aspergillus genera were identified. While Fusarium spp. was sensitive to 'warm bath' phase, Penicillium spp. was largely insensitive and accumulated in storage conditions. Surprisingly, Aspergillus spp. was poorly represented. Aflatoxin, ochratoxin A, fumonisins and T-2/HT-2 detection was performed for shell and kernel, and process phases. Higher contamination was observed on shell than in kernel samples. While aflatoxins were within the European Union (EU) limits for dry fruits, Ochratoxin exceeded the EU limits. The present study represents the first report of fumonisins and T-2/HT-2 detection in chestnuts. Fungal contamination taxa is high in chestnut fruits following postharvest handling and storage. A parametrization of process phases such as the 'warm bath' is functional to reduce the risk for some taxa. For other spoilage and mycotoxigenic genera strict sanitation procedures of equipment and water must be individuated and implemented to reduce their impact. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality

Abstract Distance covariance is a popular dependence measure for two random vectors $X$ and $Y$ of possibly different dimensions and types. Recent years have witnessed concentrated efforts in the literature to understand the distributional properties of the sample distance covariance in a high-dimensional setting, with an exclusive emphasis on the null case that $X$ and $Y$ are independent. This paper derives the first non-null central limit theorem for the sample distance covariance, and the more general sample (Hilbert–Schmidt) kernel distance covariance in high dimensions, in the distributional class of $(X,Y)$ with a separable covariance structure. The new non-null central limit theorem yields an asymptotically exact first-order power formula for the widely used generalized kernel distance correlation test of independence between $X$ and $Y$. The power formula in particular unveils an interesting universality phenomenon: the power of the generalized kernel distance correlation test is completely determined by $n\cdot \operatorname{dCor}^{2}(X,Y)/\sqrt{2}$ in the high-dimensional limit, regardless of a wide range of choices of the kernels and bandwidth parameters. Furthermore, this separation rate is also shown to be optimal in a minimax sense. The key step in the proof of the non-null central limit theorem is a precise expansion of the mean and variance of the sample distance covariance in high dimensions, which shows, among other things, that the non-null Gaussian approximation of the sample distance covariance involves a rather subtle interplay between the dimension-to-sample ratio and the dependence between $X$ and $Y$.

A Simple and Rapid Quantitative Assay for Gossypol via Reactive Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.

The development of simple and rapid analytical tools for gossypol (GSP) is important to the food industry and medical field. Here, we report a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) method for the detection of GSP by using a reactive matrix 4-hydrazinoquinazoline (4-HQ). The two aldehyde groups of GSP react with the 4-HQ and therefore improve the detection sensitivity and selectivity of GSP. Moreover, GSP forms homogeneous crystals with the 4-HQ matrix, allowing the quantification of the GSP by the proposed method. With the optimized experimental conditions, GSP could be detected at concentrations as low as 0.1 μM and quantified in a wide linear range (1-500 μM). After a brief extraction with an organic solvent, the GSP contents in cottonseeds and cottonseed kernels from different provinces of China were determined successfully. The spiked recovery of GSP in cottonseed/cottonseed kernel samples was obtained as 97.88-105.80%, showing the reliability of the assay for GSP determination in real samples.

Detecting differences in starch digestibility using in vitro methods among corn hybrids harvested at silage maturities

The objectives were to 1) determine whether genetic differences can be detected for carbohydrate fermentability among corn hybrids at silage maturities when the effects of drying and grinding are eliminated and 2) determine if in vitro starch digestibility (IVSD), a routine laboratory method used by commercial laboratories for hybrid evaluation, can detect differences when DM is controlled. Samples of whole corn plants from 3 hybrids (Brevant): B99B79SX (BMR, n = 7), B96T79SX, standard (STAN, n = 6), and B95U78SXE, floury-BMR (FL, n = 7), that had similar range of DM were selected. Sets of 8 ears were harvested concurrently and kernels removed from the ears by hand. Samples of undried kernels were quartered (QKERN) and in vitro gas production (IVGP) was measured for 120 h. More gas was produced by QKERN of FL than of STAN and BMR from 9 to 18 h (P < 0.05), and QKERN of FL produced more gas than BMR through 21 h (P < 0.05). The QKERN of FL had a shorter lag than STAN or BMR (P < 0.001) and a faster rate of gas production than BMR (P < 0.01), establishing that genetic differences are present at silage maturities. To determine whether the routine laboratory method can detect these differences, kernels (GKERN) and whole corn plants (GWP) were dried and ground to pass a 4-mm screen to measure IVSD after 3, 8, and 24 h. Particle size of GKERN was determined by dry sieving using a set of 13 sieves. For both GKERN and GWP, IVSD was lowest for BMR and highest for FL after 8 h. The IVSD8h of GKERN decreased as particle size increased at a similar rate for the 3 hybrids but the intercepts were different, with BMR being lowest and FL highest. Similarly, the IVSD8h of both GKERN and GWP decreased with increasing whole plant DM at a similar rate for the 3 hybrids and the magnitude of difference for IVSD8h was larger for the range in DM than the range in intercepts for the hybrids (0.123 vs 0.071 and 0.117 vs. 0.100, for GKERN and GWP, respectively). Plant maturity, or DM, has a larger effect on IVSD than hybrid type, and should therefore be controlled when evaluating genetic differences.

Meta-learning based blind image super-resolution approach to different degradations

Although recent studies on blind single image super-resolution (SISR) have achieved significant success, most of them typically require supervised training on synthetic low resolution (LR)-high resolution (HR) paired images. This leads to re-training necessity for different degradations and restricted applications in real-world scenarios with unfavorable inputs. In this paper, we propose an unsupervised blind SISR method with input underlying different degradations, named different degradations blind super-resolution (DDSR). It formulates a Gaussian modeling on blur degradation and employs a meta-learning framework for solving different image degradations. Specifically, a neural network-based kernel generator is optimized by learning from random kernel samples, referred to as random kernel learning. This operation provides effective initialization for blur degradation optimization. At the same time, a meta-learning framework is proposed to resolve multiple degradation modelings on the basis of alternative optimization between blur degradation and image restoration, respectively. Differing from the pre-trained deep-learning methods, the proposed DDSR is implemented in a plug-and-play manner, and is capable of restoring HR image from unfavorable LR input with degradations such as partial coverage, noise addition, and darkening. Extensive simulations illustrate the superior performance of the proposed DDSR approach compared to the state-of-the-arts on public datasets with comparable memory load and time consumption, yet exhibiting better application flexibility and convenience, and significantly better generalization ability towards multiple degradations. Our code is available at https://github.com/XYLGroup/DDSR.

The nutritional benefits of maize-soybean rotational systems in the North-Western Free State, South Africa

BackgroundMalnutrition is one of the major health concerns, particularly in the developing and under-developed world. In South Africa, maize is produced as a staple food and is the primary food for most of the country’s population. The North-Western Free State which forms part of the Nala municipality in the Lejweleputswa district of South Africa is a main producer of the country’s maize. However, the area is known for its sandy soil which contains little organic material, silt and clay. Maize in this area is normally grown in monoculture but with a focus on sustainable agriculture has recently incorporated soybean. As a means of fighting malnutrition, the objective of this study was to determine the influence that soybean incorporation as a rotation crop has on the nutritional value of maize.MethodsA trial was conducted on the farm Christinasrus in the North-Western Free State to compare the nutritional value of monoculture maize and maize in rotation with soybean over three consecutive seasons. Maize kernel samples were taken each season and its nutritional properties analyzed. Subsequent data were further analyzed using statistical analysis.ResultsResults showed that there was a seasonal effect on all nutritional properties with a general decrease in nutritional values in wetter years. Cropping systems had an effect on fibre content, with increased values observed in maize after soybean. In addition, there was a significant interaction between season and cropping systems on the total digestible nutritional value, with maize after soybean being more nutritious in wetter seasons.ConclusionResults suggest that maize in cropping systems with soybean has potential to be more nutritious while the soybean in the cropping system can act as a protein-rich companion, providing a more balanced diet for human consumption, thereby fighting malnutrition.

Ecological and health risk assessments of heavy metals and their accumulation in a peanut-soil system

Heavy metals pollution is a notable threat to environment and human health. This study evaluated the potential ecological and health risks of heavy metals (Cu, Cr, Cd, Pb, Zn, Ni, and As) and their accumulation in a peanut-soil system based on 34 soil and peanut kernel paired samples across China. Soil As and Cd posed the greatest pollution risk with 47.1% and 17.6% of soil samples exceeding the risk screen levels, respectively, with 26.5% and 20.6% of the soil sites at relatively strong potential ecological risk level, respectively, and with the geo-accumulation levels at several soil sites in the uncontaminated to moderately contaminated categories. About 35.29% and 2.94% of soil sites were moderately and severely polluted based on Nemerow comprehensive pollution index, respectively, and a total of 32.4% of samples were at moderate ecological hazard level based on comprehensive potential ecological risk index values. The Cd, Cr, Ni, and Cu contents exceeded the standard in 11.76, 8.82, 11.76 and 5.88% of the peanut kernel samples, respectively. Soil metals posed more health risks to children than adults in the order As > Ni > Cr > Cu > Pb > Zn > Cd for non-carcinogenic health risks and Ni > Cr ≫ Cd > As > Pb for carcinogenic health risks. The soil As non-cancer risk index for children was greater than the permitted limits at 14 sites, and soil Ni and Cr posed the greatest carcinogenic risk to adults and children at many soil sites. The metals in peanut did not pose a non-carcinogenic risk according to standard. Peanut kernels had strong enrichment ability for Cd with an average bio-concentration factor (BCF) of 1.62. Soil metals contents and significant soil properties accounted for 35–74% of the variation in the BCF values of metals based on empirical prediction models.

Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

Predicting oil content of Australian beauty leaf tree kernel samples using near infrared spectroscopy combined with chemometrics

This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.

High-accuracy classification and origin traceability of peanut kernels based on near-infrared (NIR) spectroscopy using Adaboost - Maximum uncertainty linear discriminant analysis

Peanut kernels, known for their high nutritional value and palatability, are classified as nut food. In this study, peanut kernel samples from six distinct cities in Shandong Province, China, were examined to categorize and trace their origins. Near-infrared (NIR) spectra of samples were captured using a portable NIR-M-R2 spectrometer. After the application of Savitzky-Golay (SG) filtering, the classification was attempted using principal component analysis (PCA) plus linear discrimination analysis (LDA). Additionally, maximum uncertainty linear discriminant analysis (MLDA) was applied for comparison. A specific number of eigenvectors could respectively maximize the classification accuracies, 81.48% for PCA + LDA and 76.54% for MLDA. In order to further improve the classification accuracies, Adaboost-MLDA was proposed to develop a stronger classifier. This method, after 18 iterations, achieved remarkable effects, achieving a high accuracy of 95.06%. In a similar vein, the enhancement with preprocessing techniques multiplicative scatter correction (MSC) + SG and standard normal variate (SNV) + SG raised accuracies to 98.77% and 97.53%, respectively. The results of classifying first-order and second-order derivative spectra using Adaboost-MLDA were also described, achieving accuracies near 100%. The experiment demonstrates that integrating Adaboost with NIR spectroscopy offers a highly accurate method for peanut kernel classification, promising for practical applications in food quality control.

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

In maize breeding studies, it is becoming common to determine the ear and kernel characteristics by image analysis. While current methods focus on measurements that can be obtained directly by image analysis, it has not been adequately addressed whether different parameters such as weight and viability can be estimated using these measurements. This study aimed to determine whether it is possible to estimate the ear weight (g), kernel weight (g), single kernel weight (g) and viability (1/0) status of maize with the help of features (area, perimeter, width, length) extracted from images of the ear and kernel samples. In this study, 233 ear and 1242 grain samples belonging to 13 maize genotypes were used as material. Digital images of the ear samples were taken with a 5 MP camera and from the kernel samples with a desktop scanner. The ear weight reference data (DV1) and the kernel weight reference data (DV2) were obtained by weighing each sample on a precision balance. Single kernel reference data (DV3) was obtained with the measurements of single kernel weights. Kernel samples underwent paper germination test and reference data (DV4) related to viability was created. Regression models were developed by using the features obtained from image analysis (area, perimeter, width, height) for each reference data set as the predictor variable. As a result of the study, it was seen that the ear weight and kernel weight can be estimated with the help of the parameters extracted from the image analysis. While moderate success was achieved in the determination of single seed weight, it was difficult to determine the viability status based on the morphometric measurements of a single kernel in maize.

Chemical differences between brown centre and white macadamia kernels

Nuts are susceptible to many quality issues, including discoloration defects in kernels. These discolouration defects can affect their market value and result in significant economic losses to both growers and processors. In macadamia, this quality issue is called brown centres and has been related to storage at high temperatures and moisture concentrations. However, the chemical differences between brown centre and white macadamia kernels have not previously been studied. We aimed to determine the chemical differences between macadamia kernels with brown centre defects and kernels that were white and cream coloured. We used brown and white kernel samples from 20 commercially processed batches of macadamia. From each batch, we selected brown centre samples and compared their chemistry with white kernel samples from the same batch. We compared the moisture concentration, sugar concentration, nutrient concentration, fatty acid profile and peroxide value of brown centre defect and white kernel samples. Our results showed that brown centres had a higher moisture concentration compared with white kernel samples (5.16 % vs 2.91 %, respectively, P < 0.05). Reducing sugars of fructose (0.61 %) and glucose (0.53 %) concentrations were only detected in brown centre, although the sucrose concentration was not significantly different. Most nutrient concentrations were higher in brown centres compared with white kernels. Nitrogen concentration was significantly higher in brown centre than white centres (2.10 % vs 1.82 %, respectively, P < 0.05). Calcium concentration was also higher in brown centre than white macadamia kernels (608.44 mg/kg vs 419 mg/kg, respectively, P < 0.05). Our study suggests that high moisture concentrations in macadamia kernels could have triggered the hydrolysis of sucrose into reducing sugars (fructose and glucose). In addition, reducing sugars and proteins can react in the Maillard reaction to form brown colouration in nuts. This is the first study to comprehensively compare the nutritional composition of brown centres and white macadamia kernels. We recommend appropriate drying and storing of macadamia nuts to prevent sucrose degradation into reducing sugars and decrease the formation of brown centres through the Maillard reaction.

Turbidity reduction efficacies of seed kernels of Mango (Mangifera indica) genotypes in Uganda

Alum and ferric salts as traditional chemical coagulants for turbidity removal in water and wastewater are expensive, and have known harmful effects. Thus, attempts to replace the chemical coagulants with safe and effective natural solutions are increasingly being made in terms of research studies to investigate the coagulation efficacies of various plants one of which is Mango (Mangifera indica). It is worth noting that M. indica has various genotypes of different origins across the world. In this study, eight (8) common M. indica genotypes in Uganda were identified, protein contents of their seed kernels determined, and coagulating efficacies investigated. Coagulation solution of each selected genotype was obtained by dissolving 5 g powder of the selected Mango seed kernel sample in 100 mL of distilled water. Next, 5 mL of this coagulant (or stock) solution was used to treat 200 mL of turbid water with turbidity ranging from 15 to 120 NTU. Using 0.01 M hydrochloric acid as an extraction solvent, protein contents of selected genotypes including Apple mango, Kate, Kent, Bire, Doodo red, Takataka, Kagoogwa, and Tommy Atkins were 38.02 %, 30.66 %, 15.94 %, 22.11 %, 21.50 %, 16.98 %, 16.36 %, and 17.87 %, respectively. Efficacies of coagulant from Apple mango, Kate, Kent, Bire, Doodo red, Takataka, Kagoogwa, and Tommy Atkins seed kernel samples were 92.2, 89.3, 66.0, 78.7, 76.9, 71.1, 68.9, and 73.1 %, respectively. Apple mango was the best performing genotype as a coagulant and this was followed by Kate. Coagulation efficacy was generally found to increase with increasing turbidity and/or coagulant's concentration. For instance, Apple Mango coagulant removed 16.7 %, 50.3 %, and 92.2 % of initial turbidity 15, 65 and 120 NTU, respectively. Kent removed 57.5, 66, and 69 % of initial turbidity 120 NTU using 5, 20, and 30 mL of stock solution, respectively. This study demonstrated the influence of the choice of a plant genotype on coagulation efficacy.

κmonty: a Monte Carlo Compton scattering code including non-thermal electrons

ABSTRACT Low-luminosity active galactic nuclei are strong sources of X-ray emission produced by Compton scattering originating from the accretion flows surrounding their supermassive black holes. The shape and energy of the resulting spectrum depend on the shape of the underlying electron distribution function (DF). In this work, we present an extended version of the grmonty code, called κmonty. The grmonty code previously only included a thermal Maxwell–Jütner electron DF. We extend the grmonty code with non-thermal electron DFs, namely the κ and power-law DFs, implement Cartesian Kerr–Schild coordinates, accelerate the code with mpi, and couple the code to the non-uniform adaptive mesh refinement grid data from the general relativistic magnetohydrodynamics code bhac. For the Compton scattering process, we derive two sampling kernels for both DFs. Finally, we present a series of code tests to verify the accuracy of our schemes. The implementation of non-thermal DFs opens the possibility of studying the effect of non-thermal emission on previously developed black hole accretion models.

Verification of a standard method based on immunoaffinity column cleanup and HPLC-FLD analysis for determination of aflatoxins in peanut kernels

Determination of aflatoxins as a group of potent contaminations in many food products is a common analysis in food quality control laboratories. For quantification of the four most toxic aflatoxins (AFs) (i,e. AF B1, B2, G1, and G2) in food and feed, a standard method involving a liquid extraction step followed by immunoaffinity purification and high-performance liquid chromatography (HPLC) is often used. In this work, this method is verified based on the requirements of ISO/IEC 17025 for the determination of aflatoxins in peanut kernel samples. All the method figures of merit including calibration, evaluation of limit of determinations (LODs) and limit of quantifications (LOQs), accuracy, precision, and uncertainty of the method are explained in detail. The accuracy of the applied method was verified by analysis of a CRM and calculation of the recovery. The relative standard deviations of the method (n = 6) were in the range of 4.99–7.85%. The LODs and LOQs of the method were found to be 0.15 and 0.50 μg. kg−1 for AFB1 and G1 and 0.06 and 0.2 μg kg−1 for AFB2 and G2, respectively. Finally, the uncertainties in the determination of aflatoxins concentrations, at the maximum permissible levels were calculated using the GUM procedure as 0.42, 0.094, 0.42, and 0.083 μg. kg−1 for AFB1, B2, G1, and G2, respectively.

Molecular half-subtractor and memory device based on a new Schiff-based colorimetric sensor for anions detection and computational studies

A new multi-analyte chromogenic sensor (L) for rapid detection of CN– and HSO4− was synthesized by reacting azo dye with furfurylamine (1: 1). The sensing ability of the L was investigated towards different anions by visual test, and UV–Vis spectroscopy in CH3CN: H2O (4: 1, v/v) media. The Job’s plot and HRMS data confirmed that the CN– and HSO4− ions bind with L in 2: 1, and 1: 1 stoichiometric ratio, respectively. The binding constants (Ka) and LOD values for CN–, and HSO4− were determined 4.45 × 10+4 M−1; 1.71 µM, and 3.67 × 10+5 M−1; 0.22 µM, respectively. Furthermore, DFT calculations supported the binding framework between L and the said anions. In addition, L was used to successfully detect CN– in peach kernel samples in aqueous media and as a colorimetric test strip for HSO4−. The UV–Vis absorption bands offer the possibility for fabrication of complex logic circuits such as complementary IMP/INH logic circuits, half subtractor and ‘set- reset’ flip flop memory elements.

Insect pests population of stored maize in four regencies of South Sulawesi

Information on species, insect pest populations that attack and storage conditions determines the control strategy. This study aims to determine the various types of post-harvest pest s population in maize kernels stored in four in South Sulawesi. Identification of post-harvest insect pests at the Pest Laboratory, Department of Plant Pests and Diseases, Faculty of Agriculture, University of Hasanuddin Makassar. Sampling using the direct capture method (hand sampling) and manual instruments. Sampling was carried out by taking samples of corn kernels in sacks from five points and taking samples of the corn kernels that were scattered on the floor of the warehouse. The results showed that 10 spesies of insects were found including 9 spesies of pests namely Sitophilus sp. Cryptolestes ferrugineus, Corcyra cephalonica, Carpophilus sp, Araecerus fasciculatus, Lophocateres pusillus, Rhyzopertha dominca, Tribolium castaneum and Oryzaephilus surinamensis and one species of parasitoid :Anisopteromalus calandrae. Sitophilus sp. is the most common species found in all districts with a total population of 636 individuals then Cryptolestes ferrugineus with 53 individuals and T. castaneum with 37 individuals. Post-harvest pest populations in all districts were found more commonly around 73% in piles of sacks compared to populations found in spilage commodities, namely around 27%.

Effects of endosperm type and storage length of whole-plant corn silage on nitrogen fraction, fermentation products, zein profile, and starch digestibility

Zeins are commercially important proteins found in corn endosperms. The objective of this study was to evaluate the effect of altering zein levels in corn inbred lines carrying endosperm mutations with differential allelic dosage and analyze the effects on the composition, nutritive value, and starch digestibility of whole-plant corn silage (WPCS) at 5 storage lengths. Three inbred lines carrying 3 different endosperm modifiers (opaque-2 [o2], floury-2 [fl2], and soft endosperm-1 [h1]) were pollinated with 2 pollen sources to form pairs of near-isogenic lines with either 2 or 3 doses of the mutant allele for each endosperm modifier. The experiment was designed as a split-plot design with 3 replications. Pollinated genotype was the main plot factor, and storage length was the subplot-level factor. Agronomic precautions were taken to mimic hybrid WPCS to the extent possible. Samples were collected at approximately 30% dry matter (DM) using a forage harvester and ensiled in heat-sealed plastic bags for 0, 30, 60, 120, and 240 d. Thus, the experiment consisted of 30 treatments (6 genotypes × 5 storage lengths) and 90 ensiling units (3 replications per treatment). Measurements included nutrient analysis, including crude protein, soluble crude protein, amylase-treated neutral detergent fiber, acid detergent fiber, lignin, starch, fermentation end products, zein concentration, and in vitro starch digestibility (ivSD). The nutritional profile of the inbred-based silage samples was similar to hybrid values reported in literature. Significant differences were found in fresh (unfermented) sample kernels for endosperm vitreousness and zein profiles between and within isogenic pairs. The o2 homozygous (3 doses of mutant allele) had the highest reduction in vitreousness level (74.5 to 38%) and zein concentration (6.2 to 4.7% of DM) compared with the heterozygous counterpart (2 doses of mutant allele). All genotypes showed significant reduction of total zeins and α-zeins during progressive storage length. In vitro starch digestibility increased with storage length and had significant effects of genotype and storage length but not for genotype by storage length interaction, which suggests that the storage period did not attenuate the difference in ivSD between near-isogenic pairs caused by zeins in WPCS. Both total zeins and α-zeins showed a strong negative correlation with ivSD, which agrees with the general hypothesis that the degradation of zeins increases ruminal starch degradability. Homozygous o2 was the only mutant with significantly higher ivSD compared with the heterozygous version, which suggests that, if all other conditions remain constant in a WPCS systems, substantial reductions in endosperm α-zeins are required to significantly improve ivSD in the silo.