Search

To study the drying stress and predict cracking of the components of the maize kernel during drying, the viscoelastic stress-strain model of the maize kernel was developed considering the differences in viscoelastic properties of the components. The results showed that at the preheating and main drying stages, the stress was mainly caused by the uneven distribution of the moisture content and gradually increased with drying. At the cooling stage, the stress significantly increased in a short time due to the decrease in temperature. The stress of the hard endosperm was higher, the pericarp had the highest stress value, and the stress of the soft endosperm and germ was low. The crack volume percentage of the germ and pericarp was zero, which was consistent with the fact that there was no crack in the pericarp and germ, the cracks were mainly in the endosperm. The crack volume percentage of the endosperm gradually increased as drying progressed and significantly increased during the cooling stage, which was similar to the increasing trend in the crack degree of the endosperm at different drying stages. The viscoelastic stress-strain model combined with the von Mises stress criterion could be used to predict the stress crack formation of components at different drying stages. • Relaxation behavior of components could be described by a two-element Maxwell model. • A viscoelastic stress-strain model was developed considering the heterogeneity of components. • Stress was closely related to the distribution of moisture and temperature. • Crack formation in components could be predicted based on a viscoelastic stress-strain model. [ABSTRACT FROM AUTHOR]

[Display omitted] • Kernel properties and carotenoid in vitro digestion were analysed in 103 commercial dent hybrids. • Tested hybrids characterized higher zeaxanthin than lutein content. • Kernel hardness traits are related to the extent of the amount of digested carotenoids. • Fiber, protein and amylose correlated with the release of carotenoids during digestion. The objective of study was to investigate the relationships between maize kernel properties and carotenoid release during simulated gastrointestinal digestion of 103 hybrids of dent type. Commercial maize hybrids significantly differed in kernel hardness, chemical composition and carotenoid profile. Across all hybrids, the amount of digestible individual carotenoids increased as follows: β-carotene < α-cryptoxanthin < β-cryptoxanthin < lutein < zeaxanthin. The amount of digested carotenoids correlated negatively with amylose content and amylose-to-amylopectin ratio, while it correlated positively with the content of neutral detergent fibres and amylopectin as well as the Stenvert index. However, the content of endosperm lipids could not be related to carotenoid digestibility. Findings clearly indicate that the carotenoid release from the kernel during digestion is related to specific physical and chemical properties, leading to a better understanding of the effects of kernel matrix structure on carotenoid digestibility in dent-type maize hybrids. [ABSTRACT FROM AUTHOR]

[Display omitted] • Epiphytic fungal community succession in maize kernels was influenced by storage temperature. • Eurotium was the dominant genus in all maize samples. • A. flavus exhibited a tendentious consumption of zein during maize storage. • The detoxification efficiency of AFB 1 was superior to that of ZEN during maize wet processing. Aspergillus flavus , a notorious saprobe and opportunistic plant pathogen, alters mycotoxin contamination and biochemical components in maize kernels during processing and storage, thereby reducing the possibilities of maize end use and compromising food safety. This study explored changes in mycotoxin production, fungal community succession and biochemical components in maize kernels stored at 20, 25 and 30 °C, exposed to A. flavus. Results showed that aflatoxin B 1 concentration increased over time, reaching 4.88 μg/kg at 20 °C, 167.23 μg/kg at 25 °C and 349.64 μg/kg at 30 °C after 15 days of storage, whereas the zearalenone production was characterized by an increase followed by a decrease. Correspondingly, the number of molds gradually increased and reached a stable stage after 10 days. High-throughput sequencing of the internal transcribed spacer (ITS) revealed that Eurotium dominated the fungal communities, with A. flavus reaching maximum abundance in maize kernels stored at 30 °C for 15 days. Correlation analysis indicated that the relative abundance of A. flavus was significantly negatively correlated with the content of zein and moisture (P < 0.05). Moreover, the wet milling process of maize effectively eliminated the concentration of aflatoxin B 1 and zearalenone from the starch. Pasting temperature and setback value of starch decreased while peak viscosity, final viscosity and breakdown value increased with storage. These findings indicate that interactions between the epiphytic fungal community and A. flavus at elevated storage temperatures aggravate both maize quality deterioration and mycotoxin contamination. Furthermore, they have a discernible impact on the pasting properties of starch. This insight informs strategies to control fungal infections during maize processing and storage. [ABSTRACT FROM AUTHOR]

The damage to maize (Zea mays) during processing is closely related to its viscoelastic properties, but the viscoelastic properties of the components of the maize kernel are unknown. This work aimed to measure the stress relaxation of components, evaluate the relaxation behavior, and observe the microstructure for interpreting relaxation properties. It was found that the stress relaxation curves of the components shifted downward with increasing moisture content. The two-element Maxwell model could successfully express the stress relaxation curves. There was a decreasing trend in the model parameters of the components as moisture content increased. The differences in the degree of the elastic behavior would result in a difference in the residual stress percentage and relaxation rate, which could be better interpreted by the microstructure analysis. The results could help optimize maize processing strategies to maintain product quality. [Display omitted] • Different relaxation behavior existed in components with different moisture contents • Relaxation behavior could be effectively described by the two-element Maxwell model • Degree of elastic behavior affected residual stress percentage and relaxation rate • Differences in relaxation behavior could be explained by the microstructure [ABSTRACT FROM AUTHOR]

A short-wave infrared (SWIR) hyperspectral imaging system (1000–2500 nm) combined with chemometric data analysis was used to detect aflatoxin B 1 (AFB 1 ) on surfaces of 600 kernels of four yellow maize varieties from different States of the USA (Georgia, Illinois, Indiana and Nebraska). For each variety, four AFB 1 solutions (10, 20, 100 and 500 ppb) were artificially deposited on kernels and a control group was generated from kernels treated with methanol solution. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA) and factorial discriminant analysis (FDA) were applied to explore and classify maize kernels according to AFB 1 contamination. PCA results revealed partial separation of control kernels from AFB 1 contaminated kernels for each variety while no pattern of separation was observed among pooled samples. A combination of standard normal variate and first derivative pre-treatments produced the best PLSDA classification model with accuracy of 100% and 96% in calibration and validation, respectively, from Illinois variety. The best AFB 1 classification results came from FDA on raw spectra with accuracy of 100% in calibration and validation for Illinois and Nebraska varieties. However, for both PLSDA and FDA models, poor AFB 1 classification results were obtained for pooled samples relative to individual varieties. SWIR spectra combined with chemometrics and spectra pre-treatments showed the possibility of detecting maize kernels of different varieties coated with AFB 1 . The study further suggests that increase of maize kernel constituents like water, protein, starch and lipid in a pooled sample may have influence on detection accuracy of AFB 1 contamination. [ABSTRACT FROM AUTHOR]

The creep recovery, stress relaxation, temperature-dependence and their frequency-dependence of maize kernel were determined within a moisture content range of 11.9% to 25.9% (w/w) by using a dynamic mechanical analyzer. The 4-element Burgers model was found to adequately represent the creep behavior of the maize seeds (R2>0.97). The 5-element Maxwell model was able to better predict the stress relaxation behavior of maize kernel than the 3-element Maxwell model. TheTgvalues for the maize kernels decreased with increased moisture content. For example, theTgvalues were 114°C and 65°C at moisture content values of 11.9% (w/w) and 25.9% (w/w), respectively. The magnitude of the loss moduli and loss tangent and their rate of change with frequency were highest at 20.7% and lowest at 11.9% moisture contents. The maize kernel structure exhibited A-type crystalline pattern and the microstructure was found to expand with increase in moisture content. [ABSTRACT FROM AUTHOR]

Abstract: A shortwave infrared (SWIR) hyperspectral imaging system with wavelength range between 1000 and 2500 nm was used to assess the potential to detect aflatoxin B1 (AFB1) contaminants on the surface of healthy maize kernels. Four different AFB1 solutions were prepared and deposited on kernels surface to achieve 10, 20, 100, and 500 ppb, respectively. A drop of 20% methanol was dipped on the surface of 30 healthy kernels in the same way to generate the control samples. Based on the standard normal variate (SNV) transformation spectra, principal components analysis (PCA) was used to reduce the dimensionality of the spectral data, and then stepwise factorial discriminant analysis (FDA) was performed on latent variables provided by the PCA's. Furthermore, beta coefficients of the first three of four discriminant factors were analyzed and key wavelengths, which can represent AFB1 and be used to differentiate different level of AFB1 were indentified. Furthermore, 150 independent samples were used as verification set to test the reproducibility of the proposed method. A minimum classification accuracy of 88% was achieved for the validation set and verification set. Results indicated that hyperspectral imaging technology, accompanied by the PCA-FDA method, can be used to detect AFB1 at concentrations as low as 10 ppb when applied directly on the maize surface. [Copyright &y& Elsevier]

• Fungal spore quantity of a single maize kernel was determined by Raman hyperspectral imaging coupled with chemometrics. • Spectral and textural feature of a single maize kernel were extracted to describe the mold. • Characteristic Raman shifts were screened to optimize the detection model. • This technique provided an in-situ alternative for the detection of fungal spore quantity of a single maize kernel. Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity. [ABSTRACT FROM AUTHOR]

• We designed a hardware system which can detect cracks accurately and efficiently. • We propose a cascade model using machine-vision to detect cracks automatically. • We find three empirical constraints to distinguish the real cracks from noises. • We integrate the algorithm and hardware to obtain an automatic crack-detection system. Stress-crack detection is important for determining seed quality. This paper presents both a machine-vision-based method and a prototype of an industrial hardware design for stress-crack detection in maize kernels. Specifically, we present a cascade model incorporating kernel-status classification, region-of-interest segmentation, and crack-detection models. The status-classification model selects kernels with the correct camera orientation, whereas the region-of-interest segmentation model locates the main axis of the kernel and supplies a kernel mask for endosperm segmentation. Further, we utilise the EDLines algorithm to detect cracks and apply three novel constraints to distinguish real cracks from noise. The results of experiments conducted indicate that our integrated hardware–software system can detect stress cracks in maize kernels effectively and automatically. The observed precision and recall of the overall system were 92.7% and 94.4%, respectively. [ABSTRACT FROM AUTHOR]

• NIR-HSI technology was used to predict oil content of single maize kernel. • Generative adversarial network was used for data augmentation of regression problems. • Deep convolutional generative adversarial network was used to expand spectral data and oil content data. • The generated fake data was highly similar to the real data. • The performance of PLSR and SVR models was improved after data augmentation. Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data. [ABSTRACT FROM AUTHOR]

Three centrifuge discs (straight, oblique, and curved) of a centrifugal maize breakage tester were investigated experimentally using discrete element method (DEM) simulation. Bonded particle model (BPM) was used to simulate the breakage behavior of maize kernels. A set of bond parameters was calibrated by comparing stiffness and rupture force of the kernel obtained from a uniaxial compression test. The breakage pattern of the kernels consisted of local disintegration and fragmentation at different impact velocities. The simulations revealed that the average velocity and breakage susceptibility (BS) were significantly affected by the rotational speed of the disc and increased linearly with an increase in the rotational speed. The BS was also significantly affected by the interaction of rotational speed and disc structure. The simulation results were the same as the experimental results, indicating that the BPM model provided an accurate representation of the kernel breakage and flow behavior in the breakage tester. [Display omitted] • Optimal centrifugal disc geometry in breakage tester was investigated by experiments and simulations. • Particle breakage and motion were analyzed in 3D during tester impacting. • Bond parameters were established for maize kernels (granules/particles). • DEM modeling explained breakage and flow pattern of kernels in three centrifugal disc. • Kinetic energies and velocity of particles obtained by DEM explained breaking mechanism. [ABSTRACT FROM AUTHOR]

• HSI and deep SSAE network were combined for recognizing the mildewed status of maize kernels. • The spectral features were represented by SSAE and the optimal wavelengths were selected using VCPA and RF for modeling. • Softmax, ELM, KELM, and SVM were developed based on different spectra features. • SSAE and KELM algorithms were fused and yielded satisfactory performances. • The best SSAE-KELM model was used to visualize the mildewed grades of kernels based on pixels-level and object-level. The early detection the mildew status of maize kernels caused by fungal infection during storage is essential. Hyperspectral imaging (HSI) combined with the deep stacked sparse auto-encoders (SSAE) algorithm was employed to recognize the early mildewed degree of kernels. HSI data of kernels were collected in the spectral range of 400–1000 nm and the mildew status was classified into four grades (health, mild mildew, moderate mildew, and heavy mildew) using the measured fungal spores as the reference. The nonlinear features of the spectra were represented using the SSAE network, and the key wavelengths were selected by variable combination population analysis and random frog methods, which were combined with four classifiers-Softmax, kernel extreme learning machine (KELM), ELM, and support vector machine-to establish models for identifying the mildew grades of kernels. The comparative results indicated that SSAE combined with KELM classifier to build the SSAE-KELM model had better identification ability than other models with high total correct recognition rate of 97.36% and 96.84% for the training and testing sets, respectively. The sensitivity and specificity were 0.92–1. The different grades were visualized on the images of maize kernels based on pixels-level and object-level. The current results demonstrated that HSI with SSAE-KELM model performed well in the early detection of mildew status of maize kernel, and can be applied as the reference method for batch and online detection of mildew kernels to improve grain storage security. [ABSTRACT FROM AUTHOR]