Kernel Moisture Research Articles

Effect of Harvest Date on Kernel Quality and Antioxidant Activity in su1 Sweet Corn Genotypes

In order to achieve a high-quality product, sweet corn should be harvested at the optimal stage of maturity. The taste of sweet corn depends largely on the kernel moisture (KM) and total sugars (TS) content while its health properties are attributed to the content of total phenols (TPH) and antioxidant activity (AO). This study aimed to estimate quality in sugary (su1) sweet corn hybrids based on the maximum content of kernel chemical compounds and the content change during maturation. During two growing seasons, five harvests by year were made at 2-day intervals from 17 to 25 days after pollination (DAP). KM decreased among hybrids from 6.6% to 14% over an eight-day period, or 0.8% to 1.8% per day. TS content was the highest at 17 DAP (16.5–28.7 mg/g DW) and decreased during maturation from 27% to 67%. Hybrids differed significantly in TPH at 17 DAP (204.4–357.1 mg GAE/100 g DW). TPH change during maturation ranged from the no significant differences up to a 29.3% decrease. DPPH- radical scavenging activity ranged among genotypes from 52.5% to 74.9% inhibition at 17 DAP and significantly increased during maturation. A well-defined hybrid-specific harvest window is necessary to maintain kernel quality in su1 sweet corn.

Study on the Dehydration Characteristics of Spring Maize after Physiological Maturity and the Suitable Period of Mechanical Grain Harvest

The high moisture content of seeds during harvesting results in a large amount of grain that breaks during mechanical harvesting, which becomes a key problem that hampers the promotion of mechanical grain harvesting technology. Maize in southern Xinjiang physiologically matures from early September to early October. In this study, 52 varieties of maize at different maturity stages were studied in 2019-2020 and were categorized in the order of physiological maturity time: early September (PD1), mid-September (PD2), late September (PD3), and early October (PD4). The goal of this study was to analyze the differences in content of kernel moisture at physiological maturity and the rate of dehydration after physiological maturity of varieties at different stages of maturity, so as to determine the suitable period for mechanical grain harvesting of maize at different stages of maturity. The results showed that the content of seed moisture at different maturity stages varied in a two-stage linear model, with periods of fast and slow dehydration. An analysis of variance (ANOVA) showed that the content of seed moisture at different stages of physiological maturity did not differ significantly ( P > 0.05 ).

Strength properties of the Bambara kernel (Vigna subterranean) as influenced by the moisture content and kernel size

The fracture resistance of food grains is an essential piece of information required for the optimum design and development of agricultural post-harvest machinery. In this study, the strength properties of two varieties of Bambara kernels (TVSU-1395 and TVSU-1353) were examined in terms of the mean rupture force, absorbed energy, and deformation as affected by the moisture content and kernel size. To achieve this, a quasi-compressive force was applied on the two varieties of Bambara kernels of varying moisture contents (5.43%, 7.24%, 9.01%, 11.54%, and 13.62% wb) and kernel sizes (small, medium, and large) in between the loading compartments of a universal Testometric device at a 20 mm/min loading rate. The experiments take ten treatments with 20 replications subjected factorially to a completely randomised design (CRD) into consideration. The results revealed that the force needed to initiate the kernel fracture increased with an increase in the kernel size and moisture content from 101.44 to 235.06 N and 74.69 to 190.49 N for TVSU-1395 and TVSU-1353, respectively; whereas the energy at the kernel fracture point increased in a range of 0.074 to 0.401 J and 0.062 to 0.141 J for TVSU-1395 and TVSU-1353, respectively. The kernel deformation increased with the moisture content and size from 0.654 to 3.746 mm. These infer that the large kernel size of the TVSU-1395 variety at a 5.4% moisture content had greater compressive strength than the TVSU-1353 variety. The kernel moisture and size exhibited a strong correlation (0.958 ≤ R<sup>2</sup> ≤ 0.997) with the strength parameters. The results of this study will help the food industry in designing energy-efficient post-harvest equipment for Bambara kernel processing. Further studies may consider the strength attributes of Bambara kernels at varying rates of loading, kernel orientations, and varieties to optimise the best process conditions for the post-harvest handling of different Bambara cultivars and develop labour-saving decorticating machines.

Divergence in Single Kernel Characteristics and Grain Nutritional Profiles of Wheat Genetic Resource and Association Among Traits.

Triticum aestivum is among the few species of crops which has been widely grown as a source of food. For seed quality trait analysis, wheat germplasm (77 genotypes) was collected from Pakistan's diverse agro-climatic regions. Significant variation (p < 0.05) was observed for tested parameters among tested genotypes. Genotypes with maximum protein content, i.e., GA2002 (16.5%) and Marvi (16.5%), moisture content, i.e., advance line 9,244 (11%), starch content, i.e., AARI 2011 (54.1%), zeleny sedimentation rate, i.e., advance line 2006 (44ml), wet gluten content, i.e., advance line 2006 (44%), kernel weight, i.e., advance line TC-4928 (41.6 ± 9.5 mg), kernel diameter, i.e., sassui (2.91 ± 0.32 mm), kernel moisture, i.e., AUQAB 2000 (11.7 ± 0.4%), Mairaj 2000 (11.7 ± 0.4%), and Barani-83 (11.7 ± 0.3%), and hardness index, i.e., Punjab 2011 (91 ± 39) are concluded as potential candidates to be explored for bakery products and the breeding program to improve quality attributes of wheat. Data were also analyzed for correlation, agglomerative hierarchical clustering, and principal component analysis (PCA). Cluster analysis clustered all genotypes into five different groups. The D2 statistics confirmed maximum diversity of cluster-V genotypes against genotypes of cluster-IV regarding single kernel characteristics, whereas cluster-II genotypes revealed maximum diversity against cluster-III genotypes relating to grain nutritional profile. The contribution of PC-I regarding single kernel characteristics toward variability was highest (48.58%) and revealed positive factor loadings for kernel weight, kernel diameter, and kernel moisture, while the contribution of PC-I with respect to grain nutritional profile toward variability was highest (59.76%) and showed positive factor loadings for moisture and starch content. Varieties having good quality attributes can be combined by breeders via various breeding methods with the aim of developing high quality wheat in the future.

An accurate and effective single-seed moisture selection method based on nuclear magnetic resonance (NMR) in maize

Kernel moisture content (KMC) is important for maize grain development, harvest, and storage. Breeders always consider KMC as a necessary indicator when conducting breeding programs. The fact that a large number of samples are required for the typical breeding process necessitates the development of accurate, high-throughput, and non-destructive detection techniques for KMC. Here, we proposed an effective method based on nuclear magnetic resonance (NMR) spectroscopy to track moisture variation in maize kernels and select offspring with low KMC at harvest. Both moisture mass and content could be accurately measured via regression analysis (R2 = 0.9924 and 0.9139, respectively). Magnetic resonance imaging technology was also applied to monitor the spatial distribution of moisture in individual kernels and ears. On the other hand, with two generations of KMC selection experiments, we demonstrated the effectiveness of the proposed method. KMC was measured for self-crossing kernels of two elite hybrids, namely Zhengdan958 and Xianyu335, and partial kernels were classified into a high-kernel-moisture group (HMG) or low-kernel-moisture group (LMG). The progenies were then selected for kernels having a high KMC in HMG and low KMC in LMG. After selection, KMCs in LMG were significantly lower than those in the HMG in both hybrids at generations F2 and F3. Finally, phenotypic response surveys revealed that KMC selection significantly affected the flowering time. Our results demonstrate the effectiveness of the NMR-based platform for optimizing KMC trait, with potential applications in maize breeding programs.

Study of corn kernel breakage susceptibility as a function of its moisture content by using a laboratory grinding method

The rate of corn kernel breakage in the grain combine harvesters is a crucial factor affecting the quality of the grain shelled in the field. The objective of the present study was to determine the susceptibility of corn kernels to breakage based on the kernel moisture content in order to determine the moisture content that corresponds to the lowest rate of breakage. In addition, we evaluated the resistance to breakage of various corn cultivars. A total of 17 different corn cultivars were planted at two different sowing dates at the Beibuchang Experiment Station, Beijing and the Xinxiang Experiment Station (Henan Province) of the Chinese Academy of Agricultural Sciences. The corn kernel moisture content was systematically monitored and recorded over time, and the breakage rate was measured by using the grinding method. The results for all grain samples from the two experimental stations revealed that the breakage rate y is quadratic in moisture content x, y=0.0796x2−3.3929x+78.779; R2=0.2646, n=512. By fitting to the regression equation, a minimum corn kernel breakage rate of 42.62% was obtained, corresponding to a corn kernel moisture content of 21.31%. Furthermore, in the 90% confidence interval, the corn kernel moisture ranging from 19.7 to 22.3% led to the lowest kernel breakage rate, which was consistent with the corn kernel moisture content allowing the lowest breakage rate of corn kernels shelled in the field with combine grain harvesters. Using the lowest breakage rate as the critical point, the correlation between breakage rate and moisture content was significantly negative for low moisture content but positive for high moisture content. The slope and correlation coefficient of the linear regression equation indicated that high moisture content led to greater sensitivity and correlation between grain breakage and moisture content. At the Beibuchang Experiment Station, the corn cultivars resistant to breakage were Zhengdan 958 (ZD958) and Fengken 139 (FK139), and the corn cultivars non-resistant to breakage were Lianchuang 825 (LC825), Jidan 66 (JD66), Lidan 295 (LD295), and Jingnongke 728 (JNK728). At the Xinxiang Experiment Station, the corn cultivars resistant to breakage were HT1, ZD958 and FK139, and the corn cultivars non-resistant to breakage were ZY8911, DK653 and JNK728. Thus, the breakage classifications of the six corn cultivars were consistent between the two experimental stations. In conclusion, the results suggested that the high stability of the grinding method allowed it to be used to determine the corn kernel breakage rates of different corn cultivars as a function of moisture content, thus facilitating the breeding and screening of breakage-resistant corn.

Predicting Single Kernel Moisture and Protein Content of Mushroom Popcorn Using NIR Spectroscopy: Tool for Determining their Effect on Popping Performance

HighlightsPrediction models for high accuracy measurement of single kernel (SK) moisture content (MC) and protein content (PC) were developed using near-infrared reflectance (NIR) spectroscopy.USDA-ARS tube SKNIR instrument sorted individual kernels based on single kernel protein content (SKPC) which enabled determining the effect of three PC levels within a popcorn variety on popping performance.Variety, MC, and PC affected popping expansion, ball rate, and number of unpopped/half-popped kernels.Within popcorn variety, increased PC significantly increased expansion and reduced number of unpopped kernels but did not affect ball rate of popped flakes.The ability to sort single kernels for specific quality parameter, such as PC, is an important and useful tool for popcorn breeders and processors to meet consumer demand for specialized products.Abstract. The increasing demand for specialized high-quality popcorn products necessitates that the popcorn industry continuously identify quality parameters that can be improved through plant breeding or manipulated or sorted for improved end-products. Relationships between protein content (PC) and popping performance (expansion, ball rate, and number of unpopped kernels) has been investigated but there has been no research on segregating individual kernels from within the same variety for specific PC ranges, which may eliminate possible interference from some underlying variety- or production-related effects. Prediction models for determination of single kernel moisture content (MC) and PC were developed for the USDA-ARS tube single kernel near infrared reflectance (SKNIR) instrument. Both parameters were predicted with high accuracies for independent validations. MC showed an R2 of 0.94 and SEP of 0.25% while PC had R2 of 0.92 and SEP of 0.35%. Popping tests showed that increased kernel PC significantly (p&amp;lt;0.05) increased expansion and lowered the number of unpopped kernels but had no effect on the ball rate of popped flakes. Thus, applications that require increased overall expansion and reduced number of unpopped kernels may be addressed by the removal of low protein popcorn kernels from a popcorn lot, which can be achieved using an automated SKNIR technique. The SKNIR technique also provides a means for plant breeders to work on targeted/specific PC or PC range based on the single kernel selection. Keywords: Ball rate, Expansion, Mushroom popcorn, NIR spectroscopy, Popcorn quality, Single kernel, Unpopped kernels.

Preharvest Application of Sulfur as Pesticide on Fresh Hull and Kernel of Pistachio (Pistacia vera L.)

In recent years, sulfur has been shown to be effective in controlling pistachio psylla. In this study, the effect of sulfur foliar application as psylla pesticide on fresh fruit and kernel of three cultivars of pistachio was determined and compared with two commercial pesticides. Refinery micronized and mineral sulfur were applied at two concentrations of 30 and 60 kg/1000 L of water. The quality of fresh pistachio hulls and kernels were investigated. In ‘Fandoghi’ cultivar, all sulfur treatments reduced the kernel moisture. The firmness and water activity of hulls and kernels were significantly affected. All sulfur treatments decreased hull lightness in ‘Fandoghi’, while it increased in ‘Akbari’ and ‘Ahmadaghaei’ cultivars. Reduction of hull chroma was observed in some cases in all cultivars. Hue of hulls was not affected by the treatments. Lightness, chroma and hue values of the testa of kernels were not affected in ‘Fandoghi’ and ‘Akbari’. In ‘Ahmadaghaei’, refinery micronized sulfur increased the testa lightness and hue and decreased chroma probably because of the softer texture of hull in ‘Ahmadaghaei’ which breaks more easily during harvest practice. Sulfur treatments positively influenced appearance and general acceptance of ‘Ahmadaghaei’ fruits. The taste and aroma of pistachios were preserved in most of treatments. High concentration (60 kg/1000 L) of both sulfur types enhanced the ethylene production in ‘Ahmadaghaei’ which was almost same as the unripe fruit because sulfur treatments reduced the percentage of unripe fruit. In ‘Ahmadaghaei’, refinery micronized sulfur treatments increased the respiration rate.

Kernel filling and desiccation in temperate maize: Breeding and environmental effects

Sowing date (SD) modifies the environmental conditions during the kernel-filling period as well as during kernel dry down after physiological maturity (PM), particularly for late-sown maize (Zea mays L.) crops that currently cover 50 % of the maize area in Argentina. The main goal of this work was to evaluate breeding effects on kernel filling and kernel desiccation traits in a set of five commercial maize hybrids released by a single breeding program between 1980 and 2016 when grown under contrasting environments in the temperate Central Pampas region. The evolution of kernel weight (KW) and kernel moisture (KM) were assessed. Maximum KW (KWMAX), maximum kernel water content (KWCMAX), kernel filling rate (KFR), kernel filling duration (KFD) and KM at silking+20 days (KMR1+20) and PM (KMPM) were computed together with husks number. Pre-PM kernel desiccation was described using an exponential decay model with k1 as the proportionality drying coefficient. For the post-PM period, kernel desiccation was modelled assuming that the change in KM in a given period is proportional to the difference between the kernel and the equilibrium moisture contents, where k2 was the corresponding proportionality drying coefficient. For KWMAX, a genetic progress of 0.26 % year −1 (p < 0.05) was estimated from 1993 to 2016. This trend was explained by the increase registered in KFD (0.32 % year−1, p < 0.01), which was partially compensated by a reduction in KFR (−0.08 % year−1, p < 0.05). No genetic progress was detected for husks number, KWCMAX, KMR1+20, KMPM, k1 and k2, but introduction of dent germplasm produced an increase in KWCMAX, KMR1+20 and KMPM and a decrease in k2. KFR and KFD as well as kernel desiccation dynamics were strongly affected by SDs. Breeding effects that extended KFD, together with the broad adoption of late sowings, promote a reduction in k2, which must be considered for the correct assessment of trade-offs between drying costs and kernel quality penalization by pests and diseases.

The variability of maize kernel drying: sowing date, harvest scenario and year

AbstractThe extent of the reduction of maize (Zea mays L.) kernel moisture content through drying is closely related to field temperature (or accumulated temperature; AT) following maturation. In 2017 and 2018, we selected eight maize hybrids that are widely planted in Northeastern China to construct kernel drying prediction models for each hybrid based on kernel drying dynamics. In the traditional harvest scenario using the optimal sowing date (OSD), maize kernels underwent drying from 4th September to 5th October, with variation coefficients of 1.0–1.9. However, with a latest sowing date (LSD), drying occurred from 14th September to 31st October, with variation coefficients of 1.3–3.0. In the changed harvest scenario, the drying time of maize sown on the OSD condition was from 12th September to 9th November with variation coefficients of 1.3–3.0, while maize sown on the LSD had drying dates of 26th September to 28th October with variation coefficients of 1.5–3.6. In the future harvest scenario, the Fengken 139 (FK139) and Jingnongke 728 (JNK728) hybrids finished drying on 20th October and 8th November, respectively, when sown on the OSD and had variation coefficients of 2.7–2.8. Therefore, the maize kernel drying time was gradually delayed and was associated with an increased demand for AT ⩾ 0°C late in the growing season. Furthermore, we observed variation among different growing seasons likely due to differences in weather patterns, and that sowing dates impact variations in drying times to a greater extent than harvest scenarios.

Biosynthesis, structure and functionality of starch granules in maize inbred lines with different kernel dehydration rate

In this study, we report important relationships between kernel starch and kernel dehydration rate for eight maize inbred lines with different dehydration characteristics. High-throughput RNA sequencing data of starch biosynthesis-related genes showed that kernel moisture content and dehydration rate were both associated with differential expression of most starch biosynthetic genes. Especially, kernel moisture content was positively correlated with the increased expression of SBEI and SBEIIb, thereby potentially inducing biosynthesis of amylose with low molecular weight and amylopectin with low content of amylopectin chains with degree of polymerization (DP) 6-12 in inbred lines with fast kernel dehydration rate. We found a negative correlation between short amylopectin chains (DP 6-12) and the starch retrogradation rate. Hence, a low amount of amylopectin chains with DP 6-12 in the inbred lines with fast kernel dehydration rate was a plausible reason for their high short- and long-term retrogradation.

Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman's rank correlation coefficient of 0.852 between wet laboratory and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR spectra data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.

Multi-Locus Genome-Wide Association Study and Genomic Selection of Kernel Moisture Content at the Harvest Stage in Maize.

Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, PLARmEB, PKWmEB, and ISIS EM-BLASSO). Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With qKMC5.6 removed, the remaining 10 stable QTNs explained <10% of the phenotypic variation, suggesting that KMC is mainly controlled by multiple minor-effect genetic loci. A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.

Effects of different moisture content and varieties on physico–mechanical properties of maize kernel and pedicel

AbstractThe aim of this study was to determine various physical, mechanical, and pedicel strengths of 28 maize cultivars, with initial moisture content ranging between 19.24 to 36.64% (w.b.). These properties are such as slenderness ratio of maize ear, dimensions, density and 100–seed weight of kernels, picking force of kernel pedicel from cob (pedicel strength). The range of the slenderness ratio of maize ear was 24.91–36.99%. The range of weight for kernel and ear were 176.68–300.09 g, 141.78–229.22 g, respectively. In terms of the sphericity of kernels which ranges between 0.62 and 0.77 depending on the cultivars. When the kernel moisture in the range of 19.24–22.50%, the range of required force for remove one kernel from the cob is 4.71–7.94 N. When the kernel moisture is greater than 22.50%, the maximum force required is 28.50 N. The force required to remove kernels from the cob along the axial direction is greater than that at the radial direction. The knowledge of these parameters is important for the development of new threshing equipment and for reducing the losses that occurred in maize production.Practical ApplicationsMaize is one of the most versatile cereal crops of the world but has a wide application in many industries. The determination of the physical and mechanical properties of maize ear, kernel, and pedicel will provide the reference to design the process of threshing equipment, postharvest machinery, and so forth, in the processing and manufacturing industries.

Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy

The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.

Difference in corn kernel moisture content between pre- and post-harvest

The harvest method of shelling corn (Zea mays L.) kernels in the field decreases labor costs associated with transporting, drying and threshing the crop. However, it was previously found that the kernel moisture content increased after field harvest, which decreased the value of corn kernels. To identify the reasons underlying the increase, we conducted a multiyear and -area trial in the Huang-Huai-Hai Plain, China and performed a staged-harvest test at several phases of kernel dry-down. The test investigated a range of parameters such as the kernel moisture content pre- and post-harvest, the kernel breakage rate, the amount of impurities, and the moisture content of various other plant tissues. An analysis of 411 pairs of pre- and post-harvest samples found that kernel moisture content after harvest was 2.2% higher than that before harvest. In the staged-harvest test, however, a significant increase was only observed when the kernel moisture content before harvest was higher than 23.9%. The increase in post-harvest kernel moisture content was positively associated with the pre-harvest kernel moisture content, breakage rate and impurity rate. Typically, at harvest time in this region, there is a significant fraction of immature crops with a high moisture content, resulting in kernels that are prone to breakage or impurities that ultimately lead to increases in water content after harvest. Therefore, we suggest using hybrids that quickly wither late in the growing stage. Additionally, farmers should delay harvest in order to minimize the pre-harvest kernel moisture content and thus reduce breakages and impurities, thereby improving the quality of kernels after harvest and the efficiency of corn kernel farming in China.

Some physical properties of kariya (Hildegardia barteri) nut/ kernel relevant to the design of its processing equipment

Kariya kernel is very rich in essential fats, oils and other valuable nutrients which may find applications in many food formulations. To harness these nutrients, processing equipment and machines are to be used. In order to effectively design these machines, the values of some physical properties of kariya nut and kernel are needed. In this study, some physical properties of the kariya nut and kernel were investigated. Results showed that mean major diameter, intermediate diameter, minor diameter and unit mass obtained at the nut moisture content of 19.83 ± 3.71 (w.b.) were 14.16 ± 0.79 mm, 10.17 ± 0.36 mm, 9.78 ± 0.28 mm and 0.503 ± 0.05g, respectively while the corresponding values obtained at the kernel moisture content of 8.89 ± 2.22% (w.b.) were 9.07 ±0.72 mm, 7.32 ±0.49 mm, 7.08 ± 0.41 mm and 0.328 ± 0.03 g, respectively. The values of calculated geometric mean diameter were 11.20 ±mm and 7.77 ± 0.36 mm, for the kariya nut and kernel, respectively. The skewness value of the sample distribution of 0.08 and -0.24 were recorded for the kariya nut and kernel, respectively. The sphericity, surface area, volume, density, bulk density and porosity were 79.27 ± 3.07%, 394.75 23.13 mm2, 738.37 ± 64.96 mm3 , 681.1 ± 20 kg/m3, 440.24 ± 0.04 kg/m3 and 36.65 ± 0.74% ; and 85.97 ± 5.27%, 189.85 ± 17.34 mm2, 246.71 ± 33.60 mm3, 1342.1 ± 136.23 kg/m3, 773.06 ± 0.06 kg/m3 and 42.28 ± 4.10% for the kariya nut and kernel respectively.

HANDY: a device for assessing resistance to mechanical crushing of maize kernel

BackgroundHow to control the physical damage during maize kernel harvesting is a major problem for both mechanical designers and plant breeders. A limitation of addressing this problem is lacking a reliable method for assessing the relation between kernel damage susceptibility and threshing quality. The design, construction, and testing of a portable tool called “HANDY”, which can assess the resistance to mechanical crushing in maize kernel. HANDY can impact the kernel with a special accelerator at a given rotating speed and then cause measurable damage to the kernel. These factors are varied to determine the ideal parameters for operating the HANDY.ResultsBreakage index (BI, target index of HANDY), decreased as the moisture content of kernel increased or the rotating speed decreased within the tested range. Furthermore, the HANDY exhibited a greater sensitivity in testing kernels at higher moisture level influence on the susceptibility of damage kernel than that in Breakage Susceptibility tests, particularly when the centrifugation speed is about 1800 r/min and the centrifugal disc type is curved. Considering that the mechanical properties of kernels vary greatly as the moisture content changes, a subsection linear (average goodness of fit is 0.9) to predict the threshing quality is built by piecewise function analysis, which is divided by kernel moisture. Specifically, threshing quality is regarded as a function of the measured result of the HANDY. Five maize cultivars are identified with higher damage resistance among 21 tested candidate varieties.ConclusionsThe HANDY provides a quantitative assessment of the mechanical crushing resistance of maize kernel. The BI is demonstrated to be a more robust index than breakage susceptibility (BS) when evaluating threshing quality in harvesting in terms of both reliability and accuracy. This study also offers a new perspective for evaluating the mechanical crushing resistance of grains and provides technical support for breeding and screening maize varieties that are suitable for mechanical harvesting.

Rapid and Non-destructive measurement of moisture content of peanut (Arachis hypogaea L.) kernel using a near-infrared hyperspectral imaging technique

This study used a rapid and non-destructive way of determining and predicting the moisture content (MC) of peanut kernels using hyperspectral imaging in the near-infrared region (900–1700 nm). Using partial least square regression (PLSR), spectral data from the peanut kernel hyperspectral images were extracted to predict MC. The MC PLSR model displayed good performance with determination coefficient of calibration (R2c), validation (R2v) and prediction (R2p) of 0.9309, 0.9083 and 0.9368, respectively. Also, the root-mean-square error of calibration (RMSEC), cross-validation (RMSEV), and prediction (RMSEP) of 1.6978, 1.9571, and 1.8715, respectively, were achieved. Optimization was done by selecting wavelengths with the highest absolute weighted regression coefficients; on this basis, 20 significant wavelengths were identified for further analysis. These wavelengths were used to build an optimized regression model which resulted in R2c of 0.9357, R2v of 0.9133, and R2p of 0.9445 as well as RMSEC, RMSEV, and RMSEP of 1.6822, 1.8316 and 1.9519, respectively. The optimized model has applied to the peanut kernel hyperspectral images in a pixel-wise manner obtaining peanut kernel moisture content distribution map. Results show promising potential of the hyperspectral imaging system in the near-infrared region combined with partial least square regression (PLSR) for rapid and non- destructive prediction of moisture content of peanut kernels.

Mısır bitkisinde sulamayı sonlandırma zamanının tane verimi, hasatta tane nemi ve bazı tarımsal özellikler üzerine etkileri

This study was conducted to investigate the effects of different irrigation termination dates on maize grain yield, kernel moisture at harvest and some other agronomic traits. The relationships between irrigation termination dates and investigated traits were visually assessed through biplot analysis. Experiments were conducted for two years (2016 and 2017) with three replications. Irrigations were terminated at eight different dates (S1: R2 blister stage; S2: R3 early milk stage; S3: R3 milk stage; S4: R3 late milk stage; S5: R4 dough stage; S6: R5 early dent stage; S7: R5 dented stage; S8: R5 late dent stage). Despite low kernel harvest moisture levels, early irrigation termination dates had low yield levels (S1:10460 kg ha-1 grain yield; 11.7% moisture). However, despite high yield levels, late irrigation termination dates had high kernel harvest moisture levels (S7:16100 kg ha-1 grain yield; and 18.8% moisture; S8:16240 kg ha-1 grain yield and 19.6% moisture). In terms of grain yield and harvest moisture, S4 (14050 kg ha-1 grain yield and 14.2% harvest moisture), S5 (14170 kg/ha grain yield and 15.0% moisture) and S6 (14880 kg ha-1 grain yield and 16.1% moisture) treatments had similar values and were considered to be as the most appropriate irrigation termination dates. Kernel weight per ear varied between 150.6 (S1) and 238.7 g (S7); number of kernels per ear varied between 677 (S1) and 741 kernels (S5), ear lengths varied between 16.7 (S1) - 24.1 cm (S4), thousand-kernel weights varied between 271 g (S1) and 381 g (S8), hectoliter weights varied between 74.3 (S1) and 78.1 kg (S4) and number of days to physiological maturity varied between 112.5 (S1) and 124.5 days (S8).