Detection of hidden insect Sitophilus oryzae in wheat by low-field nuclear magnetic resonance: Poster

Insects, either adults or larvae, living inside grains are difficlut to detect but can cause enormous loss of grain. Therefore, we explored the use of low-field nuclear magnetic resonance (LF-NMR) techniques to detect Sitophilus oryzae hidden inside wheat. Significant difference in transverse relaxation times (T2/ms) and the T2 components proportion (P2/%) was observed between wheat and S. oryzae at its four different growth stages (small larvae, large larve stage, pupal stage and adult stage). The transverse relaxation signals on the infested wheat kernels varied with S. oryzae developmental stages. LF-NMR image of uninfested wheat were very different than infested wheat with the hidden insects at its four growth stages. Therefore, LF-NMR, as a novel non-destructive method, could be used to detect insects hidden in grains to take necessary management against pest damage to grains during storage.Insects, either adults or larvae, living inside grains are difficlut to detect but can cause enormous loss of grain. Therefore, we explored the use of low-field nuclear magnetic resonance (LF-NMR) techniques to detect Sitophilus oryzae hidden inside wheat. Significant difference in transverse relaxation times (T2/ms) and the T2 components proportion (P2/%) was observed between wheat and S. oryzae at its four different growth stages (small larvae, large larve stage, pupal stage and adult stage). The transverse relaxation signals on the infested wheat kernels varied with S. oryzae developmental stages. LF-NMR image of uninfested wheat were very different than infested wheat with the hidden insects at its four growth stages. Therefore, LF-NMR, as a novel non-destructive method, could be used to detect insects hidden in grains to take necessary management against pest damage to grains during storage

Comparative role of non-stress test and colour doppler in high risk pregnancy predicted by placental histopathology and foetal outcome

Background: Assessment of the foetal wellbeing is done by various biophysical methods. Non stress test (NST) is the most commonly used test for antepartum evaluation of foetal status. It involves the use of doppler-detected foetal heart rate acceleration coincident with foetal movement perceived by mother. Duplex sonography and its off-shoot, colour duplex sonography, are relatively newer methods that combine the pulsed echo technique of sectional image formation with the doppler evaluation of blood flow.Methods: The comparative study was carried out on 200 booked term pregnant patients in the department of Obstetrics and Gynaecology, Dr. S. N. Medical College, Jodhpur Rajasthan, India. All patients were subjected to non-stress test and colour doppler and were evaluated for placental histopathology and foetal outcome in terms of low APGAR score, number of NICU admissions and perinatal mortality.Results: In our study it was found that in high-risk group 25% had non-reassuring NST and 19% had doppler findings suggestive of foetal hypoxia. In the control group 13% had non-reassuring NST and 4% had doppler findings suggestive of foetal hypoxia. It was seen that when either NST was non-reassuring or colour doppler suggested foetal hypoxia or both, these patients required admissions antenatally, had meconium stained liquor suggestive of foetal distress, had operative delivery for foetal distress, had low APGAR score, required NICU admission, and higher perinatal mortality.Conclusions: Doppler and NST are effective in predicting a normal healthy foetus. Doppler depicts chronic hypoxic changes while NST can detect acute events in presence or absence of chronic hypoxia

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (<i>Linum usitatissimum</i>)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 °C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds’ root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Quality Assessment of Dried White Mulberry (<i>Morus alba L.)</i> Using Machine Vision

Over the past decade, the fresh white mulberry (Morus alba L.) fruit has gained growing interest due to its superior health and nutritional characteristics. While white mulberry is consumed as fresh fruit in several countries, it is also popular in dried form as a healthy snack food. One of the main challenges that have prevented a wider consumer uptake of this nutritious fruit is the non-uniformity in its quality grading. Therefore, identifying a reliable quality grading tool can greatly benefit the relevant stakeholders. The present research addresses this need by developing a novel machine vision system that combines the key strengths of image processing and artificial intelligence. Two grades (i.e., high- and low-quality) of white mulberry were imaged using a digital camera and 285 colour and textural features were extracted from their RGB images. Using the quadratic sequential feature selection method, a subset of 23 optimum features was identified to classify samples into two grades using artificial neural networks (ANN) and support vector machine (SVM) classifiers. The developed system under both classifiers achieved the highest correct classification rate (CCR) of 100%. Indeed, the latter approach offered a smaller mean squared error for the training and test sets. The developed model’s high accuracy confirms the machine vision’s suitability as a reliable, low-cost, rapid, and intelligent tool for quality monitoring of dried white mulberry

Implications of Blending Pulse and Wheat Flours on Rheology and Quality Characteristics of Baked Goods: A Review

Bread is one of the most widely consumed foods in all regions of the world. Wheat flour being its principal ingredient is a cereal crop low in protein. The protein content of a whole grain of wheat is about 12&ndash;15% and is deficit in some essential amino acids, for example, lysine. Conversely, the protein and fibre contents of legume crops are between 20 and 35% and 15 and 35%, respectively, depending on the type and cultivar of the legume. The importance of protein-rich diets for the growth and development of body organs and tissues as well as the overall functionality of the body is significant. Thus, in the last two decades, there has been a greater interest in the studies on the utilization of legumes in bread production and how the incorporation impacts the quality characteristics of the bread and the breadmaking process. The addition of plant-based protein flours has been shown to produce an improved quality characteristic, especially the nutritional quality aspect of bread. The objective of this review is to synthesize and critically investigate the body of research on the impact of adding legume flours on the rheological attributes of dough and the quality and baking characteristics of bread

Effect of Laser Biostimulation on Germination of Sub-Optimally Stored Flaxseeds (Linum usitatissimum)

Sub-optimal storage of grains could deteriorate seed germination and plant viability. Recent research studies have established that laser biostimulation of seeds could be used as a safe and sustainable alternative to chemical treatment for improving crop germination and growth. Herein, the efficacy of this novel technique is evaluated to see if poor germinability caused by sub-optimal storage of flaxseeds (Linum usitatissimum) could be reversed using laser biostimulation. Healthy flaxseeds were first subjected to sub-optimal storage conditions (30 &deg;C for ten weeks) to degrade their germinability. Two low-cost lasers, including a single-wavelength red laser (659 nm) and a dual-wavelength green/infrared laser (531 and 810 nm (ratio ~10:1)) were then used on two groups viz. healthy (properly stored) and sub-optimally stored (artificially degraded (AD)) seeds and irradiated for 0 (control), 5, 10, and 15 min using total power densities of 7.8 and 6.2 mW/cm2, respectively. In the case of AD seeds, 5-min dual-wavelength laser treatment was found to be the most efficient setting as it improved the mean germination percentage, mean germination time, germination speed, germination rate index, wet weight, and dry weight by 29.3, 16.8, 24.2, 24.2, 15.7, and 20.6%, respectively, with respect to control samples. In the case of healthy seeds, dual-wavelength laser treatment could induce significant enhancement in seeds&rsquo; root length, wet weight, and dry weight (improved by 26, 23, and 8%, respectively) under 10 min of irradiation. On the other hand, the effect of applied red laser treatment was not very promising as it could only induce significant enhancement in the mean germination time of AD seeds (improved by 17%). Overall, this study demonstrates the potential of laser biostimulation in reversing the adverse effect of poor crop storage. We believe these findings could spur the development of a physical tool for manipulating seed germination and plant growth

Quality Assessment of Dried White Mulberry (Morus alba L.) Using Machine Vision

Over the past decade, the fresh white mulberry (Morus alba L.) fruit has gained growing interest due to its superior health and nutritional characteristics. While white mulberry is consumed as fresh fruit in several countries, it is also popular in dried form as a healthy snack food. One of the main challenges that have prevented a wider consumer uptake of this nutritious fruit is the non-uniformity in its quality grading. Therefore, identifying a reliable quality grading tool can greatly benefit the relevant stakeholders. The present research addresses this need by developing a novel machine vision system that combines the key strengths of image processing and artificial intelligence. Two grades (i.e., high- and low-quality) of white mulberry were imaged using a digital camera and 285 colour and textural features were extracted from their RGB images. Using the quadratic sequential feature selection method, a subset of 23 optimum features was identified to classify samples into two grades using artificial neural networks (ANN) and support vector machine (SVM) classifiers. The developed system under both classifiers achieved the highest correct classification rate (CCR) of 100%. Indeed, the latter approach offered a smaller mean squared error for the training and test sets. The developed model&rsquo;s high accuracy confirms the machine vision&rsquo;s suitability as a reliable, low-cost, rapid, and intelligent tool for quality monitoring of dried white mulberry

A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis

This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes was analyzed using several machine learning tools including the synthetic minority oversampling technique (SMOTE), information gain (IG), ReliefF, and least absolute shrinkage and selection operator (LASSO), along with various classifiers (BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest). We identified 439 differentially expressed genes (DEGs), of which only three were down-regulated (AT3G20810, AT1G31680, and AT1G30250). The performance of the top 20 genes selected by IG and ReliefF was evaluated using the classifiers mentioned above to classify stressed versus non-stressed samples. The random forest algorithm outperformed other algorithms with an accuracy of 97.91% and 98.51% for IG and ReliefF, respectively. Additionally, 42 genes were identified from all 30,380 genes using LASSO regression. The top 20 genes for each feature selection were analyzed to determine three common genes (AT5G44050, AT2G47180, and AT1G70700), which formed a three-gene signature. The efficiency of these three genes was evaluated using random forest and XGBoost algorithms. Further validation was performed using an independent RNA_seq dataset and random forest. These gene signatures can be exploited in plant breeding to improve stress tolerance in a variety of crops

Assessing the Effects of Free Fall Conditions on Damage to Corn Seeds: A Comprehensive Examination of Contributing Factors

Corn is a staple food crop grown in over 100 countries worldwide. To meet the growing demand for corn, losses in its quality and quantity should be minimized. One of the potential threats to the quality and viability of corn is mechanical damage during harvesting and handling. Despite extensive research on corn, there is a lack of reliable data on the damage its seeds undergo when they are subjected to mechanical impact against different surfaces during handling and transportation. This study is designed to investigate the effects of (a) drop height (5, 10, and 15 m) during free fall, (b) impact surface (concrete, metal, and seed to seed), seed moisture content (10, 15, 20, and 25% w.b), and ambient temperature (−10 and 20 °C) on the percentage of physical damage (PPD) and physiological damage to corn seeds. The PPD and the extent of physiological damage were determined as the percentage of seed breakage and the percentage of loss in germination (PLG), respectively. The latter parameter was specifically chosen to evaluate seeds that showed no visible external damage, thus enabling the assessment of purely internal damage that PPD did not capture. This approach enabled a comprehensive analysis of free fall’s influence on the seeds’ quality and viability, providing a complete picture of the overall impact. Total damage was then calculated as the sum of PPD and PLG. An evaluation and modeling process was undertaken to assess how corn seed damage depends on variables such as drop height, moisture content, impact surfaces, and temperatures. The results revealed that seeds dropped onto metal surfaces incurred a higher total damage (15.52%) compared to concrete (12.86%) and seed-to-seed abrasion (6.29%). Greater total damage to seeds was observed at an ambient temperature of −10 °C (13.66%) than at 20 °C (9.46%). Increased drop height increased seeds’ mass flow velocity and correspondingly caused increases in both physical and physiological damage to seeds. On the other hand, increased moisture levels caused a decreasing trend in the physical damage but increased physiological damage to the seeds. The limitations of the developed models were thoroughly discussed, providing important insights for future studies. The results of this study promise to deliver substantial benefits to the seed/grain handling industry, especially in minimizing impact-induced damage