Search

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved ( 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

As the demand for food surges and the agricultural sector undergoes a transformative shift towards sustainability and efficiency, the need for precise and proactive measures to ensure the health and welfare of livestock becomes paramount. In the egg and hatchery industry, hyperspectral imaging (HSI) has emerged as a cutting-edge, non-destructive technique for fast and accurate egg quality analysis, including detecting chick embryo mortality. However, the high cost and operational complexity compared to conventional RGB imaging are significant bottlenecks in the widespread adoption of HSI technology. To overcome these hurdles and unlock the full potential of HSI, a promising solution is hyperspectral image reconstruction from standard RGB images. This study aims to reconstruct hyperspectral images from RGB images for non-destructive early prediction of chick embryo mortality. Initially, the performance of different image reconstruction algorithms, such as HRNET, MST++, Restormer, and EDSR were compared to reconstruct the hyperspectral images of the eggs in the early incubation period. Later, the reconstructed spectra were used to differentiate live from dead embryos eggs using the XGBoost and Random Forest classification methods. Among the reconstruction methods, HRNET showed impressive reconstruction performance with MRAE of 0.0955, RMSE of 0.0159, and PSNR of 36.79 dB. This study motivated the idea that harnessing imaging technology integrated with smart sensors and data analytics has the potential to improve automation, enhance biosecurity, and optimize resource management towards sustainable agriculture 4.0.

Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.

Abstract Background Globally, non-communicable diseases (NCDs) are increasingly the primary cause of mortality and morbidity among women. Like many developing countries, Bangladesh also faces a growing burden of NCDs. The “Multisectoral Action Plan for Prevention and Control of Non-communicable Diseases, 2018–2025” signifies Bangladesh’s commitment to comprehensively combating the rising burden of NCDs. This study investigates the perceptions of those involved in developing the action plan and if/how a gender lens was incorporated into its implementation. Methods In-depth interviews were conducted with 25 key individuals involved in a high-level committee to develop and implement Bangladesh’s multisectoral action plan to address the burden of NCDs. Data were collected between July and November 2021, and thematic analysis was conducted. Results The findings revealed that interviewees believed the multisectoral action plan adopted a population-wide approach without considering gender-specific needs. This study presents the explanations for this inattention under five themes: (1) A population-level approach to NCD prevention; (2) Understanding women’s health beyond reproductive health; (3) Absence of gender-specific programs; (4) Lack of consideration of gender constraints on physical activity; and (5) Lack of collaborative efforts to address NCDs beyond the health ministry. Conclusion In conclusion, governments in countries like Bangladesh can develop more effective strategies to reduce the disease burden of NCDs among women by recognizing and addressing the gendered nature of preventive health. This can be achieved by promoting gender-responsive research, programs, and policy initiatives that consider women’s specific health concerns, ultimately leading to better health outcomes for all.

This study addresses the challenge of effectively selecting relevant variables and providing interpretable insights in spectroscopic analysis (1100–2498 nm) of corn quality (moisture, fat, protein, and starch), incorporating variable selection techniques and explainable artificial intelligence (AI). Three variable selection algorithms were used to select 36 important variables, and through combinatorial optimization, only 11 common wavelengths were chosen. Partial least squares regression (PLSR) models were developed using the full spectral range, individual variables, and common variables. Based on the root mean square errors of prediction (RMSEP), the PLSR models with common variables outperformed individual feature variables for starch (0.22 % vs. 0.24 %) and were comparable for oil (0.05 % vs. 0.05 %), water (0.02 % vs. 0.01 %) and protein (0.12 % vs. 0.09 %). The SHapley Additive exPlanation (SHAP) method was employed to explain the PLSR model and assess the contribution of common variables. This combined approach significantly improved the accuracy and interpretability of corn prediction models, providing trust in the results and facilitating the understanding of the relationship between spectral features and corn quality attributes.

The potential of hyperspectral imaging technology (HIT) for the determination of physicochemical and nutritional components, evaluation of fungal/mycotoxins contamination, wheat varieties classification, identification of non-mildew-damaged wheat kernels, as well as detection of flour adulteration is comprehensively illustrated and reviewed. The latest findings (2018–2023) of HIT in wheat quality evaluation through internal and external attributes are compared and summarized in detail. The limitations and challenges of HIT to improve assessment accuracy are clearly described. Additionally, various practical recommendations and strategies for the potential application of HIT are highlighted. The future trends and prospects of HIT in evaluating wheat quality are also mentioned. In conclusion, HIT stands as a cutting-edge technology with immense potential for revolutionizing wheat quality evaluation. As advancements in HIT continue, it will play a pivotal role in shaping the future of wheat quality assessment and contributing to a more sustainable and efficient food supply chain.

BackgroundThe global COVID-19 pandemic profoundly impacted nations worldwide, and Bangladesh was no exception. In response, the government of Bangladesh implemented community awareness initiatives aimed at containing the spread of the virus, aligned with international guidelines and recommendations. Despite these efforts, a lack of comprehensive community awareness programs played an essential role during the pandemic, not the preventive measures. A qualitative study employing framing theory was conducted to gain a deeper insight into how the social context influenced risk communication and community response throughout the COVID-19 pandemic in Bangladesh.MethodsThe study was conducted in four selected districts of Bangladesh from February to May 2022 using complementary data collection methods, including key informant interviews, in-depth interviews, and focus group discussions with purposely selected participants. Data were analyzed thematically by following six steps of the thematic analysis process. Codes were developed based on the data and summarized into themes and sub-themes grounded on the codes.ResultsThe findings indicate that the government of Bangladesh, along with development partners and non-government organizations, made a significant effort to raise awareness about COVID-19 in the community. However, there were certain limitations to this effort. These include a lack of social science and public health approaches to understanding the pandemic; inadequate coordination among the authorities for COVID-19 prevention and control; technological and geographical barriers for disseminating messages; the living conditions and lack of facilities; socio-cultural norms in understanding the COVID-19 health messages, and the gendered understanding of the messages. The findings also revealed that the awareness activities remained a one-way approach to inform the people and faced challenges to actively engage and create ownership of the community in the pandemic response.ConclusionThe study identified gaps in implementing risk communication and community engagement strategies in Bangladesh during the COVID-19 pandemic. Increasing focus on public health and prioritizing community ownership is essential to designing a more effective community awareness campaign. This approach will help ensure that health messages are communicated effectively and tailored to different communities’ needs.

Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400–1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0–98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R2p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R2p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method.

Introduction: COVID-19 pandemic induced lockdown as prevention and control measure, forced people globally to limit their movements and to stay at home for extended period of time. The objective of this study was to analyze the impact of lockdown on intimate partner violence in Bangladesh. Methods: We conducted a secondary research by employing a Poisson regression model to estimate the effect of pandemic-led lockdown policy on the change in the number of intimate partner violence-related calls during pandemic using national emergency helpline 999 call logs. Data from January 2019 to May 2020 for 64 districts produced 1088 district-month-year observations which had been used for the main analysis. Results: We found a 46% decrease in the incidence rate of intimate partner violence-related calls during the pandemic after adjusting for year, month, district fixed-effects—suggesting, non-reporting of the violence might have exacerbated during lockdown. Conclusion: While increasing rate of intimate partner violence is one side of issue, non-reporting of it has received less attention and during the lockdown non-reporting might grow large and have severe health impacts for women.

Fraud detection in meat is a challenging task for researchers, consumers, industries, and regulatory agencies. Traditional approaches for fraud detection are time-consuming, complicated, laborious, and expensive; they require technical skills. Therefore, much effort has been devoted in academia and industry to developing rapid and nondestructive optical techniques for fraud detection in meat. Among them, hyperspectral imaging has gained enormous attention and curiosity throughout the world. Hyperspectral imaging is an emerging analytical technique that combines spectroscopy and imaging in one system to acquire spectra and spatial information from an object simultaneously. Hyperspectral imaging is the only analytical technology that answers commonly asked analytical questions such as what chemical species are in the samples, how much, and most importantly, where they are located. Therefore, the technology will undoubtedly play indispensable roles in research and industry for fraud detection in the coming days.