On-site Assessment Research Articles

Design analysis of a sustainable techno-economic hybrid renewable energy system: Application of solar and wind in Sigulu Island, Uganda

Sustainable energy is central to achieving the global Sustainable Development Goals (SDGs), particularly in electrifying underserved communities. This study examines Sigulu Island, which lacks grid electricity and relies on costly, polluting diesel generators. On-site assessments revealed a daily load demand of 1,455.705 kWh. This study designed and analyzed a Sustainable Techno-economic Hybrid Renewable Energy System (STHRES) combining solar photovoltaics and wind turbines, with battery backup, to meet the island's energy needs. The research adopted both qualitative and quantitative methods, gathering atmospheric weather conditions specific to Sigulu Island. Solar panels and wind turbines were identified as the most viable options, with the system incorporating 677 units of 1 kW solar panels and 27 units of 1 kW wind turbines, generating 839.97 kW and 640.08 kW daily, respectively. Additionally, 527 Li-ion batteries were used to store approximately 1480.05 kW of surplus energy demand to manage fluctuations capable of sustaining the island for 8 hours during a total blackout. The initial installation costs are estimated at $90,393.04 for solar PV, $27,729.82 for wind turbines, $159,169.81 for batteries, and $92,407.00 for the inverter. The STHRES is projected to save $56,917.93 annually, covering 15.4 % of the installation costs compared to diesel operations. Moreover, this system will reduce Uganda's carbon footprint by 436,035.6 kgCO₂ annually, equivalent to a 0.01 % reduction in national emissions. The proposed system decreases the Net Present Cost (NPC) from $426,617.60 to $369,699.67 and the Cost of Energy (COE) from $32.12/kWh to $ 27.79/kWh. With a 9 % Internal Rate of Return (IRR) and a 3 % Return on Investment (ROI), STHRES has a payback period of 8.2 years, demonstrating its financial and environmental benefits for Sigulu Island.

Rapid on-site differentiation of turbot from different culture modes using miniaturized near infrared spectroscopy coupled with interpretable machine learning

Culture modes have a significant impact on the quality of farmed fish, such as turbots. It is urgently needed to monitor and manage turbot quality during the entire aquaculture process. Recent advances in miniaturized near infrared spectroscopy allow rapid, on-site, and non-invasive analysis of biological tissues. This proof-of-principle investigation tested whether miniaturized near infrared spectroscopy techniques, coupled with interpretable machine learning, could be applied in living turbots to differentiate their culture modes. In this work, spectra of turbots of two culture modes were collected, and spectra analysis including preliminary preprocessing, principal component analysis, machine learning modeling, and subsequent interpretation were conducted. The results show that, compared with linear discriminative analysis, support vector machine, and random forest, an XGBoost model with proper preprocessed spectra data achieved the best differentiation performance, with validation accuracy as high as 97.62 %. The precision, recall, and F1-score were 97.37 %, 97.92 %, and 97.58 %, respectively. The model interpretation results show that the absorbance at 1542.0 nm, 1415.6 nm, and 1412.3 nm held dominant positions in determining turbot culture mode. Miniaturized NIR spectroscopy coupled with machine learning proved valuable for on-site noninvasive assessment of turbot properties in vivo, providing a promising technique for more efficient and sustainable aquaculture.

Effects of Different Light Conditions on Anatomical and Histological Features of Galls in Bacterial Gall Disease of Cerasus × yedoensis.

Cerasus × yedoensis (cherry 'Somei-yoshino' Fujino) is affected by bacterial gall disease caused by Pseudomonas syringae pv. cerasicola (PSC). C. × yedoensis is often infected with PSC under weak light intensity, which indicates that susceptibility of C. × yedoensis to PSC is affected by light. To evaluate the effects of white light intensity and different light qualities, white or blue, on bacterial gall disease development, we quantitatively assessed the anatomical and histological features of bacterial-inoculated sites on branches of 2-year-old potted C. × yedoensis seedlings grown under different light intensities and qualities. The stronger the white light intensity, the less severe the gall symptoms. Gall formation was suppressed more by blue than white light of the same intensity. The validity of a simple gall index for assessing gall development with the naked eye, via quantitative evaluation of gall shape by measuring gall height, width, and volume, showed that the gall index could be used as a practical method for on-site assessments of gall development. The ratio of degeneration area in the gall remained constant, suggesting the presence of some regulatory mechanism preventing PSC from affecting the entire gall within the plant. Microscopy showed that the gall tissue is composed primarily of callus cells and has voids containing gummy material that is exuded from cracks in the gall, and the periderm develops at the gall foot but not at the gall apex, so the cells at the gall apex were necrotic or collapsed.

Pedestrian Movement Evaluation: Amman, Jordan

This study examines walkability in Amman’s residential areas, focusing on pedestrian safety and satisfaction. Three neighborhoods underwent surveys and focus group discussions. Statistical methods were used for data evaluation, while on-site assessments and photographic documentation complemented quantitative data. Main pedestrian issues included inadequate infrastructure and lack of traffic enforcement. Residents showed positive attitudes towards walkability but expressed concerns about safety and convenience. The findings stress the need for improved design, planning laws, and pedestrian infrastructure. These results serve as a valuable resource for policymakers and urban planners facing similar challenges.

Diagnosis and intervention in civil construction pathologies

Civil engineering consistently encounters challenges related to the durability and safety of buildings, making the study of pathological manifestations a critical area of inquiry. These defects not only undermine the functionality, structural integrity, and aesthetic quality of constructions but also pose significant risks to occupant safety. It is imperative to understand the origins and progression of these pathologies to mitigate structural and functional damage. The application of technical standards, while essential, is often inadequate, thereby exacerbating the occurrence of these problems. This research seeks to identify the underlying causes and origins of building pathologies and to propose effective strategies for their prevention and remediation. Through an interdisciplinary methodology, incorporating diverse sources of information such as bibliographic surveys, technical site visits, and data analysis, the study identifies key pathological manifestations in buildings, including fissures, cracks, concrete deterioration, stains, and efflorescence. The research highlights critical contributing factors such as design errors, material defects, execution failures, and insufficient maintenance. The primary aim of this study is to investigate the principal causes of pathologies and pathological manifestations in a building located in the municipality of Nanuque, and to propose preventative and corrective measures. The research methodology is divided into two distinct phases: an extensive literature review to provide a theoretical foundation, enabling the analysis of prior studies on the subject, followed by a case study involving direct on-site assessments to document the identified pathologies. The findings reveal a range of issues, including reinforcement corrosion, concrete segregation, cracks, fissures, and efflorescence. Informed by the literature, the study proposes appropriate interventions to enhance the safety, stability, aesthetics, and comfort of the building's occupants.

Developing and Validating a Multimodal Dataset for Neonatal Pain Assessment to Improve AI Algorithms With Clinical Data.

Using Artificial Intelligence (AI) for neonatal pain assessment has great potential, but its effectiveness depends on accurate data labeling. Therefore, precise and reliable neonatal pain datasets are essential for managing neonatal pain. To develop and validate a comprehensive multimodal dataset with accurately labeled clinical data, enhancing AI algorithms for neonatal pain assessment. An assessment team randomly selected healthy neonates for assessment using the Neonatal Pain, Agitation, and Sedation Scale. During painful procedures, 2 cameras recorded neonates' pain reactions on site. After 2weeks, assessors labeled the processed pain data on the EasyDL platform in a single-anonymized setting. The pain scores from the 4 single-modal data types were compared to the total pain scores derived from multimodal data. The On-Site Neonatal Pain Assessment completed using paper quality scales is referred to as OS-NPA, while the modality-data neonatal pain labeling performed using labeling software is MD-NPL. The intraclass correlation coefficient among the 4 single-modal groups ranged from 0.938 to 0.969. The overall pain intraclass correlation coefficient score was 0.99, with a Kappa statistic for pain grade agreement of 0.899. The goodness-of-fit for the linear regression models comparing the OS-NPA and MD-NPL for each assessor was greater than 0.96. MD-NPL represents a productive alternative to OS-NPA for neonatal pain assessment, and the validity of the data labels within the Multimodality Dataset for Neonatal Acute Pain has been validating. These findings offer reliable validation for algorithms designed to assess neonatal pain.

On-site burn severity assessment using smartphone-captured color burn wound images

Accurate assessment of burn severity is crucial for the management of burn injuries. Currently, clinicians mainly rely on visual inspection to assess burns, characterized by notable inter-observer discrepancies. In this study, we introduce an innovative analysis platform using color burn wound images for automatic burn severity assessment. To do this, we propose a novel joint-task deep learning model, which is capable of simultaneously segmenting both burn regions and body parts, the two crucial components in calculating the percentage of total body surface area (%TBSA). Asymmetric attention mechanism is introduced, allowing attention guidance from the body part segmentation task to the burn region segmentation task. A user-friendly mobile application is developed to facilitate a fast assessment of burn severity at clinical settings. The proposed framework was evaluated on a dataset comprising 1340 color burn wound images captured on-site at clinical settings. The average Dice coefficients for burn depth segmentation and body part segmentation are 85.12 % and 85.36 %, respectively. The R2 for %TBSA assessment is 0.9136. The source codes for the joint-task framework and the application are released on Github (https://github.com/xjtu-mia/BurnAnalysis). The proposed platform holds the potential to be widely used at clinical settings to facilitate a fast and precise burn assessment.

Near-Infrared Spectroscopy in Animal Nutrition: Historical Insights, Technical Principles, and Practical Applications

Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR’s growing role in promoting more sustainable and efficient animal production systems.

Climate change stands as the new challenge for whale watching and North Pacific gray whales (Eschrichtius robustus) in Bahia Magdalena, Mexico, after their recovery from overexploitation

IntroductionSocial-ecological systems (SES) recognize the intricate relationship between human activities and the environment and advocate for comprehensive approaches to address complex environmental issues. This study investigates the factors influencing whale watching, particularly the gray whale (Eschrichtius robustus) in northern Mexico, after significant recovery following overexploitation. Despite reaching a peak in 2015, the eastern North Pacific gray whales experienced unusual mortality events (UME), the most recent from 2019 to 2023, leading to a population decline and historically low calf production in 2022. This decline is evident in the reduced presence of whales and calves in winter breeding lagoons. Concurrently, whale watching has become a significant tourist attraction in these areas.MethodsOur objective was to develop a mental model of the SES of gray whale watching, integrating ecological and socioeconomic data to identify key variables and interactions that support system resilience. From an ecological perspective, we analyzed the long-term temporal trends of eight years of gray whale counts in the southernmost breeding and nursing lagoons within the Bahía Magdalena–Bahía Almejas Complex, Mexico. Additionally, we incorporated the current discussion in the literature about the potential impact of global climate change on gray whale populations. In the socioeconomic subsystem, we used participatory methods, including interviews, surveys, and workshops with government officials, tourism operators, and visitors. We also added on-site assessments of compliance with welfare regulations to understand tourism dynamics. ResultsOur findings identified as main external stressors the changes in ice levels in feeding areas and sea warming in breeding areas, and as internal stressors the compliance with official regulations and the number of vessels observing whales at the same time. The key socioeconomic factor of the system was tourist satisfaction, influenced by factors such as the quality of the information provided by operators. DiscussionUltimately, our mental model provides a framework for further exploration of relevant interactions and trajectories, offering insights for developing effective management strategies.

Accuracy of coronary computed tomography angiography-derived quantitative flow ratio for onsite assessment of coronary lesions.

Coronary computed tomography angiography (CCTA)-derived Murray law-based quantitative flow ratio (CT-μFR) is a novel non-invasive method for fast computation of fractional flow reserve (FFR) from CCTA images, yet its diagnostic performance remains to be prospectively validated. We aimed to evaluate the diagnostic performance of onsite CT-μFR in patients with coronary artery disease. This prospective, single-centre trial enrolled patients with ≥1 lesion with 30-90% diameter stenosis on CCTA and planned invasive coronary angiography (ICA) within 30 days. CT-μFR, ICA-derived μFR and FFR were evaluated separately in a blinded fashion. The primary endpoint was the diagnostic accuracy of CT-μFR in identifying patients with haemodynamically significant coronary stenosis defined by the invasive standard: FFR ≤0.80, or μFR ≤0.80 when FFR was not available. Between December 2020 and August 2023, 260 patients were consecutively enrolled. Paired comparison between CT-μFR and the invasive standard was obtained in 706 vessels from 260 patients. The patient-level accuracy of CT-μFR was 89.6% (95% confidence interval [CI]: 85.9-93.4%), which was significantly higher than the prespecified target of 72.0% (p<0.001). Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios for CT-μFR were 93.1%, 86.1%, 87.1%, 92.5%, 6.7, and 0.1, respectively. Out of the 231 vessels investigated by FFR, the accuracy of CT-μFR in vessels without extensive calcification was non-inferior to that of μFR (90.6% vs 88.9%; difference=1.8% [95% CI: -2.8 to 5.5%]; p for non-inferiority<0.001). The study met its prespecified primary endpoint of the diagnostic accuracy of CT-μFR in identifying patients with haemodynamically significant coronary stenosis. CT-μFR was non-inferior to ICA-derived μFR in vessels without extensive calcification. (ClinicalTrials.gov: NCT04665817).

Research on the comprehensive child life intervention program (CCLIP) for adjusting medical fear in children with central nervous system (CNS) cancers: a randomized controlled trial study protocol

BackgroundMedical fear is a common psychological reaction in hospitalized children, especially during radiotherapy for central nervous system (CNS) cancers. This fear not only causes negative emotions such as anxiety and depression but also affects children’s quality of life and treatment outcomes. It is exacerbated by factors such as unfamiliar environments during radiation therapy and separation from parents. Child Life, as a professional service, offers physical and mental support to children through medical understanding and psychological preparation, addressing their social and psychological needs, among other things. This study aims to construct a comprehensive Child Life intervention program (CCLIP), consisting of four key components: psychological adjustment and preparation, therapeutic play, pain management and coping strategies, and family support. The integration of effective intervention methods aims to reduce medical fear in children undergoing radiotherapy, promote psychological well-being, improve treatment compliance, and enhance quality of life.MethodsThis study is a protocol for a randomized controlled trial. Using a random number table method, we plan to recruit 38 eligible children who meet the inclusion criteria and then randomize them into two distinct groups: the intervention group and the control group. The intervention group will receive the CCLIP, and the control group will receive standardized care. Data will be collected through questionnaires and on-site assessments during the one-month intervention period at four distinct time points: the day of admission (T0), the first radiotherapy positioning (T1), mid-radiotherapy (T2), and postradiotherapy (T3). The primary outcome measure is the effectiveness of the CCLIP in reducing medical fear among children receiving radiation treatment for CNS cancers. Secondary outcomes include anxiety, depression, radiation adherence, quality of life among children, and parental satisfaction.DiscussionThis study aims to alleviate medical fear among children with CNS tumors undergoing radiotherapy through the implementation of the CCLIP while enhancing their mental health and quality of life. The expected outcomes of this research include providing effective intervention strategies for clinical practice, improving the treatment experience and long-term prognosis of children, and having positive impacts on children and their families.Trial registrationThis study is registered at the Chinese Clinical Trial Registry, ChiCTR2400082622. Registered 2 April, 2024.

Heritage BIM (HBIM) applied in emergency scenarios: a case study of the National Museum in Brazil

Heritage buildings, integral to preserving culture and history, play a vital role in shaping regional identity and collective memory. To address the intricate complexities of conserving such tangible cultural heritage, Heritage Building Information Modeling (HBIM) emerges as a compelling avenue, uniting technology, historic preservation, and architectural expertise. Nonetheless, a remarkable research gap exists, particularly in its application within emergency scenarios involving fire accidents during post-recovery phases. This research aims to bridge this gap by proposing key areas of focus for applying HBIM in the aftermath of fire incidents, redefining disaster recovery for heritage sites. The proposed areas are informed by a thorough assessment of the case study related to the post-recovery process undertaken by the Brazilian National Museum after the devastating fire of 2018. This case study combines gathering documentary evidence, on-site assessments, 3D model development within the BIM environment, and interdisciplinary collaboration. By emphasizing informed decision-making, precise damage assessment, and meticulous reconstruction planning, this study advances HBIM's transformative role in heritage conservation and disaster recovery, resulting in a novel approach to safeguarding cultural heritage.

Ultra-High Frequency Sensors for Detecting Partial Discharge in Power Cables

Partial discharge (PD) detection is vital for evaluating the health of power cable insulation, where failure can lead to significant disruptions. This study investigates the use of Ultra-High Frequency (UHF) sensors to detect PD signals, which typically occur in nanoseconds. The sensors operate in the GHz range and minimize interference by selecting frequencies above 400 MHz, where background noise is reduced. Through comparison with other detection methods, UHF sensors demonstrate superior sensitivity in measuring minor discharges, providing a reliable means for on-site condition assessment of high-voltage equipment. The study concludes that UHF sensors offer a practical solution for improving PD detection accuracy, particularly in challenging field conditions.

Integrating Metro Stations with the Adjacent Urban Fabric Using TOD Principles: A Case of Agargaon Metro Station, Dhaka

Transit-Oriented Development (TOD) principles offer a promising framework for integrating metro stations with their surrounding urban fabric, promoting sustainable urbanization and efficient transportation systems. Dhaka, one of the fastest-growing cities globally, faces significant challenges in traffic congestion, air pollution, and urban sprawl. Introducing metro systems offers a promising solution to alleviate these issues and enhance urban mobility. This study presents a case study of Agargaon Metro Station in Dhaka City, examining its integration with the adjacent urban fabric using TOD principles. Through a combination of field observations, spatial analysis, and stakeholder interviews, the study evaluates the current state of Agargaon Metro Station. On-site assessments examined the station's physical infrastructure, accessibility, and connectivity with nearby areas, while Geographic Information Systems (GIS) analyzed spatial data, including land use patterns and transportation networks. Semi-structured interviews with urban planners, government officials, and community members provided insights into the challenges and opportunities for implementing TOD at Agargaon. The findings reveal that the station is underutilized as a TOD hub, with inadequate pedestrian infrastructure and mixed-use developments, leading to poor connectivity and accessibility. The study highlights the necessity of improved land use planning, policy support, and community engagement to enhance the station's role in fostering TOD. These recommendations, if implemented, could alleviate traffic congestion, improve air quality, and create more livable urban spaces, thereby enhancing Dhaka's overall quality of life. Additionally, the research contributes to the social and economic dimensions of urbanization by offering a framework that can be adapted to similar metro stations in Dhaka and other rapidly urbanizing cities.

Multi-source domain adaptation network for partial discharge severity assessment in gas-insulated switchgear

Abstract Gas-insulated switchgear (GIS) partial discharge (PD) severity assessment is critical for ensuring the reliable operation of GIS systems. However, existing assessment methods often overlook the long-term dependencies of historical data and fail to adequately address challenges related to limited on-site samples and variations in sample distribution. To overcome these challenges, we propose a novel multi-source domain adaptation network (MSDAN) specifically designed for on-site GIS PD severity assessment, which is the first model developed considering distribution differences in GIS PD severity assessment for different defect types. Our approach begins with the development of a feature extractor that captures both discernible PD features and long-term dependencies. We then introduce a multi-source domain adaptation strategy to mitigate distribution disparities across PD severity samples from different defect types, ensuring effective domain alignment. Additionally, we incorporate an adaptive weighted classification mechanism to accurately assess PD severity by considering the varying contributions of different defect types to the target domain task. Experimental results demonstrate that MSDAN achieves a remarkable accuracy of 95.38% in on-site GIS PD severity assessment, outperforming other benchmark models in both accuracy and robustness. This highlights the potential of MSDAN as a robust solution for real-world GIS PD severity assessment.

Urban tree health assessment using multifaceted remote sensing datasets: A case study in Hong Kong

Although climate change is impacting various aspects of our environment, it is important to note that the overall risk to trees remains low, especially in urban areas like Hong Kong where the benefits of trees to society are significant. The trees planted in an urban setting are isolated and have several limiting factors including, excessive run-off, urban pollution, physical damage and limited root growth, which sometimes lead for tree failure incidents. The conventional on-site tree health assessment method is time consuming thus, requiring a remote sensing based method to effectively and routinely monitor the health status of urban trees. In this study several types of remote sensing datasets have been exploited to assess the health status of more than 700 Old and Valuable Trees (OVTs) and Stone Wall Trees (SWTs) around Hong Kong. These datasets include the data from Terrestrial LiDAR (Light Detection and Ranging) Surveys (TLS), Handheld Laser Scanner (HLS), Airborne LiDAR Surveys (ALS) and airborne multispectral data. For validation purpose, the in situ tree parameters data was also obtained from the Tree Management Office (TMO) of the Greening, Landscape & Tree Management Section (GLTMS) under the Development Bureau of the Hong Kong SAR Government. The results have indicated that over the period of four years (2017–2020) there has been a decline in the health of some target trees which can be attributed to the increased infestation rate in trees and severe weather conditions. The usage of LiDAR data has supported the fact that different tree structural forms can effectively be extracted and can help making informed decisions on the precise health conditions of urban trees.

Producer Welfare Benefits of Rating Area Yield Crop Insurance

Index-based insurance is an innovative concept for evaluating agricultural risks and payouts, which uses an index instead of traditional on-site loss assessment. Area yield insurance, as an index-based approach, is an effective strategy to mitigate moral hazard and adverse selection issues. This study aims to develop area yield insurance as a new insurance plan in Iran for two major crops: wheat and barley. It utilized kernel and joint kernel distributions to price the insurance and assessed producer welfare benefits by comparing the certainty equivalence (CE) of farmers’ utility with and without the policy. Data were collected from East Azerbaijan Province, including county-level yield data for irrigated and rainfed wheat and barley from 1975 to 2019 and 446 individual-level yield data from 2015 to 2019. A two-stage method was used to model yield risk: the first stage fits a trend model, while the second estimates the yield distributions with the detrended data. The results showed a significant difference in premiums calculated by the two distributions, with joint kernel distribution offering the best empirical fit and reasonable premiums. The findings indicate that area yield crop insurance provides positive welfare benefits and should serve as a viable alternative or complement to existing yield insurance plans. The successful implementation of this policy in various countries suggests it can be a suitable risk management program for developing countries like Iran.

Decentralized food safety and authentication on cellulose paper-based analytical platform: A review.

Food safety and authenticity analysis play a pivotal role in guaranteeing food quality, safeguarding public health, and upholding consumer trust. In recent years, significant social progress has presented fresh challenges in the realm of food analysis, underscoring the imperative requirement to devise innovative and expedient approaches for conducting on-site assessments. Consequently, cellulose paper-based devices (PADs) have come into the spotlight due to their characteristics of microchannels and inherent capillary action. This review summarizes the recent advances in cellulose PADs in various food products, comprising various fabrication strategies, detection methods such as mass spectrometry and multi-mode detection, sampling and processing considerations, as well as applications in screening food safety factors and assessing food authenticity developed in the past 3 years. According to the above studies, cellulose PADs face challenges such as limited sample processing, inadequate multiplexing capabilities, and the requirement for workflow integration, while emerging innovations, comprising the use of simplified sample pretreatment techniques, the integration of advanced nanomaterials, and advanced instruments such as portable mass spectrometer and the innovation of multimodal detection methods, offer potential solutions and are highlighted as promising directions. This review underscores the significant potential of cellulose PADs in facilitating decentralized, cost-effective, and simplified testing methodologies to maintain food safety standards. With the progression of interdisciplinary research, cellulose PADs are expected to become essential platforms for on-site food safety and authentication analysis, thereby significantly enhancing global food safety for consumers.

Fast and Reliable On-Site Quality Assessment of Essential Raw Brewing Materials Using MicroNIR and Chemometrics.

The interest in the quality control of the raw materials, intermediates, and final products, as well as production methods, of beer has increased significantly in recent decades due to the needs and expectations of consumers. Increasing in the industrialization and globalization of beer supply chains led to a need for novel analytical tools suitable for the rapid and reliable characterization of the materials involved. In this study, an ultracompact instrument operating in the NIR region of the spectrum, microNIR, was tested for the chemical investigation of barley malts. The essential raw materials for brewing require careful control since they deeply affect the characteristic flavor and taste of the final products. Therefore, a robust prediction model able to classify base and specialty barley malts was developed starting from NIR measurements. Soft Independent Class Analogy (SIMCA) was selected as the chemometric technique for the optimization of two prediction models, and ground and sieved materials were investigated using spectroscopy. The microNIR/chemometric approach proposed in this study permitted the correct prediction of the malt samples included in the external validation set, providing false positive and false negative rates no higher than 3.41% and 0.25%, respectively, and confirming the feasibility of the novel analytical platform.

Handheld Near-Infrared Spectroscopy for Undried Forage Quality Estimation.

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments-NeoSpectra, TrinamiX, and AgroCares-when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water's high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.