Processing Methods Research Articles

Exploring industry perspectives and preferences about calf handling and restraint methods used during spring processing of calves in western Canada

Abstract Calf processing events have important animal health, management, and sociocultural roles in the beef cattle industry. In western Canada, the three most commonly used methods for spring processing are roping and wrestling (RW), roping and Nord fork (NF), and tilt table (TT). The objective of this study was to understand the preferences and perceptions of handling event participants about calf handling and restraint methods commonly used during western Canadian beef calf processing events during the spring season. Data were collected using a mixed-methods online questionnaire. Quantitative analysis was used to describe the study participants and determine preference rankings. Qualitative, thematic analysis was used to explore participants’ perceptions about the common handling and restraint methods and to identify values within and across participants. The majority of participants were farm hands or staff members (92.8%), followed by owners (4.9%), family members (1.4%), friends (0.5%), and others (0.4%). The most preferred method to use was RW, and TT was the least preferred (χ2 = 3239.1, df = 6, p < 0.001). Participants shared values regarding calf safety and stress, processing efficiency, convenience, human safety, and labour intensity when explaining their preference to use calf handling and restraint methods for spring processing. Responses highlighted the need for understanding and skill in low stress handling and processing tasks in order for any of the methods to be effective. These values identify aspects to address when developing best practice recommendations for calf handling and restraint. Furthermore, focusing communication through the lens of these shared values will likely positively engage participants in extension efforts and community discussions.

Evaluation of Barriers to Digital Transformation in Maritime Logistics Based on A Spherical Fuzzy Multi-Crıteria Decision-Making Framework

Purpose: The objective of this study is to identify and prioritize the barriers to the adoption of digital transformation in order to ensure more efficient and effective operation of the maritime logistics sector. Methodology: The Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) method, which gives successful results in modelling uncertainty and uses Spherical fuzzy sets (SFSs), is used to rank the barriers affecting adoption of digital transformation according to their importance. Findings: In the application part of the study, firstly the barriers in the adoption of digital transformation were determined and as a result of expert evaluations, the barriers were ranked according to their importance by applying the steps of the method. When the results obtained from the study were examined, ‘Technology’ is the most important barrier category (B1) (0.341) for the adoption of digital transformation in maritime logistics, followed by the main barrier categories related to “Security” (B4) (0.266), “Environment” (B3) (0.223) and “Organisation” (B2) (0.171) respectively. Originality: This study represents a pioneering effort in the field of maritime logistics, as it is the first to identify and prioritize the barriers to digital transformation that impede operational efficiency.

Data Value-Added Service Comprehensive Evaluation Method on the Performance of Power System Big Data

With the development of digital economy, the integration and secure sharing of energy big data have become pivotal in driving innovation across the energy production, distribution, and consumption sectors. For power enterprises, leveraging data to enhance operational efficiency and drive business development will play a crucial role in value added. Firstly, based on the value-added service framework system of grid enterprises, this paper explores the basic technologies for power data applications and designs a technical roadmap for value-added services. Secondly, the proposed methodology incorporates the analytic hierarchy process (AHP) and gray comprehensive evaluation method (GCE) to determine the weights of key factors affecting the value-added services. Empirical research is conducted to validate the feasibility of typical value-added services. Additionally, this paper proposes methods for evaluating the benefits of value-added services and identifies key technologies in data mining and management, customer value discovery, and data asset utilization, providing theoretical support and practical pathways for the digital transformation of power enterprises.

A flexible multi-gate organic electrochemical synaptic transistor for image processing

In this study, a P3HT-based multi-gate frequency-dependent synaptic transistor is fabricated, which demonstrates significant advantages in mimicking the transmission characteristics of biological synaptic activities. The proposed device simulates outputs related to frequency and gate voltage modulation. This device can respond differently to inputs ranging from 0.75 to 11.11 Hz, and at the same input frequency, it exhibits different responses by varying the control gate voltage from 0 to −0.8 V. This innovative design can dynamically adjust the cutoff frequency, enhancing edge feature processing in images, thereby significantly improving the recognition accuracy of information in blurry images that can be difficult for humans to distinguish. Our results provide a hardware edge-computing image processing method, overcoming the limitations of traditional single-gate transistors that typically have fixed parameters. The recognition accuracy of information in blurry images preprocessed by this device improved significantly from 80% to 100%. Combined with the multi-gate design, this synaptic device excels not only in edge enhancement and image processing but also offers robust hardware support for future neuromorphic electronics.

Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network

Sowing uniformity is an important evaluation indicator of mechanical sowing quality. In order to achieve accurate evaluation of sowing uniformity in hybrid rice mechanical sowing, this study takes the seeds in a seedling tray of hybrid rice blanket-seedling nursing as the research object and proposes a method for evaluating sowing uniformity by combining image processing methods and the ODConv_C2f-ECA-WIoU-YOLOv8n (OEW-YOLOv8n) network. Firstly, image processing methods are used to segment seed image and obtain seed grids. Next, an improved model named OEW-YOLOv8n based on YOLOv8n is proposed to identify the number of seeds in a unit seed grid. The improved strategies include the following: (1) Replacing the Conv module in the Bottleneck of C2f modules with the Omni-Dimensional Dynamic Convolution (ODConv) module, where C2f modules are located at the connection between the Backbone and Neck. This improvement can enhance the feature extraction ability of the Backbone network, as the new modules can fully utilize the information of all dimensions of the convolutional kernel. (2) An Efficient Channel Attention (ECA) module is added to the Neck for improving the network’s capability to extract deep semantic feature information of the detection target. (3) In the Bbox module of the prediction head, the Complete Intersection over Union (CIoU) loss function is replaced by the Weighted Intersection over Union version 3 (WIoUv3) loss function to improve the convergence speed of the bounding box loss function and reduce the convergence value of the loss function. The results show that the mean average precision (mAP) of the OEW-YOLOv8n network reaches 98.6%. Compared to the original model, the mAP improved by 2.5%. Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. Finally, the actual evaluation experiment showed that the test error is from −2.43% to 2.92%, indicating that the improved network demonstrates excellent estimation accuracy. The research results can provide support for the mechanized sowing quality detection of hybrid rice and the intelligent research of rice seeder.

Tunable 1D-2D Carbon Nanomaterials for Broadband and High-Performance Microwave Absorption via Ultrasonic Spray Ice Template.

Polymer-based one- and two-dimensional (1D-2D) carbon nanomaterials are considered promising microwave-absorbing materials (MAMs) due to their high atomic utilization efficiency and tunable microscopic/macroscopic morphology. The tunable design of 1D-2D carbon nanomaterials through a facile method to meet the requirements of advanced MAMs remains a challenge. In this work, the environmentally friendly processing method of ultrasonic spray ice template (USIT) is employed to fabricate porous carbon nanomaterials based on Kapton-type polyimide, which exhibit the intriguing morphology of both 1D nanowires and 2D nanosheets. Under subsequent carbonization at 700 and 800 °C, the obtained carbon nanomaterials inherit the original morphology. Furthermore, the 1D or 2D nanomorphology can be readily controlled by adjusting the concentration of the precursor solution. For samples fabricated with lower precursor concentrations (0.1%), 1D nanowire structures are predominant. Interconnected conductive networks and heterogeneous interfaces are formed by intertwining and stacking nanowires, thereby enhancing the conductivity loss. Additionally, the abundant porous structure provides an effective channel for electromagnetic wave entrance, significantly improving the impedance matching ability. The results show that the 1D nanowire-dominated samples (700 °C carbonization) show excellent electromagnetic microwave absorption performance. The reflection loss minimum (RLmin) is -67.2 dB at 8.1 GHz and 4.65 mm, and the maximum effective absorption bandwidth (<-10 dB) is 7.7 GHz at 3.03 mm. Exemplified by MAMs, the USIT strategy has broad prospects, providing enormous potential for various practical applications and bridging the gap between polymer precursors and 1D/2D tunable carbon nanomaterials.

FireVoxel: Interactive Software for Multi-Modality Analysis of Dynamic Medical Images.

This article provides an overview of the FireVoxel software for quantitative analysis of medical images and its applications in the field. We describe FireVoxel's user interface, multi-layer design, dynamic parametric models, and several turn-key workflows. Additionally, we discuss its application in recent imaging projects. We outline basic image analysis tools such as segmentation, non-uniformity correction, and coregistration through a pictorial overview, with a focus on deformable coregistration and motion correction. Several example workflows and image-based dynamic modeling are also highlighted. Furthermore, we analyze peer-reviewed studies that utilized FireVoxel for image processing, categorizing published papers based on body structures/organs, image processing methods, and imaging modalities. For comparison, we searched the Ovid MEDLINE database to assess the general use of medical image analysis software. FireVoxel is used by over 3000 users worldwide, with 528 articles, including 413 in English, published in the past 15years. MRI is the most commonly used imaging modality (78.2%), followed by CT (14.5%) and PET (7.3%). The most frequently used methods are dynamic modeling, segmentation, texture analysis, and coregistration. FireVoxel is commonly used in abdominal and genitourinary imaging studies, where it appears to fill a niche due to the lack of alternative software. The search of the Ovid MEDLINE suggests that quantitative medical imaging studies, on the other hand, focus on the brain and cardiovascular system. FireVoxel offers an effective set of quantitative tools, particularly for abdominal and genitourinary imaging, likely due to its ability to manage patient motion and correct for MR artifacts. The software is especially valuable for processing dynamic studies. The steady increase in publications utilizing FireVoxel reflects growing interest in this software and its relevance for image-based research.

Determining and Quantifying Urban Sprawl Drivers: A Delphi-DANP Approach

Urban sprawl poses a significant and escalating challenge in developing countries, including Iran, leading to substantial transformations in urban areas. Despite efforts to manage urban spatial development, uncontrolled urban sprawl exerts considerable pressure on resources, infrastructure, and the environment. This study aims to identify and quantify the drivers of urban sprawl and investigate their interrelationships within Iranian metropolises. To achieve this objective, the study employs a mixed-method approach, commencing with a review of the existing literature and expert surveys based on PESTEL analysis and the Delphi method. This stage identified and categorized 40 key drivers (sub-factors) into six main categories (factors): political, economic, social, technological, environmental, and legal. Subsequently, the DEMATEL-based Analytic Network Process (DANP) method is utilized to explore the internal interrelationships among factors and sub-factors and to determine their relative weights, offering deeper insights into their relationships and relative importance. The findings reveal a complex interplay of political, economic, social, technological, environmental, and legal factors driving urban sprawl in Iran. Key drivers include political fragmentation, economic competition, social preferences for suburban living, rural-to-urban migration, increasing housing demand, weak legal regulations, natural constraints, inadequate transportation infrastructure, and the impact of technological advancements. Based on these findings, the study recommends a holistic approach to sustainable urban development in Iran, emphasizing the need for stakeholder engagement, participatory decision making, legal reforms, and significant investments in public transportation infrastructure.

Enhancing multiple sclerosis diagnosis: A comparative study of electroencephalogram signal processing and entropy methods.

As one of the most common neurodegenerative diseases, Multiple sclerosis (MS) is a chronic immune-driven disorder that affects the central nervous system (CNS). Due to the variety of symptoms, accurately diagnosing MS demands rigorous attention to differential diagnosis, as various disorders can closely mimic its clinical and paraclinical features. Although MR imaging techniques are gold standards in diagnosing MS, the feasibility of advanced Electroencephalogram (EEG) signal processing methods is discussed in this study to detect patients with MS disorder. EEG signals from 50 individuals were evaluated through entropy-based methods. Sixteen distinct entropy methods were employed to extract features, which were used to train several machine-learning algorithms for classifying MS patients. Furthermore, each entropy method was individually evaluated to identify the most effective approach for MS diagnosis. A regional analysis of the EEG channels was conducted to determine the most informative regions for classification. The results indicated that the proposed method outperformed previous studies and achieved highly effective results in the classification of MS patients.

Increasing chemical coverage, accuracy, and reproducibility of the processing method for polar organic chemical integrative samplers.

Despite their diffusion in research studies, passive samplers are rarely used in regulatory applications. To expand the employment of passive samplers in regulatory environmental studies, standardized procedures for processing each sampler type should be proposed and accepted, but currently, each study develops its own protocol based on previous knowledge and specific needs. In this work, six identical polar organic chemical integrative samplers in seawater were deployed to understand the importance of the sorbent transfer method prior to the elution step. A common "wet transfer" with ultra-pure water was compared to a less diffused "dry-transfer," assessing recoveries and matrix effects of 38 target compounds of emerging concern, including polar pesticides, recreational and food-related substances, pharmaceuticals, industrial additives, and ultra-violet (UV) filters. The dry-transfer procedure generally allowed better recoveries, especially for the more polar compounds, without affecting matrix effects (which remained in the range 40-130%). Along with the recovery assessment, the analysis of the non-spiked sorbent extracts revealed traces of many of the targeted emerging contaminants, quantifying perfluorooctanoic acid, UV-filters, carbamazepine, diclofenac, and triclosan. Furthermore, other compounds were found below their limits of quantitation. Ten analytes were detected only in the extracts of the dry-transferred passive samplers, highlighting the importance of applying this protocol, especially when dealing with polar compounds. This refined processing method, therefore, permits a more standardized and reproducible strategy, at the same time enlarging the set of analytes which could be detected and quantified.

Evaluating the impact of different processing methods on the flavor characteristics of Gorgon Euryale seeds using electronic tongue, electronic nose, gas chromatography-mass spectrometry, and gas chromatography-ion mobility spectrometry.

This study evaluated the volatile organic compounds (VOCs) and taste properties of Gorgon Euryale seeds processed by five methods (steaming, boiling, microwaving, roasting, and stir-frying) using electronic tongue (E-tongue), electronic nose (E-nose), gas chromatography-mass spectrometry (GC-MS), and gas chromatography-ion mobility spectrometry (GC-IMS). A total of 44 and 40 VOCs were identified by GC-MS and GC-IMS, respectively. Pyrazines (2-ethyl-3,5-dimethylpyrazine, 2,3-diethyl-5-methylpyrazine) and furans (2-pentylfuran, 2-ethylfuran) played a major role in the baked aroma characteristics of roasted and stir-fried Gorgon Euryale seeds. Six and seven marker compounds were identified by Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) models for GC-MS and GC-IMS based on 12 VOCs with odor activity value>1 and 18 VOCs with relative odor activity value>0.1, respectively. OPLS-DA and principal component analysis score plots of the E-tongue and E-nose demonstrated that samples could be effectively distinguished in terms of flavor. This research provides a comprehensive basis for evaluating the impact of processing methods on the changes in flavor of Gorgon Euryale seeds. PRACTICAL APPLICATION: This work demonstrates that the use of E-tongue, E-nose, HS-SPME-GC-MS, and GC-IMS has the capability to thoroughly analyze the flavor profile of Gorgon Euryale seeds at both macro and micro levels. This approach effectively distinguishes Gorgon Euryale products subjected to different processing treatments and provides a reliable reference for evaluating and identifying the flavor quality of Gorgon Euryale seeds.

Personalized interventions for behaviour change: A scoping review of just-in-time adaptive interventions.

Examine the development, implementation and evaluation of just-in-time adaptive interventions (JITAIs) in behaviour change and evaluate the quality of intervention reporting. A scoping review of JITAIs incorporating mobile health (mHealth) technologies to improve health-related behaviours in adults. We searched MEDLINE, Embase and PsycINFO using terms related to JITAIs, mHealth, behaviour change and intervention methodology. Narrative analysis assessed theoretical foundations, real-time data capturing and processing methods, outcome evaluation and summarized JITAI efficacy. Quality of intervention reporting was assessed using the template for intervention description and replication (TIDieR) checklist. Sixty-two JITAIs across physical activity, sedentary behaviour, dietary behaviour, substance use, sexual behaviour, fluid intake, treatment adherence, social skills, gambling behaviour and self-management skills were included. The majority (71%) aimed to evaluate feasibility, acceptability and/or usability. Supporting evidence for JITAI development was identified in 46 studies, with 67% applying this to develop tailored intervention content. Over half (55%) relied solely on self-reported data for tailoring, and 13 studies used only passive monitoring data. While data processing methods were commonly reported, 44% did not specify their techniques. 89% of JITAI designs achieved full marks on the TIDieR checklist and provided sufficient details on JITAI components. Overall, JITAIs proved to be feasible, acceptable and user-friendly across behaviours and settings. Randomized trials showed tailored interventions were efficacious, though outcomes varied by behaviour. JITAIs offer a promising approach to developing personalized interventions, with their potential effects continuously growing. The recommended checklist emphasizes the importance of reporting transparency in establishing robust intervention designs.

Effects of Thermal and Non-Thermal Sterilization Methods on the Phytocompounds, Flavor Profile, and Antioxidant Properties of High Acidity Mulberry Vinegar

This study examined the effects of three sterilization techniques—heat, microwave, and high hydrostatic pressure (HHP)—on the phytochemicals, flavor, and antioxidant properties of high-acidity mulberry vinegar. High-acidity vinegar is valued for its unique sensory properties, preservation potential, and potential health benefits, yet its quality can be significantly impacted by processing methods. Understanding how sterilization affects high-acidity vinegar is crucial for optimizing its functional and nutritional properties while maintaining consumer appeal. Thermal processing (TP) involved heating samples at 85 °C for 15 min, while microwave heating (MH) was performed at 600 W for 40 s. HHP treatments subjected samples to pressures of 400 MPa, 500 MPa, and 600 MPa at ambient temperature (~25 °C) for 15 min. Results showed that total soluble solids (TSS) remained stable at approximately 6.90% across treatments, confirming effective sterilization, especially with HHP. pH values were consistent (3.53–3.55), while total acidity varied, with HHP treatments achieving lower acidity (5.00 g/L). Phytochemical analysis indicated the control (CK) treatment preserved the highest total anthocyanin content (TAC), but HHP500 maintained notable levels. HHP treatments also yielded the highest total phenolic (TPC) and total flavonoid content (TFC), highlighting non-thermal sterilization’s advantage in preserving health-promoting compounds. Antioxidant activity, particularly DPPH, was best retained in HHP samples. HHP500 showed minimal reductions in key phenolic acids, with decreases of 6.16% in p-hydroxybenzoic acid and 7.81% in total phenolic acid. Volatile organic compound analysis revealed increased ester production, with ethyl acetate peaking at 1775.71 μg/L in HHP600. Overall, HHP at 500 and 600 MPa proved superior for producing high-quality mulberry vinegar, supporting the demand for minimally processed, health-focused food products.

Numerical simulation method for the laser powder bed fusion process by lattice Boltzmann and multi-phase field methods

Numerical simulation method for the laser powder bed fusion process by lattice Boltzmann and multi-phase field methods

Tax Administration AI: The Holy Grail to Overcome Information Asymmetry in Tax Enforcement?

Liberal democracies are based on the concept of the free person who is a rational being capable of understanding their behaviour and taking responsibility for it. The state may only interfere with individual freedom when it has a legal basis, if it is necessary to safeguard the functioning of a democratic society. Therefore, in liberal democracies, citizens have duties to fulfil and responsibilities to bear. In return, they are not and must not be surveilled by the state. Tax enforcement is built upon this concept. Tax administration Artificial Intelligence (AI) has the potential to change this concept. What is technically feasible is not necessarily appropriate for sustaining the fundamental values of a liberal society. If society wants to uphold these values, any deployment of tax administration AI must be legally framed with clear rules on the origin of the data, the method of data processing, the aim of the data processing, and the possibility of human intervention. Additional legal prerequisites may be necessary depending on the purpose of the use of tax administration AI. If it is deployed in a mindful way, it is likely to increase both the efficiency and the equality of tax enforcement. div/div

Geo-environmental GIS modeling to predict flood hazard in heavy rainfall eastern Himalaya region: a precautionary measure towards disaster risk reduction.

The Eastern Himalaya region is highly susceptible to flood and other hydrological hazards due to frizzle geophysical setup, reshaping geomorphology, and heavy annual rainfall (1600-3200 mm). Despite that, anthropogenic activities have been enhancing this susceptibility which increases the intensity and impact of floods in terms of economic loss, human loss, and environmental degradation. Addressing this environmental problem, a geospatial technology-based case study of the Kohima district, Nagaland state (India), a part of the eastern Himalaya is presented here. Various experiential models are available for computing flood hazards; however, the geospatial technique-based analytic hierarchy process (AHP) method was applied in this study due to its robustness and high accuracy level. AHP integrates reclassified GIS layers of hazard-triggering factors and sub-factors by assigning relative weights 1-9 based on their corresponding impacts on flood occurrence. Overlay operation of reclassified GIS layer (causative factors and sub-factors) in ArcMap 10.8 software generated flood spatial variability map which shows four zones, namely low, moderate, high, and very high hazard zones, covers 23%, 35%, 28%, and 14% proportion of total area (978.96 km2), respectively. The study poses a serious concern for the study area as most of the densely populated urban centers fall into moderate to very high flood hazard zones including the state capital city Kohima. So, to avert a worse flood disaster, a flood hazard zone study is the need of the hour. The present study can be used as a decision support system (DSS) for flood disaster risk reduction, infrastructural development, and land use planning in Kohima district.

Assessing physical activity/behavior of adolescents living in the Pacific with accelerometer data: 231 GENEActiv records in New Caledonia.

This paper presents a dataset related to the physical activity behavior of 206 adolescents (107 females and 99 males) from 11 to 16 years old and 25 adults (13 females and 12 males) living in rural (77 adolescents and 15 adults) and urban (129 adolescents and 10 adults) parts of New Caledonia, an archipelago of the South Pacific. Physical behavior was assessed through 60-Hz triaxial GENEActiv accelerometers worn for 5 to 7 consecutive days between July 2018 and April 2019. Participants were randomly recruited at school and trained staff fitted the devices on the nondominant wrist, at which time all were reminded of the expectations while wearing the device. The dataset contains restricted raw .bin accelerometer data provided by the GENEActiv software and open 1-second epoch .csv accelerometer data derived from the .bin files with an R script, provided in the repositories as well. Similarly, there is open information (age range and sex) and restricted information (age in months, place of living, declared cultural community and socioeconomic status) about the participants. The paper describes the file contents, describes the participants involved when collecting this dataset, and gives examples of how the data can be processed to assess the physical behavior of these participants. This high-quality dataset contains diverse sociodemographic information about the participants, which will enable the determination of correlations with their physical behavior. Moreover, since raw data with high granularity and continuous information is provided, studies interested in data processing and testing new methods on time series analyses can use the dataset.

Using ultrasound sequential images processing to predict radiotherapy-induced sternocleidomastoid muscle fibrosis

Purpose The purpose of this study was to evaluate the thickness and biomechanical parameters of the sternocleidomastoid muscle (SCM) before, during, and after radiotherapy using ultrasound elastography to predict radiotherapy-induced muscle fibrosis. Materials and methods The mean daily absorbed doses of 20 SCMs were determined. To find out the Young and shear modulus, shear wave elastography (SWE) and the B-mode sequential images processing method were implemented. In the B-mode sequential images processing method, by administering dynamic stress, the Young and shear modulus were estimated utilizing the maximum gradient and the block-matching algorithms, respectively. The imaging was done before, during the third and sixth weeks of treatment, and 3 months after radiotherapy. Results There was a statistically significant increase in the maximum thickness during the sixth week compared to before radiotherapy (p = .043). However, this parameter did not change significantly 3 months later (p = .095). The Young modulus (p = .611) derived from SWE did not differ significantly throughout any of the weeks of radiotherapy. But Young and shear modulus increased significantly in the B-mode sequential images processing method before and during the third and sixth weeks of treatment (p = .001). The outcomes observed 3 months after radiotherapy revealed a statistically significant increase in both Young modulus (p = .029) and shear modulus (p = .004) compared to pre-radiotherapy. Conclusion The Young modulus and shear modulus are introduced as biological markers used to detect the onset of the fibrosis process during the initial radiotherapy fractions.

From insoluble to soluble: An overview of processing methods for dietary fibers in okara

From insoluble to soluble: An overview of processing methods for dietary fibers in okara

Evaluation of edible quality and processing suitability of segmented products from silver carp under different thermal processing methods.

This study investigated the edible quality differences in muscle segments of silver carp (Hypophthalmichthys molitrix) and established an evaluation model for processing suitability. The results showed that steamed dorsal meat had the highest levels of total free amino acids, umami amino acids, and total volatile compounds. Fried tail meat exhibited the highest content of sweet amino acids and equivalent umami concentration (EUC) values, which were superior in all fried meat parts compared to those that were steamed. Key quality indicators for steaming included L*, crude protein, 5'-inosine monophosphate (IMP), (E)-2-Nonenal, and lysine, while IMP, moisture, 2-acetyl-1-pyrroline, and proline were key quality indicators for frying. The established processing suitability evaluation model accurately predicted overall sensory acceptability. Dorsal and belly portions of H. molitrix were best suited for steaming, whereas the tail was more suitable for frying.