Hasil untuk "Food processing and manufacture"

Ghazal Shineh, M. Mobaraki, Mohammad Jabed Perves Bappy et al.

Biofilm formation can lead to problems in healthcare, water distribution systems, food processing and packaging, industrial manufacturing, marine industries, and sanitation. These microbial communities can proliferate on biotic or abiotic surfaces, and are responsible for human disease and decreasing production efficiency and service equipment life in many industrial fields. The formation of biofilm starts with the attachment of bacteria to the surface, followed by bacterial proliferation and maturation of the microbial community. After forming a biofilm, bacteria not resistant to antimicrobial agents in their planktonic forms can turn resistant. The antibiotic resistance of bacterial biofilm, and the association of biofilms in generating infectious diseases in humans, highlight the need for designing novel and successful antibacterial, anti-biofilm, or anti-infection materials. This paper aims to review the mechanism of biofilm formation, the impact on different industries, the interaction mechanism of nanoparticles with bacteria, and strategies to design anti-biofilm materials. Examples of designing anti-infection bio-implants, coatings, medical devices, wound dressings, and sutures are reviewed.

Gabriel de Oliveira Quintana, Renata Fragoso Potenza, Sofia Lasmar Lima Oliveira

Brazil is the world’s fifth-largest methane emitter, with methane accounting for 24% of national greenhouse gas emissions and the agricultural sector responsible for 75.6% of these methane emissions, mainly from livestock. Under a business-as-usual scenario, agricultural emissions are projected to rise in line with intrinsic emissions associated with production growth, challenging Brazil’s commitments to the Global Methane Pledge and the Nationally Determined Contribution (NDC). Based on the methodology for calculating emissions from the Brazilian National GHG Inventory and the mitigation potential of different mitigation strategies aimed at the agricultural sector, such as those addressed by the ABC+ Plan. This brief compares a trend scenario against a mitigation scenario based on the adoption of proven low-emission practices and technologies in livestock and crops productions systems. The implementation of methane reduction strategies for livestock and agricultural production can reduce sectoral methane emissions by 28% by 2035 compared to 2020 levels. Policymakers must prioritize scaling of these technologies, replacing the more methane-emitting analogues and establish robust Monitoring, Reporting, and Verification (MRV) systems to ensure impact.

ZHU Xiuqing, WANG Zihan, HUANG Yuyang, GUO Ruqi, LIU Linlin, WANG Ying, ZHU Ying

Growing consumer awareness of healthy eating has stimulated interest in fat substitutes. Plant proteins, with their excellent functional properties, are widely used as emulsifiers or gelling agents and have shown significant potential in fat replacement, making them a research focus. Plant protein-based emulsion gels mimic animal fat in morphology and mouthfeel while offering nutritional benefits. Nevertheless, challenges such as soft texture and poor stability limit their practical application. This review systematically summarizes preparation methods for plant protein-based emulsion gels, with a focus on the effects and mechanisms of different additives and processing techniques on their structure and properties as fat substitutes. Furthermore, we summarize their applications in foods and propose future research directions, aiming to provide a theoretical basis for the development and application of plant-based fat alternatives.

Gert W. Meijer

Nutrition science and food science & technology are crucial for creating a healthier world through accessible nutrition and sustainable health practices. Examples of successful impact can be found in food fortification, foods with effective levels of bioactives, (re)formulation of foods to combat obesity and diet-related diseases, (re)formulation of foods to enable nutrition and health claims, and the activities by the European Technology Platform (ETP) for the food sector 'Food for Life'.In preparing, maintaining, and promoting the Strategic Research and Innovation Agenda (SRIA), the ETP aims to identify scientific and technological actions towards the transformations that are needed to achieve more optimal outcomes of the food system. The four major food system outcomes are the environment, society, citizen health & wellbeing, and economy & competitiveness. The SRIA provides essential guidance to the European Commission, Member States and Regions, the food industry, and the wider research community interested in food, in the form of Research and Innovation needs to make a real difference to the Food and Drink sector and society. The mutualism between nutritionists and food scientists and technologists is essential for achieving the transformations towards the outcomes that are needed for a more sustainable food system.

Yang PANG, Yi LI, Yue LUAN et al.

Milk and dairy products serve as crucial sources of nutrition for humans, and their composition is extensively researched. Milk exosomes are nanoscale phospholipid bilayer vesicles released from the milk source and present in the extracellular environment, which contain protein, lipid, and genetic material, among other cell-specific substances. Milk exosomes distinguish themselves from other types of exosomes in that they are rich in sources and readily producible on a large scale. They can be selectively absorbed by target cells to exert physiological effects and thereby influence various physiological and pathological processes. Milk exosomes directly participate in and regulate diverse physiological processes by carrying their endogenous bioactive molecules including proteins, microRNAs, and lipids, manifesting significant biological activity, likewise act as ideal carriers of active substances due to their outstanding biocompatibility and high loading capacity, rendering them suitable for drug delivery and transportation. Hence, this article summarizes the effects of endogenous active substances and the role of milk exosomes as signal transduction carriers, and reviews the latest research progress in regulating intestinal health, so as to facilitate in-depth research and extensive application of milk exosomes in domains such as food science, nutrition, and medicine.

Yang HE, Yiting FENG, Haowen WU et al.

To improve traditional sweet rice wine stability and flavor, yeasts with high alcohol and ester production were isolated, purified, and screened from Yunnan traditional sweet rice wine, and the strain was identified by morphological observation and molecular biology methods, with its alcohol and ester production characteristics analyzed. Effects of different yeasts synergized with Rhizopus oryzae on the quality of sweet rice wine was assessed by physicochemical indexes, sensory evaluation, and amino acid and volatile compositions. Results showed that a high alcohol production yeast strain LF05 and a high ester production yeast strain PL10 were isolated and screened, which were identified as Saccharomyces cerevisiae and Rhodotorula mucilaginosa. Compared with the control, S. cerevisiae LF05 synergised with Rhizopus oryzae fermented sweet rice wine increased the alcoholic strength (6.80vol%) and volatile alcohols (44.27 mg/L) by 23.64% and 36.60%. It reduced the bitter amino acids (329.25 μg/mL) by 41.19%. Compared with the control group Rhodotorula mucilaginosa PL10 synergized synergised with Rhizopus oryzae fermented sweet rice wine volatile esters (7.29 mg/L), sweet amino acids (425.18 μg/mL), and fresh amino acids (740.51 μg/mL) were increased by 220.39%, 154.64%, and 134.82%, as well as the sensory score (86 points) increased by 64.48%. In conclusion, the synergistic fermentation of S. cerevisiae LF05 and R. mucilaginosa PL10 with Rhizopus oryzae can improve the problem of taste and blandness of commercial sweet rice wine, which provides a reference and theoretical basis for the preparation of sweet rice wine in Yunnan Province.

Julio Cesar dos Reis, Mariana Yumi Takahashi Kamoi, Tarik Marques do Prado Tanure et al.

Diversified sustainable agricultural systems, such as integrated crop-livestock-forest systems (ICLFs), offer substantial potential for enhancing food production to meet the increasing global demand for agricultural goods while, simultaneously, conserving vital natural resources, including soil, water, and forests. However, a critical barrier to the widespread adoption of these sustainable systems in Brazil’s Amazon and Cerrado biomes, its primary agricultural commodity-producing regions, is the lack of comprehensive economic information. This paper presents case studies that evaluate the economic performance of ICLFs compared to traditional agricultural practices in these biomes (extensive livestock and large-scale cropping systems). Additionally, we employ an economic impact analysis using an input–output matrix approach to assess the economic benefits associated with ICLF adoption. The findings indicate that integrated systems exhibit superior economic performance, particularly over the long-term, as evidenced by more favorable viability indicators, such as higher internal rates of return and profitability indexes. In the Cerrado biome, the gross profit per hectare is up to USD 200 higher compared to traditional livestock and USD 26.5 higher than crop farming. While these systems necessitate higher initial investments per hectare, they provide shorter payback periods and increased profitability. Furthermore, it is observed that an ICLF expansion over degraded pasture in Brazil would promote highly positive economic impacts. Approximately 61,000 and 50,000 additional jobs would be generated in the Cerrado and Amazon biomes, respectively. In terms of production value, it would be up to USD 19.7 billion higher in the Cerrado biome and USD 16 billion higher in the Amazon biome compared to traditional livestock farming. These findings reinforce the role of public policies aimed at promoting sustainable agriculture and achieving the targets established in the Brazilian Low-Carbon Agriculture Plan.

WANG Baoyi, HU Xuefang, PEI Haisheng, ZHAI Xiaona, LIANG Liang, LI Yuanyuan

In order to make efficient use of wheat bran and to improve its quality characteristics, wheat bran was physically modified by ultrafine grinding combined with gradient glutenin addition in this study. The changes in physicochemical and functional characteristics such as particle size distribution, color, hydration characteristics, filling characteristics and flow characteristics of wheat bran ultrafine powder were analyzed. The structural changes were analyzed by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The results showed that the addition of glutenin during the ultrafine grinding of wheat bran significantly increased the whiteness of wheat bran ultrafine powder, significantly reduced the particle size of wheat bran and enhanced its uniformity (P < 0.05), and improved the agglomeration behavior of wheat bran. The oil holding capacity, solubility and swelling capacity of wheat bran were significantly increased by ultrafine grinding combined with gradient glutenin addition (P < 0.05). With increasing glutenin addition, the bulk density and tap density of wheat bran ultrafine powder increased, and the angle of repose and slip angle decreased, reflecting improved filling and fluidity of wheat bran. Ultrafine grinding combined with moderate glutenin addition (2%–4%) resulted in the breakage of the cell wall matrix of wheat bran, promoted the complete deconstruction of amorphous cellulose and the partial deconstruction of crystalline cellulose in wheat bran ultrafine powder, broke the intramolecular glycosidic bonds of cellulose and hemicellulose in wheat bran ultrafine powder, led to enhanced hydrogen bonding, and improved the quality characteristics of wheat bran ultrafine powder and the tensile properties of whole wheat flour dough. Therefore, ultrafine grinding combined with moderate glutenin holds great application potential as an important technical means for wheat bran modification.

Xinliang Zhou, Chenyu Liu, Zhisheng Chen et al.

Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks. These models leverage large-scale pre-training techniques, allowing them to generalize effectively across multiple scenarios, tasks, and modalities, thus overcoming the traditional limitations faced by conventional artificial intelligence (AI) approaches in understanding complex brain data. By tapping into the power of pretrained models, BFMs provide a means to process neural data in a more unified manner, enabling advanced analysis and discovery in the field of neuroscience. In this survey, we define BFMs for the first time, providing a clear and concise framework for constructing and utilizing these models in various applications. We also examine the key principles and methodologies for developing these models, shedding light on how they transform the landscape of neural signal processing. This survey presents a comprehensive review of the latest advancements in BFMs, covering the most recent methodological innovations, novel views of application areas, and challenges in the field. Notably, we highlight the future directions and key challenges that need to be addressed to fully realize the potential of BFMs. These challenges include improving the quality of brain data, optimizing model architecture for better generalization, increasing training efficiency, and enhancing the interpretability and robustness of BFMs in real-world applications.

Shichen Li, Ahmadreza Eslaminia, Chenhui Shao

Food drying is widely used to reduce moisture content, ensure safety, and extend shelf life. Color evolution of food samples is an important indicator of product quality in food drying. Although existing studies have examined color changes under different drying conditions, current approaches primarily rely on low-dimensional color features and cannot fully capture the complex, dynamic color trajectories of food samples. Moreover, existing modeling approaches lack the ability to generalize to unseen process conditions. To address these limitations, we develop a novel multi-modal color-trajectory prediction method that integrates high-dimensional temporal color information with drying process parameters to enable accurate and data-efficient color trajectory prediction. Under unseen drying conditions, the model attains RMSEs of 2.12 for cookie drying and 1.29 for apple drying, reducing errors by over 90% compared with baseline models. These experimental results demonstrate the model's superior accuracy, robustness, and broad applicability.

Dusan Ristic, Denisse Bender, Henry Jaeger et al.

There are several classifications of foods that also include the level of their processing, with NOVA classification appearing to be the most adopted. However scientific consensus is still missing on how to define, characterize and classify food processing. The classifications are typically based on the health impacts of foods and do not fully include the engineering perspective of processing, i.e., the application of physical, chemical, or biotechnological unit operations during food manufacturing, and the composition of a food product. This review offers an engineering perspective and definition of food processing, based on the change of mass and energy, allowing distinguishment of the impacts caused by food processing during the biomass transformation to food products. The improved understanding of the causes of undesired changes in food properties could be used for nutritional public policy recommendations and would contribute to combating some of the chronic diseases related to food consumption patterns. Proposed is the definition of “Food processing” as a sum of all intentional additions or removals of either edible matter or energy (except for any transport or for removal of inedible parts of food) between the harvest of ingredients and consumption of the product.

Ryoken Aoki, Yasuo Watanabe, Yuki Sakai et al.

Sparassis crispa, an edible mushroom, has been reported to show many kinds of physiological functions. The present paper focused on reducing body weight, subcutaneous fat, and visceral fat gain in ovariectomized (OVX) mice. Using the fruiting body powder of the indoor cultivation S. crispa (IT S. crispa: ITSc), one week after the OVX, ITSc was administered to two OVX groups by per os (p.o). In the sham group, 10 mL/kg water and 10 mL/kg saline were administered by p.o. and subcutaneous adm, respectively. OVX groups were divided into four groups. These treatments were performed on animals 6 days a week for 8 weeks. Subcutaneous and visceral fat measurements were performed under inhalation anesthesia with isoflurane using a Latheta LCT-200 X-ray CT system. The biochemical markers and the mRNA expression levels of the PPARγ, adiponectin, TNF-α, PPARα, and leptin were measured. Significant increases in body weight, fat ratio, and glucose levels were detected in OVX mice compared to sham mice. These increases were significantly blocked by ITSc, but not estradiol. Furthermore, ITSc treatment significantly increased adiponectin and leptin levels in adipose tissue. These results suggest that ITSc improves lipid abnormalities due to the less activity of women's ovary function, excluding estrogen functions.

Ting Miao, Pathairat Pastpipatkul, Xinhua Liu et al.

This study employs the stochastic frontier model (SFM) to analyze trade potential and efficiency in wheat and maize among Belt and Road Initiative (BRI) countries from 2002 to 2021, encompassing 45 countries for wheat trade and 55 for maize trade. The empirical findings reveal that economic development level, population growth, government efficiency, political stability, and regulatory quality are critical determinants of trade efficiency. Notably, World Trade Organization (WTO) membership exhibits a negative correlation with trade efficiency, potentially reflecting challenges in rule implementation and opportunity utilization among member states. In the context of maize trade, increased arable land area is inversely associated with efficiency, suggesting potential issues in managing large-scale agricultural regions or optimizing land use. The BRI’s impact on trade efficiency varies across countries, with Turkey and Hungary showing improved wheat trade efficiency, while Ethiopia and Georgia experienced declines. During the COVID-19 pandemic, effective disease management strategies and diversified trade mechanisms significantly influenced trade efficiency. Furthermore, the study reveals that larger economies do not necessarily outperform small and medium-sized economies in terms of trade potential. These findings contribute significantly to the literature on agricultural trade and offer valuable insights for policymakers, emphasizing the importance of enhancing government efficiency, political stability, and regulatory quality in the context of regional economic development initiatives such as the BRI. This research underscores the need for tailored approaches to trade policy and agricultural management, considering the unique characteristics and challenges faced by different economies along BRI.

GUO Ziyu, HU Jiaqi, YANG Feifei et al.

[Objective] This study aimed to investigate the effects of two pre-freezing methods (vacuum freezing and atmospheric pressure freezing) and two dipping methods (sucrose impregnation before pre-freezing and sucrose impregnation during freeze-drying) on the quality of freeze-dried apple slices. [Methods] The freeze-drying process was divided into six groups: vacuum pre-freezing without sucrose immersion (VFFD), atmospheric pre-freezing without sucrose immersion (CFFD), vacuum pre-freezing with sucrose immersion (BSVFFD), vacuum pre-freezing with sucrose immersion (MSVFFD), atmospheric pre-freezing with sucrose immersion (BSCFFD) and atmospheric pre-freezing with sucrose immersion (MSCFFD). The main quality indexes of freeze-dried apple slices in each group were analyzed. [Results] The color Δ<i>L</i>^* of the MSVFFD group samples is relatively lower, while Δ<i>a</i>^* and Δ<i>E</i> are relatively higher, presenting a deep red color. Sucrose impregnation significantly increased the yield of freeze-dried samples, with the highest in the MSVFFD and MSCFFD groups, followed by the BSVFFD and BSCFFD groups, and the lowest in the two control groups of VFFD and CFFD (<i>P</i><0.05). Sucrose impregnation significantly reduced the deformation rate of freeze-dried samples, and there was no significant difference among the deformation rates of the four groups with sucrose impregnation, but they were significantly lower than the two control groups of VFFD and CFFD (<i>P</i><0.05). Sucrose impregnation significantly enhanced the puncture hardness and work of freeze-dried samples. The MSVFFD group had the highest hardness, while the two control groups of VFFD and CFFD had the lowest hardness. Besides, the hardness of the three groups with vacuum freezing treatment was higher than that of the three groups with atmospheric freezing treatment, and the hardness of MSVFFD and MSCFFD groups was significantly higher than that of BSVFFD and BSCFFD groups, respectively (<i>P</i><0.05). The dried samples of MSVFFD and MSCFFD groups had the lowest moisture absorption rate, while CFFD group had a significantly higher moisture absorption rate than the other groups (<i>P</i><0.05). [Conclusion] The methods of vacuum freezing and sucrose impregnation during freeze-drying are more helpful in improving the quality of freeze-dried apple slices, and the MSVFFD group endows freeze-dried products with better overall quality.

Pengfei Zhou, Weiqing Min, Jiajun Song et al.

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://github.com/LanceZPF/KEFS.

Sanjay Mishra*

Artificial Intelligence (AI)-based computer vision systems are the vital to refining quality control in food processing and manufacturing sector. AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. These systems can investigate food products for defects, contaminants, and adherence to quality standards, confirming product safety and sinking the reliance on manual labor. The integration of AI-powered robots is transforming food processing and manufacturing operations. Robots can accomplish complex tasks such as sorting, packaging, and assembly with speed and precision, resulting in augmented productivity, reduced costs, and heightened product consistency. AI algorithms can evaluate huge amounts of data to optimize supply chain logistics. By predicting demand, managing inventory efficiently, and reshuffling transportation routes, AI in food technology improves operational efficiency, lessens costs and minimizes food waste throughout the supply chain. In view of enhancing food manufacturing with the assistance of AI it is noteworthy that AI technologies have been instrumental in streamlining food production processes. Through machine learning algorithms and automation, food manufacturers can maintain reliable product quality, reduce production costs, and minimize waste. One remarkable application is predictive maintenance, where AI predicts when production equipment is likely to fall, allowing for timely maintenance and thereby reducing downtime and costly repairs. Moreover, the challenges, limitations, and future potentials of AI in the field of food sector are summarized in this editorial as follows

Adil Riaz, F. Ali, Khurram Ashfaq et al.

PurposeThis study aims to investigate the impact of green shared vision (GSV) and green knowledge sharing (GKS) on eco-innovation types and further investigates the impact of these types on sustainable competitive advantage (SCA) and sustainable business performance (SBP) within the food manufacturing and food processing small- and medium-sized enterprises (SMEs) of a developing country.Design/methodology/approachPartial least square structural equation modeling technique was used to test the hypotheses. Simple random sampling was used, and data were collected from 312 owners/managers of food manufacturing and processing SMEs.FindingsThe results reveal a significant positive relationship between GSV, GKS and eco-innovation types. Furthermore, it was revealed that all three types of eco-innovation are significantly related to SCA and SBP.Practical implicationsThe results of this research will assist food manufacturing and food processing SMEs in reducing their eco-footprint to gain SCA and SBP. Furthermore, policymakers and governing bodies may implement strong regulations to curtail eco-pollution.Originality/valueTo the best of the authors' knowledge, this is the first study that incorporates the concept of eco-innovation in food processing and food manufacturing SMEs of a developing country in the light of the natural resource orchestration theory.

W. Martindale, Tom Æ Hollands, Sandeep Jagtap et al.

Emil Björnson, Yonina C. Eldar, Erik G. Larsson et al.

Wireless communication technology has progressed dramatically over the past 25 years, in terms of societal adoption as well as technical sophistication. In 1998, mobile phones were still in the process of becoming compact and affordable devices that could be widely utilized in both developed and developing countries. There were "only" 300 million mobile subscribers in the world [1]. Cellular networks were among the first privatized telecommunication markets, and competition turned the devices into fashion accessories with attractive designs that could be individualized. The service was circumscribed to telephony and text messaging, but it was groundbreaking in that, for the first time, telecommunication was between people rather than locations. Wireless networks have changed dramatically over the past few decades, enabling this revolution in service provisioning and making it possible to accommodate the ensuing dramatic growth in traffic. There are many contributing components, including new air interfaces for faster transmission, channel coding for enhanced reliability, improved source compression to remove redundancies, and leaner protocols to reduce overheads. Signal processing is at the core of these improvements, but nowhere has it played a bigger role than in the development of multiantenna communication. This article tells the story of how major signal processing advances have transformed the early multiantenna concepts into mainstream technology over the past 25 years. The story therefore begins somewhat arbitrarily in 1998. A broad account of the state-of-the-art signal processing techniques for wireless systems by 1998 can be found in [2], and its contrast with recent textbooks such as [3]-[5] reveals the dramatic leap forward that has taken place in the interim.

Murat Isik, Hiruna Vishwamith, Kayode Inadagbo et al.

This paper presents a novel approach to neuromorphic audio processing by integrating the strengths of Spiking Neural Networks (SNNs), Transformers, and high-performance computing (HPC) into the HPCNeuroNet architecture. Utilizing the Intel N-DNS dataset, we demonstrate the system's capability to process diverse human vocal recordings across multiple languages and noise backgrounds. The core of our approach lies in the fusion of the temporal dynamics of SNNs with the attention mechanisms of Transformers, enabling the model to capture intricate audio patterns and relationships. Our architecture, HPCNeuroNet, employs the Short-Time Fourier Transform (STFT) for time-frequency representation, Transformer embeddings for dense vector generation, and SNN encoding/decoding mechanisms for spike train conversions. The system's performance is further enhanced by leveraging the computational capabilities of NVIDIA's GeForce RTX 3060 GPU and Intel's Core i9 12900H CPU. Additionally, we introduce a hardware implementation on the Xilinx VU37P HBM FPGA platform, optimizing for energy efficiency and real-time processing. The proposed accelerator achieves a throughput of 71.11 Giga-Operations Per Second (GOP/s) with a 3.55 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency and design flexibility. Through design-space exploration, we provide insights into optimizing core capacities for audio tasks. Our findings underscore the transformative potential of integrating SNNs, Transformers, and HPC for neuromorphic audio processing, setting a new benchmark for future research and applications.