Infrared Physics & Technology最新文献

{"title":"Fabrication, microstructure and infrared optical properties of polycrystalline diamonds through surface seeding optimization","authors":"Yi Zhu ,&nbsp;Chencheng Liu ,&nbsp;Wenlong Zhang ,&nbsp;Tong Zhu ,&nbsp;Jing Jia ,&nbsp;Wei Zhang ,&nbsp;Peng Sun ,&nbsp;Hui Song ,&nbsp;Cheng-Te Lin ,&nbsp;Kazuhito Nishimura ,&nbsp;Yuezhong Wang ,&nbsp;Nan Jiang","doi":"10.1016/j.infrared.2025.105915","DOIUrl":"10.1016/j.infrared.2025.105915","url":null,"abstract":"<div><div>This study systematically investigated the impact of surface seeding density (5.5 × 10⁸ – 4.6 × 10⁹ cm⁻²) on the growth behavior, microstructure, and optical performance of polycrystalline diamond films synthesized via microwave plasma chemical vapor deposition (MPCVD). By modulating the composition of diamond suspensions, four samples (S1–S4) with controlled seeding densities were fabricated. Results demonstrated that higher seeding densities could reduce the grain sizes from ∼84 μm to ∼30 μm, lower the surface roughness (Sa: 3.81 μm to 1.53 μm), and enhance crystallinity, as were confirmed by TEM, EPR, and Raman spectroscopy. Under a seeding density of 2.7 × 10⁹ cm⁻², diamonds exhibited optimal performance with minimal lattice defects, low residual stress (0.07 GPa), and the highest infrared (IR) transmittance (∼70.9 % at 12 μm). Subsequent deposition of Y₂O₃ anti-reflective coatings on diamond substrates achieved an average transmittance of ∼93.3 % (peak: ∼96.1 %) in the 8–12 μm range, with only 4.4 % loss after thermal cycling at 800 °C. The work has established a quantitative relationship between seeding density, structural integrity, and optical properties, offering a scalable strategy for producing high-quality polycrystalline diamond materials with exceptional thermal stability and IR transparency. These advancements highlighted the material’s potential for demanding applications, such as IR windows in high-speed aircraft and high-temperature optical systems.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105915"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced yellow fluorescence emission by co-doping Tb3+ into Dy3+:LuPO4 crystal","authors":"Qingtang Xu ,&nbsp;Jianhong Li ,&nbsp;Jia Gao ,&nbsp;Chunxiao Nie ,&nbsp;Xinyue Shi ,&nbsp;Xueqing Liu ,&nbsp;Yijian Wu ,&nbsp;Bing Teng ,&nbsp;Degao Zhong ,&nbsp;Shijia Sun","doi":"10.1016/j.infrared.2025.105900","DOIUrl":"10.1016/j.infrared.2025.105900","url":null,"abstract":"<div><div>The Dy³⁺:LuPO₄ and Dy³⁺/Tb³⁺:LuPO₄ crystals were successfully synthesized by high temperature solution method. The primary characteristics, including crystal structures, chemical composition, phonon energy, J-O theory, and polarized absorption and emission spectra, were systematically analyzed. The substantial absorption characteristics demonstrated by Dy³⁺:LuPO₄ and Dy³⁺/Tb³⁺:LuPO₄ crystals at 451 nm, featuring absorption FWHMs of 6 nm and cross-sections of 5.96 × 10^-21 cm², suggest the effective compatibility with readily available blue laser diodes for optical pumping. The energy transfer mechanism between Dy³⁺ and Tb³⁺ ions was discussed in detail. Co-doped Tb³⁺ ions not only effectively improve the emission cross-section and fluorescence intensity of Dy³⁺:LuPO₄ crystal at 574 nm, but also reduce the lifetime of the upper laser energy level ⁴F_9/2, which indicates that Tb³⁺ ions can play a better sensitization effect on the emission of Dy³⁺ ions in the yellow wavelength band. The results reveal that Dy³⁺/Tb³⁺:LuPO₄ crystal is a promising yellow laser gain medium.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105900"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance terahertz detection in Weyl semimetal NbP nanosheets","authors":"Zhen Hu ,&nbsp;Bojin Zhao ,&nbsp;Yingdong Wei ,&nbsp;Xiaokai Pan ,&nbsp;Yichong Zhang ,&nbsp;Zhenhan Zhang ,&nbsp;Hailong Qiu ,&nbsp;Xiaoshuang Chen ,&nbsp;Lin Wang","doi":"10.1016/j.infrared.2025.105896","DOIUrl":"10.1016/j.infrared.2025.105896","url":null,"abstract":"<div><div>Weyl semimetals (WSMs), with their unique electronic structures and topological properties, have garnered significant interest for transport and optoelectronic applications. Among them, Type-I Weyl semimetal NbP exhibits high carrier mobility and strong nonlinear optical responses, making it a promising candidate for infrared and terahertz (THz) detection. However, challenges in conventional thin-film fabrication have limited the practical integration of heavy transition metal WSMs into functional devices. In this work, we present a chemical vapor transport (CVT)-based synthesis strategy for high-quality NbP nanosheets and successfully integrate them with a bowtie antenna structure to develop a novel THz detector. The detector operates in a zero-bias mode, leveraging photon-Weyl fermion interactions to achieve high-performance THz detection. It demonstrates a broad detection range from 0.02 to 0.30 THz, an ultrafast response time of 790 ns, a noise equivalent power as low as 6.25 × 10^-11 W Hz^−1/2, and a responsivity of 156 V/W. Furthermore, we achieve heterodyne mixing in the microwave regime with a local oscillator bandwidth exceeding 67 GHz. These findings advance the application of WSMs in optoelectronic detection and provide new insights into the interplay between topological properties and photoresponse mechanisms, paving the way for next-generation THz photonic devices.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105896"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of camellia oil adulteration by using near infrared spectroscopy combined with two dimensional correlation spectroscopy (2DCOS) analysis","authors":"Xiaorong Wang ,&nbsp;Chaojie Wei ,&nbsp;Wei Wang ,&nbsp;Daren Wang ,&nbsp;Yizhe Liu ,&nbsp;Beibei Jia ,&nbsp;Yanna Jiao","doi":"10.1016/j.infrared.2025.105902","DOIUrl":"10.1016/j.infrared.2025.105902","url":null,"abstract":"<div><div>Camellia oil (CAO), a unique edible plant oil native to China with distinct nutritional benefits, is often adulterated with cheaper oils due to its high profit. Therefore, the detection of the types and concentrations of these adulterants is crucial. In this study, the near-infrared (NIR) spectroscopy combined with two dimensional correlation spectroscopy (2DCOS) analysis method is proposed, revealing the interaction mechanism of adulterants in CAO, while enabling the rapid identification and quantification of adulteration in CAO. Firstly, Adulterated CAO samples with different concentration gradients were prepared, and the spectral characterization of the oil samples was completed. Then, the 2DCOS method was applied to distinguish highly overlapping spectral bands in adulterated oils of different concentrations. Synchronous spectra were employed to identify characteristic wavelengths from autocorrelation peaks, and asynchronous spectra were employed to analyze the sequential interaction of fatty acid fusion in adulterated oils under varying concentration gradients. Furthermore, the simplified multispectral models using support vector machine (SVM) and Partial Least Squares Regression (PLSR) were established with selected characteristic wavelengths as input variables. The established multispectral models demonstrated good performance with an accuracy of 92.78 %, the determination coefficients between 0.9601 and 0.9940, and the limits of detection between 0.70 % and 1.84 % for these adulterated oils. These results indicate that NIR spectroscopy combined with 2DCOS can be used to serve as a powerful rapid detection technique for adulteration analysis in CAO, and provides new methods for the interaction mechanism of adulterated oil components.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105902"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TMCN: Text-guided Mamba-CNN dual-encoder network for infrared and visible image fusion","authors":"Jianming Zhang ,&nbsp;Xiangnan Shi ,&nbsp;Zhijian Feng ,&nbsp;Yan Gui ,&nbsp;Jin Wang","doi":"10.1016/j.infrared.2025.105895","DOIUrl":"10.1016/j.infrared.2025.105895","url":null,"abstract":"<div><div>Infrared and visible image fusion (IVF) combines the complementary advantages of two images from different physical imaging methods to create a new image with richer information. To better address issues such as weak texture details, low contrast, and poor visual perception of overexposed and underexposed areas, we propose a text-guided Mamba-CNN dual-encoder network (TMCN). Firstly, to leverage the feature extraction capabilities of Mamba and CNN, we design a pre-training network to train a Mamba-based encoder, a CNN-based encoder, and a decoder. The structures of these encoders are also used in the image fusion stage. Then, we introduce a hybrid Mamba-CNN dual-encoder to extract global and local features from infrared and visible images, resulting in four distinct types of feature information. Secondly, we design a global fusion block (GFB) via the Mamba-based encoder, and a local fusion block (LFB) via the CNN-based encoder, to fuse the global and local features of the two modalities, respectively. Following these fusion blocks, we introduce text semantic information and utilize its stable and targeted characteristics to better solve the above problems. Therefore, we propose a plug-and-play text-guided block (TB) that first uses a CLIP-based text encoder to encode the input text, and then exploits feed-forward neural network (FFN) to extract two parameters for subsequent linear transformations, which reflect the text-guided mechanism. Finally, numerous experiments demonstrate that our method achieves excellent performance in IVF and has strong versatility. Furthermore, our method enhances the performance of downstream tasks such as object detection and semantic segmentation. The code will be available at <span><span>https://github.com/XiangnanShi-CSUST/TMCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105895"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A SEDEA-based approach for evaluating the effectiveness of thermal camouflage using image and spectral data","authors":"Ngoc Son Nguyen ,&nbsp;Anh Tuan Nguyen ,&nbsp;Tien Bao Tran ,&nbsp;Huu Khanh Vu ,&nbsp;Duc Hieu Vu ,&nbsp;Manh Thang Nguyen","doi":"10.1016/j.infrared.2025.105872","DOIUrl":"10.1016/j.infrared.2025.105872","url":null,"abstract":"<div><div>Using military camouflage technologies in modern warfare to avoid or reduce the ability of detection from opponents has rapidly developed all over the world. The method to evaluate camouflage effectiveness is investigated to ensure the practical applicability of camouflage products. Because of sensitive information, no universally accepted or reliable standards exist for measuring camouflage effectiveness domestically or internationally. This absence of standards has led to various assessment methods, broadly categorized into subjective and objective evaluations. Each approach has certain advantages and restrictions, with neither proving unequivocally superior. A comprehensive evaluation of camouflage effectiveness is a complex issue that requires considering multiple critical factors such as the characteristics of surveillance equipment, targets, background environment, atmosphere conditions, and observers. This study introduces a novel objective evaluation framework utilizing the new Data Envelopment Analysis (DEA) model, incorporating a newly developed set of assessment indicators derived from the target’s thermal image data and radiometric spectral data. This model utilizes 11 evaluation indicators, including eight characteristic indices derived from thermal images captured by a thermal camera and three spectral feature indices obtained from data collected by a spectroradiometer. Experimental analysis on multiple camouflage outfit samples demonstrate that, compared to traditional DEA models, the Super-Efficiency DEA model offers a more reliable ranking system, effectively categorizing the effectiveness of camouflage outfits. The results also suggest that the Super-Efficiency DEA model can serve as a valuable tool for standardizing and enhancing the objectivity of camouflage effectiveness assessments in the future.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105872"},"PeriodicalIF":3.1,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NOC-YOLO: An exploration to enhance small-target vehicle detection accuracy in aerial infrared images","authors":"Yuhao Zhang ,&nbsp;Zhenhua Dai ,&nbsp;Cunshu Pan ,&nbsp;Gaofeng Zhang ,&nbsp;Jin Xu","doi":"10.1016/j.infrared.2025.105905","DOIUrl":"10.1016/j.infrared.2025.105905","url":null,"abstract":"<div><div>Infrared imaging technology enhances the visibility of vehicles in the dark from an aerial perspective. However, the loss of texture and boundary features of targets in infrared images impedes the accurate detection of small-target vehicles. To address this issue, we propose Nocturne-You Only Look Once (NOC-YOLO), using YOLOv10n as the baseline model, aimed at improving the detection accuracy of small-target vehicles in drone-captured infrared images. We introduce three key improvements to the baseline model, incorporating attention mechanisms and multi-scale feature fusion to enhance the interaction between features. Extensive experiments on the DroneVehicle dataset demonstrate NOC-YOLO’s superiority: it achieves state-of-the-art mAP50 (79.5 %) and mAP50-95 (63.2 %), surpassing YOLOv10n by 1.4 % in mAP50, respectively, with only a 10 % increase in parameters and 12 % higher GFLOPs. Compared to non-YOLO methods, NOC-YOLO achieves significant performance improvements while maintaining exceptional lightweight characteristics, making it highly suitable for real-time deployment in resource-constrained environments. This balance of high precision and efficiency provides robust technical support for safety–critical applications like nighttime driving trajectory extraction and collision risk analysis.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105905"},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DFLMF-ISTD: Infrared small object detection network based on decoupled feature learning and multi-scale feature fusion","authors":"Ning Li,&nbsp;Daozhi Wei,&nbsp;Shucai Huang,&nbsp;Xirui Xue","doi":"10.1016/j.infrared.2025.105851","DOIUrl":"10.1016/j.infrared.2025.105851","url":null,"abstract":"<div><div>Infrared small object detection is widely used in small maneuvering object awareness and high threatening object detection and recognition. In recent years, the introduction of deep learning methods has greatly improved the detection performance of infrared small objects. However, the presence of clutter in infrared small object images (low signal-to-noise ratio, SNR) and the lack of shape and texture information for the objects lead to a decrease in detection performance in complex environments. As such, in this article, an infrared small object detection network based on decoupled feature learning and multi-scale feature fusion is proposed. First, utilizing disentangled feature learning, we construct Reversible Column Networks (Revcol) with C3 modules to get RevcolC3 to alleviate the issues of complex feature extraction and the loss of small-scale object information. Second, a new lighted attention spatial pyramid pooling (LASP) module is proposed. By convolving the features extracted from the backbone and performing two consecutive pooling operations, a large kernel separated attention (LSKA) mechanism is introduced to process spatial and channel information separately. This enhances the model’s multi-feature extraction capabilities while reducing computational complexity. Finally, a novel lightweight three-dimensional multi-scale feature fusion (LTDMF) module is designed to efficiently utilize the correlations between three-level pyramid feature maps and effectively extract infrared small object features. This enhances the network’s ability to detect objects while maintaining the same model size. The proposed methodology is rigorously evaluated for its feasibility and reliability on the benchmark SIRST and IRSTD-1k datasets. The experimental results indicate that the proposed methodology outperforms current state-of-the-art (SOTA) infrared small object detection techniques under conditions of complex environments, small infrared object scales, and the absence of discernible texture and shape features.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105851"},"PeriodicalIF":3.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven strength prediction for solid waste-based backfill materials using infrared radiation indices","authors":"Arienkhe Endurance Osemudiamhen ,&nbsp;Liqiang Ma ,&nbsp;Ichuy Ngo ,&nbsp;Guanghui Cao","doi":"10.1016/j.infrared.2025.105898","DOIUrl":"10.1016/j.infrared.2025.105898","url":null,"abstract":"<div><div>The resilience of underground mining systems is deeply associated with the unconfined compressive strength (UCS) of backfill components, which is key to securing safety, operational performance, and ecological balance. This investigation presents a data-centric methodology that amalgamates machine learning (ML) techniques with infrared radiation (IR) indices, specifically Average Infrared Radiation Temperature (AIRT) and Variance of Infrared Radiation Temperature (VIRT), aimed at improving the precision of UCS predictions. A thorough dataset of 180 samples focused on backfill materials taken from solid waste underwent a careful analytical methodology using progressive machine learning models, including Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), Random Forest Regression (RFR), and Least Squares Support Vector Machine (LSSVM),<!--> <!-->where LightGBM yielded stellar outcomes, reaching an R² score of 0.9276 and reflecting notably small error margins, as underlined by a mean absolute error (MAE) of 0.5618 and a root mean square error (RMSE) of 0.8803. The variance accounted for (VAF) was calculated at 0.9342, with the relative standard residual (RSR) recorded at 1.002. The prediction interval (PI) was defined as [2.0178, 5.9030], alongside a normal mean bias error (NMBE) of −0.0327. The employed methodology incorporated stringent experimental protocols, encompassing sample preparation, curing processes, UCS evaluations, and thermal imaging analyses to capture the dynamic responses elicited during loading. A user-centric graphical user interface (GUI) was engineered employing Tkinter, facilitating real-time UCS forecasts and expeditious decision-making within mining contexts. This study accentuates the transformative capacity of synergizing IR metrics with ML, presenting a replicable framework for integrating empirical testing with computational modeling. It emphasizes the necessity for larger, more heterogeneous datasets to bolster model robustness while fostering sustainable backfill design and environmentally conscientious mining practices.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105898"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid discrimination of Chinese rice Wine (Huangjiu) from various regions using Benchtop-NIR and Micro-NIR spectrometers in conjunction with chemometrics","authors":"Qiang Hu ,&nbsp;Xinxin Feng ,&nbsp;Lan Wang ,&nbsp;Lili Zhang ,&nbsp;Xueping Chen ,&nbsp;Yanyun Wang ,&nbsp;Guangfa Xie ,&nbsp;Qi Peng","doi":"10.1016/j.infrared.2025.105904","DOIUrl":"10.1016/j.infrared.2025.105904","url":null,"abstract":"<div><div>Near Infrared (NIR) technology plays a crucial role in food authenticity and origin identification. This study evaluates benchtop-NIR and Micro-NIR technologies, combined with chemometrics, to differentiate Huangjiu (Chinese rice wine) origins. Both models demonstrated strong performance, with Micro-NIR excelling in identification and prediction over benchtop-NIR. Spectral data from Huangjiu across various regions were used to develop a chemometric discrimination model. Benchtop-NIR’s database utilized first derivative preprocessing, while the sub-database employed second derivative and vector normalization. Micro-NIR used Savitzky-Golay (S.Golay), Standard Normal Variate (SNV), and Normalize Peak preprocessing, achieving 100% accuracy. Benchtop-NIR’s Q² was 0.939, R²X [1] 0.738, R²X [2] 0.202, and R² 0.94. Micro-NIR outperformed with Q² at 0.983, R²X [1] 0.981, R²X [2] 0.00627, and R² at 0.988. This study significantly advances the geographical identification of Huangjiu using NIR technology.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"149 ","pages":"Article 105904"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}