Vibrational Spectroscopy最新文献_第4页

Identification of environmental microplastics using large language models: DeepSeek-R1-Distill-Llama-8B, GPT-4o, and GPT-4o-mini 使用大型语言模型识别环境微塑料：deepseek - r1 -蒸馏- llama - 8b， gpt - 40和gpt - 40 -mini

IF 3.1 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-07-23 DOI: 10.1016/j.vibspec.2025.103842

Zijiang Yang , Hisayuki Arakawa

Microplastic pollution in the environment poses increasing risks to both ecological and human health. Identifying microplastics in environmental samples is important for monitoring and mitigation. However, current methods rely on manual interpretation of infrared (IR) spectra, which is time-consuming and labor-intensive. Thus, this study investigates the potential of large language models (LLMs) for identifying microplastics using IR spectra from environmental samples. Three models, DeepSeek-R1-Distill-Llama-8B, GPT-4o-2024–08–06 (GPT-4o), and GPT-4o-mini-2024–07–18 (GPT-4o-mini), were evaluated within a structured workflow that integrates spectral processing and model implementation. A performance evaluation framework was developed to measure identification accuracy. Results indicate that DeepSeek-R1-Distill-Llama-8B outperformed others, achieving an accuracy exceeding 0.93 across all tested polymer types, making it the preferred choice. GPT-4o proved a strong alternative, particularly when local execution is impractical, with accuracy above 0.86. GPT-4o-mini underperformed and is not recommended. Despite these promising outcomes, challenges persist, including the need to optimize spectral processing parameters and refine prompt design. As the first study to apply LLMs to microplastic identification, this work offers a foundational reference for leveraging LLM-driven spectral analysis in environmental monitoring.

环境中的微塑料污染对生态和人类健康构成越来越大的风险。鉴定环境样品中的微塑料对于监测和缓解至关重要。然而，目前的方法依赖于人工解释红外光谱，这是费时费力的。因此，本研究探讨了利用环境样品的红外光谱识别微塑料的大语言模型（LLMs）的潜力。DeepSeek-R1-Distill-Llama-8B、gpt - 40 -2024 - 08 - 06 （gpt - 40）和gpt - 40 -mini-2024 - 07 - 18 （gpt - 40 -mini）三种模型在集成了光谱处理和模型实现的结构化工作流程中进行了评估。开发了一个性能评估框架来衡量识别的准确性。结果表明，DeepSeek-R1-Distill-Llama-8B优于其他方法，在所有测试的聚合物类型中实现了超过0.93的精度，使其成为首选。gpt - 40被证明是一个强大的替代方案，特别是当本地执行不切实际时，其精度高于0.86。gpt - 40 -mini表现不佳，不推荐使用。尽管取得了这些有希望的成果，但挑战依然存在，包括需要优化光谱处理参数和改进提示设计。作为第一个将llm应用于微塑料识别的研究，本工作为利用llm驱动的光谱分析在环境监测中提供了基础参考。

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引用次数: 0

Spectroscopic and genotoxic assessment of Imazamox herbicide-induced alterations in the Allium cepa model system Imazamox除草剂致葱模型系统改变的光谱和遗传毒性评估

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-07-23 DOI: 10.1016/j.vibspec.2025.103843

Gulgun Cakmak-Arslan , Pinar Goc Rasgele

Imazamox (IMA), an imidazolinone herbicide, is commonly used to control weeds in crops such as sunflower, beans, peas and chickpeas. In the current study, the effects of 24 h exposure to different IMA concentrations (125, 250, and 500 ppm) on Allium cepa root tips were investigated at molecular level using Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy and genotoxicity tests. The ATR-FTIR results indicated that all doses of IMA caused an increase in lipid peroxidation levels and a decrease in tissue metabolic activity, along with a decrease in protein, carbohydrate and nucleic acid content and an increase in saturated lipid content. In addition, IMA caused important structural modifications including shortened lipid chains, reduced membrane disorder and fluidity, increased carbonyl content and lipid to protein ratio. Principal component analysis (PCA) and Hierarchical cluster analysis (HCA) confirmed these spectral alterations by effectively distinguishing control and IMA-treated groups across different doses. Genotoxicity assays further demonstrated that IMA induced various mitotic abnormalities, such as c-mitosis, irregular metaphase and micronuclei formation in A. cepa root tips. The observed structural and genotoxic changes were clearly dose-dependent, with higher concentrations causing more severe effects. These findings highlight the potential risks associated with IMA exposure and suggest that more caution should be exercised in the use of this herbicide. Furthermore, the successful application of ATR-FTIR spectroscopy to detect herbicide-induced molecular changes suggests that this technique, combined with chemometrics and A. cepa as a bioindicator model system, offers a rapid and reliable biomonitoring tool to evaluate pesticide toxicity.

Imazamox （IMA）是一种咪唑啉酮类除草剂，通常用于控制向日葵、豆类、豌豆和鹰嘴豆等作物的杂草。本研究利用衰减全反射-傅里叶变换红外（ATR-FTIR）光谱和遗传毒性试验，在分子水平上研究了24 h暴露于不同浓度的IMA（125、250和500 ppm）对洋葱根尖的影响。ATR-FTIR结果表明，所有剂量的IMA均引起脂质过氧化水平升高，组织代谢活性降低，蛋白质、碳水化合物和核酸含量降低，饱和脂质含量增加。此外，IMA引起了重要的结构修饰，包括缩短脂链，减少膜的无序性和流动性，增加羰基含量和脂蛋白比。主成分分析（PCA）和层次聚类分析（HCA）通过有效区分不同剂量的对照组和ima治疗组，证实了这些光谱变化。遗传毒性实验进一步表明，IMA诱导了A. cepa根尖的各种有丝分裂异常，如c-有丝分裂、不规则中期和微核形成。观察到的结构和基因毒性变化明显是剂量依赖性的，浓度越高，影响越严重。这些发现强调了与IMA暴露相关的潜在风险，并建议在使用这种除草剂时应更加谨慎。此外，ATR-FTIR光谱技术在除草剂诱导的分子变化检测中的成功应用表明，该技术与化学计量学和a . cepa作为生物指标模型系统相结合，为农药毒性评价提供了一种快速可靠的生物监测工具。

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引用次数: 0

Real-time determination of blend uniformity in veterinary products using near infrared (NIR) spectra 用近红外光谱实时测定兽药混合均匀性

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-07-20 DOI: 10.1016/j.vibspec.2025.103832

Ma Elena Tejada, Ricard Boqué

This study presents a new multivariate analytical strategy for real-time assessment of blend uniformity in powder mixtures using Near Infrared Spectroscopy (NIRS) as a Process Analytical Technology (PAT). The proposed method, called the Intensity Ratio (IR), monitors spectral variability at selected wavelengths corresponding to the excipient and the Active Pharmaceutical Ingredient (API). The approach was compared to established statistical strategies, including Relative Standard Deviation (RSD), F-test, Standard Deviation (SD), PC Score Distance Analysis (PC-SDA), and Conformity Index (CI).

For this study, a binary mixture of an active pharmaceutical ingredient (API) and excipient, together with three industrial batches used as Golden Batch (GB), according to the rationale for the number of batches for different residual risk levels described on Stage 2, ISPE [1] have been used to build the model, where the uniformity of the mixture has been determined at a predefined time and the analysis has been carried out using the reference technique, High Performance Liquid Chromatography (HPLC).

These findings suggest that PAT-based strategies can improve efficiency in pharmaceutical manufacturing by enabling real-time blend uniformity assessment, potentially reducing both process time and cost. The IR method, in particular, offers a robust and fast alternative when a characteristic API absorption peak can be reliably monitored.

本文提出了一种利用近红外光谱（NIRS）作为过程分析技术（PAT）实时评估粉末混合物混合均匀性的多元分析策略。所提出的方法，称为强度比（IR），监测与赋形剂和活性药物成分（API）对应的选定波长的光谱变异性。将该方法与已建立的统计策略进行比较，包括相对标准偏差（RSD）、f检验、标准差（SD）、PC评分距离分析（PC- sda）和符合性指数（CI）。在本研究中，活性药物成分（API）和赋形剂的二元混合物，以及三个工业批作为黄金批（GB），根据阶段2 ISPE[1]中描述的不同残留风险水平的批数的基本原理来建立模型，其中混合物的均匀性已在预定时间确定，并使用参考技术进行分析。高效液相色谱法。这些发现表明，基于pat的策略可以通过实时混合均匀性评估来提高制药效率，从而可能减少工艺时间和成本。特别是，当API的特征吸收峰可以可靠地监测时，红外方法提供了一种鲁棒和快速的替代方法。

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引用次数: 0

Fast ATR-FTIR method for quantifying silicates presence on PE plastic fragments from soil 快速ATR-FTIR法定量土壤PE塑料碎片上硅酸盐的存在

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-07-09 DOI: 10.1016/j.vibspec.2025.103833

David Picón-Borregales , Leticia Pastormerlo , Eduardo Reciulschi , Javier M. Montserrat

The interaction of plastic debris with the soil environment remains insufficiently studied. In particular, we have recently reported the incorporation of a mechanically stable clay phase—mainly composed of silicates—onto polyethylene (PE) macro-, meso-, and microplastic surfaces. This incorporation transforms plastic fragments into a composite material, potentially leading to significant changes in properties such as density, hydrophobicity, and contaminant sorption capacity. Therefore, quantifying the siliceous fraction is essential to better understand plastic–environment interactions. Determination of silicon by EDX is a conventional method, but is time-consuming, technically demanding, and not widely accessible. Moreover, the presence of clay onto the PE matrix complicates the identification of oxygen-containing functional groups due to spectral overlap between C–O and Si–O stretching vibrations in the sample's FTIR spectra. In this study, a rapid and straightforward ATR-FTIR-based methodology for the quantitative determination of silicon on weathered PE mulch fragments was developed. Furthermore, a reliable approach for the identification of Si–O and C–O functional groups in PE samples with high silicon content was established. The peak area of the Si–O stretching band showed a strong linear correlation with silicon concentration in PE–sand standards (R²=0.9878). The proposed method was validated against EDX measurements of PE samples extracted from agricultural soils, showing good agreement. Additionally, sodium citrate treatment effectively removed the siliceous fraction without the use of hazardous hydrofluoric acid, allowing for accurate determination of oxidation indices. The developed method is simple, rapid, and requires minimal sample preparation, offering a practical alternative for laboratories lacking access to advanced analytical techniques.

塑料垃圾与土壤环境的相互作用研究尚不充分。特别是，我们最近报道了在聚乙烯（PE）宏观、中观和微塑性表面上掺入一种主要由硅酸盐组成的机械稳定粘土相。这种结合将塑料碎片转化为复合材料，可能导致密度、疏水性和污染物吸附能力等性能的显著变化。因此，量化硅质组分对于更好地理解塑料与环境的相互作用至关重要。EDX法测定硅是一种传统的方法，但耗时长，技术要求高，而且应用范围不广。此外，由于样品FTIR光谱中C-O和Si-O拉伸振动之间的光谱重叠，PE基体上粘土的存在使含氧官能团的识别变得复杂。在这项研究中，开发了一种快速、直接的基于atr - ftir的方法，用于定量测定风化PE覆盖物碎片上的硅。此外，建立了一种鉴定高硅PE样品中Si-O和C-O官能团的可靠方法。Si-O拉伸带的峰面积与pe砂标准硅浓度呈较强的线性相关（R²=0.9878）。该方法与农业土壤中PE样品的EDX测量结果进行了验证，结果吻合良好。此外，柠檬酸钠处理有效地去除了硅质部分，而无需使用有害的氢氟酸，从而可以准确测定氧化指数。所开发的方法简单，快速，并且需要最少的样品制备，为缺乏先进分析技术的实验室提供了一种实用的替代方法。

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引用次数: 0

Band selection using oppositional whale optimization for hyperspectral image classification 基于对立鲸优化的高光谱图像分类波段选择

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-25 DOI: 10.1016/j.vibspec.2025.103830

Phaneendra Kumar B L N , Radhesyam Vaddi , Prabukumar Manoharan , Agilandeeswari L , Sangeetha V

This paper presents an innovative methodology for band selection pertinent to hyperspectral remote sensing imagery. The substantial volume of data, alongside its inherent redundancy and limited training samples, adversely influences the classification precision of these images. The discernment of informative, non-redundant, and uncorrelated bands from hyperspectral imagery represents a principal aim of the hyperspectral research community. In this study, we have proposed a pioneering band selection technique that emulates the hunting strategy of Whales, incorporating opposition learning to leverage the alternative candidate solutions. Subsequently, the intrinsic features are extracted from the oppositional whale bands and subjected to training via a three-dimensional convolutional neural network for classification, referred to as Modified Whale Optimization (MWO). The MWO is assessed against leading-edge methodologies across three benchmark datasets – Indian Pines, University of Pavia, and Salinas, both qualitatively and quantitatively. The reported classification accuracies are 98.67 %, 99.81 %, and 99.98 % respectively across the three datasets, achieved with a minimal number of bands. This methodology proves to be effective for applications in Land Use and Land Cover as well as Mineral identification.

提出了一种创新的高光谱遥感影像波段选择方法。大量的数据，加上其固有的冗余和有限的训练样本，对这些图像的分类精度产生了不利影响。从高光谱图像中识别信息丰富、非冗余和不相关的波段是高光谱研究界的主要目标。在这项研究中，我们提出了一种开创性的波段选择技术，该技术模拟了鲸鱼的狩猎策略，结合了对立学习来利用替代候选解决方案。随后，从对立鲸鱼波段中提取固有特征，并通过三维卷积神经网络进行分类训练，称为修正鲸鱼优化（MWO）。MWO是根据三个基准数据集（Indian Pines、Pavia大学和Salinas）的前沿方法进行定性和定量评估的。报告的分类准确率在三个数据集上分别为98.67 %，99.81 %和99.98 %，以最小的频带数量实现。该方法被证明是有效的应用于土地利用和土地覆盖以及矿物鉴定。

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引用次数: 0

Study on characteristic peaks of antibiotics and amino acids based on terahertz time-domain spectroscopy 基于太赫兹时域光谱的抗生素和氨基酸特征峰研究

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-25 DOI: 10.1016/j.vibspec.2025.103831

Yusong Zhang, Wei Shi, Yifan Li, Huanlin Li, Junnan Wang

Changes in antibiotic and amino acid concentrations are closely related to human beings, the natural environment and animal and plant life and health. Real-time and label-free detection using terahertz time-domain spectroscopy (THz-TDS) is of great significance for disease prevention and treatment and drug residue detection. However, water has a strong absorption of THz waves that cannot be ignored, which hinders the detection of water-containing samples. In this paper, the characteristic absorption peaks and molecular vibration modes of penicillin sodium and L-hydroxyproline were studied based on THz-TDS and density functional theory (DFT) using IPCA with high radiation properties. The absorption peaks of samples in different states are different, and the formation of new hydrogen bonds will cause the absorption peak to disappear, or a blue shift or a red shift occurs. The intramolecular interaction forces were then characterized based on IRI analysis. Based on this method, solutions with a minimum concentration of 842 μM can be qualitatively tested.

抗生素和氨基酸浓度的变化与人类、自然环境和动植物的生命健康密切相关。太赫兹时域光谱（THz-TDS）实时无标记检测在疾病防治和药物残留检测中具有重要意义。然而，水对太赫兹波有很强的吸收，这是不可忽视的，这阻碍了对含水样品的检测。本文利用具有高辐射特性的IPCA，基于太赫兹- tds和密度泛函数理论（DFT）研究了青霉素钠和l -羟脯氨酸的特征吸收峰和分子振动模式。不同状态下样品的吸收峰是不同的，新的氢键的形成会使吸收峰消失，或者发生蓝移或红移。然后基于IRI分析表征了分子内相互作用力。该方法可对最小浓度为842 μM的溶液进行定性检测。

引用次数: 0

Analysis of the polyether aqueous solution with temperature dependent NIR spectra 用随温度变化的近红外光谱分析聚醚水溶液

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-16 DOI: 10.1016/j.vibspec.2025.103829

Bo Zhang , Bin Lin , Shuai Yan , Hanwen Zhang , Yuewen Yu , Chenxi Li

The polyether has distinct advantage of being a coolant and a lubricant, which is often dependent on their solubility in water. In this study, the temperature dependent near-infrared spectroscopy was proposed to investigate the solubility and phase changes of polyether aqueous solution. Two-dimension correlation spectrum was applied to establish the relationship between temperature and NIR spectra, and determine the order of aggregation, turbidity and the temperature changes. The results demonstrated that the absorption peak blueshift of water and methylene exhibit good correlation with the temperature, which is related to the formation and destruction of hydrogen bonding between polyether and water molecules. Due to the destruction of hydrogen bonding, the viscosity also showed a good linear correlation with the amplitude of blueshift. The mechanism of phase change and film-forming of aqueous polyether lubricants also provide important information on development of new types of lubricants.

聚醚具有作为冷却剂和润滑剂的明显优势，这通常取决于它们在水中的溶解度。本研究提出了温度相关的近红外光谱来研究聚醚水溶液的溶解度和相变化。采用二维相关谱法建立了温度与近红外光谱之间的关系，确定了团聚度、浊度和温度变化的顺序。结果表明，水和亚甲基的吸收峰蓝移与温度有良好的相关性，这与聚醚和水分子之间氢键的形成和破坏有关。由于氢键的破坏，黏度也与蓝移振幅呈良好的线性相关。水性聚醚润滑剂的相变和成膜机理也为新型润滑剂的开发提供了重要信息。

引用次数: 0

PyQt5 Coding and optimization for heterodyne detected vibrational sum frequency generation spectroscopy PyQt5外差检测振动和频率产生谱编码与优化

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-11 DOI: 10.1016/j.vibspec.2025.103827

RongShun Wang , Yinjiao Zhang , Mingshuang Lu , Yuling Wu , Yingxue Wang , Wenhui Li , HongJuan Zhang , Dengshi Li , Feng Wei

Optoelectronic thin films play a critical role across various high-tech industries, including new materials, energy storage sectors, chip manufacturing, and biomedicine. This paper details the enhancement of optoelectronic thin film properties through the innovative use of Sum Frequency Generation (SFG) spectroscopy. This non-linear spectroscopic technique is uniquely suited to studying film surfaces and interfaces without damaging the samples, offering detailed insights into molecular arrangements and chemical states at these critical junctures. Further, this study introduces a novel Python-based application developed using the PyQt5 framework, which is designed to efficiently handle and analyze spectroscopic data. The application incorporates advanced data processing functions such as data denoising, Fourier transformation, square wave matrix extraction, inverse Fourier transformation, and data integration, providing a comprehensive tool for researchers. Our results demonstrate significant improvements in the precision and efficiency of data analysis, leading to enhanced performance and quality of optoelectronic films. The integration of interdisciplinary technological approaches with advanced programming techniques and mathematical analysis through SFG spectroscopy underscores its potential to revolutionize the field by providing a more precise characterization of the material's microstructural features and advancing the development and optimization processes of optoelectronic thin film technology.

光电薄膜在包括新材料、储能、芯片制造和生物医药在内的各种高科技产业中发挥着关键作用。本文详细介绍了通过创新地使用和频产生（SFG）光谱来增强光电薄膜的性能。这种非线性光谱技术非常适合研究薄膜表面和界面，而不会损坏样品，在这些关键节点上提供分子排列和化学状态的详细见解。此外，本研究介绍了一个使用PyQt5框架开发的基于python的新型应用程序，该应用程序旨在有效地处理和分析光谱数据。该应用程序集成了数据去噪、傅立叶变换、方波矩阵提取、傅立叶反变换、数据集成等先进的数据处理功能，为研究人员提供了一个全面的工具。我们的研究结果表明，在数据分析的精度和效率方面有了显著的提高，从而提高了光电薄膜的性能和质量。通过SFG光谱将跨学科技术方法与先进的编程技术和数学分析相结合，通过提供更精确的材料微结构特征表征和推进光电薄膜技术的开发和优化过程，强调了其革命性领域的潜力。

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引用次数: 0

A review of machine learning in hyperspectral imaging for food safety 机器学习在食品安全高光谱成像中的研究进展

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-10 DOI: 10.1016/j.vibspec.2025.103828

Mainak Das , Wan Sieng Yeo , Agus Saptoro

Manual detection methods such as human visual inspection are not quantitative and could lead to inconsistencies in food safety assessments. Conversely, traditional laboratory techniques offer quantitative assessments, but they involve expensive equipment, are time-consuming, and are destructive to the samples. To address these limitations, advances in non-destructive monitoring techniques with the implementation of machine learning (ML) algorithms can be alternative solutions. For instance, hyperspectral imaging technology, which combines spatial and spectral data to acquire a data-rich hypercube, can be integrated with ML models to assess food safety without damaging the samples. Different from the existing review studies on ML models, this review domain focuses more on staple foods and how these ML algorithms can quantify the chemical constituents in staple food sources. This study aims to differentiate the various ML models employed in food safety and discusses the challenges and future directions for effectively quantifying samples like adulterants in foods to ensure food safety. In addition, a bibliometric analysis of ML algorithms was also conducted to understand the research trends in hyperspectral imaging and ML. Besides, this review study also addresses different image-sensing technologies and contributes to research pursuing ML and deep learning for food safety purposes in agriculture.

人工检测方法，如人类目视检查，不是定量的，可能导致食品安全评估不一致。相反，传统的实验室技术提供定量评估，但它们涉及昂贵的设备，耗时，并且对样品具有破坏性。为了解决这些限制，非破坏性监测技术的进步与机器学习（ML）算法的实现可以成为替代解决方案。例如，结合空间和光谱数据获得数据丰富的超立方体的高光谱成像技术可以与ML模型相结合，在不损坏样品的情况下评估食品安全。与现有关于ML模型的综述研究不同，本综述领域更多地关注主食以及这些ML算法如何量化主食来源中的化学成分。本研究旨在区分食品安全中使用的各种ML模型，并讨论有效量化食品中掺假等样品以确保食品安全的挑战和未来方向。此外，本文还对机器学习算法进行了文献计量分析，以了解高光谱成像和机器学习的研究趋势。此外，本综述还讨论了不同的图像传感技术，并为农业食品安全领域的机器学习和深度学习研究做出了贡献。

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引用次数: 0

A feasibility study on improving the non-destructive detection accuracy of Huping jujube (Ziziphus jujuba Mill. cv. Huping) damage degree using near infrared spectroscopy 提高湖平枣（酸枣）磨无损检测精度的可行性研究。简历。利用近红外光谱分析湖平损伤程度

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-06-09 DOI: 10.1016/j.vibspec.2025.103826

Yanqing Xie , Qiang Xi , Xiangli Han , Zheng Li , Gang Li , Haixia Wang , Ming Liu , Jing Zhao

Near infrared (NIR) spectroscopy is promising for fruit quality assessment but faces robustness challenges in damage detection, as surface reflectance alone cannot fully characterize internal and external damage features. To overcome this limitation, we propose combining NIR spectroscopy with multi-position light scattering information to improve the accuracy of non-destructive jujube damage grading. The Huping jujube was impacted and the damaged jujube was taken as the sample. The NIR spectra of three kinds of samples with different damage grades are collected. With the damage degree as the reference index, five machine learning algorithms of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Radial Basis Function network(RBF), and Long Short-Term Memory (LSTM) are combined to construct the damage degree identification model of single-position spectral and multi-position detection data fusion. The test set accuracy of the optimal multi-position spectral modeling (MPSM) method is 100.00 %. Compared with the single-position spectral modeling (SPSM) method, the stability of the MPSM fusion method is significantly improved, and the accuracy rate is increased by more than 13.89 %. This study established a reliable non-destructive detection method for subtle fruit damage, demonstrating the effectiveness of multi-position spectral fusion in capturing sub-surface damage and providing a transferable framework applicable to other bruise-prone delicate fruits.

近红外（NIR）光谱技术在水果品质评估方面具有广阔的应用前景，但在损伤检测方面面临着鲁棒性的挑战，因为仅靠表面反射率不能完全表征内部和外部损伤特征。为了克服这一局限性，我们提出将近红外光谱与多位置光散射信息相结合，以提高枣无损损伤分级的准确性。以湖平枣树为研究对象，以受损枣树为研究对象。采集了三种不同损伤等级样品的近红外光谱。以损伤程度为参考指标，结合支持向量机（SVM）、随机森林（RF）、k近邻（KNN）、径向基函数网络（RBF）和长短期记忆（LSTM）五种机器学习算法，构建了单位置光谱与多位置检测数据融合的损伤程度识别模型。最优多位置光谱建模（MPSM）方法的测试集精度为100.00 %。与单位置光谱建模（SPSM）方法相比，MPSM融合方法的稳定性显著提高，准确率提高13.89 %以上。本研究建立了一种可靠的水果细微损伤无损检测方法，证明了多位置光谱融合在捕获亚表面损伤方面的有效性，并提供了一种可转移的框架，适用于其他易发生瘀伤的微妙水果。

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引用次数: 0