利用ATR-FTIR研究脂质纳米粒的鼻黏液渗透性

鼻黏液是影响疫苗和药物经鼻吸收的首要屏障。由于干扰因素复杂多变,在体评价黏液渗透性较难实施,多采用体外评价。现有的药物鼻黏液渗透性检测方法如细胞模型法、多粒子示踪技术等,存在细胞培养周期长、操作繁琐、成本高、可获得信息少且需要进行荧光标记等不足,对鼻黏膜制剂的体外评价具有很大的局限性,因此迫切需要建立一种快速、简便、灵敏的鼻黏膜制剂黏液渗透性评价方法。基于衰减全反射-傅里叶红外光谱(ATR-FTIR)对药物结构以及粘蛋白二级结构变化的敏感性,利用其对不同性质(粒径与电荷)脂质体的鼻黏液渗透性进行研究,通过FTIR图谱分析不同脂质纳米粒与黏液中粘蛋白的相互作用,建立了鼻黏膜制剂黏液渗透性的体外评价方法。方法学研究表明,对于聚乙二醇10000(PEG10000)、壳聚糖、海藻酸钠脂质体,该方法线性关系分别为Y=2.3866X+2.154、Y=1.8703X+0.2789、Y=1.13014X+0.0609,线性相关系数分别为0.9958、0.9945、0.9909,精密度RSD值分别为0.62%、0.73%、0.95%;重复性试验中RSD值分别为0.83%、0.97%、0.88%,说明该方法线性关系良好、精密度高、重复性好,可用于体外评价药物制剂在黏液中的渗透性。研究结果表明,利用ATR-FTIR在不同时间内扫描样品可得到不同脂质体制剂样品的强度增加的吸收带。对于不同粒径PEG脂质体而言,粒径越小其黏液渗透性越强;对于不同电荷脂质体而言,壳聚糖脂质体黏液渗透性最弱,海藻酸钠次之,PEG脂质体黏液渗透性最强。进一步研究表明,不同电荷脂质体黏液渗透性的差异源于其与粘蛋白相互作用,该结论可通过分析粘蛋白酰胺Ⅰ带所包含的各二级结构信息得到。综上,基于ATR-FTIR所建立的体外评价方法灵敏简便,可作为多种不同制剂的鼻黏液渗透性的快速测定,经改进后还可用于药物制剂在其他黏液中的渗透性评价,应用前景广阔。

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

Certis: Cloud Asset Management & Threat Evaluation Using Behavioral Fingerprinting at Application Layer

This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

Improving Optimal Fingerprinting Methods Requires a Viewpoint beyond Statistical Science

While being successful in the detection and attribution of climate change,the optimal fingerprinting method(OFM)may have some limitations from a physics-and-dynamics-based viewpoint.Here,an analysis is made on the linearity,noninteraction,and stationary-variability assumptions adopted by OFM.It is suggested that furthering OFM needs a viewpoint beyond statistical science,and the method should be combined with theoretical tools in the dynamics and physics of the Earth system,so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.

Rational and Continuous Measurement of Emotional-Fingerprint, Emotional-Quotient and Categorical vs Proportional Recognition of Facial Emotions with M.A.R.I.E., Second Half

Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.

Study on HPLC-DAD Fingerprint of Xueshuan Xinmaining Capsules

[Objectives]To establish the HPLC-DAD fingerprint of Xueshuan Xinmaining Capsule(XXC).[Methods]The chromatographic conditions for the analysis of XXC solution were as follows:XSelect HSS T3 column;acetonitrile-0.1%phosphoric acid water was used as mo-bile phase,gradient elution;flow rate:1.0 mL/min;column temperature 30℃;The injection volume is 10μL.The quality of XXC samples produced by different manufacturers was evaluated by similarity evaluation and cluster analysis.[Results]In theHPLC-dad fingerprints of 15 batches of XXC,23 common peaks were identified and 9 peaks were identified,and the similarity was greater than 0.95.According to the re-sults of cluster analysis,15 batches of XXC samples could be divided into two categories,S2,S5,S6,S7 and S8 batches belonged to category Ⅰ,and the rest batches belonged to category Ⅱ.[Conclusions]In this study,a representative and universal identification method of Xxc HPLC-DAD fingerprint was established.The method has high precision,stability and repeatability,is simple and reliable,and provides a pow-erful reference for further improving the quality evaluation system of XXC.

The significance of metabolic fingerprinting in carcinogenesis

Carcinogenesis describes the process through which normal cells transform into malignant cells(cancer).There were an estimated 18.1 million new cases of cancer(all cancers combined excluding non-melanoma skin cancer)worldwide in 2020:8.8 million(48%)in females and 9.3 million(52%)in males,giving a male:female ratio of 10:9.5.It may be initiated by the action of biological,physical,or chemical agents that cause a non-lethal,permanent,DNA error on the cell with a consequence of altered cell metabolism.This altered cell metabolism include the Warburg effect,altered lipid and amino acid metabolism and production of various metabolites.It also results in unique metabolic dependencies that,in some cases,can be targeted with precision medicine and nutrition,including drugs that selectively target metabolic enzymes.Metabolic fingerprinting has been applied to the study of carcinogenesis and is particularly helpful in early diagnosis,staging and choice of treatment,thus improving health outcomes.This technology could therefore be harnessed effectively while combining with other omics technologies.

ATR-FTIR光谱法检测花生油、芝麻油和菜籽油中的过氧化物

食用油在加工和储存过程中脂质易被氧化生成过氧化物,从而影响其营养价值和感官,并会产生毒素诱导多种人类疾病。基于食用油中过氧化物与三苯基膦(TPP)间1:1的定量反应,通过测定反应产物三苯基氧膦(TPPO)于542 cm-1处的红外特征吸收峰,建立检测不同食用油过氧化值的方法。实验结果表明,花生油、芝麻油和菜籽油中的三苯基氧膦(TPPO)在542 cm^(-1)处的特征吸收的峰高D(542)与其过氧化值之间存在良好的线性关系,且线性关系均不同,线性方程分别为y=3.09×10^(-4)x+1.43×10^(-4),R^(2)为0.9918,y=8.23×10^(-4)x-2.86×10^(-4),R2为0.9941,y=5.2×10^(-4)x-3.33×10^(-4),R^(2)为0.9955。建立的方法可以更加准确地检测不同食用油中的过氧化值,且使用的有机溶剂较少,可用于花生油、芝麻油和菜籽油快速、准确的质量控制分析。

基于ATR-FTIR光谱测量结晶过程溶液浓度的变量稳定加权混合收缩方法

针对衰减全反射-傅里叶变换红外(ATR-FTIR)光谱仪用于测量结晶过程溶液浓度时,因光谱谱线维度高、无关变量多,导致的标定模型预测精度低、可解释性差等问题,提出了一种变量稳定加权混合收缩的新方法。首先提出对光谱变量进行随机二进采样,将建立的优秀子模型中变量被选频率与所有子模型中变量回归系数的稳定性指标进行加权评价的稳定加权变量种群分析法(SWVCPA)。通过对变量的重要性进行排序,采用指数递减函数在迭代过程中逐渐强制滤除重要性低的变量,实现了对光谱变量空间的初步收缩,并大幅提高了收缩的稳定性。然后在收缩后的子空间继续使用一种新的动态麻雀算法(DSSA),以最小化训练预测均方根误差(RMSEC)为适应度函数进一步优化变量组合。这种混合优化方式融合了两类变量选择算法的优点,通过子模型竞争的方法确保了前期变量收缩的稳定性,防止算法陷入局部最优;通过智能优化算法避免了对剩余变量组合的遍历寻优,允许保留更多的变量进行精准选择。为了验证新方法的性能,使用L-谷氨酸溶液冷却结晶过程中6种不同浓度下采集到的ATR-FTIR光谱数据进行测试。结果表明,新方法将光谱变量数从613个减少到46个,与原始光谱相比,使用选择后变量建立的偏最小二乘法(PLSR)模型其预测均方根误差(RMSEP)为从1.727 9降低到0.165 4,预测决定系数(R^(2))从0.973 7提高到0.999 7。另外相比于特征谱段、遗传算法(GA)以及变量种群组合分析法(VCPA)选择变量建立的模型,使用新方法建立的溶液浓度预测模型具有更高的准确性和稳定性,说明该方法对提高使用ATR-FTIR光谱法测量冷却结晶过程溶液浓度准确性和可靠性具有一定的实际应用价值。

“ATR-FTIR和STA-FTIR-GC/MS的综合应用”实验教学探索

将衰减全反射红外光谱仪和同步热分析-傅里叶变换红外光谱-气相色谱质谱联用仪的综合应用引入本科《现代仪器分析》实验教学环节。以学生提供的未知塑料样品为研究对象,通过教师引导式教学,学生独立思考以及仪器实际操作,通过分析塑料样品的主要成分以及热解过程中释放出的有害物质的成分信息及其对环境的影响,加深了对仪器的工作原理、构造的理解。本实验丰富了《现代仪器分析》综合实验的教学内容,取得较好的实验教学效果,学生在实验中的主体作用、参与意识和学习兴趣都有了极大的提高,联用仪器的使用还提高了学生实践能力、知识灵活应用能力、创新能力以及各项综合能力。

ATR-FTIR结合DSC法快速鉴别食品接触用塑料包装制品

研究了一种衰减全反射红外光谱(ATR-FTIR)结合差示扫描量热法(DSC)快速鉴别食品接触用塑料包装制品单一材质的方法。红外光谱图能准确反应塑料制品的内部结构,结合DSC曲线得出其具体成分。该方法快速、准确且几乎不破坏样品。