纹党参与白条党参红外光谱的SIMCA聚类鉴别方法研究

以纹党参和白条党参的红外光谱为聚类分析的对象,研究了红外光谱结合SIMCA聚类分析法对纹党参和白条党参进行识别与分类的可行性.选取400 ～2 000 cm~(-1)范围内的光谱,通过基线补偿(Offset)和散射校正(MSC)等预处理后,采用SIMCA聚类分析法建立识别模型.结果表明,所建模型对纹党参和白条党参的识别率分别达92%和96%,拒绝率均为100%.用盲样对所建模型进行了测试,测试结果全部正确.该法可实现对纹党参和白条党参的快速鉴别.     

健康和病变关节软骨的傅里叶变换红外光谱显微成像及Fisher判别

傅里叶变换红外光谱显微成像(FTIRI)可同时获得样品的红外光谱和形貌信息,其与化学计量学方法结合,可实现生物组织中主成分含量及分布的定量研究.本研究采用FTIRI技术结合主成分分析(PCA)以及Fisher判别算法对正常和病变的关节软骨进行鉴别分析.对关节软骨切片进行实现FTIR扫描及光谱分析,再利用SPSS软件对软骨的光谱(矩阵)进行主成分分析,根据主成分得分矩阵构造分类函数,结合Fisher判别算法对样本进行分类识别.正常和病变的关节软骨样品识别准确率高达95.7％(初始案例)和94.3％(交互验证案例).本方法可准确有效地辨别关节软骨是否发生病变,为监测骨关节炎的发生和修复提供参考.     

傅里叶变换红外光谱结合模式识别法快速鉴别食用油的真伪

采用傅里叶变换红外光谱(FTIR)结合簇类独立软模式识别技术(SIMCA)建立了真伪食用油的快速鉴别方法. 该方法依据FTIR 的指纹特性, 收集并分析了53 个合格食用油和13 个伪造食用油的FTIR 谱图; 通过对谱图取二阶导数和标准化处理, 主成分分析(PCA)提取特征变量; 采用SIMCA 方法分别随机选取43 个合格食用油和9 个伪食用油样品的FTIR 谱图组成训练集, 构建得到真伪食用油的SIMCA 分类模型. 该模型经过剩余10 个合格食用油和4 个伪食用油的验证, 正确识别率达到了100%. 说明FTIR 结合SIMCA 可能成为快速鉴别食用油真伪的一种新方法.     

红外、近红外光谱-簇类的独立软模式方法识别植物调和油脂

用IR,NIR光谱法结合簇类的独立软模式(SIMCA)识别方法对植物油脂进行分类识别,建立了识别二元、三元植物调和油脂的测定方法。应用NIRCal5.2软件的SIMCA技术,分别为所制备的植物调和油脂建立了IR和NIR识别模型,并讨论了光谱处理和数据处理方法来提高模型的分类识别效果。分别以各种植物调和油脂的IR和NIR光谱为变量,随机抽取2/3的样本作训练集,建立了各个调和油的主成分分析(Princi-pal component analysis,PCA)模型;1/3作验证集,对所建模型进行验证识别。用聚类分析-主成分分析(CLU-PCA)方法考察调和油的IR,NIR光谱信息与其纯油的主成分分布。结果显示,在4000～10000cm-1光谱范围内,SIMCA可以对15种二元调和油和2种三元调和油的NIR光谱分别聚类并识别;并对10种二元调和油和2种三元调和油的IR光谱分别聚类并识别。IR以4个波数1099,1119,1746与2855cm-1的吸收值作为分析基础,选择不同的主成分数及数据预处理方法。各种油脂的SIMCA分析的分类精度均为100%,调和油的验证识别准确率100%,最低识别比例为1%,且IR识别灵敏度高于NIR。     

浓度分段模型应用于人体血清葡萄糖的FTIR/ATR光谱分析

利用傅立叶变换红外光谱(FTIR)和衰减全反射(ATR)技术,建立了人血清葡萄糖的快速定量分析方法。根据葡萄糖水溶液与纯净水差谱得到葡萄糖的指纹吸收波段(1 200~900 cm-1),分别在全谱(4 000~600 cm-1)和指纹波段建立偏最小二乘法(PLS)模型,指纹波段的预测效果明显好于全谱。选择指纹波段后,提出一种根据浓度分段分别建模然后进行组合的建模方法。按照全部样品、低浓度样品、高浓度样品分别建立模型后,根据3个模型进行综合决策。应用独立的检验集对样品进行测试表明,按葡萄糖浓度范围分段建立组合模型的预测效果优于基于全部样品建模的预测效果。对于分段阈值附近的样本,低浓度和高浓度模型的预测效果差别不大。浓度分段组合模型的预测均方根偏差(RMSEP)和预测相关系数(Rp)分别为0.732mmol/L和0.948。     

气相色谱结合化学计量学区分大米贮藏时间与产地

香气是衡量大米质量的一个主要因素,对大米的食用品质有重要影响。该文以顶空固相微萃取(SPME)技术为基础,采用气相色谱法分别分析了不同贮藏时间和不同产地大米样本的挥发性成分,通过主成分分析法(PCA)和偏最小二乘判别分析法(PLS-DA)对大米样本进行分类和判别分析。PCA及PLS投影图显示不同储藏时间的大米明显聚为4类,通过留一交叉验证法(LOO)计算PLS预报的准确率为96%,相对标准误差为8.2%。同时,PCA投影图中可将4个不同产地的大米样本进行区分,分类效果显著;所建PLSDA模型可靠,不同产地大米样本均能被准确识别,正确率为100%。以顶空固相微萃取/气相色谱检测大米中挥发性成分,利用主成分分析法和偏最小二乘判别分析法鉴别大米新鲜程度和产地具有可行性。     

基于Bayes判别的手帕纸红外光谱鉴别

手帕纸是犯罪现场常见的物证之一,在法庭科学领域备受关注.为了实现对市场上手帕纸的快速分类鉴别的目的,本文采用了具有无损检验特点的傅里叶红外光谱,结合主成分分析(PCA)与Bayes判别对8种品牌96个手帕纸样本建立分类模型.结果表明,分别利用PCA和Bayes判别对样本进行分类的准确率并不理想,采用Bayes判别对PC...     

近红外光谱法定性检测菜籽油中多效唑残留

采用近红外光谱技术结合化学计量学方法对菜籽油中多效唑残留进行定性检测。在4000~10000 cm-1光谱范围内采集126个菜籽油样本的近红外透射光谱。对原始光谱进行初步分析后,分别采用线性判别分析(LDA)、簇类独立软模式法(SIMCA)和最小二乘支持向量机(LSSVM)三种不同方法建立菜籽油中多效唑残留的定性检测模型,并对不同多效唑残留的菜籽油样本的分类正确率进行分析。研究结果表明,LDA,SIMCA及LSSVM 3种方法建立的检测模型均具有较高的判别能力,其校正集和预测集的正确率分别为93.33%,91.11%,95.56%和86.11%,88.89%,83.33%。此外,高多效唑残留样本的分类正确率大致趋于100%,而低多效唑残留样本的分类正确率则有一定波动。由此可知,利用近红外光谱技术可对菜籽油中多效唑残留进行快速、无损的定性检测。     

浓度分段模型应用于人体血清葡萄糖的FTlR/ATR光谱分析

利用傅立叶变换红外光谱（ FTlR）和衰减全反射（ ATR）技术,建立了人血清葡萄糖的快速定量分析方法。根据葡萄糖水溶液与纯净水差谱得到葡萄糖的指纹吸收波段（1200~900 cm-1）,分别在全谱（4000~600 cm-1）和指纹波段建立偏最小二乘法（ PLS）模型,指纹波段的预测效果明显好于全谱。选择指纹波段后,提出一种根据浓度分段分别建模然后进行组合的建模方法。按照全部样品、低浓度样品、高浓度样品分别建立模型后,根据3个模型进行综合决策。应用独立的检验集对样品进行测试表明,按葡萄糖浓度范围分段建立组合模型的预测效果优于基于全部样品建模的预测效果。对于分段阈值附近的样本,低浓度和高浓度模型的预测效果差别不大。浓度分段组合模型的预测均方根偏差（ RMSEP）和预测相关系数（ Rp ）分别为0.732 mmol/L和0.948。     

“动态”近红外光谱结合深度学习图像识别和迁移学习的模式识别方法研究

该文以山羊绒与山羊绒/羊毛混纺织物以及纯棉与丝光棉织物为研究对象,使用其"动态"光谱,扩大类间的光谱差异信息,通过融合其同步和异步二维相关光谱,用多张动态光谱构造一张能反映细节化学差异信息的"化学图像"。使用GoogLeNet深度神经网络图像识别模型结合迁移学习,建立了一种光谱分类的新方法。收集了234个织物样品,制备水含量分别为0、5.4%、11.2%和16.3%的样本,同时采集样品的漫反射近红外光谱。使用干基样品的多种预处理光谱,利用线性分类方法簇类独立软模式识别(SIMCA)和非线性方法支持向量机(SVM),共建立了16个分类模型。其中,山羊绒与山羊绒/羊毛混纺织物的SIMCA和SVM最优预测正确率分别为63.33%和70.09%,纯棉与丝光棉织物的分别为71.02%和72.51%,均不能实现有效分类。新方法对山羊绒与山羊绒/羊毛混纺织物的预测正确率为92.59%,纯棉与丝光棉织物的为94.74%,获得了有效分类。该文首次将图像分类方法用于光谱分类识别,开辟了一种新的研究途径。针对实际应用能收集到的样品属于小样本,不能满足深度学习需要大数据样本的问题,使用迁移学习方法使深度学习框架适应了光谱分类(小样本),为人工智能领域中先进的识别技术用于解决化学问题提供了一个成功示范。     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

Multivariate analysis for 1H-NMR spectra of two hundred kinds of tea in the world.

NMR measurements coupled with pattern-recognition analysis offer a powerful mixture-analysis tool for latent-feature extraction and sample classification. As fundamental applications of this analysis for mixtures, the 1H spectra of 176 kinds of green, black, oolong and other tea infusions were acquired by a 500 MHz NMR spectrometer. Each spectrum pattern was analyzed by a multivariate statistical pattern-recognition method where Principal Component Analysis (PCA) was used in combination with Soft Independent Modeling of Class Analogy (SIMCA). SIMCA effectively selected variables that contribute to tea categorization. The final PCA resulted in clear classification reflecting the fermentation and processing of each tea, and revealed marker variables that include catechin and theanine peaks.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

Qualitative and quantitative analysis of oxytetracycline by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enable the analysis of raw materials without time-consuming sample preparation methods. The aim of our work was to estimate critical parameters in the analytical specification of oxytetracycline, and consequently the development of a method for quantification and qualification of these parameters by NIR spectroscopy. A Karl Fischer (K.F.) titration to determine the water content, a colorimetric assay method, and Fourier transform-infrared (FT-IR) spectroscopy to identify the oxytetracycline base, were used as reference methods, respectively. Multivariate calibration was performed on NIR spectral data using principal component analysis (PCA), partial least-squares (PLS 1) and principal component regression (PCR) chemometric methods. Multivariate calibration models for NIR spectroscopy have been developed. Using PCA and the Soft Independent Modelling of Class Analogy (SIMCA) approach, we established the cluster model for the determination of sample identity. PLS 1 and PCR regression methods were applied to develop the calibration models for the determination of water content and the assay of the oxytetracycline base. Comparing the PLS and PCR regression methods we found out that the PLS is better established by NIR, especially as the spectroscopic data (NIR spectra) are highly collinear and there are many wavelengths due to non-selective wavelengths. The calibration models for NIR spectroscopy are convenient alternatives to the colorimetric method and to the K.F. method, as well as to FT-IR spectroscopy, in the routine control of incoming material.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

Accurate grading of brain gliomas by soft independent modeling of class analogy based on non‐negative matrix factorization of proton magnetic resonance spectra

Hydrogen magnetic resonance spectroscopy (¹H‐MRS) is a non‐invasive technique which provides a ‘frequency‐signal intensity’ spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in‐vivo ¹H‐MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal‐to‐noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non‐negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water‐suppressed short echo‐time ¹H‐MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non‐negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA‐based model in an independent test set. Copyright © 2015 John Wiley & Sons, Ltd.     

基于近红外光谱分析技术的食品包装塑料的定性分析

该文基于近红外漫反射光谱分析技术对食品包装材料聚乙烯、聚丙烯进行定性判别试验研究,选取不同波段范围、采用不同光谱预处理方法,使用主成分分析法(Principal component analysis,PCA)结合SIMCA、贝叶斯判别、K-近邻3种模式识别方法建立定性预测模型,并根据正确识别率比较了各模型预测性能。结果表明：使用SIMCA方法、贝叶斯判别、K-近邻3种方法建立的定性校正模型均在1 050~1 550 nm波长范围内效果较好;采用矢量归一化、标准正态变量变换、中心化、滑动均值滤波、多项式平滑滤波、一阶微分6种光谱预处理方法和上述3种模式识别方法对塑料样品近红外光谱进行了数据处理,其中在1 050~1 550 nm范围内,主成分因子数为3,采用原始光谱建立的K-近邻定性校正模型较优,对样品校正集和预测集的正确识别率均为100%。可为食品包装材料聚乙烯、聚丙烯的快速鉴别研究提供参考。     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.