Traceability of Italian garlic (Allium sativum L.) by means of HRMAS-NMR spectroscopy and multivariate data analysis

(1)H High Resolution Magic Angle Spinning-Nuclear Magnetic Resonance (HRMAS-NMR) spectroscopy was used to analyse garlic (Allium sativum L.) belonging to red and white varieties and collected in different Italian regions, in order to address the traceability issue. 1D and 2D NMR spectra, performed directly on untreated small pieces of garlic, so without any sample manipulation, allowed the assignment of several compounds: organic acids, sugars, fatty acids, amino acids and the nutritionally important fructo-oligosaccharides and allyl-organosulphur compounds. Application of Partial Least Squares projections to latent structures-Discrimination Analysis provided an excellent model for the discrimination of both the variety and, most important, the place origin, allowing the identification of the metabolites contributing to such classifications. The presence of organosulphurs, allicin and some allyl-organosulphurs found by HRMAS-NMR, was confirmed also by SPME-GC-MS; 11 molecules were identified, containing from one up to three sulphur atoms and with and without allyl moieties.     

Courtship Pheromones in Parasitic Wasps: Comparison of Bioactive and Inactive Hydrocarbon Profiles by Multivariate Statistical Methods

Cuticular hydrocarbons play a significant role in the regulation of cuticular permeability and also in the chemical communication of insects. In the parasitoid Lariophagus distinguendus (Hymenoptera: Pteromalidae), male courtship behavior is mediated by a female-produced sex pheromone. Previous studies have shown that the chemicals involved are already present in the pupal stage of both males and females. However, pheromonal activity in males decreases shortly after emergence. This pheromonal deactivation occurs only in living males, suggesting an active process rather than simple evaporation of bioactive compounds. Here, we present evidence that the sex pheromone of L. distinguendus is composed of a series of cuticular hydrocarbons. Filter paper disks treated with nonpolar fractions of cuticular extracts of freshly emerged males and females, 72-hr-old females, and yellowish pupae caused arrestment and stimulated key elements of courtship behavior in males, whereas fractions of 72-hr-old males did not. Sixty-four hydrocarbons with chain length between C₂₅ and C₃₇ were identified in the fractions by gas chromatography-mass spectrometry (GC-MS). Methyl-branched alkanes with one to four methyl groups were major components, along with traces of n-alkanes and monoalkenes. Principal component analysis, based on the relative amounts of the compounds, revealed that cuticular hydrocarbon composition differed among all five groups. By using partial least squares-discriminant analysis, we determined a series of components that differentiate bioactive and bioinactive hydrocarbon profiles, and may be responsible for pheromonal activity of hydrocarbon fractions in L. distinguendus.     

Classification of Black Beans Using Visible and Near Infrared Hyperspectral Imaging

A rapid and non-destructive method based on the visible and near infrared hyperspectral imaging technique in the wavelength range of 390–1050 nm was investigated for discriminating the varieties of black beans. In total, 300 samples of three varieties were scanned by the visible and near infrared hyperspectral imaging system, and hyperspectral data were analyzed by spectral and image processing technique respectively. A successive projection algorithm was used to obtain 13 characteristic wavelengths (504, 507, 512, 516, 522, 529, 692, 733, 766, 815, 933, 998, and 1000 nm) for spectral analysis. After the processing of successive projection algorithm, optimal image selection was carried out by principal component analysis based on the characteristic wavelengths. The first principal component image was used for the image analysis, whose contribution rate was over 98.34%. Gray level co-occurrence matrix analysis from first principal component image was applied to extract image features including 16 textural features and six morphological features. In this study, partial least squares-discriminate analysis, support vector machine, and K-nearest neighbors were used for model establishments, respectively, based on spectral feature, image feature, and the combination of spectral and image features. The results show that the best correct discrimination rate of 98.33% was achieved by applying combined spectral and image features. The study demonstrated that visible and near infrared hyperspectral imaging technique was potential for rapid classification of black beans, and the performance of the classification model can be improved by the feature combination.     

Rock and Soil Classification Using PLS-DA and SVM Combined with a Laser-Induced Breakdown Spectroscopy Library

Laser-induced breakdown spectroscopy(LIBS) has become a powerful technology in geological applications.The correct identification of rocks and soils is critical to many geological projects.In this study,LIBS database software with a user-friendly and intuitive interface is developed based on Windows,consisting of a database module and a sample identification module.The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards.The module allows easy use of the data.A sample identification module based on partial least squares discriminant analysis(PLS-DA) or support vector machine(SVM) algorithms enables users to classify groups of unknown spectra.The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils,and is thus useful for the detection of geological materials.     

不同提取方法与GC-MS进样方式对柚皮精油挥发性成分分析结果的影响

采用冷压榨、水蒸汽蒸馏及机械磨皮三种方法制备柚皮精油,比较直接进样和固相微萃取两种进样方式对柚皮精油挥发性成分种类和数量的影响,并利用主成分分析(PCA)和偏最小二乘法判别分析(PLS-DA)对三种方法提取的精油挥发性成分进行统计分析并找出特征挥发性成分。结果表明:直接进样法结合GC-MS从柚皮精油中共分离鉴定出69种物质,SPME结合GC-MS共鉴定出51种物质,直接进样比SPME进样更全面地反映出精油挥发性成分的组成。PCA分析能对直接进样法检测的三种柚皮精油进行很好的区分,其中冷榨油与磨皮油挥发性成分更为接近。PLS-DA分析发现能够将三种精油区分开的特征挥发性成分有:冷榨油中的桧烯、D-柠檬烯、α-松油醇、β-月桂烯、α-蒎烯以及4-萜品醇;水提油中芳樟醇、(R)-氧化柠檬烯、8,9-二氢新异长叶烯;磨皮油中的圆柚酮,n-棕榈酸以及(Z,Z)-9,12-十八二烯酸。其中水提油中特征挥发性物质氧化柠檬烯(5.19%)具有不良气味,磨皮油中保留指数大于2000的挥发性成分相对含量(4.61%)较高,影响澄清度和稳定性。综合以上成分分析得出,冷压榨法制备的柚皮精油的品质相对较好。     

基于氢核磁共振和偏最小二乘法- 判别分析建立鹿龟酒质量控制方法的研究

目的：建立一种基于氢核磁共振(1H-NMR)和偏最小二乘法-判别分析的鹿龟酒质量控制新方法。方法：基于1H-NMR技术测定样品的全成分信息,并转化成数据矩阵,利用SIMCA-P软件进行偏最小二乘法-判别分析(PLS-DA)。结果：鹿龟酒与不同的缺味样品及仿制品在所得的散点图中能得到明显区分。结论：氢核磁共振偏最小二乘法-判别分析法是一种操作简便,能够更全面体现其质量情况的鹿龟酒质量控制新方法。     

基于便携式近红外光谱仪的食品接触性塑料鉴别

采用便携式近红外光谱仪对聚对苯二甲酸乙二酯(PET)、高密度聚乙烯(HDPE)、低密度聚乙烯(LDPE)、聚氯乙烯(PVC)、聚苯乙烯(PS)和聚碳酸酯(PC)6类食品接触性塑料材质进行鉴别研究。用5点平滑、多元散射校正(MSC)、一阶导数和标准正态变量变换(SNV)4种方法对塑料样品光谱进行预处理;主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)分别用于塑料样品光谱空间分布分析和定性判别模型的建立。结果表明:光谱经SNV和MSC预处理后,6类塑料样品在前3个主成分空间得到了较好的分离;PLS-DA结合SNV预处理方法可得到精简的塑料材质定性判别模型,模型校正集和预测集的正确识别率(CRR)均为100%。该方法可为食品接触性塑料材质的快速鉴别提供参考。     

普洱市不同产茶区普洱生茶香气成分差异性分析

采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)结合气相色谱-质谱联用仪(gas chromatography-mass spectrometry,GC-MS)分别对普洱市5个产茶区的普洱生茶香气组分进行分析。结果表明,42个普洱生茶样品中共检测出83种香气成分,其中醇类化合物23种,碳氢类化合物20种,酯类化合物12种,甲氧基苯类化合物8种,酮类化合物8种,酸类化合物4种,酚类化合物3种,醛类化合物3种,含氮类化合物2种。不同产茶区普洱生茶中醇类、碳氢类和醛类化合物相对百分含量差异均不显著,甲氧基苯类化合物含量差异显著性比例较大,酯类、酮类、酚类、酸类和含氮类化合物含量差异显著性比例较小。以83种香气成分相对百分含量为变量进行偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA),不同产茶区的普洱生茶样品呈现明显的分离趋势,变量重要性因子(variable importance in the projection,VIP)分析,33种香气成分对不同产茶区普洱生茶样品区分起主要作用(VIP> 1),33种香气成分中甲氧基苯类、酮类、酯类和醇类物质占比最大。     

Check-all-that-apply data analysed by Partial Least Squares regression

This paper discusses the application of Partial Least Squares regression (PLS) to handle sensory data from check-all-that-apply (CATA) questions in a rapid, statistically reliable, and graphically-efficient way. We start by discussing the theory behind the CATA data and how these normally are analysed by multivariate techniques. CATA data can be analysed both by setting the CATA as the X and the Y. The former is the PLS-Discriminant Analysis (PLS-DA) version, while the latter is the ANOVA-PLS (A-PLS) version. We investigated the difference between these two approaches, concluding that there is none. This is followed by a discussion of how to get a good estimate of the uncertainty of the model parameters in the PLS model. For a PLS model this is often assessed by leave-one-respondent-out cross-validation. We will, though, show that this gives too optimistic uncertainty estimates, and a repeated split-half approach should rather be used. Finally, we will discuss the shortcomings of using univariate techniques such as the Cochran’s Q test and even the uncertainty estimates based on the Jack-knifed regression coefficients compared to the multivariate reality of the loading weights in PLS-DA. Overall, this paper provides a formal introduction as to how to utilise PLS-DA and cross validation with resampling for the investigation of CATA data.     

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R² value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R² value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.