Advancements in the design and validation of an air pollution integrated assessment model for Spain

This paper describes the design and application of the Atmospheric Evaluation and Research Integrated model for Spain (AERIS). Currently, AERIS can provide concentration profiles of NO₂, O₃, SO₂, NH₃, PM, as a response to emission variations of relevant sectors in Spain. Results are calculated using transfer matrices based on an air quality modelling system (AQMS) composed by the WRF (meteorology), SMOKE (emissions) and CMAQ (atmospheric-chemical processes) models. The AERIS outputs were statistically tested against the conventional AQMS and observations, revealing a good agreement in both cases. At the moment, integrated assessment in AERIS focuses only on the link between emissions and concentrations. The quantification of deposition, impacts (health, ecosystems) and costs will be introduced in the future. In conclusion, the main asset of AERIS is its accuracy in predicting air quality outcomes for different scenarios through a simple yet robust modelling framework, avoiding complex programming and long computing times.     

基于SIMCA模型的纸浆种类近红外光谱鉴别

研究了近红外光谱分析与SIMCA模式识别技术相结合鉴别纸浆种类的方法。收集了桉木浆、麦草浆(漂白与未漂白)、棉浆、湿地松浆等4种纸浆共90个样品,抄成不同定量纸样并采集其近红外光谱,其中部分桉木浆添加化学助剂。选择60个样品作为训练集建立SIMCA类模型,剩余30个样品用于模型检验。研究结果表明,建立的模型能完全正确识别各类纸浆,且不受抄纸定量和添加化学助剂因素的影响,为快速无损鉴别纸浆种类提供了一种准确可靠的方法。     

Tracing the geographical origin of honeys based on volatile compounds profiles assessment using pattern recognition techniques

The goal of this study was to examine the possibility of verifying the geographical origin of honeys based on the profiles of volatile compounds. A head-space solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) was used to analyze the volatiles in honeys with various geographical and floral origins. Once the analytical data were collected, supervised pattern recognition techniques were applied to construct classification/discrimination rules to predict the origin of samples on the basis of their profiles of volatile compounds. Specifically, linear discriminant analysis (LDA), soft independent modeling of class analogies (SIMCA), discriminant partial least squares (DPLS) and support vector machines (SVM) with the recently proposed Pearson VII universal kernel (PUK) were used in our study to discriminate between Corsican and non-Corsican honeys. Although DPLS and LDA provided models with high sensitivities and specificities, the best performance was achieved by the SVM using PUK. The results of this study demonstrated that GC × GC–TOFMS combined with methods like LDA, DPLS and SVM can be successfully applied to detect mislabeling of Corsican honeys.     

Classification of longan fruit bruising using visible spectroscopy

This research showed the potential of using visible spectroscopy for classification of non-bruised and bruised longan fruits. The visible spectra of bruised and non-bruised longan fruits were acquired from 400 to 700 nm with 10 nm resolution by the spectrophotometer. The principal component analysis (PCA), Partial Least Square Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA) were used to develop classification models. The Partial Least Square Discriminant Analogy (PLS-DA) showed better classification accuracy than SIMCA with 100% correctness. The result was found to be helpful for the application in the industry for on-line and portable application.     

Evaluation of SIMCA and PLS algorithms to detect adulterants in canola oil by FT-IR

Adulteration of canola oil with four potential edible oils was analysed using FT-IR and chemometric methods. The adulterants (corn, peanut, soya bean and sunflower oils) were studied in four different proportions (canola oil + adulterant oils: 90 + 10, 95 + 5, 98 + 2 and 99 + 1 in volume). Excellent classification results were obtained when multi-class approaches were performed with a maximum error of 3%, using 1630 or 16 wavenumbers as variables. In the case of one-class approaches, the selection of variables (16 wavenumbers) was necessary, improving the classification error to 5%. The differences observed using the different methods were related to the nature of each model depending on how the boundaries are set in each of them, responding either to a PCA-based or PLS-based algorithm.     

Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data

The aim of this work was to propose a quick and cost-effective procedure, which could help to identify the types of fat (rapeseed, a mixture of rapeseed and soybean, and lard oils) added to feed used for raising pigs. For this purpose, liver samples were examined and their near-infrared reflectance spectra served as data for the construction of classic and robust soft independent modeling of class analogy (SIMCA) models. The results showed that the near-infrared reflectance spectra contained information sufficient to build good classification models that enabled three types of fat additions to be distinguished. The best classification results were obtained from robust SIMCA, indicating its superior performance in terms of high sensitivity and specificity in comparison with classic SIMCA. Specifically, robust models had sensitivities of 100% and specificities of 96.05%, 97.73% and 100%, for rapeseed, mixture of rapeseed and soybean, and lard enriched feed, respectively.     

Multivariate calibration in Fourier transform infrared spectrometry as a tool to detect adulterations in Brazilian gasoline

In the present work, Fourier transform infrared spectroscopy (FTIR) in association with multivariate chemometrics classification techniques was employed to identify gasoline samples adulterated with diesel oil, kerosene, turpentine spirit or thinner. Results indicated that partial least squares (PLS) models based on infrared spectra were proven suitable as practical analytical methods for predicting adulterant content in gasoline in the volume fraction range from 0% to 50%. The results obtained by PLS provided prediction errors lower than 2% (v/v) for all adulterant determined. Additionally, Soft Independent Modeling of Class Analogy (SIMCA) was performed using all spectral data (650-3700 cm⁻¹) for sample classification into adulterant classes defined by training set and the results indicated that undoubted adulteration detection was possible but identification of the adulterant was subject to misclassification errors, specially for kerosene and turpentine adulterated samples, and must be carefully examined. Quality control and police laboratories for gasoline analysis should employ the proposed methods for rapid screening analysis for qualitative monitoring purposes.     

智舌快速检测水产品中的麦氏弧菌

探索仿生传感器--智舌在麦氏弧菌快速检测中的可行性,以期构建一种新型的水产品中致病性弧菌的快速检测技术。用智舌结合主成分分析法对11种致病性弧菌的液体培养物进行区分,以确定该法能否将11种致病性弧菌区分开及其所需的最短培养时间,并确定针对被测物的适宜电极和频率段组合;然后用智舌结合簇类独立软模式识别法构建麦氏弧菌的判别模型,并对判别模型进行回判及验证,根据判别准确率确定最佳判别模型,从而探索智舌结合簇类独立软模式识别法能否用来建立快速检测麦氏弧菌的数据库。结果显示：当弧菌在其特异性培养基中培养7h后,智舌能很好地将11种致病性弧菌区分开;6种电极与其频率段组合下的判别模型对所有样本的判别准确率均达到了100%。说明所建模型可用于麦氏弧菌的快速筛检。     

基于近红外光谱特征的赤霉病小麦籽粒SIMCA识别模型构建研究

对赤霉病小麦籽粒和未感病小麦籽粒的近红外光谱进行判别分析,为赤霉病小麦籽粒的识别、分选提供一种新方法。利用近红外光谱分析仪采集2012/13年度江苏、安徽、河南等省份25份农户田间小麦品种籽粒样品的近红外光谱信息,对获取的近红外光谱数据分别进行均值标准化、一阶求导、二阶求导和多元散射校正处理,利用全波段(950～1 650 nm)和特征波长处(985、1 130、1 160、1 190、1 235、1 320、1 385、1 410 nm)的近红外光谱数据,采用离差平方和法(Ward法)聚类分析和主成分分析等化学计量学方法,构建赤霉病小麦籽粒和未病小麦籽粒的SIMCA识别模型。模型诊断和验证结果显示,构建的SIMCA识别模型对赤霉病小麦籽粒和未感病小麦籽粒的正确识别率均为100%,识别效果良好。这一结果表明,近红外光谱技术用于赤霉病小麦籽粒的识别分选是可行的。