Migration of styrene and ethylbenzene from virgin and recycled expanded polystyrene containers and discrimination of these two kinds of polystyrene by principal component analysis

ABSTRACT

The migration of styrene and ethylbenzene from virgin and recycled expanded polystyrene (EPS) containers into isooctane was investigated using gas chromatography-mass spectrometry (GC-MS). EPS containers were in two-sided contact with isooctane at temperatures of 25 and 40°C. It was shown that recycled EPS gave greater migration ratios compared with virgin EPS, which indicated that styrene and ethylbenzene migrated more easily from recycled EPS. In addition, an analytical method to distinguish between virgin and recycled EPS containers was established by GC-MS followed by principal component analysis (PCA). The relative peak area of the identified compounds was used as input data for PCA. Distinct separation between virgin and recycled EPS was achieved on a score plot. Extension of this method to other plastics may be of great interest for recycled plastics identification.     

FTIR Spectroscopy and PLS-DA Classification and Prediction of Four Commercial Grade Virgin Olive Oils from Morocco

Fourier transform infrared (FTIR) coupled to chemometrics was shown to be a useful method to classify and predict the quality of four commercial grade virgin olive oils (VOO). FTIR and physicochemical data were collected using a set of 70 samples representing extra virgin (EV), virgin (V), ordinary virgin (OV), and lampante (L) commercial grade olive oils collected in Beni Mellal region (central Morocco). Two partial least squares discriminant analysis (PLS-DA) models using physicochemical data and FTIR data were established and compared. The PLS-DA model using only physicochemical data was not accurate enough to distinguish satisfactorily among OV, V, and EV olive oil grades. On the contrary, the PLS-DA model on FTIR data was better in the calibration, able to describe 98 % of the spectral information and predicting 93 % of the VOO grades. In the external validation, this PLS-DA model accurately classified VOO commercial grades with prediction accuracy of 100 %. The proposed procedure is fast, nondestructive, simple, and easy to operate, and it is recommended for the quick monitoring of olive oil’s quality.     

Classification and Authentication of Barley (<Emphasis Type="Italic">Hordeum vulgare</Emphasis>) Malt Varieties: Combining Attenuated Total Reflectance Mid-infrared Spectroscopy with Chemometrics

Attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy combined with chemometrics was explored as a tool to classify and authenticate Australian barley varieties. Grain samples (n = 162) were sourced from eight commercial barley varieties and analysed in the MIR range. Principal component analysis (PCA), discriminant partial least squares regression (PLS-DA), linear discriminant analysis (LDA) and soft independent modelling of class analogy (SIMCA) were used to classify the barley grain samples according to variety. PLS-DA correctly classified barley varieties between 91 and 100 %. The results have demonstrated the usefulness of ATR-MIR spectroscopy combined with chemometrics as a rapid method to classify barley grain samples according to their variety. Although MIR is not routinely available at the receival point in most of the cereal trade companies, it has the potential to be used in breeding programmes.     

The use of near infrared reflectance spectroscopy to identify the origin of oak shavings used in wine aging

Near infrared (NIR) reflectance spectroscopy combined with chemometrics was used to classify toasted and untoasted oak wood shavings sourced from two countries (France and USA). Oak wood shaving samples (n = 96) were scanned in the NIR region (680–2,500 nm) using a monochromator instrument operating in reflectance mode. Principal component analysis, partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples according to their country of origin and level of toasting. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. Correct classification rates of 83, 87 and 100 % for samples sourced from USA, France and toasted treatment were obtained using PLS-DA. For LDA, correct classification rates of 80.4, 85 and 100 % were achieved for samples sourced from USA, France and toasted treatment, respectively. These results demonstrated the ability of NIR spectroscopy to discriminate between oak wood shavings sourced from two different countries and two levels of toasting.     

A Feasibility Study on the Evaluation of Quality Properties of Chinese Rice Wine Using Raman Spectroscopy

Rapid analysis of Chinese rice wine (CRW) is an important activity for quality assurance and control investigations. In recent years, due to its insensitivity to water and fewer overlapped bands, Raman spectroscopy (RS) may provide more useful qualitative and quantitative information on functional groups of various chemical compounds in CRWs than the conventional spectroscopic technique (e.g., infrared spectroscopy); there has been a growing interest in the application of RS in the qualitative and quantitative analysis in food industry. In this study, the applicability of RS hyphenated with chemometrics using different pretreated spectra was examined to develop rapid, low-cost, and non-destructive method for quantification of four enological parameters involved in CRW quality control. Partial least square (PLS) was used for building the calibration models for the four chemical parameters based on the full RS spectrum. The model was also optimized by using efficient wavelength selection algorithm, i.e., synergy interval partial least square (SiPLS) algorithm. In addition, soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA) were used as classification techniques to predict the brands (wineries) of CRW samples. The results demonstrated that compared with the PLS model using all wavelengths of RS spectra, the prediction precision of model based on the spectral variables selected by SiPLS was significantly improved with high values of the coefficient of determination (>0.90), residual predictive deviation (>3.0), and range error ratio (>10) for all of the four quality parameters. The SIMCA and LDA results, characterized by high percentages of correct classification (96.67 and 100.00 % as average value in prediction for SIMCA and LDA, respectively), showed that samples belonging to a particular brand could be correctly classified. The overall results indicated the suitability of RS combined with efficient variable selection algorithm to rapidly control the quality of CRW.     

Discrimination of yerba mate (<Emphasis Type="Italic">Ilex paraguayensis</Emphasis> St. Hil.) samples according to their geographical origin by means of near infrared spectroscopy and multivariate analysis

Near infrared reflectance (NIR) spectroscopy combined with multivariate data analysis was used to discriminate between the geographical origins of yerba mate (Ilex paraguayensis St. Hil.) samples. Samples were purchased from the local market and scanned in the NIR region (1100–2500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were used to classify the samples based on their NIR spectra according to their geographical origin. Full cross validation was used as validation method when classification models were developed. The overall classification rates obtained were 76 and 100% using PLS-DA and LDA, respectively. The results demonstrated the usefulness of NIR spectra combined with multivariate data analysis as an objective and rapid method to classify yerba mate samples according to their geographical origin. Nevertheless, NIR spectroscopic might provide initial screening in the food chain and enable costly methods to be used more productively on suspect specimens.     

Detecting bunch withering disorder in date fruit by near infrared spectroscopy

This study introduces the application of near infrared spectroscopy (NIRs) to detect bunch withering disorder in date fruit (cv. Mazafati). The samples included intact as well as infected date fruits at different stages of ripening. Chemometric evaluation of the data was performed by soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), and principal components analysis combined with artificial neural networks (PCA–ANN). The PLS-DA algorithm was able to provide models with the best classification performance, followed by SIMCA and then PCA–ANN. The maturity stage of samples influenced the performance of the classification methods. The classification accuracy for the late harvested samples was better than those harvested at normal time and the combined data set in all classification analyses. The total accuracies of 82%, 93% and 86%, respectively for normal, late and combined data sets demonstrate that NIRs with PLS-DA has a strong potential to detect the bunch withering disorder in date fruit.     

基于近红外光谱技术的沙棘籽油鉴伪方法研究

针对市场上沙棘籽油质量参差不齐的情况,结合近红外光谱技术研究沙棘籽油快速鉴伪的方法。采用234份沙棘籽油、其他植物油、掺假沙棘籽油的近红外透反射光谱,结合簇类独立软模式法(SIMCA)、偏最小二乘判别法(PLS-DA)、支持向量机法(SVM)3种化学计量学方法,在4 000~6 000 cm-1波段范围内分别建立这3类油的判别模型,并用117份独立样品对模型进行验证。结果表明:3种建模方法均得到了满意的结果,其中SVM在训练和验证过程中均得到100%的正确率,判别效果最好;近红外光谱技术应用于识别纯沙棘籽油和区分沙棘籽油掺假类别具有实用性,近红外光谱技术应用于沙棘籽油鉴伪是可行的。     

基于连续投影算法的油菜蜜近红外光谱真伪鉴别的研究

采用连续投影算法(successive project algorithm,SPA)对177个不同产地油菜蜜样本的近红外光谱做波长选择,然后以33个特征变量作线性识别分析(LDA)。同时,也采用了主成分分析(PCA)对变量进行压缩。比较了二次识别分析(QDA)和簇类独立软模式分类法(SIMCA)的鉴别结果。SPA-LDA模型预测集的鉴别准确率为97.7%,而PCA-LDA、全谱的SIMCA和SPA-QDA预测集的正确率分别为93.2%、95.4%和90.9%;上述四种方法ROC曲线下的面积分别为0.964、0.912、0.948和0.933。SPA-LDA性能比其他三种方法要好。该方法准确、可靠,为蜂蜜真实性的现场快速检测提供了一种新方法。     

Multivariate analysis of NMR fingerprint of the unsaponifiable fraction of virgin olive oils for authentication purposes

A new approach to the geographical characterisation of virgin olive oils (VOOs) based on the ¹H NMR fingerprint of the unsaponifiable matter is presented. The ¹H NMR spectra of the unsaponifiable fraction of virgin olive oils from Spain, Italy, Greece, Tunisia, Turkey, and Syria were analysed by several pattern recognition techniques (LDA, PLS-DA, SIMCA, and CART). PLS-DA (PLS-1 approach) obtained the best classification results for all classes. Moreover, ¹H NMR spectra of the bulk oil, and its corresponding unsaponifiable fraction, as well as the subfractions of the unsaponifiable fraction (alcohol, sterol, hydrocarbon, and tocopherol fractions) were studied in the search for the markers that multivariate techniques revealed to be related to the geographical origin of olive oils. Additionally, a preliminary study regarding ¹H NMR data of the bulk oil and the corresponding unsaponifiable fraction of VOOs suggested that these spectral data contained complementary information for the geographical characterisation of VOOs.     

Detection of clenbuterol in beef meat,liver and kidney by mid‐infrared spectroscopy (FT‐Mid IR) and multivariate analysis

Mid‐infrared spectroscopy (FT‐Mid IR) coupled with multivariate analysis was used to predict clenbuterol in beef meat, liver and kidney. A SIMCA model was also developed to discriminate between pure (beef meat, liver and kidney) and spiked with clenbuterol samples (beef meat‐clenbuterol, liver‐clenbuterol and kidney‐clenbuterol). The best models to predict clenbuterol concentrations were obtained using the partial least squares algorithm (PLS) with a R² > 0.9 and SEC and standard error of prediction <0.296 and 0.324, respectively. The SIMCA model used to discriminate pure and spiked with clenbuterol samples showed 100% correct classification rate. Methods detection limit was 2 μg kg^?1. FT‐Mid IR coupled with chemometrics could be a simple and rapid screening tool for monitoring clenbuterol in beef meat, liver and kidney implicated in food poisoning. This method could be use for screening purposes.     

Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.     

应用红外光谱结合化学计量学方法检测阿拉伯胶产地和蛋白质含量

研究利用傅里叶红外光谱结合化学计量学方法来实现对苏丹阿拉伯胶的产地和蛋白质含量的快速无损检测的可行性。采集自6?个不同的产地,每个产地12?个,总计72?个阿拉伯胶样本,作为研究对象,运用线性判别分析（linear discriminant analysis,LDA）和反向区间偏最小二乘（backward interval partial least squares,Bi-PLS）法分别实现对苏丹阿拉伯胶的产地区分和蛋白质含量检测。结果表明,当主成分数为6时,LDA对样本的训练集（48?个样本）和预测集（24?个样本）的识别率都为100%。Bi-PLS法回归联合20?个光谱子区间中的4?个子区间得到最佳的蛋白质预测模型,其预测集相关系数为0.937?3,均方根误差为0.173%。因此,利用傅里叶红外光谱结合化学计量学方法可实现对苏丹阿拉伯胶的产地以及蛋白质的含量的快速无损检测。     

Preliminary study on the application of visible–near infrared spectroscopy and chemometrics to classify Riesling wines from different countries

Visible (VIS) and near infrared (NIR) spectroscopy combined with chemometrics was used in an attempt to classify commercial Riesling wines from different countries (Australia, New Zealand, France and Germany). Commercial Riesling wines (n = 50) were scanned in the VIS and NIR regions (400–2500 nm) in a monochromator instrument, in transmission mode. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise linear discriminant analysis (SLDA) based on PCA scores were used to classify Riesling wines according to their country of origin. Full cross validation (leave-one-out) was used as the validation method when classification models were developed. PLS-DA models correctly classified 97.5%, 80% and 70.5% of the Australian, New Zealand and European (France and Germany) Riesling wines, respectively. SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively. These results demonstrated that the VIS and NIR spectra contain information that when used with chemometrics allow discrimination between wines from different countries. To further validate the ability of VIS–NIR to classify white wine samples, a larger sample set will be required.     

Characterization and Classification of Almond Cultivars by Using Spectroscopic and Thermal Techniques

Three different almond cultivars (Spanish Guara, Marcona, and Butte from U.S.A.) were characterized by using attenuated total reflectance Fourier transform infrared spectroscopy (ATR‐FTIR) and thermal analysis techniques (differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). All samples were directly analyzed without the need of a previous oil extraction. Similar FTIR bands were observed for all studied cultivars corresponding to specific functional groups characteristics of almond ingredients (water, fat, protein, and carbohydrates). Significant differences were observed between cultivars according to absorbance and maximum wave number values of specific bands observed by FTIR and melting and crystallization parameters obtained by DSC. TGA showed that samples were stable up to around 220 °C. Different stages of degradation were observed with increasing temperature corresponding to the degradation of the complex matrix of the samples. Successful discrimination was obtained for all samples by applying multivariate stepwise linear discriminant analysis (LDA) separately to data obtained from FTIR and DSC. A satisfactory multidisciplinary approach was also performed by inserting together all parameters obtained from the 3 techniques as predictors ensuring higher reliability of the obtained model. The obtained results proved the suitability of the studied analytical techniques combined with LDA for an easy and fast discrimination among different almond cultivars in food processing. Practical Application: The study of spectroscopic and thermal parameters could be used as a control tool for the direct and fast assessment of almond samples in food processing, particularly for protected designation of origin products.     

Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity

A sensory analysis of 112 virgin olive oils was performed by a fully trained taste panel. The samples were divided in “defective” and “not defective” on the basis of their olfactory attributes. Then, the “not defective” samples were classified into “low”, “medium” and “high” according to the fruity aroma intensity perceived by assessors. All samples were also analysed by FT-NIR and FT-IR spectroscopy and processed by classification methods (LDA and SIMCA). The results showed that NIR and MIR spectroscopy coupled with statistical methods are an interesting technique compared with traditional sensory assessment in classifying olive oil samples on the basis of the fruity attribute. The prediction rate varied between 71.6% and 100%, as average value. The spectroscopic methods, combined with chemometric strategies, could represent a reliable, cheap and fast classification tool, able to draw a complete fingerprint of a food product, describing its intrinsic quality attributes, that include its sensory attributes.     

Confirmation of brand identity in foods by near infrared transflectance spectroscopy using classification and class-modelling chemometric techniques — The example of a Belgian beer

Food and beverage processors require tools to monitor conformance of finished goods to their defined specification; regulatory authorities need appropriate methods for detecting retail fraud. In this report, samples (n = 275) of Belgian and other European beers were collected and analysed using near infrared transflectance spectroscopy; three class-modelling techniques (soft independent modelling of class analogy, SIMCA; potential functions techniques, POTFUN; and unequal dispersed classes, UNEQ) were employed to characterise beer types (firstly Trappist and then Rochefort) while a classification method (partial least squares discriminant analysis, PLS-DA) was applied to discriminate between two final beer classes: Rochefort 8° and Rochefort 10°. The class-models and the classification rules developed were validated by means of an external prediction set. A discussion on the appropriate use of these chemometric approaches is included. Modelling of Trappist beers met with limited success while model efficiencies for Rochefort samples were highest for SIMCA and UNEQ applications i.e. 81.4% and 84.5% respectively. The classification of beers as Rochefort 8? or Rochefort 10? was possible with an average correct classification rate of 93.4%.     

Derivative FTIR spectroscopy for cluster analysis and classification of morocco olive oils

Fourier transform infrared (FTIR) spectra were employed for differentiation and classification of olive oils from several producing regions of Morocco. A preliminary treatment of the FTIR data was done by a derivative elaboration based on the Savitzky–Golay algorithm to reduce the noise and extract a largest number of analytical information from the spectra. A multivariate statistical procedure based on cluster analysis (CA) coupled to partial least squares-discriminant analysis (PLS-DA), was elaborated, providing an effective classification method. On the basis of a hierarchical agglomerative CA and principal component analysis (PCA), four distinctive clusters were recognised. The PLS-DA procedure was then applied to classify samples from the same regions, picked in different times, or unknown olive oil samples. The model was optimised by applying the Martens’ Uncertainty Test that provided to select the wavelength zones giving the most useful analytical information. The proposed method furnished results reliable in classifying olive oils from different lands with the advantages of being rapid, inexpensive and requiring no prior separation procedure.     

Classification of moroccan olive cultivars by linear discriminant analysis applied to ATR–FTIR spectra of endocarps

The potential of FTIR combined with chemometrics was studied to classify five Moroccan varieties of olives by analysis on the endocarps. Attenuated total reflectance (ATR) enabled the samples to be examined directly in the solid state. The spectral data were subjected to a preliminary derivative elaboration based on the Norris gap algorithm to reduce the noise and extract larger analytical information. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The calibration set was built by using the IR data from seventy‐five samples scanned in reflectance mode, and the ranges 3000–2400 and 2300–600 cm^?1 were selected because furnishing the most useful analytical information. PCA allowed clustering the samples in five classes by using the first two principal components with an explained variance of 98.16%. Application of LDA on an external test set of twenty‐five samples enabled to classify them into five variety groups with a correct classification of 92.0%.     

A novel approach to discriminate transgenic from non-transgenic soybean oil using FT-MIR and chemometrics

A methodology was developed to distinguish transgenic from non-transgenic soybean oils samples by using FT–MIR spectroscopy coupled with discrimination techniques, including Soft Independent Modeling of Class Analogies (SIMCA), Support Vector Machine–Discriminant Analysis (SVM-DA) and Partial Least Squares–Discriminant Analysis (PLS-DA). The discrimination success rate of these three methods was compared, and different types of preprocessing were investigated. Based on the results, the best option was PLS-DA with a 100% rate of discrimination, independent of the preprocessing method used.