SIMCA Method Research Articles

Use of Portable Fluorescence Spectroscopy and SIMCA Method to Test The Authenticity of Apis mellifera Honey From Coffee Flower Nectar Mixed With Two Artificial Sweeteners

Honey with coffee flowers nectar is native honey formed from flower nectar. In this investigation, corn syrup and rice syrup, two artificial sweeteners, were utilized as an adulterant. Portable fluorescence spectroscopy and the SIMCA method are the tools and techniques employed. There were up to 20 samples of pure Apis mellifera honey and up to 120 samples of mixed honey (MC), each used twice. Data on the emission spectra, which are excited at a wavelength of 365 nm, were measured over the wavelength range of 300-800 nm. To improve accuracy, sensitivity, and specificity, the original spectral data was altered using a number of pre-treatments. Pretreatment with the original data with smoothing moving average may accurately identify samples and provides 100% accuracy, sensitivity, and specificity. One of the steps of the SIMCA approach, the cumulative PC, has a value of 92%, which indicates that it well explains the variation of the data. The x-loading plot's values are near the peak of the waves at 378 and 460 nm, indicating the existence of phenolic and flavonoid chemicals at those wavelengths. Keywords: Apis mellifera honey, Corn syrup, Rice syrup, Portable Spectroscopy, SIMCA.

Authentication and discrimination of tissue origin of bovine gelatin using combined supervised pattern recognition strategies

Gelatin is a protein-based product derived from bovine and porcine bone and skin. Gelatin is increasingly used in food and non-food products, but there are errors in labeling concerning the origin of commercial gelatin raw materials. These mislabeling may negatively impact religious or safety communities. In gelatin studies, species identification is common, but determining the origin of the tissues, bone and skin, is also crucial. The current study demonstrated the use of Raman spectroscopy as a rapid and economical method coupled with chemometrics to differentiate tissue origins of bovine gelatin. The proposed technique could also detect adulteration of bone origin gelatin with skin gelatin with 100% sensitivity and specificity. Accordingly, Raman spectroscopy, along with DD- SIMCA and PLS-DA methods, provides a reliable technique that can easily be applied to detecting the tissue source of bovine gelatin. Also, two- step classification method with goal of finding a suitable authentication of target classes have been applied. dd-SIMCA as class modeling for authentication and PLS-DA for discrimination of groups have been combined in this strategy.

Multivariate Prognostic Model for Predicting the Outcome of Critically Ill Patients Using the Aromatic Metabolites Detected by Gas Chromatography-Mass Spectrometry.

A number of aromatic metabolites of tyrosine and phenylalanine have been investigated as new perspective markers of infectious complications in the critically ill patients of intensive care units (ICUs). The goal of our research was to build a multivariate model for predicting the outcome of critically ill patients regardless of the main pathology on the day of admission to the ICU. Eight aromatic metabolites were detected in serum using gas chromatography-mass spectrometry. The samples were obtained from the critically ill patients (n = 79), including survivors (n = 44) and non-survivors (n = 35), and healthy volunteers (n = 52). The concentrations of aromatic metabolites were statistically different in the critically ill patients and healthy volunteers. A univariate model for predicting the outcome of the critically ill patients was based on 3-(4-hydroxyphenyl)lactic acid (p-HPhLA). Two multivariate classification models were built based on aromatic metabolites using SIMCA method. The predictive models were compared with the clinical APACHE II scale using ROC analysis. For all of the predictive models the areas under the ROC curve were close to one. The aromatic metabolites (one or a number of them) can be used in clinical practice for the prognosis of the outcome of critically ill patients on the day of admission to the ICU.

Authentication of Six Indonesian Ground Roasted Specialty Coffees According to Variety and Geographical Origin using NIR Spectroscopy with Integrating Sphere

Several factors such as type of bean (Arabica or Robusta), geographical origin (elevation, soil type, temperature, and solar radiation), and agronomic/postharvest practices (fertilization, bean processing, fermentation, and type of roasting) have a great influence on the quality of final taste of the coffee. In this research, an authentication of six Indonesian ground roasted specialty coffees according to different in variety and geographical origin using NIR spectroscopy with integrating sphere was evaluated. A total of 70 coffee samples of Arabica and Robusta coffee from different geographical origins were used as samples. NIR spectral data in the range of 1175.79 nm to 1651.222 nm were obtained using a portable NIR spectrometer equipped with an integrating sphere. The unsupervised classification was performed using PCA and supervised classification was conducted using the SIMCA method. The result of PCA shows that the samples were well clustered according to variety and geographical origin along the PC1 and PC2 axis both using original and preprocessed spectra. The SIMCA performed good results both for original and preprocessed spectral data, most of the testing sample set were properly classified to their corresponding classes.

The Use of SIMCA Method and NIR Spectroscopy with Hand-Held Spectrometers Equipped with Integrating Sphere for Classification of Two Different Indonesian Specialty Coffees

The objective of this study is to evaluate the SIMCA method and NIR spectroscopy for the non-invasive and non-destructive classification of Indonesian specialty coffees that come from two geographical origins: Gayo coffee from Aceh 10 samples and Wamena from Papua 10 samples. All samples were roasted at the same condition (medium roasting at a temperature of 200°C for 10 minutes) and were ground using a home coffee grinder and then sieved using 50 mesh to obtain a homogenous particle size of 297 micrometers. Spectral data in the short and long near infrared range of 650–1650 nm was measured in a diffuse reflectance mode using two handheld spectrometers equipped with an integrating sphere (ISP-REF, Ocean Optics, USA). The result demonstrated that the classification was satisfied with 100% accuracy, sensitivity, and specificity.

Near-Infrared Spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz)

The importance of the selection and classification processes in the industry of agricultural products and the increase in the production of fruits make necessary the development and implementation of new techniques to efficiently perform these tasks. Techniques such as NIR spectroscopy have proved to have potential to accomplish this purpose. The aim of this research was to evaluate the performance of near infrared spectroscopy as a classification tool for agraz (Vaccinium meridionale Swartz), according to its state of maturity. In order to obtainthe classification models, the PCA and SIMCA methods were used. Results were obtained close to 100% accuracy in the classification for maturity stages 4 and 5 and between 81 and 90% for maturity stage 3. The NIR spectroscopy appears as a suitable technique for the classification of fruits of agraz according to their state of maturity.

Reconstruction of chemical fingerprints from an individual's time-delayed, overlapped fingerprints via laser-induced breakdown spectrometry (LIBS) and Raman spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a method for atomic emission analysis that can be used to analyze the chemical composition of samples in the forensic investigations. The combined effort of extracting, analyzing, and restoring latent fingerprints at crime scenes is an important step toward successful arrests, and hence, the purpose of this research is to estimate the temporal sequence at which a fingerprint was imprinted from an individual. The difference in the laser spectrum from a latent fingerprint at times from 0 to 60min after it was left was analyzed using LIBS and Raman spectroscopy, both of which require no chemical pre-treatment of the sample. We found that the signal intensity changes with time, 60min after the imprint was made for the wavelengths of CN band, fatty acid, amino acid, and oil components. Based on these preliminary findings, the chemometric analysis was performed on the spectrum data obtained from 60min of exposure time and the time-sequenced overlapping fingerprints were distinguished, which were imprinted at intervals of 10, 20, 30, and 40min using SIMCA and PLS-DA classification methods.

The Influence of Particle Size of Ground Roasted Coffee in Discrimination of Decaffeinated Coffee Using Ultraviolet-Visible Spectroscopy and PLS-DA Method

This present research is an application of UV-VIS spectroscopy and SIMCA method to distinguish robusta ground roasted coffee from Sumatera and Java. Spectral data measurement of the filtered and diluted aqueous coffee samples was performed using a UV-VIS spectrometer (Genesys ™ 10S UV-Vis, Thermo Scientific, USA). SIMCA model was constructed for each class, SIMCA model of Sumatera robusta coffee and SIMCA model of Java robusta coffee. Prediction results show that all predictive samples are successfully grouped correctly according to their classes using the developed SIMCA model, except for S14aPA sample that fail to be classified as robusta coffee of Sumatra. Based on the results of the discriminant power plot analysis, it is concluded that in general the contribution of wavelengths between 200-350 nm is higher than that of the wavelength between 350-600 nm. Several wavelengths with very high contribution (high discriminant power value) and seen as peaks on the plot are 245 nm, 253 nm, 264 nm, 316 nm and 327 nm.

Establishment of different origin Peganum harmala qualitative discrimination model based on near-infrared spectroscopy

The optic-fiber sensor technology combined with near-infrared diffuse reflection spectroscopy was applied to directly analyze Peganum harmala and identify different origin of P. harmala on the basis of principal component analysis, clustering analysis, SIMCA method, which resulted in the establishment of a new method to rapidly and nondestructively identify the origin of P. harmala. The original full wavelength spectrum for principal component analysis and the score of first two principal components can distinguish four origins of P. harmala basically. In the wavelength range of 866-2,507 nm, MSC as pretreatment method to establish the best model of clustering analysis to forecast the samples with the accuracy of 91.67%, can distinguish the four origins of P. harmala while in the wavelength of 1,085-2,507 nm, normalization method as pretreatment methods to establish a best model of SIMCA to forecast the sample, all the samples except for the changji sample have been identified with a total recognition rate of 97.22%. The results show that using near infrared diffuse reflectance spectroscopy combined with SIMCA is the best method that can be effectively used to identify the P. harmala.

Identification of components of Brazilian honey by 1H NMR and classification of its botanical origin by chemometric methods

The potential of NMR spectroscopy to differentiate honeys concerning to the nectar employed in its production was evaluated. The application of chemometric methods to 1H NMR spectra has allowed to discriminate the honeys produced in the state of São Paulo, being identified the signals of responsible substances for the discrimination. Application of PCA and HCA methods to 1H NMR data have resulted in the natural clustering of the samples. Wildflower honeys were characterized by higher concentration of phenylalanine and tyrosine. Citrus honeys showed higher amounts of sucrose than other compounds, while eucalyptus honeys had higher amount of lactic acid than the others. Assa-peixe honeys showed spectra similar to eucalyptus and citrus. Sugar-cane honeys showed some signals similar to eucalyptus and citrus honeys, but also showed the tyrosine and phenylalanine signals. Adulterated honeys showed 5-hydroxymethylfurfural, citric acid and ethanol signals. KNN, SIMCA and PLS-DA methods were used to build predictive models for honey classification. In the commercial honeys prediction KNN, SIMCA and PLS-DA models correctly classified 66.7; 22.2 and 72.2% of the samples, respectively.

Application of visible and near infrared spectroscopy to identification of navel orange varieties using SIMCA and PLS-DA methods

Application of visible and near infrared spectroscopy to identification of navel orange varieties using SIMCA and PLS-DA methods

A classification tool for N-way array based on SIMCA methodology

In the literature there are only few papers concerned with classification methods for multi-way arrays. The most common procedure, by far, is to unfold the multi-way data array into an ordinary matrix and then to apply the traditional multivariate tools for classification. As opposed to unfolding the data several possibilities exist for building classification models more directly based on the multi-way structure of the data. As an example, multi-way partial least squares discriminant analysis has been used as a supervised classification method, another alternative that has been investigated is to perform classification using Fisher's LDA or SIMCA on the score matrix from e.g. a PARAFAC or a Tucker model. Despite a few attempts of applying such multi-way classification approaches, no-one has looked into how such models are best built and implemented. In this work, the SIMCA method is extended to three-way arrays. Included in this work is also actual code that will work on general multi-way arrays rather than just three-way arrays. In analogy with two-way SIMCA, a decomposition model is separately built for the multi-way data for each class, using multi-way decomposition method such as PARAFAC or Tucker3. In the choice of the best class dimensionality, i.e. number of latent factors, both the results of cross-validation but mainly the sensitivity/specificity values are evaluated. In order to estimate the class limits for each class model, orthogonal and score distances are considered, and different statistics are implemented and tested to set confidence limits for these two parameters. Classification performance using different definitions of class boundaries and classification rules, including the use of cross-validated residuals and scores is compared. The proposed N-SIMCA methodology and code, besides simulated data sets of varying dimensionality, has been tested on two case studies, concerning food authentication tasks for typical food products.

Robust classification for skewed data

In this paper we propose a robust classification rule for skewed unimodal distributions. For low dimensional data, the classifier is based on minimizing the adjusted outlyingness to each group. In the case of high dimensional data, the robustified SIMCA method is adjusted for skewness. The robustness of the methods is investigated through different simulations and by applying it to some datasets.

International comparability in spectroscopic measurements of protein structure by circular dichroism: CCQM-P59

Circular dichroism is a spectroscopic technique that is widely used to obtain information about protein structure, and hence is an important tool with many applications, including the characterization of biopharmaceuticals. However, there is a lack of confidence in the technique, arising from an observed lack of comparability in the data obtained by different laboratories, or even different operators. In this study, we set out to determine the extent of comparability in the technique, by comparing the results obtained from identical protein samples by a panel of worldwide laboratories. The laboratories chosen were either national measurement institutes (NMIs) or expert laboratories nominated by an NMI. We also aimed to identify the main factors contributing to any lack of measurement comparability. Data were analysed using PCA and SIMCA methods, and we show these statistical techniques are ideal for analysing large amounts of this type of spectroscopic data. We found a startling lack of comparability among laboratories, but we also found that most of the variability arose from relatively simple problems, which can be avoided by following simple guidelines. We believe that the lack of an absolute reference or measurement traceability in circular dichroism contributes to a lack of confidence in the technique.Main text.To reach the main text of this paper, click on Final Report.The final report has been peer-reviewed and approved for publication by the CCQM Working Group on Bioanalysis (BAWG).

Classification of Brazilian vinegars according to their 1H NMR spectra by pattern recognition analysis

Abstract This work describes using 1 H NMR data and pattern recognition analysis to classify vinegars. Vinegar authenticity is linked to raw ingredient source and manufacturing conditions. Application of PCA and HCA methods resulted in the natural clustering of the samples according to the raw material used. Wine vinegars were characterized by a high concentration of ethyl acetate, glycerol, methanol and tartaric acid, while glycerol and ethyl acetate signals were not visible in alcohol/ agrin vinegars. Apple vinegars showed to be richer in alanine. The KNN, SIMCA and PLS-DA methods were used to build predictive models for classification of vinegar type wine, apple and alcohol/ agrin (27 samples – 22 as training set). The models were tested using an independent set (5 samples), no samples were wrongly classified. Validated models were used to predict the class of 21 commercial samples, which, as expected, were correctly classified. Eight commercial vinegars (honey, orange, pineapple and rice) were discriminated from these samples using PCA method. Honey vinegars did not present ethanol signals and pineapple vinegars presented the largest amount of tartaric acid. Rice and orange vinegars are richer in lactic acid and did not present the methanol signal. Alanine signals were not visible in orange vinegars.

Preliminary study on origin traceability of mutton by near infrared reflectance spectroscopy coupled with SIMCA method

Preliminary study on origin traceability of mutton by near infrared reflectance spectroscopy coupled with SIMCA method

Classification of data with missing elements and outliers

Missing elements and outliers can often occur in experimental data. The presence of outliers makes the evaluation of any least squares model parameters difficult, while the missing values influence the adequate identification of outliers. Therefore, approaches that can handle incomplete data containing outliers are highly valued. In this paper, we present the expectation-maximization robust soft independent modeling of class analogy approach (EM-S-SIMCA) based on the recently introduced spherical SIMCA method. Several important issues like the possibility of choosing the complexity of the model with the leverage correction procedure, the selection of training and test sets using methods of uniform design for incomplete data and prediction of new samples containing missing elements are discussed. The results of a comparison study showed that EM-S-SIMCA outperforms the classic expectation-maximization SIMCA method. The performance of the method was illustrated on simulated and real data sets and led to satisfactory results.

Robust SIMCA-bounding influence of outliers

In this article a robust version of SIMCA, based on spherical principal component analysis [N. Locantore, J.S. Marron, D.G. Simpson, N. Tripoli, J.T. Zhang, K.L. Cohen, Robust principal component analysis for functional data (with comments), Test 8 (1999) 1–74], is introduced for chemometrics community. The efficiency of the new approach is compared to the classical SIMCA and to its robust version proposed by Vanden Branden et al. [K. Vanden Branden, M. Hubert, Robust classification in high dimensions based on the SIMCA method, Chemometrics and Intelligent Laboratory Systems 79 (2005) 10–21]. The performances of the presented approaches are evaluated on simulated and real data sets. The results obtained from a simulation study give evidence that the proposed robust SIMCA approach offers a satisfactory efficiency when the model set does not contain outliers and is also robust, what ensures a proper classification of new objects even, when the model set used to derive classification rules is contaminated to a large extent by outlying objects.

The use of classification methods for modeling the antioxidant activity of flavonoid compounds

A study using two classification methods (SDA and SIMCA) was carried out in this work with the aim of investigating the relationship between the structure of flavonoid compounds and their free-radical-scavenging ability. In this work, we report the use of chemometric methods (SDA and SIMCA) able to select the most relevant variables (steric, electronic, and topological) responsible for this ability. The results obtained with the SDA and SIMCA methods agree perfectly with our previous model, in which we used other chemometric methods (PCA, HCA and KNN) and are also corroborated with experimental results from the literature. This is a strong indication of how reliable the selection of variables is.

Robust classification in high dimensions based on the SIMCA Method

In this paper we first investigate the robustness of the SIMCA method for classifying high-dimensional observations. It turns out that both stages of the algorithm, the estimation of principal components and the construction of a classification rule, can be highly disturbed by the presence of outliers. Therefore we propose a robust procedure RSIMCA which is based on a robust Principal Component Analysis method for high-dimensional data (ROBPCA). Various simulations and real examples reveal the robustness of our approach.