Independent Modeling Of Class Analogy Research Articles

Synthesis-identification integration: One-pot hydrothermal preparation of fluorescent nitrogen-doped carbon nanodots for differentiating nucleobases with the aid of multivariate chemometrics analysis

Most of the conventional multidimensional differential sensors currently need at least two-step fabrication, namely synthesis of probe(s) and identification of multiple analytes by mixing of analytes with probe(s), and were conducted using multiple sensing elements or several devices. In the study, we chose five different nucleobases (adenine, cytosine, guanine, thymine, and uracil) as model analytes, and found that under hydrothermal conditions, sodium citrate could react directly with various nucleobases to yield different nitrogen-doped carbon nanodots (CDs). The CDs synthesized from different nucleobases exhibited different fluorescent properties, leading to their respective characteristic fluorescence spectra. Hence, we combined the fluorescence spectra of the CDs with advanced chemometrics like principle component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA), to present a conceptually novel “synthesis-identification integration” strategy to construct a multidimensional differential sensor for nucleobase discrimination. Single-wavelength excitation fluorescence spectral data, single-wavelength emission fluorescence spectral data, and fluorescence Excitation-Emission Matrices (EEMs) of the CDs were respectively used as input data of the differential sensor. The results showed that the discrimination ability of the multidimensional differential sensor with EEM data set as input data was superior to those with single-wavelength excitation/emission fluorescence data set, suggesting that increasing the number of the data input could improve the discrimination power. Two supervised pattern recognition methods, namely KNN and SIMCA, correctly identified the five nucleobases with a classification accuracy of 100%. The proposed “synthesis-identification integration” strategy together with a multidimensional array of experimental data holds great promise in the construction of differential sensors.

Rapid authentication and identification of different types of A. roxburghii by Tri-step FT-IR spectroscopy

Anoectochilus roxburghii (Wall.) Lindl. (Orchidaceae) is a precious traditional Chinese medicinal herb and has been perennially used to treat various illness. However, there were unethical sellers who adulterated wild A. roxburghii with tissue cultured and cultivated ones. Therefore, there is an urgent need for an effective authentication method to differentiate between these different types of A. roxburghii. In this research, the infrared spectroscopic tri-step identification approach including Fourier transform infrared spectroscopy (FT-IR), Second derivative infrared spectra (SD-IR) and two-dimensional correlation infrared spectra (2D-IR) was used to develop a simple and rapid method to discriminate between wild, cultivated and tissue cultivated A. roxburghii plant. Through this study, all three types of A. roxburghii plant were successfully identified and discriminated through the infrared spectroscopic tri-step identification method. Besides that, all the samples of wild, cultivated and tissue cultivated A. roxburghii plant were analysed with the Soft Independent Modelling of Class Analogy (SIMCA) pattern recognition technique to test and verify the experimental results. The results showed that the three types of A. roxburghii can be discriminated clearly as the recognition rate was 100% for all three types and the rejection rate was more than 60%. 70% of the validated samples were also identified correctly by the SIMCA model. The SIMCA model was also validated by comparing 70 standard herbs to the model. As a result, it was demonstrated that the macroscopic IR fingerprint method and the classification analysis could discriminate not only between the A. roxburghi samples and the standard herbs, it could also distinguish between the three different types of A. roxburghi plant in a direct, rapid and holistic manner.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Authentication of an Italian PDO hazelnut ("Nocciola Romana") by NIR spectroscopy.

Common hazelnuts are widely present in human diet all over the world, and their beneficial effects on the health have been extensively investigated and demonstrated. Different in-depth researches have highlighted that the harvesting area can define small variations in the chemical composition of the fruits, affecting their quality. As a consequence, it has become relevant to develop methodologies which would allow authenticating and tracing hazelnuts. In the light of this, the present work aims to develop a non-destructive method for the authentication of a specific high-quality Italian hazelnut, "Nocciola Romana," registered with a protected designation of origin (PDO). Thus, different samples of this fruit have been analyzed by near-infrared (NIR) spectroscopy and then classification models have been built, in order to distinguish between the PDO fruits and the hazelnuts not coming from the designated region. In particular, two different classification approaches have been tested, a discriminant one, partial least squares-discriminant analysis, and a class-modeling one, soft independent modeling of class analogies. Both methods led to very high prediction capability in external validation on a test set (classification accuracy in one case, and sensitivity and specificity in the other, all higher than 92%), suggesting that the proposed methodologies are suitable for a rapid and non-destructive authentication of the product.

Development of a statistical model to detect quality and storage conditions of Atlantic salmon

The ever-increasing demand for fish as a food, has led to the development of new handling and packaging technologies resulting in premium quality fish products. In order to avoid frauds reaching the market, fish quality assurance methods need to be developed.In this study, two statistical models of biochemical processes that occur in Atlantic salmon during two weeks of storage at 0 and 4 °C were developed. These models were further used to detect salmon quality and its storage conditions. The biochemical processes were monitored using Nuclear Magnetic Resonance (NMR) spectroscopy and principal component analysis (PCA). The Soft Independent Modeling of Class Analogy (SIMCA) approach was applied to develop and evaluate the models. The fraud detection potential of the models was tested using samples of various quality and storage parameters. It was shown that the developed models are able to discriminate quality, time and temperature of stored Atlantic salmon.

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning.

Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer's disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs. We recruited patients with (n = 40) and without (n = 40) deficit schizophrenia and healthy volunteers (n = 40). The combined use of TRYCAT and CERAD features strongly segregates deficit from nondeficit schizophrenia and healthy controls. Three out of the top five most important features in LRA, SVM and SIMCA agreed, namely two different NOX/PRO TRYCAT ratios and false memory recall. SIMCA shows that deficit schizophrenia is significantly separated from nondeficit schizophrenia and controls with as top 6 features IgA responses to picolinic acid, IgM responses to 3-OH-kynurenine and kynurenic acid, and impairments in Word List Memory and Verbal Fluency Tests and Mini-Mental State Examination. Nevertheless, nondeficit schizophrenia was not significantly separated from controls. The results show that schizophrenia is not a unitary disease with mere continuous differences in severity of illness between apparent subtypes. Deficit schizophrenia is a qualitatively distinct class defined by neuroimmune (autoimmune responses to TRYCATs) and neurocognitive (episodic and semantic memory) features coupled or not with clinical (negative) symptoms.

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950–1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis.

Non-targeted detection of paprika adulteration using mid-infrared spectroscopy and one-class classification – Is it data preprocessing that makes the performance?

A method for the non-targeted detection of paprika adulteration was developed using Fourier transform mid-infrared (FT-MIR) spectroscopy and one-class soft independent modelling of class analogy (OCSIMCA). One-class models based on commercially available paprika powders were developed and optimised to provide >80% sensitivity by external validation. The performances of the established models for adulteration detection were tested by predicting spiked paprika samples with various types of fraudulent material and levels of adulterations including 1% (w/w) Sudan I, 1% (w/w) Sudan IV, 3% (w/w) lead chromate, 3% (w/w) lead oxide, 5% (w/w) silicon dioxide, 10% (w/w) polyvinyl chloride, and 10% (w/w) gum arabic. Further, the influence of data preprocessing on the model performance was investigated. Relationship between classification results and data preprocessing was identified and specificity >80% was achieved for all adulterants by applying different preprocessing methods including standard normal variate (SNV), first and second derivatives, smoothing, and combinations thereof.

Discrimination of several Indonesian specialty coffees using Fluorescence Spectroscopy combined with SIMCA method

Indonesia is one of the important producers of several specialty coffees, which have a particularly high economic value, including Civet coffee (‘kopi luwak’ in Indonesian language) and Peaberry coffee (‘kopi lanang’ in Indonesian language). The production of Civet and Peaberry coffee is very limited. In order to provide authentication of Civet and Peaberry coffee and protect consumers from adulteration, a robust and easy method for evaluating ground Civet and Peaberry coffee and detection of its adulteration is needed. In this study, we investigate the use of fluorescence spectroscopy combined with SIMCA (soft independent modelling of class analogies) method to discriminate three Indonesian specialty coffee: ground Peaberry, Civet and Pagar Alam coffee. Total 90 samples were used (30 samples for Civet, Peaberry and Pagar Alam coffee, respectively). All coffee samples were ground using a home-coffee-grinder. Since particle size in coffee powder has a significant influence on the spectra obtained, we sieved all coffee samples through a nest of U. S. standard sieves (mesh number of 40) on a Meinzer II sieve shaker for 10 minutes to obtain a particle size of 420 µm. The experiments were performed at room temperature (around 27-29°C). All samples were extracted with distilled water and then filtered. For each samples, 3 mL of extracted sample then was pipetted into 10 mm cuvettes for spectral data acquisition. The EEM (excitation-emission matrix) spectral data of coffee samples were acquired using JASCO FP-8300 Fluorescence Spectrometer. The principal component analysis (PCA) result shows that it is possible to discriminate types of coffee based on information from EEM (excitation-emission matrix) spectral data. Using SIMCA method, the discrimination model of Indonesian specialty coffee was successfully developed and resulted in high performance of discrimination with 100% of sensitivity and specificity for Peaberry, Civet and Pagar Alam coffee. This research has opened the possibility to develop a promising method to detect and evaluate authentication of Indonesian specialty coffees using fluorescence spectroscopy.

Determination of the country of origin of true mahogany (Swietenia macrophylla King) wood in five Latin American countries using handheld NIR devices and multivariate data analysis

Abstract A rapid and reliable identification of the country of origin of protected timbers is one of the measures for combating illegal logging. Mahogany (Swietenia macrophylla King) trees are distributed from Mexico to Bolivia and the Brazilian Amazon and are included in Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Near-infrared spectroscopy (NIRS) has been proven to be a promising technique for calibration based and rapid species identification. There are only a few studies concerning the origin determination of mahogany wood. The present study is dedicated to trace back mahogany wood from Bolivia, Brazil, Guatemala, Mexico and Peru by means of two different handheld NIR spectrometers. The spectra were obtained directly from the wood samples, and soft independent modeling of class analogy (SIMCA) and partial least squares for discriminant analysis (PLS-DA) models were developed for data evaluation. The SIMCA model was efficient and 67–100% and 70–98% of the origins were identified based on the spectral ranges from 1595 to 2396 nm and 950 to 1650 nm, respectively. The best results were obtained by the PLS-DA approach, in which the efficiency rates (EFR) vary from 90 to 100% with both spectrometers. In summary, both instruments were highly effective and are suitable for preliminary identification of the country of origin for mahogany wood.

Voltammetric fingerprinting of oils and its combination with chemometrics for the detection of extra virgin olive oil adulteration

In the present work, two approaches for the voltammetric fingerprinting of oils and their combination with chemometrics were investigated in order to detect the adulteration of extra virgin olive oil with olive pomace oil as well as the most common seed oils, namely sunflower, soybean and corn oil. In particular, cyclic voltammograms of diluted extra virgin olive oils, regular (pure) olive oils (blends of refined olive oils with virgin olive oils), olive pomace oils and seed oils in presence of dichloromethane and 0.1 M of LiClO4 in EtOH as electrolyte were recorded at a glassy carbon working electrode. Cyclic voltammetry was also employed in methanolic extracts of olive and seed oils. Datapoints of cyclic voltammograms were exported and submitted to Principal Component Analysis (PCA), Partial Least Square- Discriminant Analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). In diluted oils, PLS-DA provided a clear discrimination between olive oils (extra virgin and regular) and olive pomace/seed oils, while SIMCA showed a clear discrimination of extra virgin olive oil in regard to all other samples. Using methanolic extracts and considering datapoints recorded between 0.6 and 1.3 V, PLS-DA provided more information, resulting in three clusters-extra virgin olive oils, regular olive oils and seed/olive pomace oils-while SIMCA showed inferior performance. For the quantification of extra virgin olive oil adulteration with olive pomace oil or seed oils, a model based on Partial Least Square (PLS) analysis was developed. Detection limit of adulteration in olive oil was found to be 2% (v/v) and the linearity range up to 33% (v/v). Validation and applicability of all models was proved using a suitable test set. In the case of PLS, synthetic oil mixtures with 4 known adulteration levels in the range of 4–26% were also employed as a blind test set.

Simultaneous quantification of caffeine and chlorogenic acid in coffee green beans and varietal classification of the samples by HPLC-DAD coupled with chemometrics.

A chromatographic procedure (HPLC-DAD) using a relatively rapid gradient has been combined with a chemometric curve deconvolution method, multivariate curve resolution-alternating least squares (MCR-ALS), in order to quantify caffeine and chlorogenic acid in green coffee beans. Despite that the HPLC analysis (at these specific operating conditions) presents some coeluting peaks, MCR-ALS allowed their resolution and, consequently, the creation of a calibration curve to be used for the quantification of the analytes of interest; this procedure led to a high accuracy in the quantification of caffeine and chlorogenic acid present in the samples. In a second part of this study, the possibility of classifying the green coffee beans on the basis of their cultivar (Arabica or Robusta), by partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), has been explored. SIMCA resulted in 100% of sensitivity and specificity for the Arabica class, while for the Robusta, it reached 66.7% of sensitivity and 100% of specificity, or 100% of sensitivity and 100% of specificity, depending on the extraction procedure followed prior to the chromatographic analysis; PLS-DA achieved 100% of correct classification independently of the procedure used for the extraction.

FT-NIR characterization with chemometric analyses to differentiate goldenseal from common adulterants

Goldenseal (Hydrastis canadensis L.) has been a popular herb since the 1970s, with a US market share of over $32 million in 2014. Wild goldenseal has been listed in the Convention on International Trade in Endangered Species for decades. Limits in supply and greed for profit have led to adulteration with similar but more accessible and inexpensive plant materials. Fourier transform near-infrared spectroscopy (FT-NIR) coupled with three different chemometric models, partial least squares (PLS) regression, soft independent modeling of class analogy (SIMCA), and moving window principal component analysis (MW-PCA) provide fast, simple, nondestructive approaches to differentiating pure goldenseal from 4 common pure adulterants (yellow dock, yellow root, coptis, Oregon grape). All three models successfully differentiated authentic goldenseal from adulterants. The models were t-tested for detection of goldenseal intentionally mixed with individual adulterants at 2% to 95% theoretical levels made computationally. The PLS model was unable to detect adulterants mixed with goldenseal at any level. The SIMCA model was the best for detection of yellow root and Oregon grape adulteration in goldenseal, as low as 10%. The MW-PCA model proved best for detection of yellow dock at ≥ 15% and coptis adulteration ≥5% in goldenseal. This study demonstrates that NIR spectroscopy coupled with chemometric analyses is a good tool for industry and investigators to implement for rapid detection of goldenseal adulteration in the marketplace, but also indicates that the specific approach to chemometric analysis must be evaluated and selected on a case-by-case basis in order to achieve useful sensitivity and specificity.

Linear discriminant analysis, partial least squares discriminant analysis, and soft independent modeling of class analogy of experimental and simulated near‐infrared spectra of a cultivation medium for mammalian cells

AbstractCurrently, the qualification and control of medium formulations are performed based on simple methods (eg, pH and osmolality measurement of medium solutions), expensive and time‐consuming cell culture tests, and the quantification of certain critical compounds by liquid chromatography. In addition to traditional medium qualification tools, relatively new spectroscopic techniques, such as fluorescence spectroscopy, nuclear magnetic resonance, Raman and near‐infrared spectroscopies, and combinations of these techniques are increasingly being applied to medium powder investigation. A chemically defined medium powder for Chinese hamster ovary cell cultivation was investigated in this study to determine its response to heat treatments at different temperatures (30°C, 50°C, and 70°C). Because the low availability and high costs of medium powders limit the sample sizes for such experiments, 5 groups of simulated data sets were generated based on the experimental spectra to compare the efficiencies of 3 classification methods: linear discriminant analysis (LDA) based on principal component analysis (PCA), partial least squares discriminant analysis (PLS‐DA), and soft independent modeling of class analogy (SIMCA). In case of these data sets, PCA‐LDA showed better results for the classification of experimental spectra than PLS‐DA and SIMCA. Moreover, the PLS‐DA and SIMCA models yielded different results for different training set groups, while the PCA‐LDA model yielded similar results for all training sets.

Identification of ground meat species using near-infrared spectroscopy and class modeling techniques – Aspects of optimization and validation using a one-class classification model

Chemometric methods permit the construction of classifiers that effectively assist in monitoring safety, quality and authenticity of meat based on the near-infrared (NIR) spectral fingerprints. Discriminant techniques are often considered in multivariate quality control. However, when the authenticity of meat products is the primary concern, they often lead to an incorrect recognition of new samples. The performances of two class modeling techniques (CMT) in order to recognize meat sample species based on their NIR spectra was compared – a one-class classifier variant of the partial least squares method (OCPLS) and the soft independent modeling of class analogy (SIMCA). Based on obtained sensitivity and specificity values, OCPLS and SIMCA can be considered as an effective CMT for the classification of complex natural samples such as studied meat samples (with a relatively large variability). Moreover, particular attention was paid to the optimization and validation of a one-class classification model.

Is soft independent modeling of class analogies a reasonable choice for supervised pattern recognition?

A thorough survey of classification data sets and a rigorous comparison of classification methods show the unambiguous superiority of other techniques over soft independent modeling of class analogies (SIMCA – one class modeling) for classification.

Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza Using Local Variable Selection.

In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection.

Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics

The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.

Application of Vis/SNIR hyperspectral imaging in ripeness classification of pear

ABSTRACTThe objective of this research was to create supervised classification models of pear ripeness with the use of hyperspectral imaging system in the visible and short near infrared (425–1000 nm) regions. Spectra and images of 450 pear samples were studied, which were selected from three ripeness stages (unripe, ripe, and overripe). Three classification algorithms—partial least square-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were compared. Successive projection algorithm (SPA) was used to select optimal wavelengths, and gray level co-occurrence matrix (GLCM) was applied to extract four textural feature variables. The overall results revealed that the best model was PLS-DA, of which the correct classification rate (CCR) was 87.86% with the input consisted of the spectra and texture feature of images. Hence, combining spectral with texture analyses were effective for improving pear ripeness prediction. In addition, the same dataset for ripeness classification got a good performance of firmness prediction by PLS in the regression analysis. In addition, using the same input of ripeness classification to make a study on firmness prediction by partial least square analysis revealed a potential for further research, with correlate coefficient of prediction set rpre of 0.84 and root mean square error of prediction of 0.78 N.

A Fast and Inexpensive Chemometric-Assisted Method to Identify Adulteration in Acai (Euterpe oleracea) Using Digital Images

This work is concerned with an analytical method for detecting acai adulteration based on digital image (DI) assisted by mean one-class classification (OCC) chemometric approaches, namely data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS). In this study, two adulterants were considered, wheat flour and cassava. Digital images were acquired, in triplicate, using a webcam (WC040 MULTILASER of 5 Mp, 2G glass lenses with USB connection) in a closed wooden box with appropriate lighting and stored in JPEG format (24 bits) with a dimension of 2880 × 1620 pixels. For all the images, a central circular area was defined, used the working region to construct the frequency histograms in the color levels considering the standard RGB (red-green-blue), HSI (hue-saturation-intensity), and grayscale color models. Preliminary results obtained by principal component analysis (PCA) indicated the formation of two sample clusters (adulterated and unadulterated). On the other hand, the formation of sample clusters with respect to the type of adulterant (wheat and cassava) was not observed. OCC (DD-SIMCA and OC-PLS) models were built using eight and four factors, respectively, showing satisfactory fit. In the prediction of an external set of samples, the following results were obtained: error rate (ER) 2 and 31%, SEN 100% for both models, and specificity (SPE) 98.14 and 78.69 for DD-SIMCA and OC-PLS, respectively.