Class Modeling Techniques Research Articles

Geographic authentication of argentinian teas by combining one-class models and discriminant methods for modeling near infrared spectra

In this study, 110 tea samples from South American countries (Argentina, Brazil, and Paraguay) and Asian countries (India and China) were analyzed using near-infrared spectroscopy (NIRS) together with a two-step chemometric authentication strategy (class modeling techniques and discriminant analysis) to authenticate commercial teas from Argentina. In the first step, one-class models were built and validated to authenticate South American teas using preprocessed NIRS data. For this purpose, data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS) were used. The DD-SIMCA model gave the best results, with a sensitivity of 93.10%, specificity of 100%, and efficiency of 95.00%. In the second step, a support vector machine (SVM) was used to build and validate a multiclass model to discriminate between tea samples from Argentina and neighboring countries of South America. The best model was the combination of nine variables selected by the fast correlation-based filter (FCBF) method, with an accuracy of 98.30%. Therefore, we conclude that the combination of NIRS and two-step chemometric tools can be used to authenticate the geographical origin of samples with high inter-class similarity.

Addressing adulteration challenges of dried oregano leaves by NIR HyperSpectral Imaging

Dried oregano leaves are particularly prone to adulteration because of their widespread distribution and their easy mixing with leaves of other plants of lower commercial value, such as olive, myrtle, strawberry tree, or sumac. To reveal the presence of adulteration, in this study we considered an untargeted analytical approach, which instead of involving the a priori selection of specific compounds of interest is focused on defining the characteristic spectral signature of authentic oregano with respect to its most frequent adulterants. NIR HyperSpectral Imaging (NIR-HSI) represents a state-of-the-art, rapid and non-destructive technique, allowing for the collection of both spectral and spatial information from the sample, making it particularly suitable for characterizing visually heterogeneous samples.Authentication issues are typically assessed through class modelling techniques and Soft Independent Modelling of class Analogy (SIMCA) is one of the most used algorithms in this scenario. However, the high variability and heterogeneity within the authentic oregano class resulted in poor outcomes when SIMCA was applied. As an alternative, Soft Partial Least Squares Discriminant Analysis (Soft PLS-DA) algorithm was applied to differentiate authentic oregano samples from pure adulterants. Soft PLS-DA represents a hybrid approach that combines the advantages of both discriminant and class modelling techniques. The resultant classification model has indeed led to promising results, achieving a prediction efficiency of 92.9 %. Finally, based on the percentage of pixels predicted as oregano in the Soft-PLSDA prediction images, a threshold value of 10 % was established, serving as a detection limit of NIR-HSI to distinguish authentic oregano samples from adulterated ones.

PLS class modelling using error correction output code matrices, entropy and NIR spectroscopy to detect deficiencies in pastry doughs

Biscuits are a highly demanded product worldwide. Its success makes their manufacture process a challenging task, needing new strategies to maintain the high production levels and a high-quality standard. This is determined by two key processes: the kneading and the rolling. This manuscript aims to reflect the improvements that the application of a novel soft multiclass compliant classification method (PLS2-CM) entails regarding the traditional chemometric class modelling. With this new approach, the intention is to detect possible deficiencies in biscuit doughs (excess of water, lack of water or little kneading time) during both industrial processes by using NIR spectroscopy.In PLS2-CM, the coding of the classes is done using an Error Correcting Code Matrix (ECOC), which implies to employ several binary learners so that their number and structure are not predetermined beforehand but are function of the data set to be modelled. The optimization criterion in PLS2-CM is the sensitivity and specificity matrix evaluated by the Diagonal Modified Confusion Entropy (DMCEN), a new index inspired by the Shannon's entropy that is more sensitive to changes in the elements of that matrix than the usual total efficiency. The results obtained according to this index are better with this new soft classification method than the ones obtained when using other soft class modelling techniques such as soft independent modelling of class analogy (SIMCA) or unequal dispersed classes (UNEQ).In this work it is shown that it is possible to completely distinguish a correct kneaded dough from another defective one with a specificity equal to 1 during the kneading process, but the class corresponding with water deficit dough, accepts a very high percentage (80 % in training and 92 % in prediction) of the excess-water dough spectra. Despite that, after the fermentation and during the rolling process, the same doughs are modelled with complete sensitivity and specificity in prediction (100 %), which indicates that the physico-chemical changes produced during the fermentation are decisive to characterize the absence of defects in biscuit doughs kneading by NIR spectroscopy.

Multivariate curve resolution-soft independent modelling of class analogy (MCR-SIMCA)

BackgroundVarious classification, class modeling, and clustering techniques operate within abstract spaces, utilizing Principal Components (e.g., Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA)) or latent variable spaces (e.g., Partial Least Squares Discriminant Analysis (PLS-DA)). It's important to note that PCA, despite being a mathematical tool, defines its Principal Components under certain mathematical constraints, it has a wide range of applications in the analysis of real-world systems. In this research, we assess the viability of employing the Multivariate Curve Resolution (MCR) subspace within class modeling techniques, as an alternative to the PC subspace. (92). ResultsThis study evaluates the use of the MCR subspace in class modeling methods, specifically in tandem with soft independent modeling of class analogy (SIMCA), to investigate the advantages of employing the meaningful physico-chemical subspace of MCR over the mathematical subspace of PCA. In the MCR-SIMCA strategy, the model is constructed by applying MCR to training samples from a target class. The MCR model effectively partitions the data into two smaller sub-matrices: the contribution matrix and the corresponding response matrix. In the next step, the contribution matrix resulting from the decomposition of the training set develops a distance plot (DP). First, the theory of the MCR-SIMCA model is discussed in detail. Next, two real experimental datasets were analyzed, and their performance was compared with the DD-SIMCA model. In most cases, the results were as good as or even more satisfactory than those obtained with the DD-SIMCA model. (146). SignificanceThe suggested class modeling method presents a promising avenue for the analysis of real-world natural systems. The study's results emphasize the practical utility of the MCR approach, underscoring the significance of the MCR subspace advantages over the PCA subspace. (39).

An innovative screening approach for orange juice authentication using dual portable/handheld NIR spectrometers and chemometrics

In this study, we explored the feasibility of utilizing two portable/handheld near-infrared (NIR) spectrometers in combination with class modeling techniques, namely data-driven soft independent modeling of class analogy (DD-SIMCA) and soft-partial least squares-discriminant analysis (soft-PLS-DA), as well as discrimination strategies of ensemble learning and hard-PLS-DA, for developing a screening method to authenticate orange juice samples and detect the presence of adulterants (the Brix to citric acid ratio in pulp-wash). The results obtained with both NIR spectrometers (Tellspec, 900-1700 nm and Neospectra, 1350-2550 nm) coupled with DD-SIMCA exhibited perfect sensitivity and specificity of 100% in both calibration and prediction sets. Additionally, when combined with the NIR Tellspec spectrometer, ensemble learning methods including gradient boosting tree (GBT) and adaptive boosting (Adaboost) demonstrated exceptional predictive capabilities, achieving the prediction of 'Brix to citric acid' ratio in pulp-wash with high sensitivity and specificity. These results surpassed the performance of hard-PLS-DA as commonly used technique in food authentication.

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

The scope of applicability of the selected class-modelling methods

Class-modelling methods are applied to construct a mathematical model based on the similarities among samples belonging to the same category, i.e., the target class. This model is used to study the belongingness of a new sample to the class for which the model was constructed. If the sample is recognised as not belonging to the target class, it is considered as an outlier. Therefore, the class-modelling techniques are widely used for food or drug authentication and confirmation of the product origin, in order to detect samples of poor quality or potential counterfeits. Structure of the target class data might suggest which of the available class-modelling approaches is well suited for model construction. Data structure can generally be described as normal, or heterogeneous. Normal structure exhibits Gaussian distribution, whereas heterogeneous data deviates from normal distribution, e.g., the objects can have multimodal distributions, create subgroups, and form complex shapes in the feature space. Class-modelling of the normally structured data can directly be performed on an original dataset, whereas heterogeneous datasets are usually subjected to kernel transformation prior to modelling. In this study, several datasets of various structures are analysed with different class-modelling methods to test their scope of applicability and the pros and cons from the practical point of view. It is revealed that in most cases, the Support Vector Domain Description (SVDD) leads to the best classification results. However, it is also discussed and illustrated that SVDD applied to a multimodal data can lead to sub-optimal model. In that case, the Potential Functions Method (PFM) is recommended for model construction. The PFM model is based on local densities of samples in the multivariate space and in that way, it tends to be more accurate than SVDD model. Additionally, some practical remarks are given on optimisation of the Gaussian kernel width.

MIR spectroscopy versus MALDI-ToF-MS for authenticity control of honeys from different botanical origins based on soft independent modelling by class analogy (SIMCA) – A clash of techniques?

In this study, highly reproducible MIR spectroscopy and highly sensitive MALDI-ToF-MS data were directly compared for the metabolomic profiling of monofloral and multifloral honey samples from three different botanical origins canola, acacia, and honeydew. Subsequently, three different classification models were applied to the data of both techniques, PCA-LDA, PCA- kNN, and soft independent modelling by class analogy (SIMCA) as class modelling technique. All monofloral external test set samples were classified correctly by PCA-LDA and SIMCA with both data sets, while multifloral test set samples could only be identified as outliers by the SIMCA technique, which is a crucial aspect in the authenticity control of honey. The comparison of the two used analytical techniques resulted in better overall classification results for the monofloral external test set samples with the MIR spectroscopic data. Additionally, clearly more multifloral external samples were identified as outliers by MIR spectroscopy (91.3%) as compared to MALDI-ToF-MS (78.3%). The results indicate that the high reproducibility of the used MIR technique leads to a generally better ability of separating monofloral honeys and in particular, identifying multifloral honeys. This demonstrates that benchtop-based techniques may operate on an eye-level with high-end laboratory-based equipment, when paired with an optimal data analysis strategy.

Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to Non-Defective Ones.

In the context of binary class-modelling techniques, the paper presents the computation in the input space of linear boundaries of a class-model constructed with given values of sensitivity and specificity. This is done by inversion of a decision threshold, set with these values of sensitivity and specificity, in the probabilistic class-models computed by means of PLS-CM (Partial Least Squares for Class-Modelling). The characterization of the boundary hyperplanes, in the latent space (space spanned by the selected latent variables of the fitted PLS model) or in the input space, makes it possible to calculate directions that can be followed to move objects toward the class-model of interest. Different points computed along these directions will show how to modify the input variables (provided they can be manipulated) so that, eventually, a computed ‘object’ would be inside the class-model, in terms of the prediction with the PLS model. When the class of interest is that of “adequate” objects, as for example in some process control or product formulation, the proposed procedure helps in answering the question about how to modify the input variables so that a defective object would be inside the class-model of the adequate (non-defective) ones. This is the situation illustrated with some examples, taken from the literature when modelling the class of adequate objects.

Is the relationship between religiosity and filial elder-care norms declining? A comparison between two middle-aged generations

ABSTRACT We investigated whether religiosity among middle-aged adults in the Baby Boom generation and Generation-X was associated with filial elder-care norms. The sample consisted of 720 Baby Boom and 520 Gen-X respondents participating in the Longitudinal Study of Generations. A cohort-comparative model was devised using data from Baby Boomers in 1994 and Gen-Xers in 2016, when they were 42 and 40 years old, respectively. A three-step latent class modeling technique identified three religious classes in both generations: strongly religious, weakly religious, and doctrinally religious. Weakly religious Baby Boomers and Gen-Xers reported weaker filial elder-care norms than the strongly religious in each generation. No difference in these effects were detected by generation. However, only among Baby Boomers did the doctrinally religious express stronger filial norms, suggesting cross-cohort weakening in how beliefs translate into filial responsibility in the absence of congregational practice. This research opens lines of inquiry that examine long-term effects of adult children’s religiosity on the actual provision of assistance to older parents when they face health difficulties.

Identifying longitudinal-growth patterns from infancy to childhood: a study comparing multiple clustering techniques.

Most studies on children evaluate longitudinal growth as an important health indicator. Different methods have been used to detect growth patterns across childhood, but with no comparison between them to evaluate result consistency. We explored the variation in growth patterns as detected by different clustering and latent class modelling techniques. Moreover, we investigated how the characteristics/features (e.g. slope, tempo, velocity) of longitudinal growth influence pattern detection. We studied 1134 children from The Applied Research Group for Kids cohort with longitudinal-growth measurements [height, weight, body mass index (BMI)] available from birth until 12 years of age. Growth patterns were identified by latent class mixed models (LCMM) and time-series clustering (TSC) using various algorithms and distance measures. Time-invariant features were extracted from all growth measures. A random forest classifier was used to predict the identified growth patterns for each growth measure using the extracted features. Overall, 72 TSC configurations were tested. For BMI, we identified three growth patterns by both TSC and LCMM. The clustering agreement was 58% between LCMM and TS clusters, whereas it varied between 30.8% and 93.3% within the TSC configurations. The extracted features (n = 67) predicted the identified patterns for each growth measure with accuracy of 82%-89%. Specific feature categories were identified as the most important predictors for patterns of all tested growth measures. Growth-pattern detection is affected by the method employed. This can impact on comparisons across different populations or associations between growth patterns and health outcomes. Growth features can be reliably used as predictors of growth patterns.

Tutorial: multivariate classification for vibrational spectroscopy in biological samples.

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique

To develop an effective and objective diagnostic method for detecting the malignancy is of great importance. Considering that near-infrared (NIR) spectroscopy has many advantages such as being inexpensive and simple in sample preparation and class-modeling is a rather new strategy, the present paper investigates the feasibility of combining class-modeling technique other than classic classification and NIR spectroscopy for colorectal diagnosis. A total of 162 colorectal tissue slices were prepared and used to collect NIR spectra. A special variable importance (VI) index was defined to pick out 20 most significant variables. The Kennard-Stone (KS) algorithm was used to select representative 57 cancerous samples as the training set for building one-class model and the other samples served as the test set. The results showed that on the independent test set, it can achieve acceptable performance, i.e., the total accuracy of 95.2 %, the sensitivity of 96 %, and the specificity of 94.5 %. It indicates that the combination of NIR spectroscopy and one-class classifier is a potential tool for automatic cancer diagnosis.

Detection of cold chain breaks using partial least squares-class modelling based on biogenic amine profiles in tuna

The maintenance of the cold chain is essential to ensure foodstuff conformity and safety. However, gaps in the cold chain may be expected so designing analytical methods capable to detect cold chain breaks is a worthwhile issue. In this paper, the possibility of using the amount of nine biogenic amines (BAs) determined in Thunnus albacares by HPLC-FLD for detecting cold chain breaks is approached. Tuna is stored at 3 different temperature conditions for 8 storage periods. The evolution of the content of BAs is analyzed through parallel factor analysis (PARAFAC), in such a way that storage temperature, BAs and storage time profiles are estimated. PARAFAC has made it possible to observe two spoilage routes with different relative evolution of BAs. In addition, it has enabled to estimate the storage time, by considering the three storage temperatures, with errors of 0.5 and 1.0 days in fitting and in prediction, respectively. Furthermore, a class-modelling technique based on partial least squares is sequentially applied to decide, from the amount of BAs, if there has been a cold chain break. Firstly, samples stored at 25 °C are statistically discriminated from those kept at 4 °C and −18 °C; next, frozen samples are distinguished from those refrigerated. In the first case, the probabilities of false non-compliance and false compliance are almost zero, whereas in the second one, both probabilities are 10%. Globally, the results of this work have pointed out the feasibility of using the amount of BAs together with PLS-CM to decide if the cold chain has been maintained or not.

Vibrational Spectroscopy and Chemometrics in Forensic Chemistry: Critical Review, Current Trends and Challenges

The present manuscript makes an extensive review of the scientific approaches developed in the last decade involving infrared and Raman spectroscopy combined with chemometrics for solving several issues in the investigation of the most relevant forensic traces, such as questioned documents and currency, explosives, gunshot residues, illicit drugs and body fluids. In addition, current trends, main challenges and the adequate use of several chemometric techniques are discussed. Principal component analysis (PCA) was found to be the most used technique. This unsupervised approach, however, has sometimes been misunderstood as a classification technique. Discriminant analysis techniques are widely employed, leaving a range of possibilities for application of class-modeling techniques, particularly in cases of problems regarding only one target class. In addition, increasingly complex dataset structures frequently require nonlinear approaches or flexible techniques such as multivariate curve resolution-alternating least squares (MCR-ALS). Results reporting, however, still lack reliable quality parameters and sample representativeness, posing a significant challenge to the solution of forensic problems. Regarding the analytical techniques, Raman has been playing an important role, especially in the area of questioned documents and of body fluids. Portable and hyperspectral imaging infrared spectrometers have also been showing significant potential in forensic applications.

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.

Development of two dielectric sensors coupled with computational techniques for detecting milk adulteration

Milk adulteration is a challenging concern which leads to economic losses, quality deterioration of the end products in dairy industry, and consequently public health risks. The demands for reliable and fast methodologies have been already increased in dairy industry. Therefore, developing simple, rapid, and reliable instrumental methods is a necessity for detecting the milk adulterations. This research effort was aimed to combine the sensor technology with computational techniques for the milk adulteration detection. For this purpose, two dielectric sensors, parallel-plate (PPC) and cylindrical stub resonator (CS), were developed and evaluated to examine a number of prevalent milk adulterations (sodium bicarbonate, water, sugar, urea, and gelatin) with a range of variation. Dielectric power and amplitude were recorded versus frequency (swept within 0–150MHz) for the PPC and CS sensors, respectively. The spectral data were then analyzed using some computational approaches consisted of SPLINE (moving cubic spline), NIPALS (principal component analysis), CLASS (classification and class modelling techniques), MRM (multivariate range modelling), TREE (classification trees), and PLS2 (correlation between blocks of variables)). Based on the results, the dielectric power spectra of the PPC sensor showed the frequency range of 15–60MHz as the most sensitive band with respect to different adulterations and the amplitude-frequency response of the CS sensor revealed remarkable changes in the amplitude at the resonance frequencies. According to the data analysis, for PPC, two matrices were studied, with different range of the frequencies and it was proved that the data matrix with 145 variables has more discriminant information. Also, in the case of PPC data, classification tree showed the best result with 80% prediction ability for the milk samples while full classification accuracy was found for CS data. As a consequence, it was concluded that the adulterations in milk can be screened by both sensors coupled with computational techniques. Therefore, the methodologies presented here could be considered as a candidate for potential use in dairy industry.

UNEQ – An intuitive probabilistic method for class-modelling approaches

UNEQ is a parametric probabilisic class-modelling technique that makes use of the Hotelling's T2 distribution and can be considered as the modelling version of quadratic discriminant analysis. The class of interest is described by an elliptical space built around the barycentre of training data points of the class, namely the centroid vector. Orientation and eccentricity of the elliptical class space, respectively, describe correlation between the variables and their dispersion, while the width of the class space is determined according to the critical value of Hotelling’s T2 statistics at a pre-determined confidence level.

Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS) employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM). A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%).