Support Vector Machine Discriminant Analysis Research Articles

Synchrotron-Infrared Microspectroscopy of Live Leishmania major Infected Macrophages and Isolated Promastigotes and Amastigotes.

The prevalence of neglected tropical diseases (NTDs) is advancing at an alarming rate. The NTD leishmaniasis is now endemic in over 90 tropical and sub-tropical low socioeconomic countries. Current diagnosis for this disease involves serological assessment of infected tissue by either light microscopy, antibody tests, or culturing with in vitro or in vivo animal inoculation. Furthermore, co-infection by other pathogens can make it difficult to accurately determine Leishmania infection with light microscopy. Herein, for the first time, we demonstrate the potential of combining synchrotron Fourier-transform infrared (FTIR) microspectroscopy with powerful discrimination tools, such as partial least squares-discriminant analysis (PLS-DA), support vector machine-discriminant analysis (SVM-DA), and k-nearest neighbors (KNN), to characterize the parasitic forms of Leishmania major both isolated and within infected macrophages. For measurements performed on functional infected and uninfected macrophages in physiological solutions, the sensitivities from PLS-DA, SVM-DA, and KNN classification methods were found to be 0.923, 0.981, and 0.989, while the specificities were 0.897, 1.00, and 0.975, respectively. Cross-validated PLS-DA models on live amastigotes and promastigotes showed a sensitivity and specificity of 0.98 in the lipid region, while a specificity and sensitivity of 1.00 was achieved in the fingerprint region. The study demonstrates the potential of the FTIR technique to identify unique diagnostic bands and utilize them to generate machine learning models to predict Leishmania infection. For the first time, we examine the potential of infrared spectroscopy to study the molecular structure of parasitic forms in their native aqueous functional state, laying the groundwork for future clinical studies using more portable devices.

Raman spectroscopy for the identification of body fluid traces: Semen and vaginal fluid mixture

Raman spectroscopy is a versatile research tool that has found numerous practical applications recently. High specificity, non-destructive nature, and potential for on-field application make Raman spectroscopy an attractive tool for forensic science. The great potential of Raman spectroscopy for confirmatory identification of all main body fluids has been recently reported (Vyas et al. Forensic Chemistry 2020, 100247). Bringing the developed methodology from the laboratory to the crime scene requires further method development and validation for realistic, practical samples subjected to environmental conditions, contamination, substrate interference, aging, etc. This work targets the validation of the developed methodology for body fluid mixtures, semen, and vaginal fluid in particular, which are critical for sexual assault cases. Near-infrared (NIR) Raman spectra of dry semen-vaginal fluid mixtures were acquired in automatic scanning mode and subjected to advanced statistical analysis. Support Vector Machine Discriminant Analysis (SVMDA) was used to develop three binary classification models, including vaginal fluid vs mixture, semen vs mixture, and semen vs vaginal fluid. These binary models were combined to build a single tertiary model, which resulted in the 100% correct classification on the sample level for all examined mixtures (1.5% to 100% for vaginal fluid and 3% to 100% for semen). The developed analytical approach could be used for various other applications when complex binary mixtures need to be detected and analyzed.

Classification and determination of sulfur content in crude oil samples by infrared spectrometry

Determining and classifying the sulfur content of crude oil has long been of great importance because of its adverse economic and environmental effects. In this study, the total sulfur concentration in crude oil samples was determined and classified using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and chemometric methods. The methods designed for the analysis of crude oils are rapid, economical and non-destructive in the production process of the petroleum industry. Two sets of 70 and 31 samples in regression models were considered for the calibration and prediction sets, respectively. The calibration models were developed using the partial least squares regression model (PLS-R) and support vector machine regression model (SVM-R). Different pre-processing methods were also evaluated for the development of models. The preprocessing methods based on baseline correction, standard normal variate (SNV) and the auto scale were selected for regression and classification models. The use of SVM-R as a non-linear regression provided a model with significantly better root mean square error of prediction (RMSEP) values than the PLS-R model as a linear model. The ATR-FTIR spectral data were also applied by supervised classification method using the partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) for classifying crude oils based on sulfur content. The samples were classified into two classes according to the sulfur content into sweet and sour crude oil. The result of the classification found an accuracy of 96% and a classification error of 0.0384 for the prediction set in the PLS-DA algorithm. The results indicated that ATR-FTIR spectroscopy associated with multivariate calibration and classification models is a rapid and reliable approach for parallel quantification and qualification of the sulfur content present in crude oils.

Evaluation of visible spectroscopy and low-field nuclear magnetic resonance techniques for screening the presence of defects in broiler breast fillets

The potential of using simple visible spectroscopy (VIS) and low-field nuclear magnetic resonance (LF-NMR) was evaluated for differentiating three muscle conditions, normal (NORM), pale, soft and exudative (PSE), and woody breast (WB), in broiler pectoralis major. A total of 162 of pectoralis major, 54 for each condition, were collected from a commercial plant. Meat VIS spectra and transverse relaxation curve were collected using a spectrophotometer and LF-NMR, respectively. Principal component analyses (PCA) showed the better separation of PSE from NORM by VIS spectra. LF-NMR distinguished WB from NORM and PSE meat. However, the combination of VIS spectra and NMR relaxation curve differentiated the three conditions. PCA linear discriminant analysis and support vector machine discriminant analysis models further demonstrated that PCA analyses of combined VIS and LF-NMR data can correctly identify the presence of the defects in broiler breast meat.

Identification of different parts of Panax notoginseng based on terahertz spectroscopy

In this paper, the combined terahertz time-domain spectroscopy (THz-TDS) and chemometrics method is proposed to identify four different parts of Panax notoginseng rapidly and nondestructively. The research objects of the taproot, scissor, rib, and hairy root of P. notoginseng are taken. The refractive index, absorption coefficient, time-domain, and frequency-domain spectra of the samples are analyzed. It is found that the terahertz spectra of different parts of P. notoginseng are significantly different, so the absorption coefficient of samples is selected to establish models. Firstly, the baseline correction, multiple scattering correction, and normalization algorithms are used to preprocess the absorption coefficient in 0.5–2.0 THz to remove noise. Then, the Kennard–Stone (KS) algorithm is used to divide the model set and the prediction set at the ratio of 3:1, and the successive projection algorithm (SPA) is used to select the characteristic frequency points of the samples. Finally, the chosen characteristic variables are input into the support vector machine (SVM) and linear discriminant analysis (LDA) algorithm to establish the qualitative analysis models, respectively. In the SPA-SVM models, the performance of the SPA-SVM model under the linear kernel function by baseline is best, the accuracy of the training set of it is 99.50%, and the accuracy of the test set of it is 99.25%. In the SPA-LDA models, the performance of the SPA-LDA model by baseline is best, and the accuracy of the training set of it is 100%, and the accuracy of the test set of it is 100%. And the value of cumulative variance contribution is proposed to assess whether the variable is good or bad to model. The results show that the combined THz-TDS and chemometrics method can be used to realize rapid, accurate, and nondestructive identification of different parts of P. notoginseng.

Evaluation of chemometric classification and regression models for the detection of syrup adulteration in honey

Honey is a highly desirable commodity hence a target of adulteration to increase its bulk. Spectroscopic methods and chemometrics offer a fast, easy, and simple approach to the detection of syrup adulterations in honey. The idiosyncrasies associated with different instrumental data suggest varying performance for each algorithm. The objective of this study was to evaluate the performance of five commonly used classification and regression algorithms for the detection of syrup adulteration in honey. The prediction performance of the classification and regression algorithms was evaluated using data obtained from Fourier-transform infrared spectrometer with a Horizontal Attenuated Total Reflectance (HATR) Accessory analysis of honey samples. Gradient boosted discriminant analysis (GBDA) and Support vector machines discriminant analysis (SVMDA) were able to differentiate between adulterated and pure honey samples with an external validation set prediction accuracies of 0.988 and 0.981, respectively. Application of feature selection method did not lead to improved prediction accuracies. Gradient boosted regression (GBR) initialized with a ridge regression predicted the percentage level of adulteration with a regression coefficient of 1.000. The RMSE for the optimal GBR was 2.183 and 0.018 for before and after feature selection with the partial least squares (PLS) algorithm, respectively. Ensemble methods are generally better for both classification and regression of honey using FTIR-HATR spectroscopic data.

A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers

In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

Rapid detection of fumonisin B1 and B2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics

A portable near-infrared (NIR) spectrometer coupled with chemometrics for the detection of fumonisin B1 and B2 (FBs) in ground corn samples was proposed in the present work. A total of 173 corn samples were collected, and their FB contents were determined by HPLC–MS/MS. Partial least squares (PLS), support vector machine (SVM) and local PLS based on global PLS score (LPLS-S) algorithms were employed to construct quantitative models. The performance of the SVM and LPLS-S was better than that of PLS, and the LPLS-S presented the lowest RMSEP (12.08 mg/kg) and the highest RPD (3.44). Partial least squares-discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA) were used to classify corn samples according to the maximum residue limit (MRL) of FBs, and the discriminant accuracy of both the PLS-DA and SVM-DA algorithms was above 86.0%. Thus, the present study provided a rapid method for monitoring FB contamination in corn samples.

Oral Cancer Discrimination and Novel Oral Epithelial Dysplasia Stratification Using FTIR Imaging and Machine Learning.

The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800–950 cm−1. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.

Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality

In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.

Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System.

The ripening process of dry-cured meat products is characterised by the development of fungi on the product’s surface. This population plays a beneficial role, but, uncontrolled moulds represent a health risk, since some of them may produce mycotoxins, such as ochratoxin A (OTA). The aim of the present work is to assess the potential of near-infrared spectroscopy (NIRS) for the detection of OTA-producing mould species on dry-cured ham-based agar. The collected spectra were used to develop Support Vector Machines–Discriminant Analysis (SVM-DA) models by a hierarchical approach. Firstly, an SVM-DA model was tested to discriminate OTA and non-OTA producers; then, two models were tested to discriminate species among the OTA producers and the non-OTA producers. OTA and non-OTA-producing moulds were discriminated with 85% sensitivity and 86% specificity in the prediction. Furthermore, the SVM-DA model could differentiate non-OTA-producing species with a 95% sensitivity and specificity. Promising results were obtained for the prediction of the four OTA-producing species tested, with a 69% and 90% sensitivity and specificity, respectively. The preliminary approach demonstrated the high potential of NIR spectroscopy, coupled with Chemometrics, to be used as a real-time automated routine monitorization of dry-cured ham surfaces.

Rapid and Uninvasive Characterization of Bananas by Hyperspectral Imaging with Extreme Gradient Boosting (XGBoost)

Ripe fruit provides essential nutrients for the human body. To fulfill the needs of consumers, the practice of artificial ripening has become more common. Artificial ripening not only degrades the quality of fruit but also impairs health. The potential of hyperspectral imaging coupled with machine learning to quickly and uninvasively identify differently ripened bananas was explored in this study. A total of 300 banana samples that were naturally ripened or ripened by ethephon or calcium carbide were characterized by their hyperspectral images. To improve the accuracy of classification models and to reduce the effects of noise and irregular surfaces, different preprocessing strategies were investigated. Recursive feature elimination (RFE) and the successive projections algorithm (SPA) were employed to select feature wavelengths. Four classification methods – extreme gradient boosting (XGBoost), support vector machine (SVM), multi-layer perceptron (MLP), and partial least square discriminant analysis (PLS-DA) – were applied for ripening identification. The results showed that the best classification model was XGBoost based on full wavelengths, which achieved an area under the macro-average receiver operating characteristic curve of 0.9796 and high training, cross-validation, and testing accuracy values of 95.02%, 92.31%, and 91.16%, respectively. The RFE-XGBoost model reduces the data dimension and maintains satisfactory performance. Both XGBoost models achieved 100% correct differentiation between naturally ripened and artificially ripened bananas. Hence, the method may be employed to quickly and uninvasively classify differently ripened bananas.

Electroencephalography based detection of cognitive state during learning tasks: An extensive approach

Detecting cognitive states during learning tasks is an essential component in neurocognitive experiments for assessing and enhancing the cognitive performance of individuals. Studies have demonstrated that mental state recognition systems utilizing brain signals are proficient in the automated monitoring of learners’ cognitive states. The current study focuses on developing an efficient individualized and cross-subject cognitive state assessment model based on Electroencephalography (EEG) patterns during learning tasks. For this study, EEGs of 20 healthy subjects were recorded during a resting state followed by a learning task and examined EEG activations patterns in a wide perspective of feature types and rhythms. The extracted features included time-domain features such as Hjorth parameters, Wavelet-based features, and Spectral entropy. Three classifiers, Support Vector Machine, k-Nearest Neighbor, and Linear Discriminant Analysis were employed to recognize the mental state. A new EEG-based attention index using band ratios is proposed and is demonstrated as an effective predictor for recognizing attentive reading. The proposed model can yield recognition performance with an accuracy of 92.9% in the subject-dependent approach and 77.2% in the subject-independent approach with the Support Vector Machine Classifier. The findings are useful for the design and development of neurofeedback systems that monitor and enhance the cognitive performance in healthy individuals, as well as in individuals with cognitive deficits.

Assessment of a Nondestructive Method for Rapid Discrimination of Moroccan Date Palm Varieties via Mid-Infrared Spectroscopy Combined with Chemometric Models

Morocco is an important world producer and consumer of several varieties of date palm. In fact, the discrimination between varieties remains difficult and requires the use of complex and high-cost techniques. We evaluated in this work the potential of mid-IR (MIR) spectroscopy and chemometric models to discriminate eight date palm varieties. Four chemometric models were applied for the analysis of the spectral data, including principal-component analysis (PCA), support-vector machine discriminant analysis (SVM-DA), linear discriminant analysis (LDA), and partial-least-squares (PLS) analysis. MIR spectroscopic data were recorded from the wavenumber range 4000-600 cm-1, with a spectral resolution of 4 cm-1. The discriminant analysis was performed by LDA and SVM-DA with a 100% correct classification rate for the date mesocarp. PLS analysis was applied as a complementary chemometric tool aimed at quantifying moisture content; the validation of this model shows a good predictive capacity with a regression coefficient of 84% and a root-mean-square error of cross-validation of 0.50. The present study clearly demonstrates that MIR spectroscopy combined with chemometric approaches constitutes a promising analytical method to classify date palms according to their varietal origin and to establish a regression model for predicting moisture content. An alternative analytical method to discriminate date palm cultivars by FTIR-attenuated total reflection spectroscopy coupled with chemometric approaches is described.

Development of a portable oil type classifier using laser-induced fluorescence spectrometer coupled with chemometrics

Due to the recurrent small spills, oil pollution along coastal regions is still a major environmental issue. Standardized oil fingerprinting techniques are useful for oil spill identifications, but time- and resource-consuming. There have been ongoing needs for simple yet rapid approach for field screening of oil spill. Laser induced fluorescence (LIF) technology can be incorporated into a spectrometer, and with the integration of chemometrics can be consolidated as a potentially useful portable oil type classification device. Using a LIF spectrometer, 775 oil spectra were calibrated into supervised classification models and validated with 162 oil spectra. Reliability of the device to accurately remove background emission from fluorescence spectra was verified. Prediction performance and model robustness were further validated by comparison between commonly used classification models such as partial least square discriminant analysis (PLS-DA) and support vector machine-discriminant analysis (SVM-DA). Robustness in both models were comparable with PLS-DA having a lower number of misclassification (PLS-DA: 5.50%, SVM-DA: 13.8%) while SVM-DA having a lower number of unassigned samples (PLS-DA: 10.9%; SVM-DA: 16 1.39%). This study explicitly demonstrated the development of a new convenient and handy device which can be used as part of the screening process for oil spill fingerprinting.

Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue

Abstract This work reports a critical evaluation of performance of various data analysis techniques applied to the classification of biosamples using voltammetric electronic tongue. Data obtained by sensor array of carbon-paste electrodes were processed by 3 pattern recognition methods: Partial Least Squares Discriminant Analysis (PLS-DA) as the “golden standard” in electronic tongue data analysis, linear Soft Independent Modelling of Class Analogy (SIMCA) and non-linear Support Vector Machine Discriminant Analysis (SVM-DA), the two latter being less commonly used. Due to high dimensionality of the data, various preprocessing methods (autoscaling, Standard Normal Variable – SNV, Discrete Wavelet Transform – DWT) were tested for each kind of these techniques to find most suitable workflow allowing to obtain satisfactory performance. Totally 1026 models were developed and tested. In order to compare their ability to estimate class affinity of the studied biosamples, 4 performance parameters: accuracy, sensitivity, precision, and specificity, were calculated for every model for both train and test sets to obtain reliable and repeatable results. It must be underlined, that some general remarks and findings that had been concluded during classification of oligopeptides, were later additionally validated using different dataset (comprising oligopeptides and amino acids). The treatment used confirmed that the presented recommendations can be generalized and adapted to other classification tasks by means of voltammetric electronic tongues.

A hybrid recognition model of microseismic signals for underground mining based on CNN and LSTM networks

Microseismic (MS) monitoring technology has been widely used to monitor ground pressure disasters. However, the underground mining environment is complex and contains many types of noise sources. Furthermore, the traditional recognition method entails a complex process with low recognition accuracy for MS signals, so it is difficult to serve for the safe production of mines. Therefore, this study established a hybrid model combining the singular spectrum analysis (SSA) method, convolutional neural networks (CNN), and long short-term memory networks (LSTM). First, the principal components of monitoring signals were extracted with the SSA method, and then spatial and temporal features of monitoring signals were separately extracted with the CNN and LSTM. Based on actual field data collected from Xiadian Gold Mine, the hybrid model was compared with the CNN, LSTM, and back-propagation networks (BP), as well as commonly used recognition methods including the support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and linear discriminant analysis (LDA). The results show that the proposed hybrid model can accurately extract data features of monitoring signals and further improve MS signals' recognition performance. Furthermore, the recognition accuracy of mechanical signals in monitoring signals is particularly increased using the hybrid model, which avoids confusion with MS signals.

Employing supervised classification techniques in determining playability status of polyester‐urethane magnetic audio tapes

AbstractAs magnetic audio tapes age, various chemical and physical degradation processes can affect the playability of the tapes. The extent that a tape is degraded, however, is often difficult to observe without actually playing the tape. Attempting to play an extensively degraded tape can result in damage to the tape as well as to the instrument used to play the tape. Our recent work has focused on less destructive methods for determining the degree to which a tape is degraded before playing. The development of such methods would allow conservationists to predetermine if a tape should be subjected to a restoration process such as baking and eliminate any further damage from occurring. In this study, attenuated total reflectance (ATR) Fourier transform infrared (FT‐IR) spectroscopy was used as a rapid and objective method for determining the playability of polyester‐urethane magnetic tapes. A collection of 95 tapes was used to generate chemometric models based on partial least squares discriminant analysis (PLS‐DA), support vector machine discriminant analysis (SVM‐DA), artificial neural networks (ANNs), and naïve Bayes classification (NBC). Classification accuracies for playability of greater than 90% were achieved when applying the models to a separate collection of 50 tapes.

Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy.

Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550–1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers.

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen‐printed Electrode

AbstractA fast, simple and costless methodology without sample pre‐treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen‐printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non‐linear methods provide better results than linear ones.