Discrimination Technique Research Articles

How to Teach a Sociology Student Good Data Analiysis Using SPSS?

The paper presents the author’s thoughts and explores the process of teaching sociology students to analyze data using SPSS and the course book by N. Voronina, published in 2024. It also provides a brief overview of the available domestic learning materials in SPSS-based data analysis and argues that the genre of course books written by a new generation of researchers and teachers is of relevance. N. Voronina’s course book shows an understanding of the specifics of sociology students, as students who still have signs of avoiding information that is heavy in formulas and mathematical terminology, together with an understanding of how important is gaining analytical skills for professional activities. The author argues that this contradiction can be removed by bringing the research methodology to the forefront, while the analysis methods themselves should come second. The author’s high level of empathy towards students who study analysis and the use of 232 pictures embracing all the nuances of the applied processes can contribute to effective learning and application of the methods under consideration. The course book outlines all data processing and analysis approaches that may be required to develop the basic skills of a sociology research analyst. Deep and substantial description of regression analysis, decision tree method (CHAID), and discriminant analysis techniques are redundant in this context. Therewith, the author disregards the fact that the most common sociology research method is crosstabs, and its brief overview in the course book is insufficient.

Precision Denoising in Medical Imaging via Generative Adversarial Network-Aided Low-Noise Discriminator Technique

Medical imaging is significant for accurate diagnosis, and here, noise often degrades image quality, thus making it challenging to identify important information. Denoising is a component of traditional image pre-processing that helps prevent incorrect disease diagnosis. Mitigating the noise becomes difficult if there are differences in the low-level segment features. Therefore, a Generative Adversarial Network (GAN)-aided Low-Noise Discriminator (LND) is introduced to improve the denoising effectiveness in medical images with a balanced image resolution with noise mitigation. The LND function is a key that distinguishes between high- and low-noise areas based on segmented features, which are also achieved by tuning the peak signal-to-noise ratio (PSNR). Considering the training sequences, the LND-identified intervals lessen the sequences to improve the changes in pixel reconstruction. The generator function in this method is responsible for increasing the PSNR improvements over the different pixels cumulatively. The proposed method successfully improves the pixel reconstruction by 11.05% and PSNR by 9.75%, with 9.75% less reconstruction time and 13.11% less extraction error for the higher pixel distribution ratios than other contemporary methods.

Surface-enhanced Raman spectroscopy for the rapid identification of fosfomycin resistant and sensitive strains of E. Coli

Development of rapid detection and discrimination technique for the antibiotic resistant and sensitive bacterial strains is required for this purpose, Surface-enhanced Raman spectroscopy (SERS) technique is considered to have great potential. To develop a fast and sensitive detection and discrimination methodology based on SERS technique along with chemometric tools for the differentiation among fosfomycin sensitive and resistant Escherichia coli (E. coli.) strains. In this research work, three E. Coli strains resistant to fosfomycin with three fosfomycin sensitive E. coli strains were characterized in which silver nanoparticles were employed as SERS substrate. Moreover, MATLAB 7.8 (2009a) was used for the preprocessing, baseline correction and normalization of SERS spectra. Using principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA), several E. Coli strains were identified on the basis of their distinctive SERS spectral properties. The differentiating SERS spectral features which can be associated with the resistance and sensitivity against fosfomycin antibiotic, are obtained by comparing mean SERS spectrum of strains of resistant E. coli with sensitive E. coli strains. Chemometric techniques, such as principal components analysis, have been proven effective for qualitative analysis. Additionally, PLSDA was employed to classify the SERS spectra acquired from the pellets of several bacterial strains. SERS is a useful analytical method for quickly differentiating between E. Coli strains that are sensitive to fosfomycin and those that are resistant to it.

Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data

We consider the problem of anomaly detection in proportional data by investigating the Libby-Novick Beta-Liouville distribution, a novel distribution merging the salient characteristics of Liouville and Libby-Novick Beta distributions. Its main benefit, compared to the typical distributions dedicated to proportional data such as Dirichlet and Beta-Liouville, is its adaptability and explanatory power when dealing with this kind of data. Our goal is to exploit this appropriateness for modeling proportional data to achieve great performance in the anomaly detection task. First, we develop generative models, namely finite mixture models of Libby-Novick Beta-Liouville distributions. Then, we propose two discriminative techniques: Normality scores based on selecting the given distribution to approximate the softmax output vector of a deep classifier and an improved version of Support Vector Machine (SVM) by suggesting a feature mapping approach. We demonstrate the benefits of the presented approaches through a variety of experiments on both image and non-image datasets. The results demonstrate that the proposed anomaly detectors based on the Libby-Novick Beta-Liouville distribution outperform the classical distributions as well as the baseline techniques.

Selection of Optimum Enhancement Technique for the Discrimination of Alkali Granites and Syenites around Suryamalai Batholith of Central Tamil Nadu, India

Image processing techniques such as Band Ratio, Principal Component Analysis, Supervised, Unsupervised classification and Reclassification techniques are generally used as an enhancement technique for alteration zone demarcation and lithological discrimination in geosciences environment. In the present study, Landsat OLI satellite imagery has been utilized to produce various combinations of image enhancement to discriminate alkali syenite and alkali granite of Proterozoic age. These techniques were also used to differentiate three phases of granites reported in Suryamalai Batholith which is very difficult to map by conventional method due to its rugged topographic nature. 12 types of enhancement techniques were applied to differentiate rock alkali syenite from granite, however only few methods such as Kaufmanns ratio, ratio of 5/7, 3/1 and 3/5 and supervised classification were found to be suitable for the discrimination of those rocky types. Band ratio 5/7, 3/1 and 3/5, Crosta analysis and thermal reclassification techniques significantly brought to light the difference between the metamorphic rocks such as fissile hornblende biotite gneiss and hornblende biotite gneiss of Archaean age. Similarly, phase I and phase III granites were perfectly discriminated from all the image enhancement techniques. It is differentiated mainly based on its grain size and mineral variation between these two rock types. Study revealed that standard digital image processing technique can be effectively used for lithological mapping.

Research on Refined Water Injection Technology for Medium and High Water Cut Reservoirs in Nanpu 4 Block

Abstract For water injection development reservoir, the key is to control the di-rection and flow field of the injected water in the reservoir to adjust the tapping of the remaining oil between wells. This paper presents that, by applying the comprehensive discrimination technique of inefficient cir-culation flow field, a flow field evaluation model containing permeabil-ity, overwater multiplier and water saturation seepage characteristics parameters is established and the small layer flow field distribution is quantitatively evaluated by numerical simulation. Based on the geo-logical characteristics, development characteristics and grid inefficient circulation coefficients, the flow field is qualitatively classified into three types: streamline not established, streamline primary and sec-ondary, and streamline fixed and difficult to adjust. Based on the re-search of domestic and foreign literature and numerical simulation, the management system of different types of flow fields is studied to clari-fy the reasonable development technology policy of reservoir flow field adjustment and tapping the remaining oil between wells, so as to achieve the purpose of controlling the reservoir water content rise rate, reducing the water content rate and improving the recovery rate. The mine field experiments conducted in Nanpu 4 block showed that the recovery rate of the block increased by 4 percentage points, indicating that the block development achieved significant results. This is an im-portant guideline for reservoir development in low recovery, medium to high water-bearing reservoirs with water injection development.

A gamma-ray events discrimination method based on convolutional neural network in a HPGe spectrometer

Discriminating between full- and partial-energy deposition events in a high-purity germanium detection system can achieve the subtraction of background events and reduce the system’s minimum detectable activity (MDA). Traditional anticoincidence techniques and pulse shape discrimination techniques can both achieve event type discrimination, but the former has a complex equipment structure and is difficult to maintain, and the latter still has room for improvement in recognition accuracy. This paper proposes a method using convolutional neural networks (CNNs) to learn and recognize pulse waveform features for event type discrimination. We studied the impact of space-filling curve encoding methods and convolution kernel size on the training effect of the model. The results show that the zigzag curve encoding method results in a higher discrimination accuracy of the model; when the convolution kernel size is 9 × 9, the model has the best convergence speed and training effect. Finally, we compared the performance of fully connected neural network (FC network), 1D-CNN, and 2D-CNN in event type discrimination using MDA as the indicator. The results show that compared to FC network, CNNs perform better in reducing the MDA across the entire energy range, especially 2D-CNN, which performs better in the medium and low energy range compared to 1D-CNN. After using 2D-CNN for event discrimination and background suppression, the MDA is reduced by 21.23% (122 keV), 16.32% (244 keV), 8.43% (344 keV), 13.76% (662 keV), 5.54 % (1173 keV), and 26.35% (1332 keV).

Combination of in silico and molecular techniques for discrimination and virulence characterization of marine Brucella ceti and Brucella pinnipedialis.

Mammals are the main hosts for Brucella sp., agents of worldwide zoonosis. Marine cetaceans and pinnipeds can be infected by Brucella ceti and B. pinnipedialis, respectively. Besides classical bacteriological typing, molecular approaches such as MLVA, MLSA, and whole-genome sequencing (WGS) can differentiate these species but are cumbersome to perform. We compared the DNA and genome sequences of 12 strains isolated from nine marine mammals, with highly zoonotic B. melitensis, B. abortus, and B. suis, and the publicly available genomes of B. ceti and B. pinnipedialis. In silico pipelines were used to detect the antimicrobial resistance (AMR), plasmid, and virulence genes (VGs) by screening six open-source and one home-made library. Our results show that easier-to-use HRM-PCR, Bruce-ladder, and Suis-ladder can separate marine Brucella sp., and the results are fully concordant with other molecular methods, such as WGS. However, the restriction fragment length polymorphism (RFLP) method cannot discriminate between B. pinnipedialis and B. ceti B1-94-like isolates. MLVA-16 results divided the investigated strains into three clades according to their preferred host, which was confirmed in WGS. In silico analysis did not find any AMR and plasmid genes, suggesting antimicrobial susceptibility of marine Brucella, while the presence of the VGs btpA gene was variable dependent on the clade. The HRM-PCR and Suis-ladder are quick, easy, and cost-effective methods to identify marine Brucella sp. Moreover, in silico genome analyses can give useful insights into the genetic virulence and pathogenicity potential of marine Brucella strains.

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.

Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are found in urine samples from patients with RCC. In this study, we propose to investigate the use of Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) on dried urine samples for distinguishing RCC. We analyzed dried urine samples from 49 patients with RCC, confirmed by histopathology, and 39 healthy donors using ATR-FTIR spectroscopy. The vibrational bands of the dried urine were identified by comparing them with spectra from dried artificial urine, individual urine components, and dried artificial urine spiked with urine components. Urea dominated all spectra, but smaller intensity peaks, corresponding to creatinine, phosphate, and uric acid, were also identified. Statistically significant differences between the FTIR spectra of the two groups were obtained only for creatinine, with lower intensities for RCC cases. The discrimination of RCC was performed through Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM). Using PCA-LDA, we achieved a higher discrimination accuracy (82%) (using only six Principal Components to avoid overfitting), as compared to SVM (76%). Our results demonstrate the potential of urine ATR-FTIR combined with machine learning techniques for RCC discrimination. However, further studies, especially of other urological diseases, must validate this approach.

Inked Expressions: An appreciation of selected contemporary tattoo art among Ghanaians in Kumasi and Accra metropolises

Tattooing has been practiced for several years across the world. It is considered an art form that transmits cultural expressions and reflects the differences between groups of people globally. In Ghana, the popularity of contemporary tattooing has soared for various reasons. However, there is little scholarly research on tattoo culture in Ghana. Therefore, the study attempts to appreciate the aesthetic elements of selected contemporary tattoos in Kumasi and Accra metropolitan areas to reveal their social, philosophical, and other underpinnings. Qualitative ethnographic approach was employed in which a total of twenty-two (22) tattooed respondents were obtained for data collection using homogenous purposive and exponential discriminative snowball sampling techniques. Unstructured-Interviews and naturalistic observations constituted the data collection instruments. The study found that contemporary tattoo designs in Kumasi and Accra metropolises diversely included minimalist designs, religious symbols, portraits, decorative designs, philosophical symbols, among others, which aesthetically convey the religious identity, philosophy of life, and self-expression of tattoo wearers. While the study concludes that contemporary tattoo art in Ghana (Kumasi & Accra) is an embodiment of a dynamic and evolving art form that symbolically resonates with the persona, philosophy of life, sociocultural and religious expressions of tattoo wearers, it is recommended that further research should be conducted on the side effect of tattoo art on the health of tattoo wearers in Ghana. This will help determine whether or not tattoo art should be formalised as an acceptable academic field of study as well as be promoted by art institutions, galleries and museums in Ghana.

Retraction Note: Automatic speech recognition systems: A survey of discriminative techniques

Retraction Note: Automatic speech recognition systems: A survey of discriminative techniques

Identification of geological units in Alborz Mountain in Iran using Landsat-9 image

In the present study, the suitability of principal components analysis (PCA) based techniques was evaluated for identification of geological units from Landsat-9 satellite imagery. In this respect, a scene of Landsat-9 operational land imager 2 (OLI–2) data of the year 2023 was acquired and a geological map scale 1:100000 of the study area was used as the reference. The results indicated suitability of the PCA based techniques for discrimination of geological units from Landsat-9 image, especially the PCA of decorrelation stretch (DS) approach. The PCA-DS approach, which considered the advantages of both PCA and DS techniques, successfully identified all the geological units in the study area, including the Basalt, Sandstone, Dolomite, and Conglomerate. However, the performance of the PCA and DS techniques was also reasonable for this purpose. On the other hand, the study revealed weak performance of the minimum noise fraction (MNF) and PCA-MNF techniques for geological mapping using Landsat-9 imagery. In conclusion, the study demonstrated the advantage of the PCA-DS approach for geological mapping using Landsat-9 imagery; therefore, it may be useful in futures studies for geological mapping along the whole Alborz Mountain with similar lithological and geomorphological conditions.

Self-Referenced Au Nanoparticles-Coated Glass Wafers for In Situ SERS Monitoring of Cell Secretion.

Surface-enhanced Raman spectroscopy (SERS) is a powerful technique for discrimination of bimolecules in complex systems. However, its practical applications face challenges such as complicated manufacturing procedures and limited scalability of SERS substrates, as well as poor reproducibility during detection which compromises the reliability of SERS-based analysis. In this study, we developed a convenient method for simultaneous fabrication of massive SERS substrates with an internal standard to eliminate the substrate-to-substrate differences. We first synthesized Au@CN@Au nanoparticles (NPs) which contain embedded internal standard molecules with a single characteristic peak in the Raman-silent region, and then deposited the NPs on 6 mm glass wafers in a 96-well plate simply by centrifugation for 3 min. The one-time obtained 96 SERS substrates have excellent intrasubstrate uniformity and intersubstrate repeatability for SERS detection by using the internal standard (relative standard deviation = 10.47%), and were able to detect both charged and neutral molecules (crystal violet and triphenylphosphine) at a concentration of 10-9 M. Importantly, cells can be directly cultured on glass wafers in the 96-well plate, enabling real time monitoring of the secretes and metabolism change in response to external stimulation. We found that the release of nucleic acids, amino acids and lipids by MDA-MB-231 cells significantly increased under hypoxic conditions. Overall, our approach enables fast and large-scale production of Au@CN@Au NPs-coated glass wafers as SERS substrates, which are homogeneous and highly sensitive for monitoring trace changes of biomolecules.

Single nucleotide polymorphism rs7961894, platelet morphological parameters and lipid profile in children with type 1 diabetes: a potential relationship

Increased cardiovascular risk has been associated with certain platelet morphological parameters, and several single nucleotide polymorphisms (SNPs) have been reported to be linked. Still, little is known about their role among children with type 1 diabetes mellitus (T1DM). So, we aimed to investigate platelet parameters and lipid profile changes in relation to rs7961894 SNP in children with T1DM. Eighty children with T1DM and eighty apparently healthy controls participated in this cross-sectional study. Platelet count, mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), HbA1c, triglycerides, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol were measured, and atherogenic indices were calculated. Using a real-time polymerase chain allelic discrimination technique, rs7961894 SNP was genotyped. Children with T1DM had significantly higher MPV, PDW, TC, and LDL-C compared to controls. 25% of patients had rs7961894 CT genotype with significantly higher MPV, PDW, PCT, LDL-C, triglycerides, Castelli’s risk index II (CRI II), and atherogenic index of plasma (AIP) compared to CC genotyped patients. MPV correlated significantly with CRI II and AIP, PDW with CRI II, while PCT correlated substantially with HbA1c, LDL-C, CRI II, and AIP. rs7961894 CT genotype was a significant dependent predictor of the changes in MPV, PDW, and PCT in multivariate regression analysis.Conclusion: In children with T1DM, rs7961894 CT genotype is significantly linked to MPV, PDW, and PCT changes, which showed a substantial relationship to CRI II and AIP, highlighting the importance of monitoring these patients to identify potential cardiovascular risks early.What is Known:• Platelets and dyslipidemia are involved in atherosclerosis pathogenesis• Changes in platelet activity and morphological parameters in diabetes mellitus are contradictory• rs7961894 single nucleotide polymorphism is associated with significant changes in mean platelet volume (MPV) with no available data in childrenWhat is New:• Children with type 1 diabetes mellitus exhibited significantly higher values of MPV and platelet distribution width (PDW)• rs7961894 CT genotype was a dependent predictor of the changes in MPV, PDW, and plateletcrit (PCT) values• Diabetic children with the rs7961894 CT genotype showed substantial alterations in lipid parameters with a strong correlation between MPV, PDW, and PCT and Castelli’s risk index II and the atherogenic index of plasma

Simultaneous flow and particle measurements for multiphase flows in hydraulic engineering: A review and synthesis of current state

While multiphase flows are abundant in both natural environments and engineering applications, their analysis and quantification present challenges. In particular, simultaneously measuring the flow field and quantifying particle behavior pose many challenges. Methods based on Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) principles have been used in the last two decades to measure flow fields and dispersed (particle) phases, respectively. Despite the extensive application of these principles to multiphase flow, there are numerous approaches and techniques used to synchronize and combine PIV and PTV measurements. Combined PIV and PTV data acquisition also requires consideration of phase discrimination to obtain distinct data for the fluid and dispersed phases. In the literature, various methods have been proposed and applied to achieve phase discrimination. This review paper aims to consolidate and classify various phase discrimination techniques used in specialized applications in hydraulic engineering. These methods are categorized into optical (spectral, temporal, and hybrid) and post-processing methods, with a particular emphasis on their applicability within the realm of hydraulic engineering. Moreover, this review expands on several emerging PIV/PTV technologies and applications, where the combination of equipment and algorithms has led to significant strides in the non-intrusive measurement of multiphase flow. By consolidating and critically evaluating current methods for particle discrimination, this paper aims to enhance the scientific community's understanding of simultaneous phase velocity measurements, thereby setting the stage for advancements in multiphase flow visualization techniques.

Optimization of fast output from SiPM array for pulse shape discrimination techniques at high counting rates

Plastic scintillators with pulse shape discrimination (PSD) capability, when combined with silicon photo-multipliers (SiPMs), enable the development of compact neutron detectors with lower power consumption and insensitivity to magnetic fields. However, the SiPM array has a significantly large pulse width of the output signal, which leads to discrimination difficulties at high counting rates. There are two outputs of a SiPM array: a standard output for energy measurement and a fast output for timing measurement. The pulse width of the standard output signal is in the range of hundreds nanoseconds, while the pulse width of the other one is in the range of tens nanoseconds. Fast output signals have traditionally been considered suitably for fast timing, however utilizing them for PSD has been little explored. In our previous work, we successfully implemented PSD by using fast output signals from a SiPM. Despite the obvious negative overshoot in fast signals, we achieved PSD of fast signals by using longer sampling times. However, this approach was still limited at high counting rates. This study proposes a compensation network for fast output signals, which is composed of a compensation unit consisting of a resistor and a capacitor. This design effectively compensates the overshoot of the fast output and shortens the pulse width from 60 ns to 44 ns. Additionally, it highlights the frequency amplitude differences between the neutron and gamma signals from fast output in the high-frequency component, thus improving the PSD capability of the fast output.

A scintillating-fiber detector for making high-time-resolution secondary D-T neutron measurements in KSTAR.

A scintillating fiber (Sci-Fi) detector for the middle neutron flux range was installed in KSTAR as part of a collaboration between the National Institute for Fusion Science and the Korea Institute of Fusion Energy. The detector could make relatively high-time-resolution measurements of secondary deuterium (D)-tritium (T) neutron fluxes to investigate the degradation of D-D-born triton confinement, which is crucial for demonstrating alpha particle confinement, particularly above 0.9 MA in KSTAR. The pulse-height spectrum of the Sci-Fi detector exhibited two peaks, the higher of which corresponded to D-T neutrons. A discrimination technique was applied to extract the D-T neutron signal, revealing the time evolution of the D-T neutron flux during relatively high plasma current discharges with a 50ms temporal resolution. Future research will involve investigating the causes of the degradation of the triton burnup ratio above 0.9 MA in KSTAR.

Linkage between ACE2 Gene Polymorphisms and SARS-CoV-2 infection in Burkina Faso, sub-Saharan Africa

The ACE2 gene polymorphisms (rs143936283, rs146676783, and rs4646116) in infected and noninfected persons by SARS-CoV-2 in Burkina Faso. Our cross-sectional study population comprised 137 SARS-CoV-2 infected persons and 181 non-infected persons. Three ACE2 gene polymorphisms rs143936283, rs146676783, and rs4646116, were genotyped using the real-time PCR standard TaqMan allelic discrimination technique. The association between SARS-CoV-2 infection and the polymorphisms were evaluated by a binary logistic regression. There was no association between the polymorphisms rs143936283, rs4646116 haplotypes, and SARS-CoV-2 infection in our study population. However, in the female population, the heterozygous genotype CT of rs146676783 increased by two and half the risk (OR=2.58 95%CI (1.2-5.48), p= 0.014) of being infected by SARS-CoV-2. Additionally, carrying the homozygous minor allele (genotype TT) of rs146676783 increased by more than five and half the risk (OR=5.57 95%CI (1.64-18.78), p=0.006) of being infected by SARS-CoV-2 among females. This study showed that the ACE2 gene variant rs146676783 was associated with an increased risk of being infected by SARS-CoV-2 in females, suggesting a need for further investigation to contribute to a better understanding of the African COVID-19 enigma.

How do visual and cognitive non-driving related tasks affect drivers’ visual attention and takeover performance in conditionally automated driving?

This study examines changes in visual attention during drivers’ engagement in visual or cognitive NDRTs during conditionally automated driving, and determines how these changes affect takeover performance. Seventy-five participants took part in a driving simulator study, performing three pre-takeover tasks: the three pre-takeover tasks: an auditory-imagery-verbal task (cognitive NDRT); a video-watching task (visual NDRT); and a monitoring task (baseline/non NDRT). Also, there were two hazardous events (breakdown or sudden merging of the vehicle ahead) leading to takeover requests issued with 7-s or 5-s lead times. The results revealed that NDRTs negatively affected visual attention, which caused lower saccade frequency between different areas of interest and shorter saccade amplitude. Drivers paid more visual attention to the road ahead and in-vehicle information system when performing cognitive and visual NDRTs, respectively. The visual attention of drivers performing NDRTs negatively affected takeover performance (e,g longer reaction time, heavier maximal brake pedal input, etc.). The reduction in takeover request lead time impaired takeover performance. The findings will support the design of eye tracker-based “out-of-loop” discrimination techniques and human-machine interaction interfaces in automated vehicles. This study contributes to the literature by examining how different types of NDRTs affect takeover performance, specifically focusing on drivers’ visual attention.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.