Correct Discrimination Research Articles

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans.

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000-4000 cm-1) and preprocessing (Standardization, Savitzky-Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90-180-270-360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models.

Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.

Color characteristics are a crucial indicator of green tea quality, particularly in needle-shaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea.

Discrimination of Fungicide-Contaminated Lettuces Based on Maximum Residue Limits Using Spectroscopy and Chemometrics

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making.

Application of X-STRs for Forensic Identification in Mixed DNA Profile: A Case Report

Autosomal polymorphisms (STRs) or Y-Chromosome polymorphisms (Y-STRs) are usually used for the study and deconvolution of mixed DNA profiles in forensic genetics, accompanying data interpretation with biostatistical evaluations (e.g., RMP, RMNE, LR). Sometimes, however, some mixed DNA profiles are so complex that autosomal and Y markers are not sufficient for correct discrimination and identification. In this work is reported a robbery case in which the analysis of the polymorphic markers of the X Chromosome (X-STRs) was applied to the mixed profiles obtained from the traces. This falls outside the classic use of the X-STRs. Indeed, the aim of the authors is to encourage the usage of X-STRs not only in parental relationships, but also in pure forensic cases for interpreting complex mixed DNA profiles, since their application in case resolution could be more decisive than autosomal STRs and Y-STRs.

Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics

In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.

Carcass traits and meat quality assessment of two slow-growing chicks strains fed Acheta domesticus larval meal

Presently, there has been a noticeable rise in the consumption of poultry meat within the general population, particularly focusing on poultry sourced from alternative rearing systems as opposed to intensive ones. This study evaluated the impact of incorporating house cricket (Acheta domesticus,AD) larvae meal into the diet of 2 slow-growing chicken strains on their carcass traits and meat quality. A total of 256 one-day-old male chicks were utilized, with 128 belonging to the Red (R) strain and 128 to the Naked Neck (NN) strain. Chicks from each strain were randomly assigned to 16 replicates of 8 chicks each. Eight replicates were allocated to 2 distinct treatments (n=64 chicks/treatment) based on the diet employed: the control treatments for the Red (R-C) and Naked Neck (NN-C) strains were fed soybean meal, while the other 2 treatments were fed AD meal (R-AD and NN-AD). Three different rations were used throughout the growth cycle of the animals, tailored to meet the nutritional needs of the birds. All chickens were slaughtered at 95 d of age, and eight chickens per treatment were randomly chosen to assess carcass traits and meat quality. Near-infrared reflectance spectroscopy (NIRS) was employed to classify meat based on the feed and the chick strain, achieving a correct discrimination of 100% of the samples. The carcass traits exhibited significant alterations due to the inclusion of insect larval meal in the diet, resulting in reduced values across all parameters for chickens consuming AD meal, irrespective of genotype. The pH and cooking losses were generally higher for chicks fed cricket meal, as occurred with moisture. However, protein content was lower in the meat of chicks consuming cricket meal. Fat content showed lower values for NN chickens. There were minimal differences in the fatty acid profile, with lower C18:3n-3 values for chickens fed cricket meal. Therefore, including Acheta domesticus meal in the diets of slow-growing chick strains is a viable alternative to replace soybean meal. It provides meat quality characteristics comparable to those obtained in conventional systems.

인공지능 활용에 따른 차별에 대한 법적 고찰

As AI has a significant impact on daily life, the economy, and industry, various problems are raised. Among them, special attention should be paid to the problem of unreasonable discrimination. If big data continues to expand and AI technology develops further in the future, and AI is used in all areas of life, the problem of discrimination will become a very serious social problem. As there is a high possibility that the problem of unreasonable discrimination due to the use of AI will surface, legal countermeasures must be carefully considered. So this paper examines legal measures to prevent and respond to the problem of unfair discrimination caused by the use of AI. We briefly summarize the meaning and types of discrimination, examine the causes of discrimination due to the use of AI, the risks of such discrimination, and the necessity and difficulty of legal regulation regarding this, and then based on this discussion, present legal measures to prevent and regulate discrimination.&#x0D; There is a high risk that AI will not only reflect the biases inherent in our society, but also strengthen and perpetuate them. On the other hand, due to the opacity and complexity unique to AI, it is difficult to recognize and correct discrimination and resolve disputes. Accordingly, we must recognize the seriousness of the risk that AI will further solidify prejudice and discrimination in our society, and prepare normative response measures that meet the characteristics of such risk.&#x0D; First of all, standards for equality and fairness that should be reflected in legal regulations must be established. Reasonable conclusions must be made about the meaning and content of discrimination, the values of non-discrimination and fairness that must be observed, standards for evaluating the legitimacy of discriminatory treatment, and proper indicators. Next, it is important to conduct impact assessment and inspect the risks of AI systems in advance and ensure transparency to prevent unreasonable discrimination. In addition, the entity responsible for unreasonable discrimination that occurs in various contexts must be clearly identified, and procedural rules regarding dispute resolution must be clarified. Basically, it is necessary to hold the user who adopts the AI system accountable and appoint a person responsible for preventing bias. Above this, the fact that AI is being used must be notified to the person being evaluated, and the input and output data of the algorithm must be disclosed afterwards. Also, expansion of public data disclosure and relaxation and transition of the burden of proof for unreasonable discrimination should also be considered.

Combination of fingerprint and chemometric analytical approaches to identify the geographical origin of Qinghai-Tibet plateau rapeseed oil

Verification of the geographical origin of rapeseed oil is essential to protect consumers from fraudulent products. A prospective study was conducted on 45 samples from three rapeseed oil-producing areas in Qinghai Province, which were analyzed by GC-FID and GC-MS. To assess the accuracy of the prediction of origin, classification models were developed using PCA, OPLS-DA, and LDA. It was found that multivariate analysis combined with PCA separate 96% of the samples, and the correct sample discrimination rate based on the OPLS-DA model was over 98%. The predictive index of the model was Q2 = 0.841, indicating that the model had good predictive ability. The LDA results showed highly accurate classification (100%) and cross-validation (100%) rates for the rapeseed oil samples, demonstrating that the model had strong predictive capacity. These findings will serve as a foundation for the implementation and advancement of origin traceability using the combination of fatty acid, phytosterol and tocopherol fingerprints.

Unambiguous correct discrimination of linearly dependent states with multiple copies

Unambiguous correct discrimination of linearly dependent states with multiple copies

PRAME Is an Effective Tool for the Diagnosis of Nevus-Associated Cutaneous Melanoma.

(1) Background: Nevus-associated cutaneous melanoma (CM) is relatively common in the clinical practice of dermatopathologists. The correct diagnosis and staging of nevus-associated cutaneous melanoma (CM) mainly relies on the correct discrimination between benign and malignant cells. Recently, PRAME has emerged as a promising immunohistochemical marker of malignant melanocytes. (2) Methods: PRAME immunohistochemistry (IHC) was performed in 69 cases of nevus-associated CMs. Its expression was evaluated using a score ranging from 0 to 4+ based on the percentage of melanocytic cells with a nuclear expression. PRAME IHC sensitivity, specificity, positive predictive values, and negative predictive values were assessed. Furthermore, the agreement between morphological data and PRAME expression was evaluated for the diagnosis of melanoma components and nevus components. (3) Results: PRAME IHC showed a sensitivity of 59%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 71%. The diagnostic agreement between morphology and PRAME IHC was fair (Cohen's Kappa: 0.3); the diagnostic agreement regarding the benign nevus components associated with CM was perfect (Cohen's Kappa: 1.0). PRAME was significantly more expressed in thick invasive CMs than in thin cases (p = 0.02). (4) Conclusions: PRAME IHC should be considered for the diagnostic evaluation of nevus-associated CM and is most useful in cases of thick melanomas. Pathologists should carefully consider that a PRAME-positive cellular population within the context of a nevus could indicate a CM associated with the nevus. A negative result does not rule out this possibility.

Awake ripples enhance emotional memory encoding in the human brain

Enhanced memory for emotional experiences is hypothesized to depend on amygdala-hippocampal interactions during memory consolidation. Here we show using intracranial recordings from the human amygdala and the hippocampus during an emotional memory encoding and discrimination task increased awake ripples after encoding of emotional, compared to neutrally-valenced stimuli. Further, post-encoding ripple-locked stimulus similarity is predictive of later memory discrimination. Ripple-locked stimulus similarity appears earlier in the amygdala than in hippocampus and mutual information analysis confirms amygdala influence on hippocampal activity. Finally, the joint ripple-locked stimulus similarity in the amygdala and hippocampus is predictive of correct memory discrimination. These findings provide electrophysiological evidence that post-encoding ripples enhance memory for emotional events.

Non-invasive optical property-based strategy to distinguish poly(ethylene terephthalate) containers of different recycled content

For Poly(ethylene terephthalate) (PET), one of the most widely used and re-used plastics, no definitive analytical method has been established to distinguish between the virgin form and its recycled counterpart. Numerous efforts have been made to achieve this purpose by mainly measuring thermal or mechanical properties and in this sense, the challenge arises from the fact that several factors can affect the characteristics of PET, recycled or not. In this study, the optical properties and their correlations for 28 PET containers with different mass, thickness and content of recycled material have been studied, normalized and parameterized to ultimately differentiate between both forms. Diffuse reflectance UV–Vis (DRS) spectra were unique for each sample and, while near-infrared (NIR) and Fourier-transform infrared-attenuated total reflectance (FT-IR-ATR) provided constant overall parameters regardless of the recycled %, fluorescence and Raman results indicated some differences between non-recycled and recycled PET. Furthermore, the Raman identification accuracy of the two groups was 96.4% and the fluorescence intensity could be related to the content of recycled PET. Moreover, water permeability tests were carried out on some samples, concluding that this property was correlated with the thickness of the plastic and not the recycled %. The study was completed by performing a multivariate analysis (PCA, box plot, K-means and hierarchical clustering analysis) to better understand the correlations within the variables studied and finally confirm the selection of those that allowed for a correct and sequential discrimination of the groups, resulting in a fast and non-invasive strategy that allows for an almost complete vPET-rPET distinction.

Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex

Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.

Brain dynamics during mnemonic discrimination in preclinical and prodromal Alzheimer’s disease

AbstractBackgroundMnemonic discrimination of similar lures and targets is emerging as a promising marker of subtle memory decline in early Alzheimer’s disease (AD) due to its reliance on regions most affected by AD pathology. Tau pathology initially targets medial temporal lobe (MTL) regions in an anterior‐temporal (AT) brain network, which is preferentially involved in object processing, before it can be found in the posterior‐medial (PM) network associated with scene processing. Tests of object and scene mnemonic discrimination may therefore serve as behavioural indicators of the functional integrity of AT and PM regions, respectively. We investigated behavioural and neural changes in mnemonic discrimination across the AD spectrum.Method201 DELCODE cohort participants underwent fMRI scanning during an object‐scene mnemonic discrimination task at four time points from baseline to three‐year follow‐up. At baseline, participants were split by diagnostic group (cognitively unimpaired = CU, subjective cognitive decline = SCD, mild cognitive impairment = MCI, dementia of the Alzheimer’s type = DAT; Table 1). We identified brain regions showing lower activity to repeated targets but not similar lures, indicative of successful pattern separation of overlapping memory representations.ResultThese findings present cross‐sectional baseline data. Behavioural data showed a main effect of group but no group by category (objects, scenes) interaction. CUs and SCDs outperformed patients across domains and CUs scored higher than SCDs only in scene discrimination. Amyloid positive and negative CUs and SCDs did not differ in performance. Successful mnemonic discrimination engaged hippocampus, parahippocampal cortex, precuneus, angular gyrus, and inferior frontal regions for both objects and scenes. Correct object discrimination was additionally associated with amygdala and transentorhinal cortical activity. Reduced brain activation in regions of the anterior‐temporal and posterior‐medial network during successful object and scene mnemonic discrimination, respectively, was found in patient groups (MCI+DAT) compared to cognitively normal (CU+SCD) older adults.ConclusionOur findings lend further support to the utility of mnemonic discrimination as indicator of memory impairment in early AD. Object and scene tasks engage partially dissociable brain networks and individuals with cognitive impairment show reduced neural pattern separation effects. These findings lay the groundwork for future longitudinal analyses to track these changes based on clinical and biomarker status.

Critical evaluation of key parameters in single particle ICP-MS data processing for the correct determination of platinum nanoparticles in complex environmental and biological matrices.

There is an urgent need for the harmonization of critical parameters in single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) and they have been deeply studied and optimized in the present work using platinum nanoparticles (PtNPs) as a representative case of study. Special attention has been paid to data processing in order to achieve an adequate discrimination between signals. Thus, a comparison between four different algorithms has been performed and the method for transport efficiency calculation has also been thorougly evaluated (finding the use of a well-characterized solution of the same targeted analyte (30nm PtNPs) as adequate). The best results have been obtained after the application of a deconvolution approach for the data processing and using 5ms as dwell time and 40,000 data points for data acquisition. Under the optimized conditions, a correct discrimination between NP events and background signal up to 100 or 750ng L-1 of added ionic Pt was reached for 30 and 50nm PtNPs, respectively. The suitability of the developed method for the characterization of PtNPs in relevant environmental (water samples) and biological (cell culture media) matrices has also been demonstrated.

Proposed Changes in Federally Mandated Race and Ethnic Classifications May Undermine Their Original Purpose

The Office of Management and Budget (OMB) is proposing an overhaul of “Statistical Policy Directive 15,” commonly referred to informally as OMB15 , the mandated method for collecting data on and classifying the race and ethnic status of the American population. Initiated in 1977, the standards for collecting data on race and ethnicity are mandated to be used by the Census Bureau and all federal agencies, and eventually all state, local, and private entities that receive federal funds, and/or come under civil rights compliance provisions designed to monitor disparities and correct discrimination. The classification was developed to monitor compliance with several civil rights laws affecting many sectors of American life that were passed in the 1960s and in later years. The widespread use and enormous implications of changes in how the United States collects race and ethnicity data mean that we should be very cautious about changes like the ones that have been proposed, and we think it would be wise to make these mandated data classifications both as simple as possible for the U.S. population to understand their meaning and to apply them in the many areas where they are to be used.

A PVDF-based colorimetric sensor array for noninvasive detection of multiple disease-related volatile organic compounds.

Detection of human-generated volatile organic compounds (VOCs) is a new pathway for assessing health. Herein, a polyvinylidene fluoride (PVDF)-based colorimetric sensor array was designed for detecting disease-related VOCs (DVOCs) within 15min, using a complex of Cu metal-organic framework, graphene aerogel, and dyes as response materials. Fingermaps derived from 28 DVOCs were obtained for further data processing. Pattern recognition was successfully employed in the correct discrimination of 28 DVOCs in low (10μM), medium (100μM), and high (300μM) concentrations. Importantly, the sensor array also presented excellent discrimination ability and application potential when detecting VOCs produced by human cancer and normal cells. In general, VOC acquisition is noninvasive and harmless, and the PVDF-based sensor arrays are simple and visual. Such advantages expand their further application potential.

Investigation of Non-Equilibrium and Dynamic Behavior of Soil Water Characteristic Curves through Field Monitoring

In this paper, soil water characteristic curves (SWCCs) were reconstructed through simultaneous field measurement of soil water content and suction value. The objective of this study was to evaluate whether field-based monitoring could capture the different SWCC paths in various seasons. Moreover, field measurement of SWCC makes it possible to quantify the hysteresis phenomenon, analyzes the effect of various temporal resolutions on SWCC, and highlights the differences in reconstructed SWCC for the same soil during various weather conditions. Field data from one expansive subgrade site in Texas, USA were collected through moisture and suction sensors over a 3-year monitoring period. Analyzing the rainfall and evapotranspiration data, three distinct wetting and drying cycles were identified which allowed correct discrimination of the cycles. The pattern of changes in SWCC was identified in the various wetting and drying cycles. While the field data was fitted through the Van Genuchten equation, variations of the unsaturated soil parameters ( α, n, m) were identified in various cycles. Air entry value indicator parameter α ranged from 0.05 to 0.11 kPa−1 while the slope of the SWCC curve parameter n value varied from 1.1 to 1.45. Comparing these results with previous studies, it was concluded that wetting and drying cycle parameters do not have any predefined correlation. However, these unsaturated parameters required for numerical modeling are dependent on the climatic events happening year-round.

Characterization of Radix Angelicae sinensis by Fluorescence and Near-Infrared Spectroscopies

Angelica sinensis (Oliv.) diels is a perennial herbaceous plant. Its dried root, referred to as radix Angelicae sinensis (RAS), has been used for edible and medicinal purposes. This study is dedicated to spectroscopic characterization and quality detection of RAS powder material or aqueous solution using fluorescence excitation–emission matrix and near-infrared (NIR) spectroscopies. It is the first time that the fluorescence combined with net analyte signal–classical least-squares was able to accurately determine two active substances in 131 RAS batches from five geographical origins in Gansu, China. The concentrations of Z-ligustilide and L-tryptophan were 1.17 ± 0.43 mg g−1 and 0.59 ± 0.19 mg g−1, respectively. A satisfactory NIR-principal discriminant variate model was achieved, enabling the RAS batches to be discriminated by the location. The correct discrimination rates reached 100% for calibration and 87.50% for validation. The work provides an alternative and holistic strategy for reliable fingerprint authentication of RAS materials and helps promote its utility.

Rapid Discrimination of Pseudomonas aeruginosa ST175 Isolates Involved in a Nosocomial Outbreak Using MALDI-TOF Mass Spectrometry and FTIR Spectroscopy Coupled with Machine Learning

The goal of this study was to evaluate matrix-assisted laser desorption ionization–iime of flight mass spectrometry (MALDI-TOF MS) and Fourier-transform infrared spectroscopy (FTIR-S) as diagnostic alternatives to DNA-based methods for the detection of Pseudomonas aeruginosa sequence type (ST) 175 isolates involved in a hospital outbreak. For this purpose, 27 P. aeruginosa isolates from an outbreak detected in the Hematology department of our hospital were analyzed by the above-mentioned methodologies. Previously, these isolates had been characterized by pulse-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS). Besides, eight P. aeruginosa isolates were analyzed as unrelated controls. MALDI-TOF MS spectra were acquired by transferring several colonies onto the MALDI target and covering them with 1 µl of formic acid 100% and 1 µl of α-ciano-3,4-hidroxicinamic acid matrix. For the analysis with FTIR-S, colonies were resuspended in 70% ethanol and sterile water according to the manufacturer’s instructions. Spectra from both methodologies were analyzed using Clover Biosoft Software, which allowed data modeling using different algorithms and validation of the classifying models. Three outbreak-specific biomarkers were found at 5,169, 6,915, and 7,236 m/z in MALDI-TOF MS spectra. Classification models based on these three biomarkers showed the same discrimination power displayed by PFGE. Besides, K-nearest neighbor algorithm allowed the discrimination of the same clusters provided by WGS and the validation of this model achieved 97.0% correct classification. On the other hand, FTIR-S showed a discrimination power similar to PFGE and reached correct discrimination of the different STs analyzed. In conclusion, the combination of both technologies evaluated, paired with machine learning tools, may represent a powerful tool for real-time monitoring of high-risk clones and isolates involved in nosocomial outbreaks.