Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

BACKGROUND: No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. METHODS: In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. RESULTS: From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. CONCLUSION: This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

Uncovering the phenolic diversity of Guabiju fruit: LC-MS/MS-based targeted metabolomics approach.

The comprehensive composition of phenolic compounds (PC) from seven genotypes of guabiju were analyzed by high-performance liquid chromatography coupled to a diode array detector and mass spectrometry (HPLC-ESI-qTOF-MS/MS), and a targeted metabolomic approach was utilized to explore the PC-related similarities among the genotypes. Sixty-seven phenolic compounds were annotated and twenty-four were quantified in all genotypes of guabiju. The phenolic acids and anthocyanins were the major PC, representing more than 63% (w/w) of the total PC. Di-O-galloylquinic and tri-O-galloylquinic acids and ellagitannins were reported for the first time in guabiju. The results of hierarchical clustering and principal components analysis (PCA) suggested seven groups as suitable clusters to be formed according to phenolic composition. Eleven PC were selected as relevant for sample clustering, and six of them were highlighted as the most informative (in decreasing order of importance): epicatechin, catechin, (epi)gallocatechin gallate II, di-O-galloylquinic acid I, tri-O-galloylquinic acid and delphinidin 3-O-glucoside. To the best of our knowledge, this study contributes to the literature with the most complete phenolic profile of guabiju genotypes up to date. Moreover, guabiju susceptibility to fungal infestation related to PC composition was briefly discussed based on a parallel study using the same genotypes.

Determination of the most informative chemical elements for discrimination of rice samples according to the producing region.

Several approaches to assess the authenticity of food products have been developed, given that fraudulent products may impact consumers' confidence, affect commercial trades and lead to health risks. This paper proposes an approach to identify the chemical elements that optimally discriminate rice samples according to their producing region in the South of Brazil, the largest rice producer outside Asia. A combinatorial procedure on the concentration of 26 elements determined using inductively coupled mass spectrometry (ICP-MS) and liquid chromatography hyphenated with ICP-MS from 640 rice samples was coupled with Support Vector Machine. The assessed elements included nonmetal and metal elements of 3 types of rice collected from 5 rice-producing regions. The framework selected Mn, Fe, Cu, Zn, Ni, Mo, Cd, Cs, As, Rb, Se, and iAs as the most informative elements for tracking samples' origin. The concentration of such elements is strongly affected by fertilization procedures and soil composition.

Development and Validation of a Predictive Model of Success in Bariatric Surgery.

PURPOSE: There are no criteria to establish priority for bariatric surgery candidates in the public health system in several countries. The aim of this study is to identify preoperative characteristics that allow predicting the success after bariatric surgery. MATERIALS AND METHODS: Four hundred and sixty-one patients submitted to Roux-en-Y gastric bypass were included. Success of the surgery was defined as the sum of five outcome variables, assessed at baseline and 12 months after the surgery: excess weight loss, use of continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) as a treatment for obstructive sleep apnea (OSA), daily number of antidiabetics, daily number of antihypertensive drugs, and all-cause mortality. Partial least squares (PLS) regression and multiple linear regression were performed to identify preoperative predictors. We performed a 90/10 split of the dataset in train and test sets and ran a leave-one-out cross-validation on the train set and the best PLS model was chosen based on goodness-of-fit criteria. RESULTS: The preoperative predictors of success after bariatric surgery included lower age, presence of non-alcoholic fatty liver disease and OSA, more years of CPAP/BiPAP use, negative history of cardiovascular disease, and lower number of antihypertensive drugs. The PLS model displayed a mean absolute percent error of 0.1121 in the test portion of the dataset, leading to accurate predictions of postoperative outcomes. CONCLUSION: This success index allows prioritizing patients with the best indication for the procedure and could be incorporated in the public health system as a support tool in the decision-making process.

Surgery scheduling heuristic considering OR downstream and upstream facilities and resources.

BACKGROUND: Surgical theater (ST) operations planning is a key subject in the healthcare management literature, particularly the scheduling of procedures in operating rooms (ORs). The OR scheduling problem is usually approached using mathematical modeling and made available to ST managers through dedicated software. Regardless of the large body of knowledge on the subject, OR scheduling models rarely consider the integration of OR downstream and upstream facilities and resources or validate their propositions in real life, rather using simulated scenarios. We propose a heuristic to sequence surgeries that considers both upstream and downstream resources required to perform them, such as surgical kits, post anesthesia care unit (PACU) beds, and surgical teams (surgeons, nurses and anesthetists). METHODS: Using hybrid flow shop (HFS) techniques and the break-in-moment (BIM) concept, the goal is to find a sequence that maximizes the number of procedures assigned to the ORs while minimizing the variance of intervals between surgeries' completions, smoothing the demand for downstream resources such as PACU beds and OR sanitizing teams. There are five steps to the proposed heuristic: listing of priorities, local scheduling, global scheduling, feasibility check and identification of best scheduling. RESULTS: Our propositions were validated in a high complexity tertiary University hospital in two ways: first, applying the heuristic to historical data from five typical ST days and comparing the performance of our proposed sequences to the ones actually implemented; second, pilot testing the heuristic during ten days in the ORs, allowing a full rotation of surgical specialties. Results displayed an average increase of 37.2% in OR occupancy, allowing an average increase of 4.5 in the number of surgeries performed daily, and reducing the variance of intervals between surgeries' completions by 55.5%. A more uniform distribution of patients' arrivals at the PACU was also observed. CONCLUSIONS: Our proposed heuristic is particularly useful to plan the operation of STs in which resources are constrained, a situation that is common in hospital from developing countries. Our propositions were validated through a pilot implementation in a large hospital, contributing to the scarce literature on actual OR scheduling implementation.

Wavenumber selection based on Singular Value Decomposition for sample classification.

The dissemination of falsified medicines is a public health risk. Techniques such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy are commonly adopted for fraudulent drug detection. However, the spectrum generated by the ATR-FTIR typically results in hundreds of wavenumbers, reducing the performance of classification methods aimed at discriminating between authentic and falsified medicines. This article proposes a novel method for selecting a reduced size subset of wavenumbers that improves the classifier performance. The singular value decomposition SVD is used to generate a wavenumber importance index. An iterative process creates k-nearest neighbor (KNN) models by adding the wavenumbers in a decreasing order according to the importance index. Wavenumbers that increase classification accuracy are selected. When applied to Cialis® ATR-FTIR data, the proposed approach retained average 0.51% of the original wavenumbers with 100% accurate classifications; as for the Viagra® data set, the method yielded perfect classifications retaining average 0.17% of the original wavenumbers.

Decision Support for Breast Cancer Detection: Classification Improvement Through Feature Selection.

Several statistical-based approaches have been developed to support medical personnel in early breast cancer detection. This article presents a method for feature selection aimed at classifying cases into categories based on patients' breast tissue measures and protein microarray. The effectiveness of this feature selection strategy was evaluated against the commonly used Wisconsin Breast Cancer Database-WBCD (with several patients and fewer features) and a new protein microarray data set (with several features and fewer patients). Features were ranked according to a feature importance index that combines parameters emerging from the unsupervised method of principal component analysis and the supervised method of Bhattacharyya distance. Observations of a training set were iteratively categorized into malignant and benign cases through 3 classification techniques: k-Nearest Neighbor, linear discriminant analysis, and probabilistic neural network. After each classification, the feature with the smallest importance index was removed, and a new categorization was carried out until there was only one feature left. The subset yielding maximum accuracy was used to classify observations in the testing set. Our method yielded average 99.17% accurate classifications in the testing set while retaining average 4.61 out of 9 features in the WBCD, which is comparable to the best results reported by the literature on that data set, with the advantage of relying on simple and widely available multivariate techniques. When applied to the microarray data, the method yielded average accuracy of 98.30% while retaining average 2.17% of the original features. Our results can aid health-care professionals during early diagnosis of breast cancer.

Enhancing counterfeit and illicit medicines grouping via feature selection and X-ray fluorescence spectrometry.

In this paper, we propose a novel framework to select the most relevant X-Ray Fluorescence (XRF) energy values (i.e., features) to enhance the clustering (grouping) of counterfeit and illicit medical tablets. The framework is based on the integration of multidimensional scaling (MDS) and Procrustes analysis (PA) multivariate techniques. MDS provides a projection of the original data into a lower dimension, while PA finds a projection matrix from the original data. Such outputs give rise to a feature importance index that guides an iterative feature selection process; after each feature is inserted in the subset, an optimization procedure based on a greedy search method is carried out to maximize the clustering quality assessed through the Silhouette Index (SI). The inorganic chemical fingerprinting of 41 commercial samples (Viagra®, Cialis®, Lazar®, Libiden®, Maxfil®, Plenovit®, Potent 75®, Rigix®, V-50®, Vimax® and Pramil®) and 56 seized counterfeit samples (Viagra and Cialis) was used to validate the proposed framework. From the original 2048 data points in the full spectra, we identified a subset comprised of 41 energy values that substantially improved clustering quality; the obtained groups were assessed by visual inspection of the PCA plots.

Simultaneous identification of low-molecular weight phenolic and nitrogen compounds in craft beers by HPLC-ESI-MS/MS.

Phenolic and nitrogenous compounds from different styles craft beers were identified by high performance liquid chromatography and mass spectrometry in order to stratify beer samples according to their style. For this, an exploratory assessment relying on Linear Discriminant Analysis was performed. Fifty-seven phenolic compounds were reported and twelve of them were found for the first time in beer: benzoic acids, 2,4-dihydroxybenzoic acid, 2,3-dihydroxybenzoic acid, dimethoxybenzoic acid; phenolic acid conjugates, 3-p-coumaroylquinic acid, 4-p-coumaroylquinic acid, 3-feruloylquinic acid, 4-feruloylquinic acid, 5-feruloylquinic acid; flavonoids, taxifolin hexoside, quercetin dihexoside, apigenin-6,8-dipentoside, and isofraxidin hexoside. Additionally, 11 nitrogenous compounds belonging to the phenolamide class were found. Two discriminant functions were generated and allowed a satisfactory separation among all beer styles. 3-Caffeoylquinic acid, 3-p-coumaroylquinic acid, 4-p-coumaroylquinic acid, 5-caffeoylquinic acid, coumaric acid, kaempferol-3-O-rutinoside, proanthocyanidin B dimer III and proanthocyanidin B dimer V were the compounds that showed the highest capacity of discriminate the beer styles (IPA, Lager and Weiss).

Comparison between counterfeit and authentic medicines: A novel approach using differential scanning calorimetry and hierarchical cluster analysis.

Erectile dysfunction medicines such as Cialis and Viagra are very popular worldwide and are between the most prevalent counterfeit medicines in Brazil. A range of analytical methods has been used to analyze Cialis and Viagra, such as ATR-FTIR, GCMS and UPLC-MS. Until now, there are no data available of DSC methods for analysis of counterfeit medicines of Cialis and Viagra. DSC is a thermal analysis that provides useful information of physico-chemical events, and however is almost not used for forensic purposes. In this study, thermal analysis of 25 counterfeit Viagra and Cialis seized by Brazilian Federal Police were performed by DSC and compared to their authentic medicines and analytical standards, along with chemometric tools. Authentic samples of Viagra displayed a similar thermal profile with the API, while Cialis were different with additional endothermic peaks, that could be related to excipients interference. Thermograms of Viagra counterfeit samples were similar to authentic samples, while Cialis showed an enlargement and displacement of endothermic peaks. Also, some Cialis counterfeit samples showed melting peaks attributed to sildenafil, the API of Viagra, instead tadalafil, confirming previous results obtained by UPLC-MS. Multivariate analysis with application of Hierarchical Cluster Analysis classified different groups of samples, including a cluster with counterfeit Cialis and Viagra, indicating the use of same API for both counterfeit medicines and possibly the same illicit production; and a cluster with authentic Viagra and counterfeit Cialis, confirming the addition of sildenafil instead tadalafil to Cialis counterfeit samples. Here for the first time we described the use of DSC for chemical profiling of Cialis and Viagra and showed that even when applied to a small group of samples, DSC along with chemometric tools can be considered as a good auxiliary method in forensic casework samples. DSC provided useful data to perform the identification of counterfeit and authentic medicines, with low cost and a simple method.

Lean-Oriented Layout Design of a Health Care Facility.

BACKGROUND: In this article, we propose a method that integrates systematic layout planning techniques to lean health care practices aided by multicriteria decision analysis that could be applied to reformulate the layout of health care facilities. METHODS: We analyze a high-variety sterilization unit of a large public hospital located in Brazil. The unit is currently implementing lean practices, and layout changes are required to provide more efficient materials and information flows. RESULTS: Traditional design of health care facilities is not aligned with lean implementation and its underlying practices and principles. We propose the integration of such approaches to enhance their benefits. To rank and select the best layout alternative, a multicriteria decision analysis method (analytic hierarchy process) is adopted. CONCLUSIONS: There are 3 contributions here: the integration of lean principles into traditional health care facility design practices, the use of multicriteria decision analysis to refine the determination of the best layout solution, and the application of our propositions in a real case study.

A Six Sigma Approach to Analyze Time-to-Assembly Variance of Surgical Trays in a Sterile Services Department.

INTRODUCTION: We analyze the assembly of surgical trays in a hospital's sterile services department. The department assembles 520 different tray setups. However, tray assembly times are unknown, imposing a challenge to production planners. To respond to demand, workers from other departments are often called, leading to higher operational costs and more frequent quality problems due to workers' poor training and inconsistency. METHODS: Conducting traditional time-motion studies is infeasible in such a high variety production setting. Thus, we used design of experiments to optimize the data acquisition. Assembly times of 36 trays were sampled using a 2-factor nested factorial design. Through regression analysis, we built a model to estimate completion times of trays not sampled in the experiment. RESULTS: A prediction model with 90.8% accuracy was obtained from the experimental data. The model was validated with assembly times from several trays not included in the experiment. Predicted assembly times had an absolute error of 7.83% on average compared with observed assembly times. CONCLUSIONS: Design of experiments and regression analysis combined were able to optimize time data acquisition using a small sample of trays, resulting in a model that predicted assembly times within an acceptable margin of error.

A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening.

Colorectal cancer (CRC) a leading cause of death by cancer, and screening programs for its early identification are at the heart of the increasing survival rates. To motivate population participation, non-invasive, accurate, scalable and cost-effective diagnosis methods are required. Blood fluorescence spectroscopy provides rich information that can be used for cancer identification. The main challenges in analyzing blood fluorescence data for CRC classification are related to its high dimensionality and inherent variability, especially when analyzing a small number of samples. In this paper, we present a hierarchical classification method based on plasma fluorescence to identify not only CRC, but also adenomas and other non-malignant colorectal findings that may require further medical investigation. A feature selection algorithm is proposed to deal with the high dimensionality and select discriminant fluorescence wavelengths. These are used to train a binary support vector machine (SVM) in the first level to identify the CRC samples. The remaining samples are then presented to a one-class SVM trained on healthy subjects to detect deviant samples, and thus non-malignant findings. This hierarchical design, together with the one class-SVM, aims to reduce the effects of small samples and high variability. Using a dataset analyzed in previous studies comprised of 12,341 wavelengths, we achieved much superior results. Sensitivity and specificity are 0.87 and 0.95 for CRC detection, and 0.60 and 0.79 for non-malignant findings, respectively. Compared to related work, the proposed method presented a better accuracy, required fewer features, and provides a unified approach that expands CRC detection to non-malignant findings.

Text mining approach to predict hospital admissions using early medical records from the emergency department.

OBJECTIVE: Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. DESIGN: We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). MEASUREMENTS: Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. RESULTS: Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. CONCLUSIONS: The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams.

A genetic algorithm-based framework for wavelength selection on sample categorization.

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd.

Selecting relevant Fourier transform infrared spectroscopy wavenumbers for clustering authentic and counterfeit drug samples.

This paper proposes a novel method for selecting subsets of wavenumbers provided by attenuated total reflectance by Fourier transform infrared (ATR-FTIR) spectroscopy able to improve the clustering of medicine samples into two groups; i.e., authentic or fraudulent. For that matter, we apply principal components analysis (PCA) to ATR-FTIR data, and derive two variable importance indices from the PCA parameters. Next, an iterative variable (i.e. wavenumbers) elimination procedure and sample clustering through k-means and Fuzzy C-means techniques are carried out; clustering performance is assessed by the Silhouette Index (SI). The performance of the proposed method is compared with a greedy variable selection method, the "leave one variable out at a time" approach, in terms of clustering quality, percent of retained variables, and computational time. When applied to Viagra ATR-FTIR data, our propositions increased the average SI from 0.5307 to 0.8603 using 0.61% of the original 661 wavenumbers; as for Cialis ATR-FTIR data, clustering quality increased from 0.7548 to 0.8681 when 1.21% of the original wavenumbers were retained in the procedure. The retained wavenumbers, located in the 1091-1046cm(-1) region, comprise the lactose typically hailed as key substance to discriminate between authentic and counterfeit samples.

PLS-DA wavenumber selection for the categorization of medicine samples based on multiple criteria.

ATR-FTIR spectra may include a large number of noisy and correlated wavenumbers that tend to affect and reduce the performance of exploratory and classificatory multivariate techniques. We propose a method based on Partial Least Square Discriminant Analysis (PLS-DA) for identifying the relevant subsets of ATR-FTIR wavenumbers aimed at classifying Viagra and Cialis into authentic or fraudulent categories. In our propositions, the PLS-DA is applied to ATR-FTIR data, and four indices aimed at evaluating wavenumber importance are derived from PLS-DA parameters. Next, an iterative wavenumber elimination and classification procedure integrating PLS-DA and the proposed indices is carried out: the wavenumber with the smallest index is removed, and a new classification is performed using the remaining wavenumbers. The classification performance is assessed through multiple criteria, i.e., sensitivity, specificity and percent of wavenumbers retained; the recommended wavenumber subset is chosen based on the distance between the candidate subsets and a hypothetical ideal solution. The proposed method significantly reduced the percent of wavenumbers to be assessed, and slightly improved classification performance for Viagra and Cialis data when compared to classification on all the original wavenumbers.

A framework for selecting analytical techniques in profiling authentic and counterfeit Viagra and Cialis.

Several analytical techniques aimed at profiling drugs are deemed costly and time consuming, and may not be promptly available for analysis when required. This paper proposes a method for identifying the analytical techniques providing the most relevant data for classification of drug samples into authentic and unauthentic categories. For that matter, we integrate principal components analysis (PCA) to k-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification tools. PCA is first applied to data from five techniques, i.e., physical profile, X-ray fluorescence (XRF), direct infusion electrospray ionization mass spectrometry (ESI-MS), active pharmacological ingredients profile (ultra performance liquid chromatography, UPLC-MS), and infrared spectroscopic profile (ATR-FTIR). Subsets of PCA scores are then combined with a "leave one subset out at a time" approach, and the classification accuracy using KNN and SVM evaluated after each subset is omitted. Subsets yielding the maximum accuracy indicate the techniques to be prioritized in profiling applications. When applied to data from Viagra and Cialis, the proposed method recommended using the data from UPLC-MS, physical profile and ATR-FTIR techniques, which increased the categorization accuracy. In addition, the SVM classification tool is suggested as more accurate when compared to the KNN.

A multivariate-based wavenumber selection method for classifying medicines into authentic or counterfeit classes.

Attenuated total reflectance (ATR), a sampling technique by Fourier transform infrared (FTIR) spectroscopy, has been adopted as an analytical tool for detecting fraudulent medicines. The spectrum generated by FTIR-ATR typically relies on hundreds of equally spaced wavenumbers which may reduce the performance of techniques tailored to classify samples into classes, i.e., authentic or fraudulent. This paper proposes a novel method for selecting subsets of wavenumbers (variables) that better classify samples into such classes. For that matter, principal components analysis (PCA) is integrated to the k-nearest neighbor (KNN) classification technique. PCA is applied to FTIR-ATR data, and a variable importance index is built on the PCA outputs. An iterative backward variable elimination is started guided by that index; after each variable removal, samples are categorized into authentic or fraudulent classes using KNN, and the classification accuracy is measured. The wavenumber subset compromising high accuracy and reduced percent of retained variables is chosen. When applied to Cialis FTIR-ATR data, the proposed approach retained only average 1.84% of the original variables and increased the classification accuracy average 2.1%, to 0.9897 from 0.9689; as for Viagra data, the method increased average classification accuracy 1.56%, from 0.9135 to 0.9278, using only 7.72% of the original variables.

Counterfeit Cialis and Viagra fingerprinting by ATR-FTIR spectroscopy with chemometry: can the same pharmaceutical powder mixture be used to falsify two medicines?

This paper proposes a direct and efficient method to discriminate between counterfeit and authentic Cialis and Viagra samples by combining attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with multivariate techniques. The chemical profile of 53 commercial samples (Viagra(®), Cialis(®)) and 104 counterfeit samples (Viagra and Cialis) from distinct seizures were obtained from ATR-FTIR data derived from 10mg of crushed core tablets. Principal component analysis (PCA) technique was employed to classify samples based on the fingerprint region mid-infrared spectra (1800-525 cm(-1)) using OMNIC v.7.2 software; PCA enabled categorizing samples in groups with different chemical profiles, successfully distinguishing between authentic and counterfeits samples in forensic routine. The existence of active pharmaceutical ingredients (API) and technological adjuvant others than specified on the medicine package were also detected in counterfeit samples. In addition, we applied the similarity match (SM) method to demonstrate that a mixture of pharmaceutical powders deriving from a common origin may have been used to manufacture both counterfeit Cialis and Viagra tablets from distinct seizures.