Percentage Of Classification Research Articles

Genç Çiftçi Projesi Desteğinden Yararlanma Durumunu Etkileyen Faktörlerin Belirlenmesi: Akdeniz Bölgesi Örneği

This study aims to determine the characteristics of young farmers and their businesses that benefit from and cannot benefit from young farmer support in the Mediterranean Region and determine the factors that affect the benefit of young farmer project support. In 2016, a survey was conducted with all 160 producers who benefited from young farmer support, and a survey was conducted with 56 producers who applied for young farmer project support but could not benefit from it to make comparisons between groups. The tendency of farmers to benefit from the young farmer support project was determined using artificial neural networks and logistic regression analysis. It was determined that the majority of the producers who received support only made animal production and mixed production (livetock production and vegetable production), while the majority of the producers who did not receive support made only plant production. With both analysis methods, it was determined that the most critical variables that affect the benefit of young farmer project support are the type of activity, the share of non-agricultural income in total income, the number of farmers in the family, the education period, the status of having non-agricultural income and family size. The total correct classification rate was found to be 87.04% in the logistic regression analysis and 91.20% in the artificial neural network analysis, and it was seen that the classification percentages obtained by both methods were quite close to each other.

A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification

The ubiquitous usage of feature selection in search space optimization, information retrieval, data mining, signal processing, software fault prediction, and bioinformatics is paramount to expert and intelligent systems. Most of the conventional feature selection methods implemented are based on filter and wrapper approaches that suffer from poor classification accuracy, high computational cost, and selection of irrelevant and redundant features. This is due to the limitations of the employed objective functions leading to overestimation of the feature significance. On the contrary, hybrid feature selection methods formulated from information theory and nature-inspired metaheuristic algorithms are preferred because of their high computational efficiency, scalability in avoiding redundant and less informative features, and independence from the classifier. However, these methods have three common drawbacks: (1) poor trade-off between exploration and exploitation phase, (2) getting stuck into an optimal local solution, and (3) avoiding irrelevancy and redundancy of selected features. The first and the second drawback is related to metaheuristic algorithm implementation, while the third is concerned with applied information-theoretic paradigms. To address the aforementioned problems, we developed a new hybrid feature selection method, namely, the Iterative Feature Selection using Dynamic Butterfly Optimization Algorithm based Interaction Maximization (IFS-DBOIM) that combines Dynamic Butterfly Optimization Algorithm (DBOA) with a mutual information-based Feature Interaction Maximization (FIM) scheme for selecting the optimal feature subset. There is evidence that DBOA performs better in exploration, exploitation, and avoidance of local optima entrapment, and FIM comparatively scores the maximum relevancy with minimum redundancy of the new features with previously selected ones. The performance of the proposed method is compared using twenty publicly available datasets with ten baseline feature selection approaches. The results revealed that IFS-DBOIM outperforms other approaches on most datasets, maximizing the percent classification accuracy with the least number of features. The nonparametric Wilcoxon rank test confirms the statistical significance of these outcomes. Moreover, this method realizes the best trade-off between accuracy and stability.

Geometric morphometric discrimination between seven populations of Kawakawa Euthynnus affinis (Cantor, 1849) from Peninsular Malaysia

Small epipelagic and migratory, Euthynnus affinis (kawakawa) is one of the commercially significant tunas of Indo-Pacific tropical and subtropical waters. Unfortunately, the management and stock structure of certain migratory species in the area is not clear. The current study aimed to discriminate the E. affinis through shape and body size variations and to evaluate the variations among seven populations of E. affinis. A total of 114 individuals of E. affinis were collected from two main geographic areas; the Straits of Malacca and the South China Sea. Multivariate analyses, such as discriminant function analyses (DFA) and principal component analyses (PCA) of 11 homologous landmarks and seven morphometric variables were carried out to discriminate seven populations of E. affinis. The results revealed a significant variations among the body shape of the seven populations of E. affinis. Morphological homogenous occurred between populations obtained from the east coast of Peninsular Malaysia (Terengganu, Kelantan, and Johor). However, populations found on the west coast of Peninsular Malaysia (Selangor) were separated and formed another cluster. The variations in the body shape of E. affinis occurred in the body depth and the overall body shape. The percentage of overall correct classification for all seven populations of E. affinis is 88.6%. This present study is the first report using a geometric morphometric method performed on the E. affinis from Peninsular Malaysian waters.

Face recognition based on scale invariant feature transform and fuzzy reasoning

To alleviate the limitations that existing face recognition methods, this paper describes a new face recognition method based on scale invariant feature transform (SIFT) and fuzzy reasoning. Experimental results demonstrate that the proposed method yields a better performance in terms of the correct classification percentages compared with the recent face recognition algorithms.

Extrapolation of Loan Default using Predictive Analytics: A Case of Business Analysis

The research assesses the validity of a customer's appropriateness for a loan using a machine learning approach called predictive modeling. Banks and Non-Banking Financial Companies (NBFCs) are at danger of significant Non-Performing Assets (NPAs) due to customer non-payment of loans (Non-Performing Assets). The data for this study came from Kaggle, and eight different prediction models were employed to determine if the borrower would be able to repay the loan. Adaboost, κ-Nearest Neighbors (k-NN), Logistic Regression, Support Vector Machines (SVM), Decision Tree, Naive Bayes, Neural Networks, and Random Forest (RF) are the eight models, respectively. The purpose is to back up decisions made on the basis of factual evidence rather than subjective reasons. Classification Accuracy, Precision, Recall, and F-1 scores are the four performance parameters used to determine the results. With 70% and 30% respectively, the dataset is separated into train and test datasets. The whole analysis is done in two phases, with the first being a full model that is trained on 70% of the train data and the second being observed on 30% of the test data. The purpose of this study is to see how objective characteristics influence borrowers to default on loans, to identify the most common reasons for default, and to predict which customers would default. There are two evaluations we did for the research, wherein, first we took overall train set and make predictions using predictive modeling. The Adaboost predictive model delivers the greatest results, with a recall rate of 0.384, classification accuracy of 59.2 percent, true-positive rate of 69.74 percent. Second, we performed feature selection and discovered that Credit History with 31 percent had the utmost impact on loan default detection. By partitioning the dataset into Credit_History 1 and 0, we discovered that Credit History 1 produces superior results, with a rate of 0.444, 60.5 percent classification accuracy, and a true-positive rate of 68.7%.

Sex estimation using humeral and femoral head diameters in contemporary and prehispanic mexican populations

In bioarchaeology and forensic anthropology the most reliable skeletal element for sex estimation is the pelvis; nevertheless, when it is missing, other postcranial elements must be used. The main goal of this research is to provide sectioning points for sex assessment from humeral and femoral head diameters for three prehispanic and two contemporary Mexican populations. Using a sliding caliper, a total of 386 (45.3% female and 54.6% male) humeral and femoral head diameters were recorded. The sectioning point was calculated as the mean between sexes, and univariate independent sample t-tests were performed to test significant differences between sexes. The results demonstrate significant sexual differences in all populations and high percentages of correct sex classification (90%-94%). We conclude that the proposed cut-off points can be used as an alternative for sex estimation in Mexican populations, in contexts with incomplete skeletons and/or fragmented bones.

Sensitivity Analysis by Variables in the Classification of Renal Insufficiency Using Artificial Neural Networks VS Multinomial Logistic Regression

Purpose: The aim of the research is to see to what extent Neural Networks (non-parametric technique) could be used as opposed to Multinomial Logistic Regression (parametric technique) in the analysis of sensitivity by variable in the classification of "Renal insufficiency" (three categories: NOT, MODERATE and ADVANCED). The analysis of sensitivity by variable, in our case, consists of eliminating from the model the three most influential variables (one by one): Blood creatine, Urine creatine and Urea, in that order. Once a variable is removed from the model, it will not reenter the model. Methods: This study collects data from the University Hospital of Salamanca (Spain), configuring a file of renal insufficiency data with 184 cases and 9 variables. First, we do descriptive-exploratory analysis of data for renal insufficiency data, obtained experimentally through a pilot survey. The comparison between ANNs and MLR is carried out by classification in the categories NOT, MODERATE and ADVANCED. Results: The descriptive-exploratory analysis of data shows the high value of the Coefficient of variation, Kurtosis and Skewness of the variables Blood creatine, Urine creatine and Urea, all of them well above 66%, 10 and 2.70, respectively. The study shows that when all the variables in the model are considered, the highest classification percentage concerns the Multinomial Logistic Regression (MLR), while, for the analysis of sensitivity by variables, the classification percentages are favourable to the Artificial Neural Network model (ANN). Conclusions: The joint classification percentages in the analysis of sensitivity by variables are favourable to the artificial neural network model (perceptron). That is, the non-parametric technique (ANNs) would surpass the parametric technique (MLR) in the classification of a patient's renal insufficiency.

EVALUATION OF MACHINE LEARNING HYPERPARAMETERS PERFORMANCE FOR MICE PROTEIN EXPRESSION DATA IN DIFFERENT SITUATIONS

In this study, the aim was to assess the effect and significance of hyperparameters in four different datasets containing different values for observation numbers and variable counts with the machine-learning methods of support vector machines and artificial neural networks. With this aim, a dataset comprising 15 repeats of 77 protein levels from 38 healthy and 34 down syndrome mice was used. A total of 138 different models and model classification performance criteria were obtained from the datasets in the study comprising combinations of hyperparameters in machine-learning methods. Comparison of the models used criteria like accurate classification percentage, kappa statistic, mean absolute error and square root of mean error squares. According to performance criteria, the first dataset with 1080 observations x 77 variables had 71.30% accurate classification percentage for assumed parameters with the support vector machines polynomial kernel function, while changing the hyperparameter variables increased this rate to 99.44%. Similarly, the second dataset had 50.65% accurate classification percentage with the artificial neural network single hidden layer 2 neuron model, while changing the hyperparameter values increased this rate to 90.46%. In conclusion, in situations with low variable and observation numbers, the machine learning methods were determined to display lower performance. However, in datasets, it is very important for classification performance in artificial neural networks and support vector machines, especially polynomial and radial basis function kernel functions, to set hyperparameters according to the dataset. In situations with low variable numbers, especially, the effect of hyperparameters was determined to gain importance.

Authentication of Transylvanian Spirits Based on Isotope and Elemental Signatures in Conjunction with Statistical Methods.

The potential association between stable isotope ratios of light elements and mineral content, in conjunction with unsupervised and supervised statistical methods, for differentiation of spirits, with respect to some previously defined criteria, is reviewed in this work. Thus, based on linear discriminant analysis (LDA), it was possible to differentiate the geographical origin of distillates in a percentage of 96.2% for the initial validation, and the cross-validation step of the method returned 84.6% of correctly classified samples. An excellent separation was also obtained for the differentiation of spirits producers, 100% in initial classification, and 95.7% in cross-validation, respectively. For the varietal recognition, the best differentiation was achieved for apricot and pear distillates, a 100% discrimination being obtained in both classifications (initial and cross-validation). Good classification percentages were also obtained for plum and apple distillates, where models with 88.2% and 82.4% in initial and cross-validation, respectively, were achieved for plum differentiation. A similar value in the cross-validation procedure was reached for the apple spirits. The lowest classification percent was obtained for quince distillates (76.5% in initial classification followed by 70.4% in cross-validation). Our results have high practical importance, especially for trademark recognition, taking into account that fruit distillates are high-value commodities; therefore, the temptation of “fraud”, i.e., by passing regular distillates as branded ones, could occur.

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.

Correlation between quality of life and sleep quality of military firefighters

Introduction: Among the professions that are impacted by the work context, the military firefighter is mentioned, who carries out activities with danger, physical and emotional exhaustion, which can have an impact on quality of life. Objective: To analyze the relation between quality of sleep and quality of life of Brazilian Military Firefighters. Methods: Research with a quantitative, transversal, correlational and analytical approach, carried out with 129 military firefighters working in Rio Grande do Sul, Brazil, that had been performing operational and administrative assistance for more than six months. Questionnaires containing socio-occupational and lifestyle data, the Pittsburgh Sleep Quality Index and the World Health Organization Quality of Life to assess quality of life were used. Descriptive and analytical statistics were used. Results: The analysis of quality of life shows a higher average in the physical domain (77.52), a direct correlation between the physical domain and the other quality of life domains, and inverse correlations between global sleep quality and the domains of quality of life. Regarding sleep quality, a higher percentage of poor classification (71.3%) is observed in military firefighters. Conclusion: There was a predominance of workers with poor sleep quality and good perception of general quality of life. An inverse correlation was identified between sleep quality and the domains of quality of life. When comparing quality of life with sleep quality domains, a significant difference was identified.

Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals.

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.

A Novel Approach for Outlier Detection in Multivariate Data

Outlier detection is a challenging task especially when outliers are defined by rare combinations of multiple variables. In this paper, we develop and evaluate a new method for the detection of outliers in multivariate data that relies on Principal Components Analysis (PCA) and three-sigma limits. The proposed approach employs PCA to effectively perform dimension reduction by regenerating variables, i.e., fitted points from the original observations. The observations lying outside the three-sigma limits are identified as the outliers. This proposed method has been successfully employed to two real life and several artificially generated datasets. The performance of the proposed method is compared with some of the existing methods using different performance evaluation criteria including the percentage of correct classification, precision, recall, and F-measure. The supremacy of the proposed method is confirmed by abovementioned criteria and datasets. The F-measure for the first real life dataset is the highest, i.e., 0.6667 for the proposed method and 0.3333 and 0.4000 for the two existing approaches. Similarly, for the second real dataset, this measure is 0.8000 for the proposed approach and 0.5263 and 0.6315 for the two existing approaches. It is also observed by the simulation experiments that the performance of the proposed approach got better with increasing sample size.

Road classification using built-in self-scaling method of Bayesian regression

This paper proposes the use of the built-in self-scaling (BS) method for ISO classification of road roughness. The technique employs the transfer function between the vehicle body acceleration as input and the suspension travel as output. This transfer function has a nonzero dc gain, which is important for application of the BS method. Frequency response magnitude patterns corresponding to this transfer function are estimated via Bayesian regression, capitalizing on the inherent properties of the BS method where the prior dc gain is incorporated into the formulation. This strategy leads to high classification accuracy. The proposed approach requires only low-cost sensors. It possesses a short detection time of 0.5s and a short training time of 5s for each road class. The method is model-free and does not require recalibration when the load carried by the vehicle changes. Additionally, it is capable of handling varying vehicle velocity and is effective for both passive and active suspensions. A laboratory-scale experiment shows that the proposed technique increases the percentage of correct classification by an average of 34% in the case of constant road profiles, compared with a state-of-the-art method using augmented Kalman filtering. A corresponding value of 24% is achieved for a varying road profile. The significant improvement in the accuracy of road classification is impactful as it will enable controller design for suspension systems to be enhanced resulting in more comfortable ride and higher vehicle stability.

Evaluation of Convolutional Neural Network Architecture for Feasibility Analysis on Fetal Abdomen and Brain Images

This research aims to evaluate the possibilities of fetus ultrasound image classification using machine learning algorithms as normal or abnormal. Most of the earlier research works have produced a high percentage of false-negative classification results—recent research work aimed to reduce the rate of false-negative diagnoses. Also, the number of sonologists for analyzing prenatal ultrasound worldwide is very less and solved by developing an efficient algorithm, which reduces the percentage of false negatives in the diagnosis output. Several earlier research works focused on analyzing fetal abdominal image or fetal head images, making the medical industry use two different diagnostic modules separately. This work aims to design and implement a convolution frame-work named as two Convolution Neural Network (tCNN) model for diagnosing any fetal images. The proposed tCNN model diagnoses the fetal abdominal and fetal brain images and classify them as normal or abnormal. CNN1of tCNN performs segmentation and classification based on the acceptance of abdomen circumference and stomach bubble, umbilical vein, and amniotic fluid measurements. CNN2shows based on head circumference and head and abdominal circumference, femur, crown-rump, and humerus lengths measured.With clinical validation, an extensive experiment carried out and the results compared with the experts in terms of segmentation accuracy and the obstetric measurements. This paper provides a foundation for future multi-classification research works on diagnosing fetal intracranial abnormalities and differential diagnosis using machine learning algorithms.

Correct Classification Level of Young Adults Who Abuse and Do Not Abuse Their Partner in The Romantic Relationship of Forgiveness

In this study, the correct classification level of whether forgiving oneself, others and the situation is abusing their partners was determined by logistic regression analysis. There are 221 young adults ranging from 19-30 in this study, which was designed in the scanning model. Heartland Forgiveness Scale and Information Form were used in the study. In the initial model of the analysis, all participants were classified in the group that exploited their partner, with a classification percentage of 62.9%. The biggest contribution to the initial model comes from the variable of forgiving others, respectively, the variables of self-forgiveness and forgiveness. Cox & Snell RSquare value for the final model was calculated as .10. This finding shows that when the predictor variables are included in the model, 10% of the predicted variable is explained. Accordingly, the model has a good fit. Of the 82 individuals who did not abuse the result model, 34 were classified correctly and 48 were incorrect, with a percentage of correct classification of 41.5%. Of 139 individuals abused, 120 were correct and 19 were incorrectly classified, with an accurate classification percentage of 86.3%. One-unit increase in the self-forgiveness variable is 7.90% in the odds of abuse [(1-.921).100]; One-unit increase in forgiveness to others causes an 8.10% [(1-.919).100] increase in exploit odds. Findings show that the variables of self-forgiveness and forgiveness for others make significant contributions to classifying individuals who abuse and do not. The variables of forgiving yourself and others increase the predictive power of the model created.

Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank. 
 Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value >0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively. 
 Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue >0.3Absolute<0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue. 
 Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

Resting echocardiographic predictors for true-severe aortic stenosis in patients with low-gradient severe aortic stenosis: A dobutamine stress echocardiography study.

Dobutamine stress echocardiography (DSE) is not always feasible in patients with low-gradient severe aortic stenosis (LG-SAS), and there are limited data available on the resting echocardiographic predictors for true-severe aortic stenosis (TSAS). This study investigated resting echocardiographic predictors for TSAS. Clinical data of 106 LG-SAS patients who underwent DSE were retrospectively analyzed. LG-SAS was defined as an aortic valve area index (AVAi)<.6cm2 /m2 , and a mean AV pressure gradient<40mm Hg. The velocity ratio (VR) was calculated as the peak left ventricular outflow tract velocity/peak AV velocity. TSAS was defined as a projected AVAi<.6cm2 /m2 . The mean age was 79.3 ± 7.3 years, and 45 (42.5%) were men. The resting AV data were as follows: AVAi, .50 ± .07 cm2 /m2 ; mean AV pressure gradient, 23.0 ± 7.4mm Hg; and VR, .25 ± .05. The projected AVAi was .58 ± .09 cm2 /m2 , and TSAS was documented in 65 (61.3%) patients. In multivariate analysis, the independent predictors of TSAS were AVAi (p=0.012) and VR (p=0.004) with respective best cut-off values of .52cm2 /m2 and .25 on receiver-operating characteristic curve analysis. According to incremental numbers of the predictors, correct classification percentages of TSAS significantly increased with the Cochran-Armitage trend test (16.2% in no predictors, 65.2% in one predictor, and 95.7 % in two predictors; p<0.001). Resting AVAi and VR were independent predictors of TSAS in LG-SAS patients. The true severity might be predictable using the combination of resting parameters.

Authentication of the Montanera Period on Carcasses of Iberian Pigs by Using Analytical Techniques and Chemometric Analyses

Simple SummaryFor the Iberian pork industry, the authentication of the raw material is of great importance. For this reason, the main object of this study was the classification of Iberian pig carcasses in accordance with the time the animals have spent in the montanera period and the breed. To do this, the potential of Near Infrared Spectroscopy and Gas Chromatography–Mass Spectrometry techniques was studied. Different sampling methods and mathematical treatments of the data were assayed. The results obtained show high percentages of correct classification both for days in the montanera and according to the breed for both techniques. The advantages for the industry are that none of the selected techniques require any chemical treatment of the sample before analysis and the proposed sampling is easy to use by the operator. It is also not necessary to identify individual compounds. This leads to the conclusion that both technologies are fast and useful for the authentication of raw material.The potential of two complementary analytical techniques (near infrared spectroscopy, NIRS and gas chromatography–ion mobility spectrometry, GC-IMS) was used to establish the time that Iberian pigs have been fed on acorns and pasture and to verify their genetic purity. For both techniques it was neither necessary to carry out any chemical treatment in advance nor to identify individual compounds. The results showed that both the NIR spectrum and the spectral fingerprint obtained by GC-IMS were affected by the time that the Iberian pig feeds on natural resources. High percentages of correct classification were achieved in the calibration for both techniques: >98% for the days of montanera and >96% for the breed by NIRS and >99% for the days of montanera and >98% for the breed by GC-IMS. The results obtained showed that NIR spectra taken from intact samples is a quick classification method according to the time of montanera and breed.

Factors Associated with Cancer-Related Pain Requiring High-Dose Opioid Use in Palliative Cancer Patients.

Background: There are no universal tools to predict the necessity of high-dose opioid use for cancer-related pain. Early recognition and interventions for intractable cancer pain could minimize the distress of palliative patients.Objective: We sought to identify the clinical factors associated with high-dose opioid use in advanced cancer patients to recognize palliative patients who would develop intractable cancer pain, as early as possible.Setting/Subjects: Among 385 in-hospital cancer patients from April 1, 2014 to July 31, 2019, who were referred to the palliative care team for cancer-related pain, clinical factors significantly correlated to high-dose opioid use were retrospectively analyzed.Measurements: We conducted a multiple logistic regression analysis to identify variables significantly related to high-dose opioid use (>120 mg/day oral morphine equivalent dose).Results: Independent factors of high-dose opioid use included younger age (odds ratio [OR] 0.965, 95% confidence interval [CI] 0.944–0.986, p = 0.001), respiratory cancers (OR 1.882, 95% CI 1.069–3.312, p < 0.001), and opioid switch (OR 2.869, 95% CI 1.497–5.497, p = 0.001). The percentage of correct classifications of the regression equation was 86.9%.Conclusions: Younger age, respiratory cancers, and opioid switch were related to high-dose opioid use. Our findings may help palliative caregivers to deal with intractable cancer pain in palliative patients, and thus relieve their distress.