Highest Correct Classification Rate Research Articles

Red Propolis: Chemical and Mid-Infrared Spectroscopic Characterization and Classification by Geographic Origin

In this paper, we report the characterization of Brazilian red propolis from two origins. The commercial value of this product is due to its composition and bioactivity, which depend on geographical factors. Total and individual phenolic, antioxidant, and medium infrared spectroscopic (MIR) characterization of 72 red propolis samples from two origins (Bahia (BA) and Rio Grande do Norte (RN), Brazil) were performed. Samples from RN showed higher concentrations of biomarkers. The BA samples showed higher antioxidant capacity despite having lower concentrations of the red propolis biomarker molecules when compared to the RN samples. The MIRS showed differences regarding the origin. The principal component analysis confirmed the differences, generating well-defined groups. Linear discriminant analysis or discriminant analysis and and partial least squares discriminant analysis showed excellent model performance. The models with MIRS data from raw propolis have a high correct classification rate and require no sample preparation. These findings can affect red propolis production, boosting sustainable extractivism, and changing the reality of small communities.

Quantification and Detection of Ground Garlic Adulteration Using Fourier-Transform Near-Infrared Reflectance Spectra.

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.

Canine dimensions for estimation of sex in adult and non-adult individuals with external validation by aDNA.

Using discriminant functions obtained from canine dimensions for sex estimation in human skeletons has frequently been proposed as a promising approach within assemblages, even when used in non-adult individuals. However, applicability of this method to adult and non-adult individuals from other assemblages was rarely investigated, probably due to frequently observed inter-population differences in tooth dimensions. In the present study, discriminant functions obtained for permanent canine dimensions at the cemento-enamel junction in a previous study of the early medieval assemblage from Greding, were applied to individuals from a late medieval Jewish cemetery at Erfurt, Germany. The results were validated by aDNA analyses. Prior to the application of the functions, canine dimensions of the assemblages were compared. The comparison showed largely corresponding canine dimensions between the two assemblages. The application of the formulae obtained on the early medieval assemblage to the late medieval assemblage at Erfurt revealed a 100 % correct classification rate in the adult individuals. In non-adults, the correct classification rate was poorer, with 7 of 9 (77.8 %) individuals correctly classified. The study showed that the application of discriminant functions for sex estimation from canine measurements to assemblages other than those for which the functions were developed can lead to high correct classification rates in adults if the average canine dimensions are similar in the respective assemblages. An application to non-adult individuals should only be made with caution as canine dimensions in the "non-survivors" can lead to an over-estimation of the proportion of female non-adults.

Association of general health and lifestyle factors with the salivary microbiota - Lessons learned from the ADDITION-PRO cohort.

Previous research indicates that the salivary microbiota may be a biomarker of oral as well as systemic disease. However, clarifying the potential bias from general health status and lifestyle-associated factors is a prerequisite of using the salivary microbiota for screening. ADDDITION-PRO is a nationwide Danish cohort, nested within the Danish arm of the Anglo-Danish-Dutch Study of Intensive treatment in People with Screen-Detected Diabetes in Primary Care. Saliva samples from n=746 individuals from the ADDITION-PRO cohort were characterized using 16s rRNA sequencing. Alpha- and beta diversity as well as relative abundance of genera was examined in relation to general health and lifestyle-associated variables. Permutational multivariate analysis of variance (PERMANOVA) was performed on individual variables and all variables together. Classification models were created using sparse partial-least squares discriminant analysis (sPLSDA) for variables that showed statistically significant differences based on PERMANOVA analysis (p < 0.05). Glycemic status, hemoglobin-A1c (HbA1c) level, sex, smoking and weekly alcohol intake were found to be significantly associated with salivary microbial composition (individual variables PERMANOVA, p < 0.05). Collectively, these variables were associated with approximately 5.8% of the observed differences in the composition of the salivary microbiota. Smoking status was associated with 3.3% of observed difference, and smoking could be detected with good accuracy based on salivary microbial composition (AUC 0.95, correct classification rate 79.6%). Glycemic status, HbA1c level, sex, smoking and weekly alcohol intake were significantly associated with the composition of the salivary microbiota. Despite smoking only being associated with 3.3% of the difference in overall salivary microbial composition, it was possible to create a model for detection of smoking status with a high correct classification rate. However, the lack of information on the oral health status of participants serves as a limitation in the present study. Further studies in other cohorts are needed to validate the external validity of these findings.

Chemometric Screening of Oregano Essential Oil Composition and Properties for the Identification of Specific Markers for Geographical Differentiation of Cultivated Greek Oregano

The present study investigated the potential interconnection between the place of cultivation of Greek oregano samples and the composition and properties of their essential oils (EOs). In addition, it attempted to identify characteristic chemical features that could differentiate between geographical origins with the use of chemometric tools. To this end, a total of 142 samples of commercially available Greek oregano (Origanum vulgare ssp. hirtum) plants harvested during the calendar years 2017–2018 were obtained for this study. The samples came from five different geographical areas of Greece and represented twelve localities. After appropriate processing, the oregano samples were subjected to hydrodistillation (HD), and the resulting EOs were analyzed for their total phenolic content (TPC), antioxidant activity, and chemical composition. The acquired data were subjected to the chemometric methods of multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) to investigate the potential of classifying the oregano samples in terms of geographical origin. In addition, stepwise LDA (SLDA) was used as a final step to narrow down the number of variables and identify those wielding the highest discriminatory power (marker compounds). Carvacrol was identified as the most abundant component in the majority of samples, with a content ranging from 28.74% to 68.79%, followed by thymol, with a content ranging from 7.39% to 35.22%. The TPC values, as well as the Trolox equivalent antioxidant capacity (TEAC) values, showed no significant variations among the samples, ranging from 74.49 ± 3.57 mg GAE/g EO to 89.03 ± 4.76 mg GAE/g EO, and from 306.83 ± 5.01 μmol TE/g EO to 461.32 ± 7.27 μmol TE/g EO, respectively. The application of the cross-validation method resulted in high correct classification rates in both geographical groups studied (93.3% and 82.7%, respectively), attesting to a strong correlation between location and oregano EO composition.

Non-targeted analysis of VOCs by HS-SPME-G C/MS coupled with chemometrics as a potential tool for authentication of White Kołuda oat goose

Abstract This study tested the possibility of using non-targeted analysis of volatile organic compounds by headspace solid-phase microextraction-gas chromatography-mass spectrometry coupled with chemometrics as a potential tool for differentiating leg meat of oat- and wheat-fed (ad libitum) White Kołuda geese. Thirty-six classification models were obtained for which the correct classification rate and classification accuracy for oatfed and wheat-fed geese were calculated based on a seven-fold cross-validation. Generally, the most advantageous method of the sample preparation was the high-temperature heat treatment version, whereas the highest correct classification rate was obtained when the chemometric analysis was carried out on the female, then male, and finally male + female variant of group comparisons (P&lt;0.01). Furthermore, log-transformation appeared to be a slightly better data preprocessing technique in comparison to systematic ratio normalization. The obtained classification models can potentially differentiate the meat of oat-fattened from wheat-fattened White Kołuda geese.

Future foods protection: Supervised chemometric approaches for the determination of adulterated insects’ flours for human consumption by means of ATR-FTIR spectroscopy

In recent years, the possibility of reintroducing edible insects in the human diet in the areas where this habit had fallen in disuse has been revaluated. The reasons lie in the noticeable nutritional properties of some edible insects (generally rich in protein, vitamins, mineral elements, polyphenols and fatty acids) and their sustainable breeding which requires a moderate amount of water, and no peculiar exigencies. Consequently, insect-based snacks, insect flours, and derived products are currently spreading on the market all over the world. Although “future foods” have been a hot and debated topic in the last years, research has not kept pace with the times, and, in the literature, only a few studies on the characterization and the authentication of edible insects (and their derived products) have been published. In the light of this, the present work aims at developing a non-destructive infrared spectroscopy (IR)-based tool for detecting adulterations in insects’ flours for human consumption. Mixtures of cricket and buffalo worm flours, prepared to mimic adulterated samples, together with pure flours of the same insects, have been analysed by FT-IR spectroscopy. Eventually, classification tools for discriminating between pure and adulterated samples, i.e., Sequential Preprocessing through ORThogonalization Discriminant Analysis (SPORT-DA) and Soft Independent Modelling of Class Analogy (SIMCA), have been employed. Both discriminant and class-modelling approaches demonstrated to be suitable for the purpose. The highest correct classification rate on cricket flour was achieved by means of SPORT-DA, which correctly predicted 100% of test samples; whereas, the most accurate predictions on the buffalo worm flour data set were obtained using SIMCA, which allowed the erroneous classification of only three (over thirty) test objects.

Development Model of College English Education Service Talents Based on Nonlinear Random Matrix

The development of artificial intelligence makes people’s life and work easier and more effective, and computer-based online exams and marking not only improve students’ learning efficiency but also reduce the pressure of teachers’ marking work. For objective questions, marking has gone from manual marking to cursor reader marking to computerized character matching, and the correct rate of marking has soared to 100%; for subjective questions, foreign systems such as PEG and E-rater have been used, and domestic systems such as those using English large corpus similarity matching and those based on natural language understanding using intelligent algorithms have been used for marking. Most of these systems are based on some shallow linguistic features such as rules and LSAs for marking, and there is no deep perception of English language sense. Although the current intelligent marking systems have made a lot of achievements, they do not fundamentally solve the problem of the rationality of intelligent marking of subjective questions. In this article, we propose a regularized discriminant analysis algorithm with good estimation of the mean, and a dimensionality reduction algorithm for high-dimensional missing data by using the relevant research results of random matrix theory to address the problems of traditional machine learning methods in high-dimensional data analysis. Although the linear discriminant analysis algorithm performs well in solving many practical problems, it works poorly in dealing with high-dimensional data. The specific analysis is as follows: in terms of age characteristics, the mobile population under the age of 35 has a significant preference for urban consumer comfort, and it increases with age, peaking at the stage of 30–35 years old and then decreasing rapidly. For this reason, a regularized discriminant analysis algorithm based on random matrix theory is proposed. First, a good estimate of the high-dimensional covariance matrix is made by the nonlinear shrinkage method or the eigenvalue interception method, respectively; then, the estimated high-dimensional covariance matrix is used to calculate the discriminant function values and perform the classification. The classification experiments conducted on simulated and real datasets show that the proposed algorithm is not only more widely applicable but also has a high correct classification rate.

3D reconstruction based novel methods are more effective than traditional clinical assessment in breast cancer axillary lymph node metastasis prediction

The status of axillary lymph node metastases determines the treatment and overall survival of breast cancer (BC) patients. Three-dimensional (3D) assessment methods have advantages for spatial localization and are more responsive to morphological changes in lymph nodes than two-dimensional (2D) assessment methods, and we speculate that methods developed using 3D reconstruction systems have high diagnostic efficacy. This exploratory study included 43 patients with histologically confirmed BC diagnosed at Second Xiangya Hospital of Central South University between July 2017 and August 2020, all of whom underwent preoperative CT scans. Patients were divided into a training cohort to train the model and a validation cohort to validate the model. A 3D axillary lymph node atlas was constructed on a 3D reconstruction system to create various methods of assessing lymph node metastases for a comparison of diagnostic efficacy. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic values of these methods. A total of 43 patients (mean [SD] age, 47 [10] years) met the eligibility criteria and completed 3D reconstruction. An axillary lymph node atlas was established, and a correlation between lymph node sphericity and lymph node metastasis was revealed. By continuously fitting the size and characteristics of axillary lymph nodes on the 3D reconstruction system, formulas and models were established to determine the presence or absence of lymph node metastasis, and the 3D method had better sensitivity for axillary lymph node assessment than the 2D method, with a statistically significant difference in the correct classification rate. The combined diagnostic method was superior to a single diagnostic method, with a 92.3% correct classification rate for the 3D method combined with ultrasound. In addition, in patients who received neoadjuvant chemotherapy (NAC), the correct classification rate of the 3D method (72.7%) was significantly higher than that of ultrasound (45.5%) and CT (54.5%). By establishing an axillary lymph node atlas, the sphericity formula and model developed with the 3D reconstruction system achieve a high correct classification rate when combined with ultrasound or CT and can also be applied to patients receiving NAC.

A Test of the (hu)MANid Program on a Diverse Sample of Mandibles

The (hu)MANid program is a free, web-based software using mandibular metric and morphoscopic variables to estimate sex and ancestry. The database consists of 1,745 individuals from 15 populations (modern, historic, and prehistoric). The required use of a mandibulometer presents two limitations. It is an expensive piece of equipment not available in all labs, and many users do not have experience using a mandibulometer, so error can be high. To address this, three-dimensional surface scans (3D scans) provide an alternative method for data collection. Here, the authors assess the accuracy of the (hu)MANid program on a diverse sample, compare accuracy from 3D scans and physical measurements, and evaluate whether mandibulometer measurements increase classification rates. Six metric and morphoscopic variables were collected from 3D scans (Ntotal = 555) and from bone (Ntotal = 41). Three additional mandibulometer measurements were collected from bone. Mixture discriminant analysis and non-stepwise options were applied due to reported higher correct classification and no significant difference when using stepwise options. Test samples with matching reference groups from the digital measurements had higher than chance average correct classifications for ancestry and sex/ancestry combined. Pooled sexes had correct classifications higher than chance for all comparisons except females using digital measurements. Physical measurements had a higher correct classification rate than digital measurements despite the small sample size. Including mandibulometer measurements did not significantly increase classification rates for physical measurements. Results indicate that hu(MAN)id is better than chance but suggest additional testing due to a lack of diversity in the physical measurements.

Gender-related differences in involvement of addiction brain networks in internet gaming disorder: Relationships with craving and emotional regulation

BackgroundAbnormal interactions among addiction brain networks associated with intoxication, negative affect, and anticipation may have relevance for internet gaming disorder (IGD). Despite prior studies having identified gender-related differences in the neural correlates of IGD, gender-related differences in the involvement of brain networks remain unclear. MethodsOne-hundred-and-nine individuals with IGD (54 males) and 111 with recreational game use (RGU; 58 males) provided resting-state fMRI data. We examined gender-related differences in involvement of addiction brain networks in IGD versus RGU subjects. We further compared the strength between and within addiction brain networks and explored possible relationships between the strength of functional connectivities within and between addiction brain networks and several relevant behavioral measures. ResultsThe addiction brain networks showed high correct classification rates in distinguishing IGD and RGU subjects in men and women. Male subjects with versus without IGD showed stronger functional connectivities between and within addiction brain networks. Moreover, the strength of the connectivity within the anticipation network in male IGD subjects was positively related to subjective craving. However, female subjects with versus without IGD showed decreased functional connections between and within addiction brain networks. The strength of connectivity between the anticipation and negative-affect brain networks in female IGD subjects was negatively related to maladaptive cognitive emotion-regulation strategies. ConclusionsAddiction brain networks have potential for distinguishing IGD and RGU individuals. Importantly, this study identified novel gender-related differences in brain-behavior relationships in IGD. These results help advance current neuroscientific theories of IGD and may inform gender-informed treatment strategies.

On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance

The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers.

Deep learning approaches for force feedback based void defect detection in friction stir welding

The Friction Stir Welding (FSW) process is known as a solid state welding process comparably stable against external disturbances in its steady state. Therefore, the process is commonly applied with fixed welding parameters utilizing axial force control or position control strategies. External and internal process disturbances, introduced by the workpieces, gap tolerances, tool wear or machine/tool inadequacies are rarely monitored and no conclusion towards the weld seam quality is drawn. In most applications the sole process monitoring feature is the axial force and quality control measures are often only applied to external weld seam features. Based on prior analytic process analysis and quality models based on analytic algorithms, machine learning approaches are employed to find suitable quality criteria to detect internal weld seam defects (void defects) over a wide range of welding parameters. The recorded welding force data is examined using Long Short-Term Memory (LSTM) Networks, Bidirectional Long Short-Term Memory (BiLSTM) Networks and different versions of Convolutional Neural Networks (CNN), differing in Layer architecture and Node count (and thus resulting complexity). In an effort to reduce data and speed up development and training, manual feature extraction was performed. Features were selected based on the cyclical nature of the FSW-process and prior research into in-plane process force data quality relevancy. The features as well as raw recorded force data were used to train previously mentioned networks. Overall the viability of the approaches was shown by a high rate of correct void defect classification, with CNNs exceeding 93% test set classification rate. Network training had to be closely monitored as a small dataset and large number of input features increased the risk of overfitting.

Classification of Sleep Apnea Events Using Nasal Air Flow (NAF) Signals. (Dept. E)

Detecting and diagnosing different types of Sleep Apnea (Obstructive, Central and Hypopneaisone of the major tasks in sleep medicine. Clinically, analyzing Nasal Airflow (NAF) signal is the most sufficient and direct reliably effective method for the automatic detection of Sleep Apnea events by checking the airflow amplitude reduction. This paper investigates and compares the performance of three classifiers: a kohen self-organizing map (SOM), Adaptive Neuro Fuzzy Inference System ?(ANFIS) AND Hidden Markov Model (HMM) for the classification of Sleep apnea events. The results have shown that the highest correct classification rate is 95.7% when using HMM with order 13.

Intra- and Inter-Specific Variation in Edible Jellyfish Biomarkers and Implications for Origin Traceability and Authentication

With the continuous development of jellyfish fisheries and food products around the world, an effective traceability system has become increasingly prominent. This study provides insight into the origin traceability and authentication of two commercially important jellyfish species, flame jellyfish Rhopilema esculentum and Nomura’s jellyfish Nemopilema nomurai, while investigating the intra- and inter-specific variation in fatty acid (FA) profiles and carbon and nitrogen stable isotope ratios (δ13C and δ15N). Results showed significant differences in FA profiles and isotopic values in fresh bell tissues between wild and farmed R. esculentum and among geographic origins, possibly due to different food sources, nutritional status, and energy costs that each group experiences at a given location. The linear discriminant analysis indicated that δ13C, δ15N, C16:0, C17:0, C18:0, C16:1n7, and C20:5n3 were suitable discriminatory variables with a high rate of correct classification for distinguishing origins of R. esculentum. In addition, inter-specific FA profiles/biomarkers, combined with isotopic values, suggests the variety of dietary sources and trophic positions of sympatric similar-sized R. esculentum and N. nomurai and the potential use of biomarkers, especially stable isotope analysis, for distinguishing sympatric jellyfish species. These results highlighted the complementarity of FA and stable isotope analyses and provide an alternative approach for improving the origin traceability and authenticity evaluation of untreated edible jellyfish. Furthermore, this study adds new information regarding the biochemical compositions of jellyfish species.

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Retinal optical coherence tomography (OCT) with intraretinal layer segmentation is increasingly used not only in ophthalmology but also for neurological diseases such as multiple sclerosis (MS). Signal quality influences segmentation results, and high-quality OCT images are needed for accurate segmentation and quantification of subtle intraretinal layer changes. Among others, OCT image quality depends on the ability to focus, patient compliance and operator skills. Current criteria for OCT quality define acceptable image quality, but depend on manual rating by experienced graders and are time consuming and subjective. In this paper, we propose and validate a standardized, grader-independent, real-time feedback system for automatic quality assessment of retinal OCT images. We defined image quality criteria for scan centering, signal quality and image completeness based on published quality criteria and typical artifacts identified by experienced graders when inspecting OCT images. We then trained modular neural networks on OCT data with manual quality grading to analyze image quality features. Quality analysis by a combination of these trained networks generates a comprehensive quality report containing quantitative results. We validated the approach against quality assessment according to the OSCAR-IB criteria by an experienced grader. Here, 100 OCT files with volume, circular and radial scans, centered on optic nerve head and macula, were analyzed and classified. A specificity of 0.96, a sensitivity of 0.97 and an accuracy of 0.97 as well as a Matthews correlation coefficient of 0.93 indicate a high rate of correct classification. Our method shows promising results in comparison to manual OCT grading and may be useful for real-time image quality analysis or analysis of large data sets, supporting standardized application of image quality criteria.

Otolith shape analysis of the Brazilian silverside in two northeastern Brazilian estuaries with distinct salinity ranges

The hypothesis that salinity and temperature influence otoliths of Atherinella brasiliensis populations was investigated by comparing the morphologies and shapes of otoliths from two contrasting estuaries (positive and hypersaline). Overall, 462 otoliths were extracted from adult fish collected in both estuaries. For each otolith, the morphometric measurements (height, width, and length) and were recorded. We compared the morphology and shape of the otoliths from Brazilian silversides in different estuaries and estuarine zones (upper and lower) using multivariate analyses of variance and discriminant function analysis. Overall, two morphotype patterns were revealed through elliptic Fourier descriptors based on 30 harmonics. Significant differences in the morphologies and shapes of otoliths were found among and within estuaries, particularly in the population from the hypersaline estuary, which presented higher otolith areas and perimeter and height values in comparison with that from the positive estuary. The otolith morphometric variables were correctly classified into their original groups at more than 98.3 %, indicating a high rate of correct classification of individuals into their original populations. The weight of the otoliths was inversely related to the condition factor of fishes. The results highlight the significant correlations of environmental variables on otolith morphology, with both temperature and salinity being significant drivers of differences between populations.

One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools

The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the dd-SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.

A Generalized Diagnostic Classification Modeling Framework Integrating Differential Speediness: Advantages and Illustrations in Psychological and Educational Testing

To advance the theoretical foundation of incorporating response times (RTs) into diagnostic classification models (DCMs), this study attempts to further derive, test and illustrate a generalized modeling framework (known as the JVRT-LCDM) that can simultaneously analyze response accuracy and differential speediness based on an existing method (Zhan et al., British Journal of Mathematical and Statistical Psychology, 71(2), 262–286, 2018). The JVRT-LCDM not only provides fine-grained diagnostic feedback without strict model constraints but also clarifies the specific speed trajectory of individuals. Moreover, some existing models from psychometric literatures are included in the JVRT-LCDM as special cases. The feasibility of the JVRT-LCDM is investigated via a Monte Carlo simulation study using a Bayesian estimation scheme, and two empirical datasets are then analyzed to illustrate the applicability of the JVRT-LCDM in practice. The results indicate that (1) as a generalized and flexible model, the JVRT-LCDM realizes high correct classification rates and accurate speed parameter recovery; (2) the JVRT-LCDM outperforms the existing models in terms of model-data fit, diagnostic consistency, and estimation of specific individuals in practical cognitive diagnosis assessments; and (3) the JVRT-LCDM provides reliable evidence for nonconstant speed modeling.

Curated Data In - Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing.

New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.