Set Of Test Samples Research Articles

Dual-tiered insights: cross-examining entities in free text electronic health records

Abstract Background Poor determination and processing of actions at the primary care level are found to be associated with unstructured and cluttered electronic health records (EHRs). Physicians recommend that EHRs must highlight entities, such as action items, findings, follow-ups, and reasons for medication changes. There’s consensus around the success of large language models (LLMs) in detecting clinical and non-clinical entities in EHRs. However, end-to-end systems make limited efforts to go beyond entity and relationship detection to support clinical decision-making. Methods We propose a two-tier system that generates case-based comparative reports for entities highlighted by physicians in EHRs. First, we fine-tuned a custom Bidirectional Encoder Representations from Transformers (BERT) model that detects the required entities within the EHRs. Second, a clustering-based service generates a report comparing the detected entities of a specific EHR with other EHRs sharing the same conditions determined by the healthcare professional. Results The system leverages the MIMIC IV deidentified free-text clinical notes dataset. The first-tier successfully classified entities with a macro-score average range of [0.87 - 0.94]. For the second tier, preliminary simulations on a set of test samples were made to cluster the entities. The simulations leverage a custom statistical tests engine offering a suite of entity-type specific tests. The results of these tests are used to generate the case-based detected entities comparative report. Conclusions Highlighting clinical and non-clinical entities bridges the communication gap between primary and secondary care. Clustering these entities extends the capability of LLMs in clinical support decision systems by providing healthcare professionals with the ability to run comparative analyses between EHRs. The comparative analysis reports provide a factual comparison between a selected number of detected entities and their thresholds. Key messages • Clustering clinical and non-clinical entities extends the capacity of named entity recognition in clinical decision-making systems. • Ability to generate a comparative analysis report based on figures extracted from recognised entities in free-text electronic health records.

Classification of Kudoa thyrsites infected and uninfected fish using a handheld near-infrared spectrophotometer, SIMCA and PLS-DA.

Kudoa thyrsites infection of marine fish typically results in myoliquefaction, which is only apparent 24 to 56 h post-mortem. The traditional methods for the detection of K. thyrsites infected fish are time-consuming and destructive, reducing its marketability. This poses a challenge for the fish industry to remove infected fish before it reaches the market or further processing activities. This study investigated the use of near-infrared (NIR) spectroscopy, in combination with soft independent modelling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA), for discriminating K. thyrsites infected fish from uninfected fish. Performance of the classification models was evaluated by calculating the sensitivity, specificity and precision. A total of 334 fish samples (200 sardine, 64 hake and 70 kingklip) were used for this study. Infection of K. thyrsites was determined with the use of qPCR assays. Ninety per cent (90%) of the sardine samples, 78% of the hake samples and 37% of the kingklip samples were infected. Class groups of infected and uninfected fish samples were created for the purpose of generating SIMCA and PLS-DA classification models for each species of fish, as well as for a species independent data set. Principal component analysis (PCA) of NIR spectra did not show any clustering for infected and uninfected samples. Calibration and test sample sets were generated for the purpose of building and testing the SIMCA and PLD-DA classification models. SIMCA and PLS-DA were unable to classify test samples correctly into the two classes. The number of misclassifications (NMC) was higher for the SIMCA models than for the PLS-DA models, with more than 60% incorrectly classified. SIMCA classified most of the test samples into both classes. The precision for PLS-DA were 89% for sardine, 81% for hake, 0% for kingklip and 87% for species independent models, however, most samples were classified at infected. The use of NIR spectroscopy and classification models such as SIMCA and PLS-DA showed limited use as a method to distinguish between K. thyrsites infected and uninfected fish samples. Textural and chemical changes during extended frozen storage of the fish samples may have masked the effects associated with K. thyrsites infection. Further studies are suggested where NIR spectroscopy is used in combination with texture analysis and image spectroscopy.

Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patients

ObjectiveTo develop a model for preoperatively predicting postcardiotomy cardiogenic shock (PCCS) in patients with poor left ventricular (LV) function undergoing cardiac surgery. MethodsFrom the Society of Thoracic Surgeons Adult Cardiac Database, 11,493 patients with LV ejection fraction ≤35% underwent isolated on-pump surgery from 2018 through 2019, of whom 3428 experienced PCCS. In total, 68 preoperative clinical variables were considered in machine-learning algorithms trained and optimized using scikit-learn software. ResultsCompared with patients with ideal recovery, those that did were younger (65 vs 67 years), more likely female, Black, with low LV ejection fraction (26.5 vs 28.9%), previous myocardial infarction, chronic lung disease, diabetes, reoperation, or advanced heart failure. Among those with PCCS versus ideal recovery, operative mortality was 27% (925/3428) versus 0.1% (5/8065). PCCS occurred more often after coronary artery bypass grafting with concomitant mitral valve repair or after longer perfusion and clamp times. Reliable preoperative PCCS predictors were more advanced cardiac, liver, and renal failure; frailty; and greater white cell count. Out of sample test set receiver operating curve achieved an area under the curve of 0.74 with acceptable calibration Hosmer-Lemeshow statistic χ2 = 1.33, P = .25. ConclusionsIn patients with severe LV dysfunction undergoing cardiac surgery, risk of PCCS is elevated by preoperative failure of other organ systems and complexity of the planned operation that prolongs myocardial ischemia and cardiopulmonary bypass. This risk calculator could serve as an important tool to preoperatively identify patients in need of advanced levels of support.

Classification of tomato seedling chilling injury based on chlorophyll fluorescence imaging and DBO-BiLSTM

IntroductionTomatoes are sensitive to low temperatures during their growth process, and low temperatures are one of the main environmental limitations affecting plant growth and development in Northeast China. Chlorophyll fluorescence imaging technology is a powerful tool for evaluating the efficiency of plant photosynthesis, which can detect and reflect the effects that plants are subjected to during the low temperature stress stage, including early chilling injury.MethodsThis article primarily utilizes the chlorophyll fluorescence image set of tomato seedlings, applying the dung beetle optimization (DBO) algorithm to enhance the deep learning bidirectional long short term memory (BiLSTM) model, thereby improving the accuracy of classification prediction for chilling injury in tomatoes. Firstly, the proportion of tomato chilling injury areas in chlorophyll fluorescence images was calculated using a threshold segmentation algorithm to classify tomato cold damage into four categories. Then, the features of each type of cold damage image were filtered using SRCC to extract the data with the highest correlation with cold damage. These data served as the training and testing sample set for the BiLSTM model. Finally, DBO algorithm was applied to enhance the deep learning BiLSTM model, and the DBO-BiLSTM model was proposed to improve the prediction performance of tomato seedling category labels.ResultsThe results showed that the DBO-BiLSTM model optimized by DBO achieved an accuracy, precision, recall, and F1 score with an average of over 95%.DiscussionCompared to the original BiLSTM model, these evaluation parameters improved by 9.09%, 7.02%, 9.16%, and 8.68%, respectively. When compared to the commonly used SVM classification model, the evaluation parameters showed an increase of 6.35%, 7.33%, 6.33%, and 6.5%, respectively. This study was expected to detect early chilling injury through chlorophyll fluorescence imaging, achieve automatic classification and labeling of cold damage data, and lay a research foundation for in-depth research on the cold damage resistance of plants themselves and exploring the application of deep learning classification methods in precision agriculture.

Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging.

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes.

Foam density mapping via THz imaging

Plastic foams, near-ubiquitous in everyday life and industry, show properties that depend primarily on density. Density measurement, although straightforward in principle, is not always easy. As such, while several methods are available, plastic foam industry is not yet supported with a standard technique that effectively enables to control density maps. To overcome this issue, this paper proposes Terahertz (THz) time-of-flight imaging using normal reflection measurements as a fast, relatively cheap, contactless, non-destructive and non-dangerous way to map plastic foam density, based on the expected relationship between density and refractive index. The approach is demonstrated in the case of polypropylene foams. First, the relationship between the estimated effective refractive index and the polypropylene foam density is derived by characterizing a set of carefully crafted samples having uniform density in the range 70–900 kg/m3. The obtained calibration curve subtends a linear relationship between the density and the refractive index in the range of interest. This relationship is validated against a set of test samples, whose estimated average densities are consistent with the nominal ones, with an absolute error lower than 10 kg/m3 and a percentage error on the estimate of 5%. Exploiting the calibration curve, it is possible to build quantitative images depicting the spatial distribution of the sample density. THz images are able to reveal the non-uniform density distribution of some samples, which cannot be appreciated from visual inspection. Finally, the complex spatial density pattern of a graded foam sample is characterized and quantitatively compared with the density map obtained via X-ray microscopy. The comparison confirms that the proposed THz approach successfully determines the density pattern with an accuracy and a spatial scale variability compliant with those commonly required for plastic foam density estimate.

LIBS және хемометриканы қолданып кварцты жіктеу және сандық талдау әдісі

Vein quartz often becomes the subject of chemical analysis. Laser-induced breakdown spectroscopy (LIBS) is faster, cheaper, and more environmentally friendly compared to traditional silicate analysis. This study demonstrates the feasibility of using LIBS in combination with design of experiment and chemometric methods for the classification analysis of amorphous quartz and the quantitative determination of several constant impurities. The combination of objects and methods used in this work has not been previously published. Vein quartz samples were collected from various geographically distant locations. The sample preparation for LIBS involved cutting and polishing a flat surface of the quartz specimen. The LIBS instrument settings were optimized using design of experiment method. The concentrations of several trace elements in the training and control sets of samples were determined using atomic absorption spectroscopy (AAS). The spectra and analytical data of the training set were used for multivariate calibration using Partial Least Squares Regression (PLS-R). Sample classification was performed using Principal Component Analysis (PCA). It was shown that in the score plot in the space of the first three principal components, the samples confidently group according to their sources of origin. The results were validated on a test set of samples. The accuracy of quantitative determination of Mg, Ca, Na, K, Al, Ti using LIBS exceeded 90%. A method for the quantitative determination of several elements in quartz and an approach to identifying the place of origin of a sample are proposed.

Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.

Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPIcr) equation without race and the estimated glomerular filtration rate (eGFR) equation developed for the Chinese population - displayed suboptimal performance in patients with neurogenic lower urinary tract dysfunction (NLUTD), which limited their clinical application for detecting changes in GFR levels in all cohorts. To develop a neural network model based on multilayer perceptron (MLP) for evaluating GFR in Chinese NLUTD patients, and compare the diagnostic performance with Cr-based multiple linear regression equations for Chinese and the CKD-EPIcr equation without race. Single-center, cross-sectional study of GFR estimation from serum Cr, demographic data, and clinical characteristics in Chinese patients with NLUTD. A total of 204 NLUTD patients, from 27 different geographic regions of China, were selected. A random sample of 141 of these subjects was included in the training sample set, and the remaining 63 patients were included in the testing sample set. The reference GFR (rGFR) was assessed by the technetium-99m-labeled diethylenetriaminepentaacetic acid (99mTc-DTPA) double plasma sample method. A neural network model based on MLP was developed to evaluate GFR in the training sample set, which was then validated in the testing sample set and compared with Cr-based GFR equations. The MLP-based model showed significant performance improvement in evaluating the difference, absolute difference, precision, and accuracy of GFR estimation compared with the Cr-based GFR equations. Additionally, compared with the rGFR, we found that the MLP-based model provided an acceptable level of accuracy (greater than 85%, which was within a 30% deviation from the rGFR). The MLP-based model offered significant advantages in estimating GFR in Chinese NLUTD patients, and its application could be suggested in clinical practice.

Risk Identification of Mountain Torrent Hazard Using Machine Learning and Bayesian Model Averaging Techniques

Frequent mountain torrent disasters have caused significant losses to human life and wealth security and restricted the economic and social development of mountain areas. Therefore, accurate identification of mountain torrent hazards is crucial for disaster prevention and reduction. In this study, based on historical mountain torrent hazards, a mountain torrent hazard prediction model was established by using Bayesian Model Average (BMA) and three classic machine learning algorithms (gradient-boosted decision tree (GBDT), backpropagation neural network (BP), and random forest (RF)). The mountain torrent hazard condition factors used in modeling were distance to river, elevation, precipitation, slope, gross domestic product (GDP), population, and land use type. Based on the proposed BMA model, flood risk maps were produced using GIS. The results demonstrated that the BMA model significantly improved upon the accuracy and stability of single models in identifying mountain torrent hazards. The F1-values (comprehensively displays the Precision and Recall) of the BMA model under three sets of test samples at different locations were 3.31–24.61% higher than those of single models. The risk assessment results of mountain torrents found that high-risk areas were mainly concentrated in the northern border and southern valleys of Yuanyang County, China. In addition, the feature importance analysis result demonstrated that distance to river and elevation were the most important factors affecting mountain torrent hazards. The construction of projects in mountainous areas should be as far away from rivers and low-lying areas as possible. The results of this study can provide a scientific basis for improving the identification methods of mountain torrent hazards and assisting decision-makers in the implementation of appropriate measures for mountain torrent hazard prevention and reduction.

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools.

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2µgL-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

Propranolol chiral analysis using voltammetric sensor based on triterpenoid-graphene oxide hybrid material

Enantioselective sensors are potentially in demand at all stages of the production, storage and use of drugs, including identifying impurities of undesirable isomers and improving stereoselective synthesis methods. A key aspect for enantioselective sensors design is the development of appropriate molecular architectures that make it possible to create recognition sites with different affinities for enantiomers. This experimental work proposes a new chiral sensor based on glassy carbon electrode (GCE) modified by graphene oxide (GO) covalent functionalized with pentacyclic triterpenoid betulonic acid (BetA) for voltammetric recognition and determination of propranolol (Prop) enantiomers. Characterization of the materials was carried out using FTIR spectroscopy, scanning electron microscopy and cyclic voltammetry. Differential pulse voltammetry was used for Prop enantiomers recognition and quantification. The potential difference reached 30 mV along with enanitoselectivity coefficient ipS/ipR equal to 1.30. The calculated binding energy for S-Prop/GO-BetA complex is higher by 4.801 kcal moll−1 compared to R-Prop/GO-BetA indicating a more favorable mutual arrangement of molecules. The linear determination range was established as 5.0 × 10-6 ∼ 1.0 × 10-4 mol·L-1 and 1.0 × 10-4 ∼ 4.0 × 10-4 mol·L-1 for both enantiomers. The detection limits were determined to be 3.9⋅10-7 mol·L-1 and 5.0⋅10-7 mol·L-1 for S-Prop and R-Prop, respectively. The standard spike-recovery tests were carried out in human biological fluids with recoveries 97.3 % − 106.9 % for both enantiomers. The proposed sensor was used to determine the ratio of Prop enantiomers in mixture by regression analysis with projection to latent structures (PLS). The purpose was to build a PLS-model based on a calibration set containing a known number of Prop enantiomers and recognize a test set prepared independently. All samples of test set are correctly identified and the RSD did not exceed 8.7 %. GCE modified by covalent functionalized GO with BetA showed better stability compared with noncovalent functionalization in terms of electrode degradation.

The Li battery digital twin – Combining 4D modelling, electro-chemistry, neutron, and ion-beam techniques

Knowledge driven materials and component design is key for improving the performance of Li-ion batteries and solving the remaining hurdles for next-generation battery concepts like all-solid-state batteries. While the spatial and time dependent distribution of Li would help elucidating performance bottlenecks and degradation phenomena, only a few analysis techniques are available, due to the unique nature of Li, especially as Li+-ion. In fact, only two non-destructive techniques with good time resolution can combine spatial information with absolute quantification of Li, one being Neutron Depth Profiling (NDP), the other Ion-Beam-Analysis (IBA). While both exploit nuclear processes, the information gained is complementary. NDP provides high depth resolution, but only limited lateral resolution, whereas IBA has high lateral, but only limited depth resolution. In this study, we benchmark both techniques for the first time using a set of Li-battery test-samples and show the strengths and synergies of both techniques. The derived information regarding the depth dependent Li-concentration is then used to validate a microstructure resolved continuum model of charge, discharge, and relaxation behavior of the cells and electro-chemical analysis. This fundamental work demonstrates a new route to optimize Li batteries on material and component level by combining advanced characterization and digital twin modelling.

Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples

Covariate shift in the test data is a common practical phenomena that can significantly downgrade both the accuracy and the fairness performance of the model. Ensuring fairness across different sensitive groups under covariate shift is of paramount importance due to societal implications like criminal justice. We operate in the unsupervised regime where only a small set of unlabeled test samples along with a labeled training set is available. Towards improving fairness under this highly challenging yet realistic scenario, we make three contributions. First is a novel composite weighted entropy based objective for prediction accuracy which is optimized along with a representation matching loss for fairness. We experimentally verify that optimizing with our loss formulation outperforms a number of state-of-the-art baselines in the pareto sense with respect to the fairness-accuracy tradeoff on several standard datasets. Our second contribution is a new setting we term Asymmetric Covariate Shift that, to the best of our knowledge, has not been studied before. Asymmetric covariate shift occurs when distribution of covariates of one group shifts significantly compared to the other groups and this happens when a dominant group is over-represented. While this setting is extremely challenging for current baselines, We show that our proposed method significantly outperforms them. Our third contribution is theoretical, where we show that our weighted entropy term along with prediction loss on the training set approximates test loss under covariate shift. Empirically and through formal sample complexity bounds, we show that this approximation to the unseen test loss does not depend on importance sampling variance which affects many other baselines.

Abstract 6182: Artificial intelligence (AI)-powered quantification of glypican-3 (GPC3) expression facilitates patient selection for GPC3-targeted therapy in solid tumors

Abstract Introduction: GPC3 is a glycosyl-phosphatidylinositol-anchored cell surface glycoprotein that functions to control cell growth and differentiation in early development. Membrane GPC3 is highly expressed in hepatocellular carcinoma (HCC) and moderately in squamous non-small cell lung cancer (SQ-NSCLC) with limited expression in normal tissues which makes it an attractive target for immune cell engagers. This study aims to develop an AI-powered algorithm to evaluate GPC3 expression and compare it with manual immunohistochemistry (IHC) scoring in HCC, adeno-NSCLC and SQ-NSCLC. Methods: 147 NSCLC (94 SQ-NSCLC and 53 adeno-NSCLC) and 113 HCC FFPE tumor blocks were obtained from commercial sources. GPC3 protein level was quantified by a qualified IHC assay for tumor cell membrane and cytoplasm individually. PD-L1 expression is reported as tumor cell proportion score (TPS), combined positive score (CPS) and immune cell proportion score (IPS/IC) TPS, regardless of staining pattern and intensity. FFPE slides were stained for GPC3+ (GC33, Ventana), PD-L1+ (22C3, Dako) cells and then scanned at 20x using the Aperio Versa8 scanner. Machine learning (ML) models were developed using the digitized whole slide images to identify GPC3+ tumor area. Data-driven cutoffs were applied to model-generated human-interpretable features of GPC3+ percentage of tumor area to classify samples as positive and negative. As an orthogonal approach to the GPC3+ percentage cutoff method, tissue and cell annotations were used to train a convolutional neural network (CNN) to classify GPC3 positivity. Results: The epidemiology profiling results revealed that GPC3 protein is highly expressed in HCC (57.5% cases with > 1% positive cells), followed by SQ-NSCLC (52.1%) and adeno-NSCLC (5.7%). GPC3 expression was only detected in tumor cells with two staining patterns observed: membrane + cytoplasm and cytoplasm-only. There was no significant correlation between GPC3 and PD-L1 expression across tumor types, although a trend of lower level of PD-L1 was observed in the samples with GPC3 ≥50%. ML model quantification of GPC3+ and GPC3- showed high concordance with the consensus score of 2 independent pathologists on a test set of held-out samples not used in training. This concordance was comparable to that of individual annotators to consensus. Instead, GPC3 positivity results using both cutoff and CNN methods showed moderate concordance with 2-way pathologist consensus. Conclusions: Here we profiled GPC3 expression across tumor types and showed high level of expression in HCC followed by SQ-NSCLC. We have established an AI-powered digital pathology platform that can provide a standardized, scalable, and reproducible method of characterizing GPC3 positivity to support further patient selection in clinical study. Citation Format: Ellen Meng, Robin Meng, Ying Tong, Yuchen Li, Hong Wang, Paola Fiorentini, Elham Attieh, Qi Tang, Asma Kefsi, Serena Masciari, Giovanni Abbadessa, Cecile Combeau, Lei Tang, Benoit Pasquier, Rui Wang. Artificial intelligence (AI)-powered quantification of glypican-3 (GPC3) expression facilitates patient selection for GPC3-targeted therapy in solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6182.

Geographic origin classification and simultaneous determination of methylxanthines in vietnamese tea using chemometrics based on the near infrared reflectance spectroscopy

This paper reported the results of classification of geographic origin and simultaneous analysis of three methylxanthines (caffeine, theobromine, theophylline) in Vietnamese tea samples by the infrared reflectance spectrophotometry coupled with chemometrics. The spectral range was 10,000-4,000cm-1 and each spectrum was measured at 2 cm-1 intervals. For the purpose of geographic origin classification, this study used FT-NIR spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA), and Principal Component Analysis-Discriminant Analysis (PCA-DA). The ability to determine the origin of tea samples in the prediction set of PLS-DA model is 100%. Using the same IR spectral database combined with the partial least squares (PLS), three methylxanthines in tea samples are also quickly quantified. The PLS model based on the spectra of 24 tea samples in which the contents of 3 analytes were determined by high performance liquid chromatography- HPLC) were applied for simultaneous determination of caffeine, theobromine and theophylline in samples. The determination of methylxanthines in 7 tea samples in test set gave the good accuracy of the PLS model. The correlation coefficients (R2) in the prediction set were of 0.9582, 0.8894 and 0.9303 for theobromine, theophylline, and caffeine, respectively. This work demonstrated that infrared reflectance spectrophotometry combined with chemometrics could be applied to rapidly classify the geographic origin and simultaneous determination of main contents in green tea.

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

Multiple marine algae identification based on three-dimensional fluorescence spectroscopy and multi-label convolutional neural network

Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosynthetic pigments can constrain the efficacy of fluorescence methods. This study introduces a multi-label classification model that combines a specific Excitation-Emission matric convolutional neural network (EEM-CNN) with three-dimensional (3D) fluorescence spectroscopy to detect single and mixed algal samples. Spectral data can be input directly into the model without transforming into images. Rectangular convolutional kernels and double convolutional layers are applied to enhance the extraction of balanced and comprehensive spectral features for accurate classification. A dataset comprising 3D fluorescence spectra from eight distinct algae species representing six different algal classes was obtained, preprocessed, and augmented to create input data for the classification model. The classification model was trained and validated using 4448 sets of test samples and 60 sets of test samples, resulting in an accuracy of 0.883 and an F1 score of 0.925. This model exhibited the highest recognition accuracy in both single and mixed algae samples, outperforming comparative methods such as ML-kNN and N-PLS-DA. Furthermore, the classification results were extended to three different algae species and mixed samples of skeletonema costatum to assess the impact of spectral similarity on multi-label classification performance. The developed classification models demonstrated robust performance across samples with varying concentrations and growth stages, highlighting CNN's potential as a promising tool for the precise identification of marine algae.

An optimized back propagation neural network on small samples spectral data to predict nitrite in water

Accurate detection of pollutant levels in water bodies using fusion algorithms combined with spectral data has become a critical issue for water conservation. However, the number of samples is too small and the model is unstable, which often leads to poor prediction and fails to achieve the measurement goal well. To address these challenges, this paper proposes a practical and effective method to precisely predict the concentrations of nitrite pollution in aquatic environments. The proposed method consists of three steps. Firstly, the dimension of the spectral data is reduced using Kernel Principal Component Analysis (KPCA), followed by sample augmentation using Generative Adversarial Network (GAN) to reduce calculation cost and increase the diversity and scale of the data. Secondly, several improvement strategies, including multi-cluster competitive and adaptive parameter updating, are introduced to enhance the capability of the Particle Swarm Optimization (PSO) algorithm. The improved PSO algorithm is then applied to optimize the initialization weights and biases of the Back Propagation neural network, thereby improving the model fitting and training performance. Finally, the developed prediction model is employed to predict the test set samples. The result suggests that the R2, RMSE, and MAE values are 0.976290, 0.008626, and 0.006617, which outperform the state-of-the-art and provided a promising model for the prediction of nitrite concentration in water.

Assessment of Beeswax Adulteration by Paraffin and Stearic Acid Using ATR-IR Spectroscopy and Multivariate Statistics-An Analytical Method to Detect Fraud.

A spectroscopic investigation of beeswax adulteration by paraffin and/or stearic acid was undertaken via Attenuated Total Reflectance Infra-Red spectroscopy (ATR-IR) combined with multivariate statistical analyses. Principal Component Analysis (PCA) was successfully applied for the first time as an exploratory tool for the differentiation among pure beeswax and adulterated beeswax by paraffin and stearic acid with detection limits (LOD) of ~5% and 1%, respectively. Partial Least Square (PLS) modelling was used to build chemometric models based on beeswax/paraffin and beeswax/stearic acid calibration mixtures and subsequently used to predict concentrations of paraffin and stearic acid on a set of unknown test samples. PLS predictions demonstrated that beeswax adulteration by paraffin is much more prominent (74%) than the one by stearic acid (26%) and that commercial beeswax products (candles, pearls, blocks, etc.) are more prone to adulteration (27%) than honeycomb-type samples (12.5%).

Rapid determination of alprazolam in gin tonic cocktails based on the coupling of IR spectroscopy and chemometrics: A feasibility study

“Drug-facilitated sexual assault" (DFSA) is a sexual assault perpetrated against a person rendered unconscious by a substance that changes her/his physical and/or mental condition, such as ethanol or drugs. Several active pharmaceutical ingredients, whether used alone or with alcoholic beverages, can produce anterograde amnesia and loss of inhibition. The most common pharmaceuticals found in DFSAs are GHB (-hydroxybutyric acid), benzodiazepines (Valium, Xanax, or Roipnol), antidepressants (Venlafaxine), muscle relaxants (cyclobenzaprine), antihistamines, sleeping pills (diphenhydramines), hallucinogens, and opioids. Biological samples are typically examined in cases of suspected DFSA; however, occasionally, samples are sent to labs a long period after being collected, jeopardizing the accuracy of the analysis. As a result, in recent years, the focus has shifted to directly detecting the presence of drugs in alcoholic beverages. In light of this, the purpose of the current study is to build a FT-IR-based approach for the determination of alprazolam in a common long drink (gin and tonic). To achieve this goal, pure (Class Pure) and spiked gin tonics (Class Spiked) were analyzed by Fourier-transform infrared spectroscopy (FT-IR). Afterward, two classifiers were used: Sequential preprocessing through ORThogonalization Linear Discriminant Analysis (SPORT-LDA) and Soft Independent Modeling of Class Analogies (SIMCA). Both approaches provided good results: SPORT-LDA achieved a 95% and a 98% accuracy rate (on the external test set of samples) for spiked and pure cocktails, respectively. This corresponds to the misclassification of 5 spiked and 1 pure drinks. The SIMCA model of class pure achieved 98.2% and 91.7% of specificity and sensitivity, respectively, coinciding with 55 pure samples (over 60) correctly accepted and 2 (over 110) erroneously rejected by the model. In conclusion, the SIMCA model of class pure seems preferable, because it minimizes the type II error. Eventually, the study was circumscribed to the spiked cocktails and a novel SPORT model was used to quantify alprazolam in spiked cocktails. This provided noteworthy results, in fact, it led to a Root Mean Square Error in Prediction (RMSEP) of 0.95, and a R2pred of 0.98.