Utilization of multiple-dilution fluorescence fingerprint facilitates prediction of chemical attributes in spice extracts

Fluorescence Fingerprint (FF) is a powerful tool for rapid quality assessment of various foods and plant-derived products. However, the conventional utilization of FFs measured at a single dilution level (DL) to substitute chemical analyses is extremely challenging, especially for multicomponent materials like spice extracts because fluorescence intensity and concentration widely differ between components, with complex phenomena like inner filter effects. Here, we proposed a new strategy to use the meta-data comprised of FFs measured at multiple DLs with machine learning to estimate common chemical attributes including total polyphenol and flavonoid contents, and antioxidant abilities. This strategy achieved more consistently satisfactory performance in estimation of all chemical attributes of spice extracts compared to using a single DL. Hence, the workflow employed in this study is expected to serve as an alternative method to quickly evaluate the chemical quality of spice extracts, as well as other plant products and food materials.

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

Comparing silver and gold nanoislands' surface plasmon resonance for bisacodyl and its metabolite quantification in human plasma

Gold and silver nanoparticles have witnessed increased scientific interest due to their colourful colloidal solutions and exceptional applications. Comparing the localized surface plasmon resonance (LSPR) of gold and silver nanoparticles is crucial for understanding and optimizing their optical properties. This comparison informs the design of highly sensitive plasmonic sensors, aids in selecting the most suitable nanoparticles for applications like surface-enhanced infrared spectroscopy (SEIRA) and biomedical imaging, and guides the choice between gold and silver nanoparticles based on their catalytic and photothermal properties. Ultimately, the study of LSPR facilitates the tailored use of these nanoparticles in diverse scientific and technological applications. Two SEIRA methods combined with partial least squares regression (PLSR) chemometric tools were developed. This development is based on the synthesis of homogeneous, high-dense deposited metal nanoparticle islands over the surface of glass substrates to be used as lab-on-chip SEIRA sensors for the determination of bisacodyl (BIS) and its active metabolite in plasma. SEM micrographs revealed the formation of metallic islands of colloidal citrate-capped gold and silver nanoparticles of average sizes of 29.7 and 15 nm, respectively. BIS and its active metabolite were placed on the nanoparticles' coated substrates to be directly measured, then PLSR chemometric modelling was used for the quantitative determinations. Plasmonic citrate-capped gold nanoparticle substrates showed better performance than those prepared using citrate-capped silver nanoparticles in terms of preparation time, enhancement factor, PLSR model prediction, and quantitative results. This study offers a way to determine BIS and its active metabolite in the concentration range 15-240 ng/mL in human plasma using inexpensive disposable glass-coated substrates that can be prepared in 1 h to get results in seconds with good recovery between 98.77 and 100.64%. The sensors provided fast, simple, selective, molecular-specific and inexpensive procedures to determine molecules in their pure form and biological fluid.

Fast analysis of straw proximates based on partial least squares using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid measurement technique based on the spectroscopic absorption bands of specific functional groups within biomass. Its main advantages include simple preparation, precise analysis, and the ability to analyze multiple components simultaneously. Fast analysis of straw proximates (moisture, ash, and fixed carbon) has been investigated by means of NIRS. A total of 144 samples were collected, the spectral data were analyzed by partial least squares (PLS) regression and support vector regression (SVR) with four wavelength selection methods. PLS combined with competitive adaptive reweighted sampling (CARS) provided excellent predictive performance for moisture, ash, and fixed carbon. For moisture prediction, the values of RP2, RMSEP and RPD were 0.7202, 0.8196, and 2.11, respectively. For ash prediction, the values of RP2, RMSEP and RPD were 0.9307, 0.5901, and 3.69, respectively. For fixed carbon prediction, the values of RP2, RMSEP and RPD were 0.8504, 0.2735, and 2.76, respectively. Fast analysis of proximates of corn stover was possible using this NIRS system.

Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence

OBJECTIVES

To compare facial growth prediction models based on the partial least squares and artificial intelligence (AI).

MATERIALS AND METHODS

Serial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared.

RESULTS

On average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively.

CONCLUSIONS

PLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.

Quantitative prediction of rice starch digestibility using Raman spectroscopy and multivariate calibration analysis

Digestibility is an important characteristic of rice starch. It is affected by the growing environment, such as temperature and soil, so that even in the same genetic cultivar the digestibility of each product will be different. Here, we predicted rice starch digestibility by Raman scattering spectroscopy. A partial least squares (PLS) regression analysis was performed between biochemically quantified digestibility index values and Raman scattering spectra of purified starch from rice samples of different cultivars and growing conditions. The prediction model obtained by analyzing the individual cultivars was able to predict digestibility with a high accuracy, with an R2 of 0.95 and RMSEP of 0.43, whereas a mixture of all cultivars resulted in more than two times worse accuracy. Our finding suggests that the molecular structures affecting digestibility fluctuate depending on the growing environment while maintaining a unique balance regulated by cultivar-specific starch synthesis mechanisms.

Authentication of aged beef in terms of aging time and aging type by 1H NMR spectroscopy

Meat authenticity addresses parameters such as species, breed, sex, housing system and postmortem treatment. Seventy-four beef backs from two breeds ('Fleckvieh' and 'Schwarzbunt') and three cattle types (heifer, cow, young bull) were dry-aged and wet-aged up to 28 days and analyzed by 1H NMR spectroscopy. Statistical models based on partial least squares regression and discriminant analysis were performed to classify the beef samples by breed, cattle type, aging time, and aging type based on their 1H NMR spectra. The aging time of beef samples can be predicted with an error ± 2.28 days. The cattle type model has an accuracy of cross-validation of 99.2 %, the breed models of 100 % and the aging type model for 28-days aged samples of 99.6 %. These models allow the authentication of beef samples in terms of breed, cattle type, aging time, and aging type with a single 1H NMR measurement.

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

Application Research of Visible Near-Infrared Spectroscopy Technology for Detecting Intracerebral Hematoma

OBJECTIVE

To explore the visible near-infrared spectroscopic (VNIRS) characteristics of intracerebral hematoma, and provide experimental basis for hematoma localization and residual detection in hypertensive intracerebral hemorrhage (HICH) surgery.

METHODS

Using VNIRS, spectral data of cerebral hematoma and cortex were collected during HICH craniotomy, and characteristic spectra were matched with paired-sample T-test. A partial least squares (PLS) quantitative model for cerebral hematoma spectra was established.

RESULTS

The reflectance of cerebral hematoma spectra in the 500-800 nm band was lower than that of the cortex, and there were statistically significant differences in the 510, 565, and 630 nm bands (P < 0.05). The calibration correlation coefficient of the PLS quantitative model for cerebral hematoma spectra was R2 = 0.988, the cross-validation correlation coefficient was R2cv = 0.982, the root mean square error of calibration was RMSEC = 0.101, the root mean square error of cross-validation was RMSEV = 0.122, the external validation correlation coefficient was CORRELATION = 0.902, and the root mean square error of prediction was RMSEP = 0.426, indicating that the model had high fitting degree and good predictive ability.

CONCLUSIONS

VNIRS as a noninvasive, real-time and portable analysis technology, can be used for real-time detection of hematoma during HICH surgery, and provide reliable basis for hematoma localization and residual detection.

Modeling and identification of affective parameters on cadmium's durability and evaluating cadmium pollution indicators caused by using chemical fertilizers in long term

Soil contamination by anthropogenic heavy metals has become a global issue. This study aimed to investigate cadmium (Cd) concentration, mobility, and contamination indices of Cd in soils in the Hamadan province, west of Iran. To investigate the concentration of Cd in soil, one hundred soil samples from wheat farms and five samples from control lands were collected. Pollution indexes, including Cd mobility, enrichment factor, geoaccumulation index, contamination index, and availability ratio, were investigated. The structural equation model was also used to evaluate effective parameters on cadmium durability in soil. Results showed that mean values of available phosphorus (P) were 83.65, 129, and 65 (mg kg-1) in three land-use types rainfed, irrigated, and controlled, respectively. The mean values of Cd in different land-use types of rainfed, irrigated, and controlled were 0.15, 0.18, and 0.08 (mg kg-1), respectively. The results indicated that the amount of Cd in both forms (available and total) in ones that received fertilizer, especially P fertilizers, was higher than in the controlled one. Other pollution indexes revealed that the study area had been slightly contaminated due to anthropogenic activities. Lime, clay, lead, and OM were identified as affective parameters on cadmium durability. Finally, the results demonstrated that the mobility rate was high. Cd had a higher potential mobility in soil samples in the rain-fed and irrigated land than in the controlled land, and Cd had a low retention time.

The spatiotemporal evolution and impact mechanism of energy consumption carbon emissions in China from 2010 to 2020 by integrating multisource remote sensing data

The spatiotemporal evolution patterns of carbon emissions and their influence mechanisms are important topics for regional climate change monitoring and research on sustainable development goals. At present, due to the limitation of statistical data collection scale, it is difficult to analyze the spatiotemporal variation of carbon emission and its influence mechanism at a finer scale in China. With the development of new remote sensing platforms and technologies, multisource remote sensing data such as nighttime light remote sensing data and XCO2 concentration data have become important information resources for carbon emission monitoring. Therefore, this study monitors the spatiotemporal evolution of carbon emissions in China based on multisource remote sensing data and conducts impact mechanism research. The main conclusions of this study include: (1) The partial least squares carbon emission estimation model and the downscaled inversion model estimate carbon emissions with high accuracy. The estimated carbon emissions of both have high correlation with statistical carbon emissions, with R2 of 0.86 and 0.87, respectively, and no significant overestimation or underestimation. (2) The overall spatial pattern of energy consumption carbon emissions in China from 2010 to 2018 is high in the east and low in the west and high in the north and low in the south, but this spatial distribution pattern is gradually weakening. China's energy consumption carbon emissions varied considerably from 2010 to 2018, with an overall slow positive growth trend. (3) The mechanisms of population growth, economic development, urbanization and industrialization on carbon emissions are more complex, and most of their influencing factors promote carbon emission generation, while carbon emission impacts have spatial spillover. This study designs and studies a regional energy consumption carbon emission estimation model in China based on multisource remote sensing data, and explores the characteristics of regional multiscale carbon emission spatiotemporal variation and its influence mechanism, so as to provide scientific references for China's carbon emission reduction targets.

Identifying the active ingredients of carbonized Typhae Pollen by spectrum-effect relationship combined with MBPLS, PLS, and SVM algorithms

Typhae Pollen (TP) and its carbonized product (carbonized Typhae Pollen, CTP), as cut-and-dried herbal drugs, have been widely used in the form of slices in clinical settings. However, the two drugs exhibit a great difference in terms of their clinical efficacy, for TP boasts an effect of removing blood stasis and promoting blood circulation, while CTP typically presents a hemostatic function. Since the active ingredients of CTP, so far, still remain unclear, this study aimed at identifying the active ingredients of CTP by spectrum-effect relationship approach coupled with multi-block partial least squares (MBPLS), partial least squares (PLS), and support vector machine (SVM) algorithms. In this study, the chemical profiles of a series of CTP samples which were stir-fried for different duration (denoted as CTP0∼CTP9) were firstly characterized by UHPLC-QE-Orbitrap MS. Then the hemostatic effect of the CTP samples was evaluated from the perspective of multiple parameters-APTT, PT, TT, FIB, TXB2, 6-keto-PGF1α, PAI-1 and t-PA-using established rat models with functional uterine bleeding. Subsequently, MBPLS, PLS and SVM were combined to perform spectrum-effect relationship analysis to identify the active ingredients of CTP, followed by an in vitro hemostatic bioactivity test for verification. As a result, a total of 77 chemical ingredients were preliminarily identified from the CTP samples, and the variations occurred in these ingredients were also analyzed during the carbonizing process. The study revealed that all the CTP samples, to a varying degree, showed a hemostatic effect, among which CTP6 and CTP7 were superior to the others in terms of the hemostatic effect. The block importance in the projection (BIP) indexes of MBPLS model indicated that flavonoids and organic acids made more contributions to the hemostatic effect of CTP in comparison to other ingredients. Consequently, 9 bioactive ingredients, including quercetin-3-O-glucoside, kaempferol-3-O-rutinoside, quercetin, kaempferol, isorhamnetin, 2-methylenebutanedioic acid, pentanedioic acid, benzoic acid and 3-hydroxybenzoic acid, were further identified as the potential active ingredients based on PLS and SVM models as well as the in vitro verification. This study successfully revealed the bioactive ingredients of CTP associated with its hemostatic effect, and also provided a scientific basis for further understanding the mechanism of TP processing. In addition, it proposed a novel path to identify the active ingredients for Chinese herbal medicines.

A new process analytical technology soft sensor based on electrical tomography for real-time monitoring of multiphase systems

BACKGROUND

Electrical tomography is widely recognized for its high time resolution and low cost. However, the implementation of electrical tomographic solutions has been hindered by the high computational overhead associated, which causes delays in the analysis, and numerical instability, that results in unclear reconstructed images. Therefore, it has been mostly applied offline, for qualitative tasks and with some delay. Applications requiring fast response times and quantification have been hindered or ruled out.

RESULTS

In this article, we propose a new process analytical technology soft sensor that maps directly electrical tomography signals to the relevant parameter to be monitored. The data acquisition and estimation steps occur almost instantaneously, and the final accuracy is very good (R2 = 0,994).

SIGNIFICANCE AND NOVELTY

The proposed methodology opens up good prospects for real-time quantitative applications. It was successfully tested on a pilot piping installation where the target property is the interface height between two immiscible fluids.

Comparison of data-driven identified hypertension-protective dietary patterns among Chinese adults: based on a nationwide study

PURPOSE

Diet pattern (DP) is a key modifiable and cost-effective factor in hypertension (HTN) management. The current study aimed to identify and compare the hypertension-protective DPs among Chinese adults.

METHODS

52,648 participants aged over 18 years were included from China Nutrition and Health Surveillance (CNHS) 2015-2017. Reduced rank regression (RRR) and partial least square regression (PLS) was applied to identify the DPs. Multivariable-adjusted logistic regression was used to assess the association between the DPs and HTN.

RESULTS

DPs derived by RRR and PLS were both featured by higher consumption of fresh vegetables and fruits, mushrooms and edible fungi, seaweeds, soybeans and related products, mixed legumes, dairy products, fresh eggs, and lower of refined grain consumption. Compared to the lowest quintile, participants in the highest quintile had lower odds of HTN (RRR-DP: OR = 0.77, 95% CI = 0.72-0.83; PLS-DP: OR = 0.76, 95% CI = 0.71-0.82; all p < 0.0001). Simplified DP scores were observed the same protective tendencies (Simplified RRR-DP: OR = 0.81, 95% CI = 0.75-0.87; Simplified PLS-DP: OR = 0.79, 95% CI = 0.74-0.85; all p < 0.0001) and showed effective extrapolation in subgroups defined by gender, age, location, lifestyle, and different metabolic conditions.

CONCLUSIONS

The identified DPs had high conformity with East Asian dietary habits, and significantly negative associations with HTN among Chinese adults. The simplified DP technique also indicated the potential for improving the extrapolation of the results of DP analysis related to HTN.

Dietary patterns, obesity markers and leukocyte telomere length among Brazilian civil servants: cross-sectional results from the Pro-Saude study

OBJECTIVE

Dietary patterns express the combination and variety of foods in the diet. The partial least squares method allows extracting dietary patterns related to a specific health outcome. Few studies have evaluated obesity-related dietary patterns associated with telomeres length. This study aims to identify dietary patterns explaining obesity markers and to assess their association with leukocyte telomere length (LTL), a biological marker of the ageing process.

DESIGN

Cross-sectional study.

SETTING

University campuses in the state of Rio de Janeiro, Brazil.

PARTICIPANTS

478 participants of a civil servants' cohort study with data on food consumption, obesity measurements (total body fat, visceral fat, BMI, leptin and adiponectin) and blood samples.

RESULTS

Three dietary patterns were extracted: (1) fast food and meat; (2) healthy and (3) traditional pattern, which included rice and beans, the staple foods most consumed in Brazil. All three dietary patterns explained 23·2 % of food consumption variation and 10·7 % of the obesity-related variables. The fast food and meat pattern were the first factor extracted, explaining 11-13 % variation of the obesity-related response variables (BMI, total body fat and visceral fat), leptin and adiponectin showed the lowest percentage (4·5-0·1 %). The healthy pattern mostly explained leptin and adiponectin variations (10·7 and 3·3 %, respectively). The traditional pattern was associated with LTL (β = 0·0117; 95 % CI 0·0001, 0·0233) after adjustment for the other patterns, age, sex, exercise practice, income and energy intake.

CONCLUSION

Leukocyte telomere length was longer among participants eating a traditional dietary pattern that combines fruit, vegetables and beans.

An improved successive projections algorithm version to variable selection in multiple linear regression

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Short communication: Predicting blood plasma non-esterified fatty acid and beta-hydroxybutyrate concentrations from cow milk-addressing systematic issues in modelling

Negative energy status in early lactation is linked to a variety of metabolic disorders, reduced fertility, and decreased milk production. To improve the energy status of cows by breeding and management, the identification of negative energy status is crucial. While biomarkers such as non-esterified fatty acid (NEFA) concentration and beta-hydroxybutyrate (BHB) in blood plasma could be used to identify a negative energy state, measuring them directly from blood is both invasive and expensive. In this work, we developed prediction equations for blood plasma NEFA and BHB levels based on mid-IR spectral measurements of milk. The models were fitted using partial least squares regression and evaluated using both cross-validation and independent-herd validation. A total of 3 183 spectral records from 606 lactations originating from three different herds were utilised. R2 values of 0.53 (RMSE = 0.206 mmol/l, RMSE of cross-validation (RMSECV) 0.217 mmol/l) for NEFA and 0.63 (RMSE = 0.326 mmol/l, RMSECV = 0.353 mmol/l) for BHB were obtained. Furthermore, relatively similar prediction accuracies were found for BHB (RMSE of prediction (RMSEP) 0.411 mmol/l and 0.422 mmol/l) and NEFA (RMSEP = 0.186 mmol/l and 0.221 mmol/l) when model training was done using two herds and validated on the third herd. The results from the model fits confirm that it is possible to build blood plasma BHB and NEFA models based on mid-IR spectra that are sufficiently accurate for practical use.

Social-Economic Backgrounds to US County-Based COVID-19 Deaths: PLS-SEM Analysis

A complex interplay of social, economic, and environmental factors drove the COVID-19 epidemic. Understanding these factors is crucial in explaining the racial disparities observed in COVID-19 deaths. This research investigated various hypotheses, including ecological, racial, demographic, economic, and political party factors, to determine their impact on COVID-19 deaths. The study utilized data from the National Center for Health Statistics (NCHS), specifically focusing on COVID-19 deaths categorized by race and Hispanic origin in US counties, with over 100 recorded deaths as of July 11, 2022.

METHOD

To analyze the data, the study employed partial least squares (PLS) as the statistical approach, considering the presence of multicollinearity in the county-level socioeconomic data. SmartPLS4 software was utilized to illustrate paths depicting variance and covariance and to conduct significance tests. The analysis encompassed overall COVID-19 deaths and deaths among White, Black, and Hispanic Americans, utilizing the same latent variables and paths.

RESULTS

The results revealed that the number of residents aged 65 years or older in a county was the most influential predictor of COVID-19 deaths, irrespective of race. Economic factors emerged as the second strongest predictors. However, when considering each racial group separately, distinct factors aligned with the five hypotheses emerged as significant contributors to COVID-19 deaths. Furthermore, the diagrams illustrating the relationships between these factors (covariates) varied among racial groups, indicating that the underlying social influences differed across races.

DISCUSSION

In light of these findings, it becomes evident that a "one-size-fits-all" approach to prevention strategies is suboptimal. Instead, targeted prevention efforts tailored to specific racial and social classes at high risk of COVID-19 death could have provided more precise messaging and necessitate direct engagement.

Anti-inflammation activity of Zhizi Jinhua Pills and overall quality consistency evaluation based on integrated HPLC, DSC and electrochemistry fingerprints

ETHNOPHARMACOLOGICAL RELEVANCE

Zhizi Jinhua Pills (ZZJHP), a compound preparation composed of 8 traditional Chinese medicines (TCM), is widely used clinically to clearing heat, purging fire, cooling blood and detoxifying. However, the studies on its pharmacological activity and the determination of active compounds are relatively few. There is a lack of quality control methods that can reflect the effectiveness of the drug.

AIM OF THE STUDY

The objective was to construct fingerprint profiles, conduct a spectrum-effect relationship study and establish an overall quality control method for ZZJHP through anti-inflammatory and redox activity studies.

MATERIALS AND METHODS

Firstly, anti-inflammatory activity was tested using the xylene-induced ear edema model in mice. Then, Five-wavelength fusion HPLC fingerprint, electrochemical fingerprint, and Differential scanning calorimetry (DSC) profiling were established to evaluate ZZJHP more comprehensively, where Euclidean quantified fingerprint method (EQFM) was proposed for the similarity assessment of these three fingerprints. Moreover, the spectrum-activity relationship of HPLC-FP and DSC-FP with electrochemical activity helped explore the active components or ranges in the fingerprint. Finally, integrated analysis of HPLC, DSC and electrochemistry were used for the quality screen of samples from different manufacturers.

RESULTS

ZZJHP was found to significantly decrease the levels of both TNF-α and IL-6 in the mice. Qualitatively, the integrated similarity S_m of 21 samples were all greater than 0.9, indicating the great consistency in chemical composition. Quantitatively, 9 batches of samples were classified as Grade1∼4; 6 batches of samples were classified as Grade5∼7 due to higher P_INT; 6 batches of samples were classified as Grade4∼5 due to lower P_INT. EQFM can qualitatively and quantitatively characterize the fingerprint profile information from an overall perspective.

CONCLUSIONS

This strategy will contribute to the quantitative characterization of TCM and promote the application of fingerprint technology in the phytopharmacy field.

Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia

BACKGROUND

Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD).

METHOD

Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity.

RESULTS

Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes.

CONCLUSIONS

Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.

Machine learning algorithm inversion experiment and pollution analysis of water quality parameters in urban small and medium-sized rivers based on UAV multispectral data

To examine and analyze the applicability of UAV multispectral images to urban river monitoring, this paper, taking the Fuyang River in the urban area of Handan Municipality as the object, the orthogonal image data of the river in different seasons were acquired by unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and at the same time, the water samples were collected for physical and chemical indexes detection. Based on the image data, a total of 51 modeling spectral indexes were obtained by constructing three forms of band combinations ranging from the difference index (DI), ratio index (RI), and normalization index (NDI) and combining six single-band spectral values. Through the partial least squares (PLS), random forest (RF), and lasso prediction models, six fitting models of water quality parameters were constructed: turbidity (Turb), suspended, substance (SS), chemical oxygen demand (COD), ammonia nitrogen (NH₄-N), total nitrogen (TN), and total phosphorus (TP). After verifying the results and evaluating the accuracy, the following conclusions were drawn: (1) The inversion accuracy of the three types of models is generally the same-summer is better than spring, and winter is the worst. (2) Water quality parameter inversion model based on two kinds of machine learning algorithms has more prominent advantages than PLS. RF model has good performance in the inversion accuracy and generalization ability of water quality parameters in different seasons. (3) The prediction accuracy and stability of the model are positively correlated to a certain extent with the size of the standard deviation of sample values. To sum up, by using the multispectral image data acquired by UAV and adopting the prediction models built upon machine learning algorithms, water quality parameters in different seasons can be predicted in different degrees.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R²= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

Negative-emotion-induced reduction in speech-in-noise recognition is associated with source-monitoring deficits and psychiatric symptoms in mandarin-speaking patients with schizophrenia

BACKGROUND

Patients with schizophrenia (SCH) have deficits in source monitoring (SM), speech-in-noise recognition (SR), and auditory prosody recognition. This study aimed to test the covariation between SM and SR alteration induced by negative prosodies and their association with psychiatric symptoms in SCH.

METHODS

Fifty-four SCH patients and 59 healthy controls (HCs) underwent a speech SM task, an SR task, and the assessment of positive and negative syndrome scale (PANSS). We used the multivariate analyses of partial least squares (PLS) regression to explore the associations among SM (external/internal/new attribution error [AE] and response bias [RB]), SR alteration/release induced by four negative-emotion (sad, angry, fear, and disgust) prosodies of target speech, and psychiatric symptoms.

RESULTS

In SCH, but not HCs, a profile (linear combination) of SM (especially the external-source RB) was positively associated with a profile of SR reductions (induced especially by the angry prosody). Moreover, two SR reduction profiles (especially in the anger and sadness conditions) were related to two profiles of psychiatric symptoms (negative symptoms, lack of insight, and emotional disturbances). The two PLS components explained 50.4% of the total variances of the release-symptom association.

CONCLUSION

Compared to HCs, SCH is more likely to perceive the external-source speech as internal/new source speech. The SM-related SR reduction induced by the angry prosody was mainly associated with negative symptoms. These findings help understand the psychopathology of SCH and may provide a potential direction to improve negative symptoms via minimizing emotional SR reduction in schizophrenia.

Determination of Bendamustine, Gemcitabine and Vinorelbine (BEGEV) regimen in spiked human plasma using multivariate model update chemometric methods

Combination of bendamustine (BEN), gemcitabine (GEM), and vinorelbine (VIB), (BEGEV) regimen, has been proved to be a tolerable, safe and effective regimen in relapsed/refractory classical hodgkin lymphoma (R/R cHL). Two chemometric models named principal component regression (PCR) and partial least squares (PLS) were established for determination and quantification of BEN, GEM and VIB simultaneously in the ranges of 5-25 µg/mL for each of BEN and VIB, while in the range of 10 -30 µg/mL for GEM in pure and spiked plasma using their UV absorbance. The updated methods have been proven their ability to predict the concentrations of the studied drugs and validated according to FDA guidelines showing good results. There was no significant difference between the developed methods and the reported LC-MS/MS method upon statistical comparison was applied. Furthermore, the updated chemometric methods have advantages of being sensitive, accurate and cost effective for estimation of BEN, GEM and VIB and monitoring their concentration.

Leveraging visible and near-infrared spectroelectrochemistry to calibrate a robust model for Vanadium(IV/V) in varying nitric acid and temperature levels

Spectroelectrochemistry and optimal design of experiments can be used to rapidly build accurate models for species quantification and enable a greater level of process awareness. Optical spectroscopy can provide vital elemental and molecular information, but several hurdles must be overcome before it can become a widely adopted analytical method for remote analysis in the nuclear field. Analytes with varying oxidation state, acid concentration, and fluctuating temperature must be efficiently accounted for to minimize time and resources in restrictive hot cell environments. The classic one-factor-at-a-time approach is not suitable for frequent calibration/maintenance operations in this setting. Therefore, a novel alternative was developed to characterize a system containing vanadium(IV/V) (0.01-0.1 M), nitric acid (0.1-4 M), and varying temperatures (20-45 °C). Spectroelectrochemistry methods were used to acquire a sample set selected by optimal design of experiments. This new approach allows for the accurate analysis of vanadium and HNO₃ concentration by leveraging UV-Vis-NIR absorption spectroscopy with robust and accurate chemometric models. The top model's root mean squared error of prediction percent values were 3.47%, 4.06%, 3.40%, and 10.9% for V(IV), V(V), HNO₃, and temperature, respectively. These models, efficiently developed using the designed approach, exhibited strong predictive accuracy for vanadium and acid with varying oxidation states and temperature using only spectrophotometry, which advances current technology for real-world hot cell applications. Additionally, Nernstian analysis of the V(IV/V) standard potential was performed using traditional absorbance methods and multivariate curve resolution (MCR). The successful tests demonstrated that MCR Nernst tests may be valuable in highly convoluted spectral systems to better understand the redox processes' behavior.

A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury

This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm^-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R² values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.

Machine learning-assisted laser-induced breakdown spectroscopy for monitoring molten salt compositions of small modular reactor fuel under varying laser focus positions

Next-generation advanced nuclear reactors based on molten salts are interested to apply machine learning (ML) technology to minimize human error and realize effective autonomous operation. Owing to harsh environments with limited access to molten salts, laser-induced breakdown spectroscopy (LIBS) has been investigated as a possible option for remote online monitoring. However, the height of molten salts is easily fluctuated by vibration. In addition, the level of molten salts could change during normal operation through the insertion of a controlling structure. While these uncertainties should be considered, their effects have not been studied yet. In this study, LIBS has been actively coupled with ML to automate the online monitoring of difficult-to-access molten salt systems. To practically apply a prediction model with ML, we intentionally defocus the measurement by manipulating the sample position. This study investigates the focusing and defocusing spectra of Sr and Mo as fission products for constructing the two prediction models using partial least squares and artificial neural network methods. For each method, the prediction models trained with focusing spectra only or focusing and defocusing spectra simultaneously are constructed and compared to each other. While the prediction model using only focusing spectra resulted in a root mean square error of prediction (RMSEP) of 0.1943-0.2175 wt%, a prediction model using both spectra led to approximately 10 times enhanced RMSEP (0.0210-0.0316 wt%). This study implies that not only focusing data but also defocusing data are needed to construct the prediction model while considering its practical usage in a real system, especially in the complex processes of the nuclear industry.

Exploring quantitative structure-property relationship models for environmental fate assessment of petroleum hydrocarbons

The rate and extent of biodegradation of petroleum hydrocarbons in the different aquatic environments is an important element to address. The major avenue for removing petroleum hydrocarbons from the environment is thought to be biodegradation. The present study involves the development of predictive quantitative structure-property relationship (QSPR) models for the primary biodegradation half-life of petroleum hydrocarbons that may be used to forecast the biodegradation half-life of untested petroleum hydrocarbons within the established models' applicability domain. These models use easily computable two-dimensional (2D) descriptors to investigate important structural characteristics needed for the biodegradation of petroleum hydrocarbons in freshwater (dataset 1), temperate seawater (dataset 2), and arctic seawater (dataset 3). All the developed models follow OECD guidelines. We have used double cross-validation, best subset selection, and partial least squares tools for model development. In addition, the small dataset modeler tool has been successfully used for the dataset with very few compounds (dataset 3 with 17 compounds), where dataset division was not possible. The resultant models are robust, predictive, and mechanistically interpretable based on both internal and external validation metrics (R² range of 0.605-0.959. Q²_(Loo) range of 0.509-0.904, and Q²_F1 range of 0.526-0.959). The intelligent consensus predictor tool has been used for the improvement of the prediction quality for test set compounds which provided superior outcomes to those from individual partial least squares models based on several metrics (Q²_F1 = 0.808 and Q²_F2 = 0.805 for dataset 1 in freshwater). Molecular size and hydrophilic factor for freshwater, frequency of two carbon atoms at topological distance 4 for temperate seawater, and electronegative atom count relative to size for arctic seawater were found to be the most significant descriptors responsible for the regulation of biodegradation half-life of petroleum hydrocarbons.

Factors that influence the impact of Chronic Non-Cancer Pain on daily life: A partial least squares modelling approach

BACKGROUND

Chronic Non-Cancer Pain is pain of more than three months' duration and is not associated with an oncological condition. There is ample literature that recognises that Chronic Non-Cancer Pain impacts numerous areas of the life of the person who suffers from it. This impact is difficult to determine and quantify because Chronic Pain is a subjective experience.

OBJECTIVE

The objective of this study was to test a recursive model of hypothesised factors that comprise the concept of Chronic Non-Cancer Pain Impact on daily life using Partial Least Squares-Structural Equation Modelling.

DESIGN

A cross-sectional study was carried out. The sample size was calculated using G*Power V.3.1.9.4 with five parameters (two-tailed, large effect size (f² = 0.35), power of 0.95, statistical significance of 95% (α = 0.05) and 36 predictors). The minimum number of subjects was considered to be 137.

METHODS

A recursive model was built based on data from a sample of 395 people over 18 years of age with Chronic Non-Cancer Pain. Data collection was conducted between January and March 2020 at Pain Units and Primary Healthcare Centres belonging to the Spanish Public Health System in the province of Seville (Spain). Analyses were based on Partial Least Squares-Structural Equation Modelling. The internal consistency, convergent validity and discriminant validity of the internal measurement model were assessed. For the external measurement model, global model adjustment and structural validity were assessed. The predictive capacity of the final model was also evaluated. All analyses were performed using SmartPLS version 3.3.2 in consistent mode.

RESULTS

Findings showed an adequate validity of the proposed model, which comprised nine factors: pain catastrophising, hopelessness due to pain, support network, proactivity, treatment compliance, self-care, mobility, resilience, and sleep. The internal validity of the model (Cronbach's alpha and rho_A > 0.70; Average Variance Extracted>0.50; standardised outer loadings>0.60; Heterotrait-Monotrait-Ratio < 0.85), goodness of fit (Standardised Root Mean Square Residuals<0.08; Geodesic and Euclidean distance p-value<0.05) and predictive power with out-of-sample values (Stone-Geisser test>0.5) were adequate. The hypothesised structure of the instrument has also been confirmed (path coefficients>0.3; R² > 0.1; f² > 0.2).

CONCLUSIONS

The results have shown an adequate internal consistency, convergent validity and discriminant validity of the model. Likewise, the model has shown an adequate goodness of fit, and the validity of its structure and the hypothesis have been confirmed. However, more research is needed in this regard as the possible interaction between the different factors evaluated in the model with the confounding or moderating variables that may exist.

Simultaneous quantitative Analysis of Salmeterol and Fluticasone in Inhalation Spray Using HPLC and Fast Spectrophotometric Technique Combined with Time Series Neural Network and Multivariate Calibration Methods

BACKGROUND

Chromatographic methods have been used for the simultaneous determination of salmeterol (SMT) and fluticasone (FLU), which take a lot of time to analyze, need large amount of solvents and sample pre-treatment, as well as it is costly.

OBJECTIVE

The aim of this paper was to propose a simple, quick, and low-cost method for the determination of SMT and FLU using time series neural network and multivariate calibration methods, including partial least squares (PLS) and principal component regression (PCR).

METHODS

The simultaneous spectrophotometric determination of SMT and FLU in binary mixtures and anti-asthma spray was performed by applying multivariate calibration methods and intelligent approach.

RESULTS

The coefficient of determination (R2) of the time series neural network was obtained 1 and 0.9997 for SMT and FLU, respectively. The mean recovery of PLS and PCR methods was found 99.29%, 99.84% and 102.05%, 103.72% for SMT and FLU, respectively. Furthermore, root mean square error (RMSE) of SMT and FLU were 0.187, 0.156 and 0.693, 0.714 for PLS and PCR, respectively.

CONCLUSION

The analyzing inhalation spray was assessed using high-performance liquid chromatography and its results were compared with chemometrics methods via analysis of variance (ANOVA) test.

HIGHLIGHTS

Intelligent and multivariate calibration methods were proposed.Simultaneous spectrophotometric determination of salmeterol and fluticasone was studied in the anti-asthma spray.HPLC as a reference method was performed and compared with chemometrics methods.Rapid, simple, low-cost, and accurate are the benefits of the proposed approaches.

Fast detection of volatile fatty acids in biogas slurry using NIR spectroscopy combined with feature wavelength selection

To analyze the state of anaerobic digestion (AD), fast detection models of volatile fatty acids (VFAs) were constructed using near-infrared transmission spectroscopy combined with partial least squares regression to measure concentrations of the acetic acid (AA), propionic acid (PA) and total acid (TA) in biogas slurry. CARS-SA-BPSO algorithm was proposed based on competitive adaptive reweighted sampling (CARS) and simulated annealing binary particle swarm optimization algorithm (SA-BPSO) for selecting feature wavelengths of the AA, PA and TA. Regression models were established with the determination coefficient of prediction (R_p²) of 0.989, root mean squared error of prediction (RMSEP) of 0.111 and residual predictive deviation (RPD) of 9.706 for AA; R_p² of 0.932, RMSEP of 0.116 and RPD of 3.799 for PA; R_p² of 0.895, RMSEP of 0.689 and RPD of 3.676 for TA. It is sufficient to meet the fast detection needs of the AA and PA concentrations in biogas slurry, and basically meet the measuring demand of the TA concentration. CARS-SA-BPSO effectively improves the performance of the calibration model using sensitive wavelength selections, which provides theoretical support for establishing the spectral quantitative regression model to meet the requirements of practical application.

Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared spectroscopy combined with chemometrics

As a mature platform compound, citric acid (CA) is mainly produced by Aspergillus niger (A. niger) through submerged fermentation. However, the CA fermentation process is still regulated based on experience and limited offline data, so real-time monitoring and intelligent precise control of the fermentation process cannot be carried out. In this study, near-infrared (NIR) spectroscopy combined with different chemometrics methods was used to quantify the substrate, product, and cell concentration of CA fermentation online. The predictive performance of total sugar (TS), CA, and dry cell weight (DCW) concentrations were compared between traditional partial least squares (PLS) and intelligent stacked auto-encoder (SAE) modeling methods. Theresults showed that both PLS and SAE models had good performance in predicting TS and CA. The performance, accuracy, and precision of the PLS models are slightly better than those of the SAE models in predicting TS and CA. SAE model was superior to the PLS model in predicting DCW concentration. The SAE modeling method has advantages in predicting the concentration of complex components. In this study, the multi-parameter online prediction was realized in the complex system of CA fermentation, which provided the basis for real-time intelligent control of the fermentation process.

Morphable models of the lumbar spine to vary geometry based on pathology, demographics, and anatomical measurements

The shape of the lumbar spine influences its function and dysfunction. Yet examining the influence of geometric differences associated with pathology or demographics on lumbar biomechanics is challenging in vivo where these effects cannot be isolated, and the use of simple anatomical measurements does not fully capture the complex three-dimensional geometry. The goal of this work was to develop and share morphable models of the lumbar spine that allow geometry to be varied according to pathology, demographics, or anatomical measurements. Partial least squares regression was used to generate statistical shape models that quantify geometric differences associated with pathology, demographics, and anatomical measurements from the lumbar spines of 87 patients. To determine if the morphable models detected meaningful geometric differences, the ability of the morphable models to classify spines was compared with models generated from random labels. The models for disc herniation (p < 0.04), spondylolisthesis (p < 0.001), and sex (p < 0.01) all performed significantly better than the random models. Age was predicted with a root mean square error of 14.1 years using the age-based model. The morphable models for anatomical measurements were able to produce instances with root mean square errors less than 0.8°, 0.3 cm², and 0.7 mm between desired and resulting measurements. This method can be used to produce morphable models that enable further analysis of the relationship among shape, pathology, demographics, and function through computational simulations. The morphable models and code are available at https://github.com/aclouthier/morphable-lumbar-model.

Automated malarial retinopathy detection using transfer learning and multi-camera retinal images

Cerebral malaria (CM) is a fatal syndrome found commonly in children less than 5 years old in Sub-saharan Africa and Asia. The retinal signs associated with CM are known as malarial retinopathy (MR), and they include highly specific retinal lesions such as whitening and hemorrhages. Detecting these lesions allows the detection of CM with high specificity. Up to 23% of CM, patients are over-diagnosed due to the presence of clinical symptoms also related to pneumonia, meningitis, or others. Therefore, patients go untreated for these pathologies, resulting in death or neurological disability. It is essential to have a low-cost and high-specificity diagnostic technique for CM detection, for which We developed a method based on transfer learning (TL). Models pre-trained with TL select the good quality retinal images, which are fed into another TL model to detect CM. This approach shows a 96% specificity with low-cost retinal cameras.

Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

Classification of local diesel fuels and simultaneous prediction of their physicochemical parameters using FTIR-ATR data and chemometrics

Class identification and prediction of physicochemical variables of eight diesel fuel brands collected from several stations within the Atlanta metropolitan area in the State of Georgia were investigated using principal component analysis (PCA), partial least squares discriminant analysis (PLS2-DA), and partial least squares regression (PLSR) as modeling techniques. The fuels were from a common pipeline, therefore, assumed to have very similar characteristics. Ten FTIR-ATR spectra per fuel brand were collected over the 650 - 4000 cm^-1 mid-infrared region, and the 80 x 3351 matrix was submitted to PCA to determine if there were any clusters. Following PCA, the 80 x 3351 matrix was split into a training matrix (56x3351) and a test matrix (24x3351). PLS2-DA models were built and evaluated for class identification using dummy variables (I,0) as input matrix. For physicochemical variable predictions, models were developed via PLSR using the FTIR-ATR spectra training matrix and physicochemical variables obtained from the Georgia Department of Agriculture Labs as input. Correlation coefficients of the eight fuels ranged from 0.9960 to 0.9998. PCA revealed all eight clusters of the diesel fuels, regardless of the tight correlation coefficients range. With a 1.0 ± 0.1 cut-off for fuel identification, the PLS2-DA models showed 100% correct predictions for four or five fuel brands, and 75% correct prediction for all eight fuel brands. PLSR predicted 100% correct physicochemical variables, with a RMSEP range of 0.019 to 1.132 for all 80 variables targeted.

Topic based quality indexes assessment through sentiment

This paper proposes a new methodology called TOpic modeling Based Index Assessment through Sentiment (TOBIAS). This method aims at modeling the effects of the topics, moods, and sentiments of the comments describing a phenomenon upon its overall rating. TOBIAS is built combining different techniques and methodologies. Firstly, Sentiment Analysis identifies sentiments, emotions, and moods, and Topic Modeling finds the main relevant topics inside comments. Then, Partial Least Square Path Modeling estimates how they affect an overall rating that summarizes the performance of the analyzed phenomenon. We carried out TOBIAS on a real case study on the university courses' quality evaluated by the University of Cagliari (Italy) students. We found TOBIAS able to provide interpretable results on the impact of discussed topics by students with their expressed sentiments, emotions, and moods and with the overall rating.

Formative-reflective scheme for the assessment of tourism destination competitiveness: an analysis of Italian municipalities

In this article, we propose a formative-reflective scheme for the assessment of Tourism Destination Competitiveness (TDC) based on a combined use of Partial Least Squares-Path Modelling (PLS-PM) and the method recently proposed by Fattore, Pelagatti, and Vittadini (FPV). TDC is conceived as a construct reflecting the tourism performance of a destination, and several determinants are considered, including endowed resources, created resources, and supporting factors. The proposed scheme is applied to a case study on 1575 Italian municipalities for which the Italian National Institute of Statistics released data on tourist flows. Our contribution is innovative for three aspects: (i) the consistency of the formative-reflective scheme for TDC assessment is discussed on a theoretical basis; (ii) an empirical comparison between PLS-PM and the FPV method is performed; (iii) data with higher granularity than most studies on TDC assessment are employed. Our findings highlight that endowed resources are the primary driver of TDC, followed by created resources and supporting factors, and emphasize that the best ranked destinations are big cities with a multifaceted tourism alongside sea and mountain destinations with cultural attractions.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH₄-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN₄-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH₄-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH₄-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method

OBJECTIVES

To develop a facial growth prediction model incorporating individual skeletal and soft tissue characteristics.

MATERIALS AND METHODS

Serial longitudinal lateral cephalograms were collected from 303 children (166 girls and 137 boys), who had never undergone orthodontic treatment. A growth prediction model was devised by applying the multivariate partial least squares (PLS) algorithm, with 161 predictor variables. Response variables comprised 78 lateral cephalogram landmarks. Multiple linear regression analysis was performed to investigate factors influencing growth prediction errors.

RESULTS

Using the leave-one-out cross-validation method, a PLS model with 30 components was developed. Younger age at prediction resulted in greater prediction error (0.03 mm/y). Further, prediction error increased in proportion to the growth prediction interval (0.24 mm/y). Girls, subjects with Class II malocclusion, growth in the vertical direction, skeletal landmarks, and landmarks on the maxilla were associated with more accurate prediction results than boys, subjects with Class I or III malocclusion, growth in the anteroposterior direction, soft tissue landmarks, and landmarks on the mandible, respectively.

CONCLUSIONS

The prediction error of the prediction model was proportional to the remaining growth potential. PLS growth prediction seems to be a versatile approach that can incorporate large numbers of predictor variables to predict numerous landmarks for an individual subject.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)_average=0.0107,R_average²=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

A stacked regression ensemble approach for the quantitative determination of biomass feedstock compositions using near infrared spectroscopy

Rapid, robust, and accurate biomass compositional analyses are required in the bioenergy industry to accurately determine the chemical composition of biomass feedstocks. A stacked regression ensemble approach using near infrared spectroscopic method was developed for the quantitative determination of glucan, xylan, lignin, ash, and extract in biomass feedstocks. A comprehensive comparison of the performance of various machine learning techniques including support vector regression (linear and radial), least absolute shrinkage and selection operator (LASSO), ridge regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed in the training set data (n = 188). The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning algorithm which collates the performance of the abovementioned machine learning regression techniques. Results show that the stacked regression primarily outperformed other machine learning techniques (Root mean square error of prediction (RMSEP)_average=1.660%wt,R²=0.907) across all five constituents in the validation set data (n = 81). Further results also show that the RMSEP of the stacked ensemble technique is significantly different than that of the partial least squares (PLS) approach in predicting glucan, ash, lignin, and extract components in biomass samples. The stacked ensemble learning approach offers an alternative method for a more accurate prediction of biomass compositions than the traditional PLS technique.

Sex-specific associations in verbal memory brain circuitry in early psychosis

Hippocampal circuitry and related cortical connections are altered in first episode psychosis (FEP) and are associated with verbal memory deficits, as well as positive and negative symptoms. There are robust sex differences in the clinical presentation of psychosis, including poorer verbal memory in male patients. Consideration of sex differences in hippocampal-cortical circuitry and their associations with different behavioral dimensions may be useful for understanding the underlying pathophysiology of verbal memory deficits and related symptomatology in psychosis. Here, we use a data-driven approach to simultaneously capture the complex links between sex, verbal memory, symptoms, and cortical-hippocampal brain metrics in FEP. Structural magnetic resonance imaging and behavioral data were acquired from 100 FEP patients (75 males, 25 females) and 87 controls (55 males, 32 females). Multivariate brain-behavior associations were examined in FEP using partial least squares to map sociodemographic, verbal memory, and clinical data onto brain morphometry. The analysis identified two sex-dependent patterns of verbal memory, symptoms, and brain structure. In male patients, verbal memory deficits and core psychotic symptoms were associated with both increased and decreased frontal and temporal cortical thickness and reductions in CA2/3 hippocampal subfield and fornix volumes. In female patients, fewer negative/depressive symptoms were associated with a more attenuated cortical thickness pattern and more diffuse reductions in hippocampal white matter regions. Taken together, the results contribute towards better understanding the underlying pathophysiology of psychosis by highlighting the unique contribution of specific hippocampal subfields and surrounding white matter and their connections with broader cortical networks in a sex-dependent manner.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

Social support as a mediator in the relationship between perceived stress and nomophobia: An Investigation among Malaysian university students during the COVID-19 pandemic

This study examined the mediating role of social support in the relationship between perceived stress and nomophobia among Malaysian university students during the COVID-19 pandemic. A cross-sectional study was conducted with N = 547 university students. Participants answered a self-administered questionnaire measuring nomophobia, social support, and perceived stress. Exploratory analyses were conducted using partial least square structural equation modelling. We found that perceived stress was positively associated with nomophobia during the COVID-19 pandemic, whilst social support partially mediated the relationship between perceived stress and nomophobia. The results of this study indicated that stress may be buffered by social support in individuals with higher levels of nomophobia.

Effect of lean manufacturing tools on sustainability: the case of Mexican maquiladoras

The Mexican maquiladora industry is applying Lean Manufacturing Tools (LMT) in its production lines; however, few studies have investigated its relationship with sustainability (social, economic, and environmental). This paper presents a second-order structural equation model (SEM) relating 8 LMT integrated into three independent latent variables: continuous improvement (Kaizen and Gemba), supporting tools (Andon, visual management, and Poka-yoke), and machinery and equipment (total productive maintenance, overall equipment effectiveness, and Jidoka) that are related to social, economic, and environmental sustainability as dependent variables. The model is validated with information obtained from 249 companies using partial least squares. Findings show that the application of LMT in the Mexican maquiladora industry avoids the generation of waste and reprocessing. Likewise, the improvement of production processes reduces the waste emitted into the environment and reduces energy consumption. Also, when companies have environmental programs, the work environment is safe, and labor relations are improved, increasing morale and the commitment to work for the company, gaining economic and ecological benefits.

Scale invariance in fNIRS as a measurement of cognitive load

Scale invariant neural dynamics are a relatively new but effective means of measuring changes in brain states as a result of varied cognitive load and task difficulty. This study tests whether scale invariance (as measured by the Hurst exponent, H) can be used with functional near-infrared spectroscopy (fNIRS) to quantify cognitive load, paving the way for scale-invariance to be measured in a variety of real-world settings. We analyzed H extracted from the fNIRS time series while participants completed an N-back working memory task. Consistent with what has been demonstrated in fMRI, the current results showed that scale-invariance analysis significantly differentiated between task and rest periods as calculated from both oxy- (HbO) and deoxy-hemoglobin (HbR) concentration changes. Results from both channel-averaged H and a multivariate partial least squares approach (Task PLS) demonstrated higher H during the 1-back task than the 2-back task. These results were stronger for H derived from HbR than from HbO. This suggests that scale-free brain states are a robust signature of cognitive load and not limited by the specific neuroimaging modality employed. Further, as fNIRS is relatively portable and robust to motion-related artifacts, these preliminary results shed light on the promising future of measuring cognitive load in real life settings.

Diffuse Reflection Infrared Fourier Transform Spectroscopy and Partial Least Squares Regression Analysis for Temperature Prediction of Irreversible Thermochromic Paints

Temperature measurement of internal components of a jet engine is a crucial control parameter to ensure its component life and efficiency. Particularly for thermal analysis of internal components of jet engines, irreversible thermochromic paints (TPs) have been developed at Rolls-Royce plc to evaluate the surface temperature of engine components where it is otherwise impossible. Thermochromic paints change color with respect to an increased temperature whereby the resulting change in the TP color corresponds to the maximum temperature experienced by the surface of engine components during testing. To improve the reliability and reproducibility of the temperature measurement by TPs, this work explored the potential use of diffuse reflection Fourier transform infrared spectroscopy (DRIFTS) combined with partial least squares regression (PLSR) analysis. The outcome of the prediction of the raw and pre-processed datasets was compared and discussed. The major contributors to the prediction models were the change in the property of the surface M-OH bonds, the structural change of the inorganic pigments and fillers, and their solid-state reaction at a higher temperature. The result showed improved reliability of the prediction model after the combined pre-process treatments with reported RMSEC of 4.5 °C and RMSECV of 13.0 °C using three latent variables.

Quantitative image analysis as a robust tool to assess effluent quality from an aerobic granular sludge system treating industrial wastewater

Quantitative image analysis (QIA) is a simple and automated method for process monitoring, complementary to chemical analysis, that when coupled to mathematical modelling allows associating changes in the biomass to several operational parameters. The majority of the research regarding the use of QIA has been carried out using synthetic wastewater and applied to activated sludge systems, while there is still a lack of knowledge regarding the application of QIA in the monitoring of aerobic granular sludge (AGS) systems. In this work, chemical oxygen demand (COD), ammonium (N-NH₄⁺), nitrite (N-NO₂^-), nitrate (N-NO₃^-), salinity (Cl^-), and total suspended solids (TSS) levels present in the effluent of an AGS system treating fish canning wastewater were successfully associated to QIA data, from both suspended and granular biomass fractions by partial least squares models. The correlation between physical-chemical parameters and QIA data allowed obtaining good assessment results for COD (R² of 0.94), N-NH₄⁺ (R² of 0.98), N-NO₂^- (R² of 0.96), N-NO₃^- (R² of 0.95), Cl^- (R² of 0.98), and TSS (R² of 0.94). While the COD and N-NO₂^- assessment models were mostly correlated to the granular fraction QIA data, the suspended fraction was highly relevant for N-NH₄⁺ assessment. The N-NO₃^-, Cl^- and TSS assessment benefited from the use of both biomass fractions (suspended and granular) QIA data, indicating the importance of the balance between the suspended and granular fractions in AGS systems and its analysis. This study provides a complementary approach to assess effluent quality parameters which can improve wastewater treatment plants monitoring and control, with a more cost-effective and environmentally friendly procedure, while avoiding daily physical-chemical analysis.

Advanced chemometric methods as powerful tools for impurity profiling of drug substances and drug products: Application on bisoprolol and perindopril binary mixture

Impurity profiling has a rising importance nowadays due to the increased health problems associated with impurities and degradation products found in several drug substances and formulations. Three advanced, accurate and precise chemometric methods were developed as impurity profiling methods for a mixture of bisoprolol fumarate (BIS) and perindopril arginine (PER) with their degradation products which represent drug impurity or a precursor to such impurity. The methods applied were Partial Least Squares (PLS-1), Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Artificial Neural Networks (ANN). Genetic Algorithm (GA) was used as a variable selection tool to select the most significant wavelengths for the three chemometric models. For proper analysis, a 5-factor 5-level experimental design was used to establish a calibration set of 25 mixtures containing different ratios of the drugs and their degradation products (impurities). The validity of the proposed methods was assessed using an independent validation set. The designed models were able to predict the concentrations of the drugs and the degradation products/impurities in the validation set and pharmaceutical formulation. The proposed methods presented a powerful alternative to traditional and expensive chromatographic methods as impurity profiling tools.