Partial Least Squares Model Research Articles

Rapid Method for Simultaneous Determination of γ-Oryzanol Compounds in Rice (Oryza sativa) Grains using UV-Vis Spectroscopy and Chemometrics

Rice (Oryza sativa) contains γ-oryzanol, which consists of 4 main compounds: Cycloartenol ferulate, 2,4-methylenecycloartanyl ferulate, campesterol ferulate, and β-sitosterol ferulate, that contribute to the health benefits of rice. This research aimed to develop a rapid method to determine the 4 major γ-oryzanol compounds in 45 varieties covering pigmented (black and red) and non-pigmented (white) rice grain. The method was developed by integrating UV-Vis spectroscopy with chemometrics, specifically principal component analysis (PCA) and partial least square (PLS) regression. Sampling was performed across the Indonesian archipelago collecting 180 samples, which comprises 60 samples for each type of rice in form of rice husk, bran, whole grain, and polished rice. The results of PCA on the spectroscopic data successfully identified distinction for the 3 types of rice attributable by different type and levels of chemical compound in the grain. White rice exhibited a characteristic absorption at 325 nm while in pigmented (red and black) rice showed a maximum absorbance at 280 nm, indicating the presence of different composition of chemical compounds. A reliable model to predict the 4 major γ-oryzanol compounds was established with R2 calibration, R2 cross-validation, and R2 prediction higher than 0.9 was obtained using the spectroscopic data in the range from 200 to 400 nm. However, the PLS modeling was unsuccessful for red and black rice samples most likely due to the interfering high absorption of red colored compounds. Compared to the existing techniques for analyzing individual compounds of the γ-oryzanol by high-performance liquid chromatography, the newly developed approach using UV-Vis spectroscopy combined with chemometrics is more practical, faster, and cost-efficient and mainly, solvent and residues free. HIGHLIGHTS A rapid method was developed to determine 4 major γ-oryzanol compounds in rice. This method integrates UV-Vis spectroscopy with PCA and PLS regression chemometrics. PCA successfully identified the distinction among white, red, and black rice types. Reliable PLS models were established for white rice with R2 values above 0.9. The developed approach is more practical, faster, and residue-free than HPLC. GRAPHICAL ABSTRACT

Medición de la pobreza multidimensional en México mediante un análisis bibliométrico y de ecuaciones estructurales

Objective. Multidimensional poverty in Mexico was measured through a bibliometric and structural equation analysis. Design/Methodology/Approach. We used the technique developed by the National Council for the Evaluation of Social Development Policy (CONEVAL, in Spanish) in Mexico, based on information from the National Household Income and Expenditure Survey (ENIGH, in Spanish). The Smart PLS software also used the partial least squares (PLS) technique. Regarding the bibliometric part, a descriptive analysis was performed using the Scopus database and VOSviewer as a processing tool. Results/Discussion. The results showed that basic housing services, access to food, quality, housing spaces, and income significantly impact multidimensional poverty. The PLS model showed adequate predictive superiority and goodness of fit. From the bibliometric point of view, there was clear interest on the part of the academic and scientific community in developing knowledge in this field. Conclusions. The effectiveness of the PLS model for measuring multidimensional poverty in Mexico is demonstrated, and several determinants are highlighted. Future studies are recommended to improve social policies and poverty reduction strategies. Originality/Value. The study provides a novel approach by using the PLS model to measure multidimensional poverty in Mexico. It applies a robust exploratory methodology that can be replicated in similar contexts and is supported by bibliometric contrast.

Deciphering stratified structure and microbiota assembly of biofilms from a pyrite-based biofilter driven by mixotrophic denitrification

The precise structure and assembly process of pyrite-based biofilms remain poorly understood. The polysaccharides (PN), proteins (PS), and extracellular DNA were enriched in the soluble extracellular polymeric substance (EPS), loosely bound EPS, and tightly bound EPS, respectively, indicating a significant stratified structure of biofilms. The tryptophan facilitated mixotrophic metabolic processes. Both dominant (>1%) and rare species (<0.01 %) harbored core bacteria, including sulfur autotrophic bacteria, sulfate-reducing bacteria, and heterotrophic bacteria. Furthermore, partial least-squares path modeling quantified the contributions of total phosphorus (TP) (λ = 0.32), dissolved organic matter (DOC) (λ = 0.29), and NH4+–N (λ = 0.26) to variations in the microbial community. Nonmetric multidimensional scaling analysis revealed three distinct stages in biofilm development: colonization (0–36 d), succession (36–149 d), and maturation/old (149–215 d). Furthermore, neutral community model indicated that stochastic processes drove the colonization and maturation/old stages, while deterministic processes dominated the succession stage. This study offered valuable insights into the regulation of pyrite-based engineered ecosystems.

Simultaneous estimation of prasugrel and aspirin in bulk drugs by chemometric methods

A UV–Vis spectrophotometric method enhanced by chemometric techniques, specifically Principal Component Regression (PCR) and Partial Least Squares (PLS) regression, was developed and validated for the simultaneous quantification of prasugrel (PRA) and aspirin (ASP) in bulk drugs and pharmaceutical formulations. The method demonstrated high accuracy, precision, and robustness, achieving mean recoveries of 100.63% for PRA and 100.08% for ASP with relative standard deviations (RSD) below 3%. Both PCR and PLS models showed excellent predictive capabilities, with RMSEP values of 0.45–0.48 for PRA and 0.78–1.13 for ASP, indicating the models’ reliability. In line with green and white chemistry principles, the method minimizes environmental impact by reducing solvent consumption and waste generation compared to traditional chromatographic methods. The Analytical Eco-Scale score was 84, reflecting excellent compliance with green chemistry standards. The method’s simplicity, low energy consumption, and reduced chemical waste further support its alignment with sustainability goals. However, acetonitrile, a hazardous solvent, was still used in small quantities, and solvent recycling was not implemented, slightly affecting the eco-score. To evaluate the method’s greenness, the RGB12 algorithm was applied, achieving a high score of 94.4%, with the majority of parameters related to reagent consumption, waste production, energy efficiency, and safety scoring optimally. The method’s safety, cost-effectiveness, and minimal environmental footprint make it suitable for routine pharmaceutical analysis, particularly in quality control environments where resource efficiency and sustainability are prioritized. Thus, the developed method offers a sustainable, efficient, and environmentally friendly solution for the simultaneous analysis of prasugrel and aspirin in pharmaceutical formulations, making it a valuable tool for routine analysis in the pharmaceutical industry.

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 105 cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium.

Revealing synergistic mechanisms of biochar-assisted microbial electrolysis cells in enhancing the anaerobic digestion performance of waste activated sludge: Extracellular polymeric substances characterization, enzyme activity assay, and multi-omics analysis

Although biochar (BC)-assisted microbial electrolysis cells (MEC) has been shown to improve anaerobic digestion (AD) performance of waste activated sludge (WAS), the underlying mechanisms remain unclear. This study conducted an in-depth investigation into the mechanism based on analyses of extracellular polymeric substances (EPS) characteristics, enzyme activities and multi-omics. The results showed that compared with the control group, methane production improved by 16.73 %, 21.32 %, and 29.37 % in the BC, MEC, and BC-assisted MEC (BC-MEC) groups, respectively. The reconfiguration of the protein secondary structure increased the hydrophobicity of the EPS, thereby promoting microbial aggregation. In addition, partial least-squares path modeling (PLS-PM) and mantel test based on the enzyme activity and multi-omics analyses revealed that the promotional effect of MEC on the hydrolysis of WAS was superior to that of BC, while BC was more advantageous in promoting electron transfer and biofilm formation regulated by quorum sensing. The synergistic effects of BC and MEC were exemplified in the BC-MEC group. g_norank_Aminicenantales responsible for the hydrolysis of WAS was enriched (29.6 %), and the activities of hydrolytic enzymes including α-glucosidases and proteases were increased by 29.1 % and 43.6 %, respectively. Further, the expressions of genes related to acyl homoserine lactones (AHLs) and diffusible signal factor (DSF) in quorum sensing systems, as well as the genes related to hydrogenase involved in electron transfer (mbhJKL, hyfB-JR, hypA-F, and hoxFHUY), were up-regulated in the BC-MEC group. This facilitated electron transfer and microbial communication, consequently enhancing methane production. This research significantly advances the understanding of the mechanism by which BC-assisted MEC enhances AD performance and provides valuable insights into strategies for improving energy recovery from WAS.

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610–0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520–0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119–0.148, MAE-LOOTRAIN = 0.132–0.177, predictive R2 or Q2F1 = 0.538–0.567, Q2F2 = 0.500–0.565, MAETEST = 0.122–0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (−ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.

Partial least squares regression based rapid quantification of intracellular biopolymers from a Sudan black absorption assay

Rapid quantitation of intracellular polyhydroxyalkanoate (PHA) is essential for monitoring and possibly intervening in ongoing bioprocesses. The traditional multistep protocol involving cell separation, lysis, organic solvent extraction, and purification of intracellular polymers is time intensive. In this study, indirect Sudan black (ISB) adsorption data combined with culture optical density and intracellular protein measurements were used to develop partial least squares (PLS) regression models to predict the intracellular PHA concentration in Cupriavidus necator. ISB analysis supported the hypothesized partitioning of Sudan black into residual biomass, PHA, and abiotic compartments. The PLS regression model developed with autoscaled data offered excellent fit on cross-validation (R2CV of 0.987) and independent prediction (R2P of 0.994) sets. The method limit of detection for the PLS model (0.036 g l−1) was reasonably low to track biopolymer accumulation in microbial culture, even in early growth phase. The conservative estimate suggests a significantly shorter processing time with the machine learning-based approach (190 min) than with the traditional protocol (940 min) while retaining analytical interchangeability.

Raman laser intensity and sample clarification on biochemical monitoring over Zika-VLP upstream stages

In the current biopharmaceutical scenario, constant bioprocess monitoring is crucial for the quality and integrity of final products. Thus, process analytical techniques, such as those based on Raman spectroscopy, have been used as multiparameter tracking methods in pharma bioprocesses, which can be combined with chemometric tools, like Partial Least Squares (PLS) and Artificial Neural Networks (ANN). In some cases, applying spectra pre-processing techniques before modeling can improve the accuracy of chemometric model fittings to observed values. One of the biological applications of these techniques could have as a target the virus-like particles (VLP), a vaccine production platform for viral diseases. A disease that has drawn attention in recent years is Zika, with large-scale production sometimes challenging without an appropriate monitoring approach. This work aimed to define global models for Zika VLP upstream production monitoring with Raman considering different laser intensities (200 mW and 495 mW), sample clarification (with or without cells), spectra pre-processing approaches, and PLS and ANN modeling techniques. Six experiments were performed in a benchtop bioreactor to collect the Raman spectral and biochemical datasets for modeling calibration. The best models generated presented a mean absolute error and mean relative error respectively of 3.46 × 105 cell/mL and 35 % for viable cell density (Xv); 4.1 % and 5 % for cell viability (CV); 0.245 g/L and 3 % for glucose (Glc); 0.006 g/L and 18 % for lactate (Lac); 0.115 g/L and 26 % for glutamine (Gln); 0.132 g/L and 18 % for glutamate (Glu); 0.0029 g/L and 3 % for ammonium (NH4+); and 0.0103 g/L and 2 % for potassium (K+). Sample without conditioning (with cells) improved the models' adequacy, except for Glutamine. ANN better predicted CV, Gln, Glu, and K+, while Xv, Glc, Lac, and NH4+ presented no statistical difference between the chemometric tools. For most of the assessed experimental parameters, there was no statistical need for spectra pre-filtering, for which the models based on the raw spectra were selected as the best ones. Laser intensity impacts quality model predictions in some parameters, Xv, Gln, and K+ had a better performance with 200 mW of intensity (for PLS, ANN, and ANN, respectively), for CV the 495 mW laser intensity was better (for PLS), and for the other biochemical variables, the use of 200 or 495 mW did not impact model fitting adequacy.

Chemometrics Evaluation of Lemongrass Essential Oils: Authenticity, Quality and Selection Of Bioactives against t. cruzi.

AbstractThe effectiveness of treatments against the parasite Trypanosoma cruzi, the causative agent of Chagas disease, is limited in the chronic phase of the disease. The essential oils of lemongrass (Cymbopogon citratus and Cymbopogon flexuosus) have shown activity against this parasite. This study aimed to use principal component analysis (PCA) to analyze the data of lemongrass essential oils submitted to gas chromatography with flame ionization detector (GC‐FID) and proton nuclear magnetic resonance (1H NMR) as well as partial least squares (PLS) regressions to correlate chemical variability with the results of biological assays. The PLS models highlighted geraniol, caryophyllene, and limonene as the main compounds responsible for antioxidant activity. Regarding cytotoxicity, the PLS models indicated that this activity is a cumulative effect of multiple compounds. For the trypanocidal effect, median lethal concentration (CL50) values ranged from 15.12 ± 3.96 to 110.10 ± 11.22 μg mL−1, resulting in selectivity index (SI) values of 2.99 to 22.55 times more selective for T. cruzi compared to host cells. PLS indicated that geraniol, neral and geranial were the main contributors to the trypanocidal effect, suggesting the potential for somatic or synergistic effects against trypomastigote forms.

The effect of process parameters on a continuous blending process monitored in-line by near-infrared spectroscopy

The continuous feeding-mixing system ensures the composition uniformity down to the tableting continuous manufacturing line so that a quality end-product is consistently delivered. Near-infrared spectroscopy (NIRS) enables in-line assessment of the blend's critical quality attributes in real-time. In this study, the effect of total feed rate and impeller speed on the continuous blending process monitored in-line by NIRS was examined by principal component analysis (PCA), ANOVA simultaneous component analysis (ASCA) and partial least squares (PLS) regression. Process data were generated by a factorial experimental design with process parameters and a constant formulation comprised of: 30 % (wt/wt) ibuprofen, 67.5 % (wt/wt) microcrystalline cellulose, 2 % (wt/wt) of sodium starch glycolate and 0.5 % (wt/wt) of magnesium stearate. The PCA hinted at the prevalence of impeller speed effect on ibuprofen concentration due to path length variation of the NIR light caused by the fluidized behaviour in the powder blend as a result of high speed ranges (>300 rpm). The ASCA model indicated that while both impeller speed and total feed rate effects were statistically significant (p-value=0.004), the impeller speed was the factor that contributed the most to the spectral variance (55.5 %). The PLS regression model for the ibuprofen content resulted in a RMSECV of 1.3 % (wt/wt) and showed that impeller speed was yet again the factor that exerted the major influence on spectral variance, owing to its wavelength-dependent effect that prevents common pre-processing techniques from eliminating it across the entire NIR range. The best sample presentation to the NIR probe was achieved at low impeller speed ranges (<600 rpm) and low total feed rates (<15 kg/h), such that it enhanced the PLS model ability to predict the ibuprofen concentration in the blend.

Exploring the analytical potential of total reflection X-ray fluorescence (TXRF) combined with partial least square (PLS) for simple determination of ash content in various coal types

Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R2) and mean square error (MSE) of the prediction set reaching 0.9736 and 0.99 %, respectively. Moreover, the measurement accuracy of this model was six times greater than that obtained with traditional X-ray fluorescence (XRF). Presented analytical results display the possibilities of combining TXRF with PLS for coal quality evaluation.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Haemonchus contortus alters distribution and utilization of protein and amino acids in different tissues of host sheep

The objective was to determine host animal protein/amino acid redistribution and use among the abomasum, duodenum and muscle of sheep infected with Haemonchus contortus. Sixteen male Ujumqin sheep (32.4 ± 3.9 kg) were dewormed and randomly assigned to two groups, infected or not infected with H. contortus (GIN and CON). The GIN group had lower (P < 0.05) dry matter intake, average daily gain, and live body weight than CON, with extensive focal infiltration of lymphocytes in the lamina propria and bottom of the abomasal epithelium. In the abomasum and duodenum, there were 100 and 220 genes, respectively, that were up-regulated, whereas 56 and 149 were down-regulated. In the abomasum, the most enriched KEGG pathways were related to immunity and inflammation reaction, including: viral protein interaction with cytokine and cytokine receptor (P = 0.017), influenza A (P = 0.030), IL-17 signaling pathway (P = 0.030). In the duodenum, KEGG pathways were more enriched in nutrient metabolism, including pancreatic secretion (P < 0.001), protein digestion and absorption (P < 0.001), graft-versus-host disease (P = 0.004). Furthermore, most genes related with the above KEGG pathways were increased in the abomasum but decreased in the duodenum. Amino acid profiles in abomasum and duodenum of CON and GIN groups were clustered in a partial least-squares discriminant analysis model, with significant changes in 36 and 19 metabolites in abomasal and duodenal chyme, respectively. Further confirmed by transcriptome-targeted metabolome association analysis, GIN mainly enhanced metabolism of arginine and sulphur amino acids in abomasum and those metabolic pathways were associated. Meanwhile, GIN mainly decreased pyruvate related amino acid metabolism in duodenum. Moreover, concentrations of Arg (P = 0.036), His (P = 0.027), and Cys (P = 0.046) in longissimus thoracis et lumborum were decreased in GIN, whereas concentrations of Gly (P = 0.012) and Ala (P = 0.046) were increased. In conclusion, H. contortus enhanced metabolism of arginine and sulphur amino acids in the abomasum; decreased pyruvate metabolism in the duodenum; and drove more protein/amino acids for abomasal tissues to resist physical and immune damage, reducing protein and amino acids in duodenum and muscle for support host growth. Specific nutrients (such like arginine, histidine, and cysteine) may play important role in control gastrointestinal nematode infection for ruminant.

PLS-based hellinger distance method for fault detection in chemical engineering systems

Fault detection is vital in chemical engineering systems to maintain operational efficiency, product quality, and safety through timely identification and correction of deviations from expected behavior. Although partial least squares (PLS) has proven effective in monitoring due to its ability to handle highly correlated variables, traditional detection metrics of PLS may fail to identify small abnormal changes as they rely solely on recent observations. This paper integrates PLS modeling framework with Hellinger Distance (HD)-based fault detection index to overcome the limitations of conventional detection metrics. The utilization of HD is motivated by its sensitivity to quantifying any dissimilarity between distributions, which makes it well-suited for detecting small deviations in process behavior. The HD-based index will be computed between the residuals obtained from the model in the offline stage and the online stage. The HD metric involves careful inspection and comparison of the residuals, which enables it to capture the sensitive details in the data, thus, enhancing the detection of faults. For increased flexibility, kernel density estimation is employed to establish the reference threshold of the PLS-HD approach. The performance of this approach will be evaluated using data from simulated Continuous Stirred-Tank Heater (CSTH) and Continuous Stirred-Tank Reactor (CSTR) processes, by considering various fault types such as bias, freezing, and sensor drift faults. The results demonstrate the superior performance of the proposed PLS-HD approach compared to conventional PLS monitoring methods.

Data driven latent variable adaptive control for nonlinear multivariable processes

This article aims at a new partial least squares (PLS) control design and analysis in a data-driven framework for nonlinear multivariable processes whose mechanistic models are completely unknown. First, a general nonlinear autoregressive moving average with the exogenous input (NARMAX) model is used as the dynamic nonlinear PLS model. Then, by introducing a dynamic linearisation approach in each latent variable (LV) space, the unknown NARMAX-based PLS model is transformed to a linear dynamic PLS data model (dPLSDM), which can be improved in real time by estimating its unknown parameter using the latent input and output (I/O) data. Next, a data-driven latent variable adaptive control (DDLVAC) is proposed in each LV loop. By virtue of the dPLSDM, the multivariable nonlinear process is decoupled into multiple single-loop systems and the high dimensions of the process data are reduced such that the corresponding DDLVAC is simplified. Further, the DDLVAC only depends on the I/O data without requiring any model information of the original process. Theoretical analysis confirms the validity of the DDLVAC. The simulation study demonstrates the advantages of the DDLVAC such as less storage space, smaller computation burden, less control cost, as well as more robustness against uncertainties.

A self-adaptive model for sensing total phosphorus in natural water bodies using Fourier transform mid-infrared attenuated total reflectance spectroscopy

As the most economically developed area in China, the environmental water quality of Guangdong-Hong Kong-Macao Greater Bay Area has received extensive attention, and the spatial variations of total phosphorus (TP) in the Greater Bay were significant, conventional laboratory analysis is difficult to meet the requirements of TP monitoring due to time and cost consuming, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) technology, combined with the self-adaptive partial least squares (SA-PLS) model, was used to determine the TP concentration. The results showed that P-O vibrations were observed in the wavenumber range of 1200–900 cm−1, and prediction models were established by using this range. For the conventional partial least squares (PLS) model, the R2, RMSE and RPD were 0.817, 0.022 mg L−1 and 2.335, respectively, while for the SA-PLS model the prediction was improved with the values of 0.965, 0.010 mg L−1, 5.296, respectively, and the SA-PLS mode prediction was satisfied when the TP content in water was more than 0.05 mg L−1. It was found that the TP determination was interfered by sulfate, when the sulfate content was < 100 mg L−1, both SA-PLS and the conventional PLS model could be used for quantitative analysis of TP; when sulfate content was > 100 mg L−1, PLS model could not be used while the SA-PLS model still achieved an excellent prediction. Therefore, FTIR-ATR combined with SA-PLS model can rapidly determine TP in water, providing an alternative strategy for monitoring TP in natural water bodies.

Litter application increases soil multinutrient cycling in alpine meadow ecosystems on the Tibetan Plateau

In soil, multiple nutrients are cycled simultaneously (multinutrient cycling), rather than a single measurable process. While the impact of litter decomposition on individual soil nutrient cycling in alpine meadow ecosystems on the Tibetan Plateau is well-documented, its effects on soil multinutrient cycling remain unclear. We deployed a three-year litter application experiment in an alpine meadow on the Tibetan Plateau to examine the responses of soil microclimate, extracellular enzyme activities, and bacterial and fungal communities to litter applications, as well as the correlations between these vital factors with soil multinutrient cycling induced by litter application. We showed that litter application raised the soil multinutrient cycling index, temperature, and moisture content by an average of 190 %, 1.2 °C, and 12 %, respectively. Litter application increased the activities of soil extracellular enzyme β-1,4-glucosidase, β-1,4-xylosidase, β-D-cellobiosidase, L-leucine aminopeptidase, acid phosphatase, and phenol oxidase. Moreover, litter application increased the richness and diversity of both soil bacterial and fungal communities, and altered their community structure, but with larger effects on bacterial communities (R2 = 0.43 for bacterial community and R2 = 0.19 for fungal community). This indicates that bacterial communities are more responsive to litter application than fungal communities in alpine meadow soils on the Tibetan plateau. Partial least-square path modeling indicated that soil bacterial and fungal communities and extracellular enzyme activities were significantly positively correlated with soil multinutrient cycling after litter application. The relative abundances of bacterial phyla of Proteobacteria, Actinobacteria, and Verrucomicrobiota, and fungal phyla of Basidiomycota were positively related to most of the critical soil nutrients, indicating that those microbial taxa are the main drivers of soil multinutrient cycling. Overall, this study provides explicit evidence that litter application accelerates Tibetan meadow soil multinutrient cycling, which contributes to an enhanced understanding of the role of litter in sustaining the functions and services of alpine meadow ecosystems on the Tibetan Plateau.

Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies.

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models

The utilization of the soil pedotransfer functions (PTFs) developed based on the basic soil propertied is an alternative, fast, cost-effective and applicable approach for the prediction of field capacity (FC) and permanent wilting point (PWP). In addition, the Visible–Near-Infrared (Vis-NIR) spectra in soil science has gained prominence due to its practicality and relevance. In this paper, we used the data of the soil PWP and FC of 135 soil samples, easily measurable soil properties and Vis-NIR spectroscopy. The multiple linear regression (MLR) model was utilized to formulate PTFs model and Vis-NIR spectroscopy combined with MLR and partial least-squares (PLSR) was used to develop Spectrotransfer Function (STF). Results showed that among the easily measurable soil properties, particle-size diameter (dg) with Beta of −0.72 and −0.63 the most influential parameters for predicting FC and PWP, respectively, followed by the clay content. Developed PTFs for both FC and PWP with a R2 of 0.71 and 0.68, respectively, had a better performance than other previous developed PTFs. Results also revealed that the PLSR with a higher R2 (0.81) and lower RMSE (4 %) significantly performed better in comparison to STF for both FC and PWP prediction.