Key Factors for Activated Carbon Adsorption of Pharmaceutical Compounds from Wastewaters: A Multivariate Modelling Approach

Projection to Latent Structures (PLS) regression, a generalization of multiple linear regression, is used to model two datasets (40 observed data points each) of adsorption removal of three pharmaceutical compounds (PhCs), of different therapeutic classes and physical–chemical properties (carbamazepine, diclofenac, and sulfamethoxazole), from six real secondary effluents collected from wastewater treatment plants onto different powdered activated carbons (PACs). For the PLS regression, 25 descriptors were considered: 7 descriptors related to the PhCs properties, 10 descriptors related to the wastewaters properties (8 related to the organic matrix and 2 to the inorganic matrix), and 8 descriptors related to the PACs properties. This modelling approach showed good descriptive capability, showing that hydrophobic PhC-PAC interactions play the major role in the adsorption process, with the solvation energy and log Kow being the most suitable descriptors. The results also stress the importance of the competition effects of water dissolved organic matter (DOM), namely of its slightly hydrophobic compounds impacting the adsorption capacity or its charged hydrophilic compounds impacting the short-term adsorption, while the water inorganic matrix only appears to impact PAC adsorption capacity and not the short-term adsorption. For the pool of PACs tested, the results point to the BET area as a good descriptor of the PAC capacity, while the short-term adsorption kinetics appears to be better related to its supermicropore volume and density. The improvement in these PAC properties should be regarded as a way of refining their performance. The correlations obtained, involving the impact of water, PhC and PAC-related descriptors, show the existence of complex interactions that a univariate analysis is not sufficient to describe.

Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

Efficiency and its determinants in the Algerian banks: network data envelopment analysis and partial least squares regression

PurposeThe purpose of this paper is to evaluate the efficiency of Algerian banks and examine the effects of explanatory factors on their performance.Design/methodology/approachIn this paper, a methodology of two-stage network data envelopment analysis (DEA) is used to explore the efficiency of a sample of 13 Algerian banks during the 2013–2017 period. In the first stage, the network DEA is used to assess the overall and stages efficiencies. In the second stage, the partial least squares (PLS) regression is conducted to determine the potential effects of explanatory factors on stages efficiency.FindingsThe main empirical results indicate that Algerian banks need an efficiency improvement in both stages. The overall efficiency of the Algerian banking system improves over the study period. The deposit producing efficiency is positively affected by bank size and bank age. The revenue earning efficiency is negatively associated with bank size and bank age. The domestic banks are more efficient than foreign banks in the deposit producing stage and the foreign banks are more efficient than domestic banks in the revenue earning stage.Practical implicationsThe results might be used as guidelines for both managers and policymakers in order to improve banks and banking system performance.Originality/valueTo the best of our knowledge, this study is the first that uses the DEA in investigating the efficiency of Algerian banks by dividing the overall efficiency into deposit producing and revenue earning efficiencies. Unlike most studies that have usually used OLS regression, Tobit regression and bootstrapped truncated regression, this study is the first in the bank efficiency literature that uses PLS regression to investigate the potential effect of explanatory variables on deposit producing and revenue earning efficiencies.

Predictive evaluation of multicomponent direct compress model tablets by integrating sphere UV-Vis spectroscopy and chemometrics

BACKGROUND: Control of the pharmaceutical manufacturing process and active pharmaceutical ingredients (API) is essential to product formulation and bioavailability. OBJECTIVE: The aim of this study is to predict tablet surface API concentration by chemometrics using integrating sphere UV-Vis spectroscopy, a non-destructive and contact-free measurement method. METHODS: Riboflavin, pyridoxine hydrochloride, dicalcium phosphate anhydrate, and magnesium stearate were mixed and ground with a mortar and pestle, and 100 mg samples were subjected to direct compression at a compaction pressure of 6 MPa at 7 mm diameter. The flat surface tablets were then analyzed by integrating sphere UV-Vis spectrometry. Standard normal variate (SNV) normalization and principal component analysis were applied to evaluate the measured spectral dataset. The spectral ranges were prepared at 300–800 nm and 500–700 nm with SNV normalization. Partial least squares (PLS) regression models were constructed to predict the API concentrations based on two previous datasets. RESULTS: The regression vector of constructed PLS regression models for each API was evaluated. API concentration prediction depends on riboflavin absorbance at 550 nm and the excipient dicalcium phosphate anhydrate. CONCLUSION: Integrating sphere UV-Vis spectrometry is a useful tool to process analytical technology.

Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.

Influence of Digital Finance on Efficacy of Entrepreneurship by Returning Migrant Workers

Digital finance provides an ideal entrepreneurial environment for returning migrant workers (RMWs). From the perspective of entrepreneurs, many scholars have quantified the factors affecting entrepreneurship, as well as the entrepreneurial environment, theorized the importance, motives, and internal/external impactors of RMW entrepreneurship, and put forward quite a lot of countermeasures. This paper innovatively evaluates how digital finance influences the efficacy of RMW entrepreneurship. Firstly, the authors established an influencing factor analysis model and an RMW entrepreneurship model and explained principles for the structural equation modeling of the influence of digital finance on RMW entrepreneurship efficacy. Next, the traditional partial least squares (PLS) regression was optimized, the optimal initial iteration values (IIVs) were obtained, and the algorithm convergence was achieved. Finally, a multilayer structural equation model (SEM) was constructed to evaluate the influence of digital finance on RMW entrepreneurship efficacy. The proposed algorithm and model were proved valid and feasible through experiments.

Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

Codot coffee from Tanggamus, Lampung is one of Indonesian specialty coffee with a very limited production. In this research, an authentication study for the Codot ground roasted coffee was conducted using UV-vis spectroscopy and chemometrics. A total of 330 samples of pure and adulterated Codot coffee was prepared. The adulterated Codot coffee samples were intentionally created by adding a regular coffee (non-Codot coffee) into pure Codot coffee samples with three levels of adulterations: low (10-20%), medium (30-40%), and high level (50-60%). All samples were 0,29 mm in particle size. The extraction procedure was performed with hot distilled water (98°C). The spectral data of coffee samples were acquired using a benchtop UV-visible spectrometer in the range of 190-1100 nm using a transmittance mode. The result showed that the pure and adulterated samples could be discriminated along PC1 and PC2 axis. The classification model was developed using LDA with 90,91% of accuracy could be obtained. The LDA model was used to classify the new samples and resulted in a sensitivity (SEN) of 100%, specificity (SPEC) of 76,67%, precision (PREC) of 78,13%, and accuracy (ACC) of 87,27% could be obtained. Using PLS regression, a PLS model was developed to quantify the percentages of Codot coffee adulteration and resulted in high of coefficient of determination both in calibration and validation (R2kal = 0,99 and R2val = 0,98). These results showed that UV-vis spectroscopy and chemometrics are suitable for authentication of Codot specialty coffee with RMSEP = 2,68% and RPD in prediction of 6,49. Keywords: authentication, LDA, PCA, PLS regression, UV-vis spectroscopy