QSAR modeling for cytotoxicity of sulfur-containing Shikonin oxime derivatives targeting HCT-15, MGC-803, BEL-7402, and MCF-7 cell lines.

Targeting cancer cells through drug-based treatment or combination therapy protocols involving chemical compounds can be challenging due to multiple factors, including their resistance to bioactive compounds and the potential of drugs to damage healthy cells. This study aims to investigate the relationship between the structure of novel sulfur-containing shikonin oxime compounds and the corresponding cytotoxicity against four cancer types, namely colon, gastric, liver, and breast cancers, through computational chemistry tools. This investigation is suggested to help build insights into how the structure of the compounds influences their activity and understand the mechanisms behind it and subsequently might be used in multi-cancer drug design process to propose novel optimized compounds that potentially exhibit the desired activity. The findings showed that the cytotoxic activity against the four cancer types was accurately predictable (R2 > 0.7, NRMSE <20%) by a combination of search and machine learning algorithms, based on the information on the structure of the compounds, including their lipophilicity, surface area, and volume. Overall, this study is supposed to play a crucial role in effective multi-cancer drug design in cancer research areas.

Flexible Synthesis of Bio-Hydroxyapatite/Chitosan Hydrogel Beads for Highly Efficient Orange G Dye Removal: Batch and Recirculating Fixed-Bed Column Study.

The use of fish waste as a source material for the development of functional beads has significant potential applications in the fields of materials science and environmental sustainability. In this study, a biomaterial bead of chitosan was cross-linked with bio-hydroxyapatite (Bio-Hap/Cs) through the encapsulation process to create a stable and durable material. The beads are characterized using scanning electron microscopy combined with energy dispersive X-ray spectrometry, Fourier transform infrared spectroscopy, and X-ray diffraction techniques. The adsorption efficiency of Bio-Hap/Cs hydrogel beads was evaluated by using Orange G (OG) dye in both batch and recirculating column systems, and the effect of various parameters on the adsorption capacity was investigated. In the batch study, it was found that OG removal increased with an increasing pH and adsorbent dose. However, in the recirculating column system, a higher bed height and lower flow rate led to increased removal of the OG dye. The kinetic study indicated that the pseudo-second-order model provided a good description of OG adsorption onto Bio-Hap/Cs beads in both batch and recirculating processes, with a high coefficient correlation. The maximum adsorbed amounts are found to be 19.944 mg g-1 and 9.472 mg g-1 in batch and recirculating processes, respectively. Therefore, Bio-Hap/Cs hydrogel beads have demonstrated an effective and reusable material for OG dye remediation from aqueous solutions using recirculating adsorption processes.

Simultaneous Quantification of Two Neonicotinoids Using QuEChERS-LC-MS/MS in Moroccan Spearmint (Mentha Spicata.L): Qualimetry of the Method by Uncertainty Estimation Using Generalized Pivotal Quantities Approach and Monte Carlo Simulation.

BACKGROUND: Neonicotinoids (NEOs) are used for the phytosanitary treatment of Mentha Spicata.L crops, and this practice requires precise control of these harmful substances at very low concentrations. OBJECTIVE: The objective of this study is to apply an approach allowing simultaneously validation and evaluation of measurement uncertainty based on total error methodology, in order to accurately quantify the presence of two NEOs in Mentha Spicata.L utilizing a Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS)-LC-MS/MS methodology. METHODS:  The quantification of imidacloprid and acetamiprid employing a QuEChERS extraction method, coupled with LC-MS/MS, ensuring the accuracy of the analytical method and managing the risks associated with its routine use. A complete and exhaustive validation approach based on the "ß-content, γ-confidence" tolerance interval was used for the uncertainty assessment, using the generalized pivot quantity (GPQ) concept and Monte Carlo simulation, which avoids the need for additional data while achieving intermediate precision for each concentration level within predetermined acceptable limits. RESULTS: The validation procedure is based on the choice of a quadratic model for the two NEOs, allowing the validation of acetamiprid and imidacloprid by LC-MS/MS assay within the range of working concentration. The flexibility of the uncertainty profile intervals was demonstrated with a variation in ß-content values (66.7, 80, and 90%) and risk values (10 and 5%), which remained within the acceptability limits of 20%, and the relative expanded uncertainty did not exceed 15 and 11%. CONCLUSION: A QuEChERS-LC-MS/MS method for the analysis of two NEOs has been successfully fully validated using the uncertainty profile strategy. HIGHLIGHTS: Implementation of an overall validation strategy, which involves both the validation and uncertainty assessment known as the uncertainty profile, for the quantification of two important NEOs in Mentha Spicata.L using QuEChERS-LC-MS/MS. This qualimetric approach has been conducted by computing the measurement uncertainty of the method utilizing data from analytical validation under conditions of intermediate precision at each level of concentration without additional effort. After that we have demonstrated the flexibility of this strategy for the LC-MS/MS quantification of acetamiprid and imidacloprid, using a decision tool that enables the choice and modification of ß-content and γ-confidence values.

Simultaneous Hydrodistillation of Healthy Cedrus atlantica Manetti and Infected by Trametes pini and Ungulina officinalis: Effect on Antibacterial Activity Utilizing a Mixture-Design Method.

The Atlas cedar belongs to the Pinaceae family of trees and can be found in a crucial resinous mountain forest in Morocco that spans 133,000 hectares. This endemic species is valued for its wood quality and essential oil (EO), which has various biological activities. However, pathogenic fungi, particularly Trametes pini and Ungulina officinalis, frequently attack Atlas cedarwood, causing significant damage and loss of value. This study aims to extract EO from both healthy and infected Atlas cedarwood to promote its valorization and to assess the antibacterial properties of the resulting EOs. The EOs from healthy and sick cedarwood, as well as a combination of these woods, were extracted using hydrodistillation and simultaneous hydrodistillation. Gas chromatography and mass spectrometry were used to examine the chemical compositions of the EOs. In addition, the disk diffusion method and a measurement of the minimum inhibitory concentration were used to assess the EOs' antibacterial activity against two bacterial strains, namely, Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The results show that the extraction yields of healthy cedarwood, cedarwood infected by Trametes pini, and cedarwood infected by U. officinalis were 1.43 ± 0.03, 0.56 ± 0.03, and 0.26 ± 0.06%, respectively, Moreover, the antibacterial results showed that neither the healthy nor the diseased cedar oil had any impact on either strain. However, the EOs from some binary mixtures (75:25, 50:50, and 25:75%) of cedarwood infected by Trametes pini and cedarwood infected by U.ngulina officinalis and the mixture of healthy cedarwood and cedarwood infected by the two fungi inhibited the growth of S. aureus with different MIC values. The findings of this research could lead to the development of new products with antibacterial properties, such as natural disinfectants, and reduce the amount of waste generated by the cedar industry.

Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy.

Olive oil is a key component of the Mediterranean diet, rich in antioxidants and beneficial monounsaturated fatty acids. As a result, high-quality olive oil is in great demand, with its price varying depending on its quality. Traditional chemical tests for assessing olive oil quality are expensive and time-consuming. To address these limitations, this study explores the use of near infrared spectroscopy (NIRS) in predicting key quality parameters of olive oil, including acidity, K232, and K270. To this end, a set of 200 olive oil samples was collected from various agricultural regions of Morocco, covering all three quality categories (extra virgin, virgin, and ordinary virgin). The findings of this study have implications for reducing analysis time and costs associated with olive oil quality assessment. To predict olive oil quality parameters, chemical analysis was conducted in accordance with international standards, while the spectra were obtained using a portable NIR spectrometer. Partial least squares regression (PLSR) was employed along with various variable selection algorithms to establish the relationship between wavelengths and chemical data in order to accurately predict the quality parameters. Through this approach, the study aimed to enhance the efficiency and accuracy of olive oil quality assessment. The obtained results show that NIRS combined with machine learning accurately predicted the acidity using iPLS methods for variable selection, it generates a PLSR with coefficients of determination R2 = 0.94, root mean square error RMSE = 0.32 and ratios of standard error of performance to standard deviation RPD = 4.2 for the validation set. Also, the use of variable selection methods improves the quality of the prediction. For K232 and K270 the NIRS shows moderate prediction performance, it gave an R2 between 0.60 and 0.75. Generally, the results showed that it was possible to predict acidity K232, and K270 parameters with excellent to moderate accuracy for the two last parameters. Moreover, it was also possible to distinguish between different quality groups of olive oil using the principal component analysis PCA, and the use of variable selection helps to use the useful wavelength for the prediction olive oil using a portable NIR spectrometer.

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples.

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.337, respectively. Moreover, the possibility of optimizing and boosting the performance of the method by optimizing the predictive model performance has been suggested. Therefore, a features selection approach was conducted to disqualify non-relevant wavelengths and stress only relevant ones in order to make them the only contributors to a final optimized model. The best selected features subset was composed of 28 wavelengths out of 121, found by applying genetic algorithm combined to partial least squares regression as a feature selection method, on spectra preprocessed consecutively by the first-order savitzky-golay derivative calculated with 7-point quadratic SG filter, and multiplicative scatter correction method. The overall results show the possibility of combining HSI and ML for fast monitoring of water content in cement samples.

The Flexibility of ß-Content, γ-Confidence Tolerance Intervals to Qualimetry a Simultaneous 22 Aromatic Amines Derived from Azo Dyes in Fabric Using a Sensitive GC-MS Technique.

BACKGROUND: Azo dyes are among the most widely used dyes in the textile industry, releasing a series of carcinogenic aromatic amines that can be absorbed through the skin. OBJECTIVE: This work aims to show that 22 azo dye amines in a textile matrix can be quantified using a GC-MS method. METHODS: Based on the notion of total error and ß-content, γ-confidence tolerance intervals (ß,γ-CCTI), a chemometric approach known as the "uncertainty profile" has been used to completely validate a GC-MS method for the simultaneous assay of 22 azo amines in fabrics. According to International Organization for Standardization (ISO) in ISO 17025 guidelines, analytical validation and measurement uncertainty estimates have evolved to be two main principles for ensuring the accuracy of analytical results and controlling the risk associated with their use. RESULTS: The calculated tolerance intervals allowed for the determination of the uncertainty limits at each concentration level. These limits when compared to the acceptable limits show that a significant portion of the expected outcomes is in conformity. Additionally, the relative expanded uncertainty values, calculated with a proportion of 66.7% and a 10% risk, do not exceed 27.7, 12.2, and 10.9% for concentration levels 1, 15, and 30 mg/L, respectively. CONCLUSION: The capability and flexibility of the ß-content, γ-confidence intervals have been established through the use of this innovative approach to carrying out qualimetry of the GC-MS method depending on the behavior, required conformity proportion, and acceptable tolerance limits of each amine. HIGHLIGHTS: An efficient GC-MS technique for the simultaneous determination of 22 azo amines in a textile matrix has been developed. Analytical validation using a new strategy based on the uncertainty concept is reported, uncertainty associated to measurement results is estimated, and the applicability of our approach to the GC-MS method is investigated.

An Overall Validation Approach Based on ß-Content, γ-Confidence Tolerance Interval, and Uncertainty Profile: Application to LC-MS/MS Quantification of Carbendazim in Drinking Water.

BACKGROUND: Carbendazim is a fungicide which can seep into the water supply, presenting a public health risk, and therefore the accurate trace determination of this substance is very important. OBJECTIVE: The purpose of the study is to take a top-down analytical validation approach in order to determine the amount of carbendazim in drinking water by using an SPE-LC-MS/MS technique. METHODS: Quantification of carbendazim using solid-phase extraction coupled with LC-MS/MS was used in order to ensure the accuracy of the analytical method and to control the risk of its routine application. An overall validation methodology based on two-sided tolerance interval type ß-content, γ-confidence has been applied for the validation and estimation of uncertainty by building a decision graphical tool called the "uncertainty profile" by using the statistical process known as the Satterthwaite approximation with no recourse to additional data by satisfying intermediate precision condition for each concentration level within the acceptance limits fixed in advance. RESULTS: The process of validation is based on the selection of a linear weighted 1/X model enabling validation of the carbendazim dosage using LC-MS/MS in the range of working concentrations as the ßγ-CCTI fell inside acceptable limits of ±10%, and the relative expanded uncertainty did not surpass 7% regardless of the ß values (66.7, 80, and 90%) and the 1- γ = risk (10 and 5%). CONCLUSION: The application of the uncertainty profile approach for full validation of a SPE-LC-MS/MS assay for the quantification of carbendazim has been successfully achieved. HIGHLIGHTS: Implementation of a full validation strategy based on validation and measurement uncertainty with no additional effort using data from analytical validation under intermediate precision conditions at each level of concentration for carbendazim quantification in drinking water using SPE-LC-MS/MS. So we have shown the flexibility of this approach for carbendazim assay by LC-MS/MS. Indeed, It provides an efficient decision-making tool that allows selection and modification of ß-content and γ-confidence values.

Intra-GUM comparisons for the estimation of measurement uncertainty: Application to a chromatographic assay of four statins in a dry form.

The chromatographic method (HPLC) is well-known for its dependability and precision in pharmaceutical analyses for assessing the quality and efficacy of drug products. However, the characteristics of the HPLC method make sense only after a careful evaluation of the measurement uncertainty. Nevertheless, it has been observed that precise identification of the sources of uncertainty resulting from the operational mode using the HPLC technique to assess their standard uncertainty based on the ISO-GUM approach is not obvious. Because of our extensive bibliographic search, we noticed that many studies published on the estimate of the uncertainty in chromatographic methods (HPLC) using the Eurachem/Citac guide are somewhat diverse in their use of this approach. The documentary research revealed four mathematical models namely, GUM1, GUM2, GUM3, and GUM4 that are most used to estimate the measurement uncertainty of the HPLC technique. Hence, a comparison between the four GUM mathematical models was carried out graphically and statistically using one-way ANOVA followed by Tuckey and Scheffe tests. The comparison results show a significant difference, which may create confusion in the reporting of measurement results as well as problems in making sound decisions. This leads to the suggestion that alternative approaches, such as the approach of a total error or ISO 11325, be used to check the ISO-GUM method's calculations of measurement uncertainty.

The Uncertainty Profile Used for Full Validation of the HPLC Method to Determine 22 Azo Amines in Fabrics.

BACKGROUND: Azo dyes are synthetic dyes that can degrade to carcinogenic amines, therefore determining their exact concentration is crucial. OBJECTIVE: The purpose of this study is to demonstrate that an HPLC method may be used to quantify 22 azo dye amines in a textile matrix. METHODS: The HPLC-DAD (Diode Array Detector) technique for measuring 22 amines in a textile matrix was tested using a new graphical statistical strategy based on the building of ß-content, γ-confidence tolerance intervals, which allows for good qualimetry of analytical procedures. In this way, we established the approach's ability to perform a full validation of the analytical technique as well as its ability to estimate measurement uncertainty in a simple and uncomplicated way with minimum effort, utilizing only the data collected during the analytical validation. RESULTS: The findings suggest that this technique can properly assess the validity of the HPLC method for simultaneous determination of 22 amines, with 67-90% of compliance results falling within acceptable limits. CONCLUSION: Using the uncertainty profile methodology, the full validation of an HPLC method for the determination of 22 azo amines in a textile matrix was successfully established. HIGHLIGHTS: The development and evaluation of an efficient HPLC technique for the simultaneous determination of 22 azo amines in a textile matrix have been completed. The total reliability of the analytical procedure was established by evaluating its measurement uncertainty at each concentration level using the suggested uncertainty profile approach and confirming proportions 67 to 90% of compliance.

Development and Validation of a UPLC-DAD Method for the Simultaneous Quantification of Eight Antihypertensive Drugs in the Pharmaceutical Matrix.

BACKGROUND: Hypertension is a critical health problem; it is a prevalent risk factor for cardiovascular disease. Many treatments to combat hypertension are available, however many patients are resistant to the standard therapeutic approaches. The association of two or more substances in a fixed-dose combination is effective and tolerated as a substitute for the standard therapeutic approach. OBJECTIVE: The new ultra performance liquide chromatography method was developed and validated to assay a combination of eight antihypertensive drugs including a diuretic: hydrochlorothiazide, dihydropyridine calcium channel blocker: Amlodipine and angiotensin II type 1 receptor blockers (sartans): valsartan, candesartan, eprosartan, olmesartan, losartan, and irbesartan in the pharmaceutical matrix. METHODS: Chromatographic separation was performed on an Acquity® UPLC C18 1.7 µm 2.1 × 100 mm column, with a gradient of buffer solution and acetonitrile, in the proportion of (80:20 v/v). RESULTS: Good resolution was obtained, and an optimal analysis time of less than 5 min was achieved. The method was validated according to the International Conference on Harmonization guidelines following the classical approach and accuracy profile, and it is shown to be suitable for intended applications. The method was successfully used for quality control laboratories and the determination of these drugs combinations in pharmaceutical dosage forms.

Application of Design Space, Uncertainty, and Risk Profile Strategies to the Development and Validation of UPLC Method for the Characterization of Four Authorized Phosphodiesterase Type 5 Inhibitors to Combat Counterfeit Drugs.

BACKGROUND: Counterfeit medicines are an increasing scourge that are difficult to identify and they have become industrialized and widespread through highly organized illegal channels. OBJECTIVE: This research aims to develop a robust method to determine four phosphodiesterase type-5 inhibitors in counterfeit drugs based on ultra-performance liquid chromatography. METHOD: Experimental design methodology (DOE) and design space (DS) recommended by ICH Q8 were used side-by-side in the development phase to define the optimal parameters as well as the robustness of the chromatographic method. Moreover, both the uncertainty and risk profile derived from the ß-content and γ-confidence tolerance interval were investigated during the validation phase to examine the performance of this method. RESULTS: Successful chromatographic results, in a high resolution between the four active ingredients and an optimal analysis time of less than 1.6 min, were achieved at the end of the optimization phase. In addition, validation results show a low risk of future measurements outside acceptance limits set at 5%. CONCLUSIONS: Our procedure was successfully applied in the routine phase to identify 23 illicit formulations of an erectile dysfunction drug. HIGHLIGHTS: An efficient method for the characterization of 4 authorized phosphodiesterase in less than 1.6 min was established. A DS approach was applied to test the performance of this analytical method during analytical development. A risk profile was then carried out to approve the validity of the analytical method through the uncertainty profile approach.

Development and validation of QSPR models for corrosion inhibition of carbon steel by some pyridazine derivatives in acidic medium.

Statistical modeling of the corrosion inhibition process by twenty-one pyridazine derivatives for mild steel in acidic medium was investigated by the quantitative structure property relationship (QSPR) approach. This modeling was established by the correlation between the corrosion inhibition efficiency (IE %) and a number of the electronic and structural properties of these inhibitors such as: the E HOMO (highest occupied molecular orbital energy), the E LUMO (lowest unoccupied molecular orbital energy), the energy gap (E L-H ), the dipole moment (µ), the hardness (η), the softness (σ), the absolute electronegativity (χ), the ionization potential (IP), the electron affinity (EA), the fraction of electrons transferred (ΔN), the electrophilicity index ω the molecular volume (V m ), the logarithm of the partition coefficient (Log P), and the molecular mass (M), in addition to the inhibitor concentration (C i ). The structure electronic properties was calculated by the use of the density functional theory method (DFT), at B3LYP/6-31G (d, p) level of theory and the analysis of dimensionality and redundancy as well as the test of collinearity between descriptors are carried out using principal component analysis (PCA). Whereas, the correlation between EI % and molecular structure is performed through the development of tree mathematical models, based-QSPR approaches: the partial least squares regression (PLS), the principal component regression (PCR) and the artificial neural networks (ANN). Indeed, the statistical quantitative results revealed that PCR and ANN were the most relevant and predictive models in comparison with the PLS model. This pertinence was demonstrated by using leave one-out cross-validation as an efficient method for testing the internal stability and predictive capability of said models with a high cross-validated determination coefficient R 2 cv = 0.92 and predicted determination coefficient R 2 pred = 0.92 and R 2 pred = 0.90 for PCR and ANN respectively; in addition to an extrapolation test set as an external validation with a significant external coefficient of determination: R 2 test = 0.94 and R 2 test = 0.92, for the two correspondingly models.

Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy.

The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis. It is a rapid technique, and inexpensive relatively. However, this technique requires a calibration step using different machine learning and chemometrics tools. This study aims to develop predictive models for total soil phosphorus and extractable phosphorus by the Olson method (P-Olson) using three regression methods, namely partial least squares (PLS), regression support vector machine (RSVM) and backward propagation neural network (BPNN), combined with a proposed variable selection algorithm (PARtest) and a genetic algorithm PLS (GA-PAS). Also, it aims to investigate the effect of the texture on the accuracy of the prediction. The results show that PARtest combined with PBNN outperform the other used algorithms with an R2t = 0.86, RMSEt = 1104 mg kg-1, and RPD = 3.23 for the TP. For P-Olson the RSVM coupled with GA-PLS outperforms all other methods with an R2t = 0.77, RMSEt = 20.09 mg kg-1, and RPD = 1.90. The use of hierarchical ascendant clustering (HAC) helps to reduce the heterogeneity of soil and helps to increase the quality of prediction. The obtained results show that the models for clayey and loamy soils yielded an excellent prediction quality with an R2t = 0.88, RMSEt = 857.33 mg kg-1, and RPD = 4.10 using BPNN with PARtest for TP. Furthermore, an R2 = 0.83 RMSE = 8.30 mg kg-1, RPD = 11.00 3.11using RSVM with GA-PLS for P-Olson. Thus, the texture has a significant effect on the prediction accuracy.

Validation and Measurement Uncertainty Assessment of a Microbiological Method Using Generalized Pivotal Quantity Procedure and Monte-Carlo Simulation.

Recently, a novel and effective statistical tool called the uncertainty profile has been developed with the purpose of graphically assessing the validity and estimating the measurement uncertainty of analytical procedures. One way to construct the uncertainty profile is to compute the ß-content, γ-confidence tolerance interval. In this study, we propose a tolerance interval based on the combination of the generalized pivotal quantity procedure and Monte-Carlo simulation. The uncertainty profile has been applied successfully in several fields. However, in order to further confirm its universality, this newer approach has been applied to assess the performance of an alternative procedure versus a reference procedure for counting of Escherichia coli bacteria in drinking water. Hence, the aims of this research were to expose how the uncertainty profile can be powerfully applied pursuant to ISO 16140 standards in the frame of interlaboratory study and how to easily make a decision concerning the validity of the procedure. The analysis of the results shows that after the introduction of a correction factor, the alternative procedure is deemed valid over the studied range because the uncertainty limits lie within the acceptability limits set at ±-0.3 log unit/100 ml for a ß = 66.7% and γ = 90%.

Large-scale retrospective evaluation of regulated liquid chromatography-mass spectrometry bioanalysis projects using different total error approaches.

The current approach in regulated LC-MS bioanalysis, which evaluates the precision and trueness of an assay separately, has long been criticized for inadequate balancing of lab-customer risks. Accordingly, different total error approaches have been proposed. The aims of this research were to evaluate the aforementioned risks in reality and the difference among four common total error approaches (ß-expectation, ß-content, uncertainty, and risk profile) through retrospective analysis of regulated LC-MS projects. Twenty-eight projects (14 validations and 14 productions) were randomly selected from two GLP bioanalytical laboratories, which represent a wide variety of assays. The results show that the risk of accepting unacceptable batches did exist with the current approach (9% and 4% of the evaluated QC levels failed for validation and production, respectively). The fact that the risk was not wide-spread was only because the precision and bias of modern LC-MS assays are usually much better than the minimum regulatory requirements. Despite minor differences in magnitude, very similar accuracy profiles and/or conclusions were obtained from the four different total error approaches. High correlation was even observed in the width of bias intervals. For example, the mean width of SFSTP's ß-expectation is 1.10-fold (CV=7.6%) of that of Saffaj-Ihssane's uncertainty approach, while the latter is 1.13-fold (CV=6.0%) of that of Hoffman-Kringle's ß-content approach. To conclude, the risk of accepting unacceptable batches was real with the current approach, suggesting that total error approaches should be used instead. Moreover, any of the four total error approaches may be used because of their overall similarity. Lastly, the difficulties/obstacles associated with the application of total error approaches in routine analysis and their desirable future improvements are discussed.