Liquid Detergent Compositions Research Articles

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer.Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models.An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses.It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions between two at-line instruments installed at two liquid detergent production plants

Calibration transfer of partial least squares (PLS) quantification models is established between two Raman spectrometers located at two liquid detergent production plants. As full recalibration of existing calibration models is time-consuming, labour-intensive and costly, it is investigated whether the use of mathematical correction methods requiring only a handful of standardization samples can overcome the dissimilarities in spectral response observed between both measurement systems. Univariate and multivariate standardization approaches are investigated, ranging from simple slope/bias correction (SBC), local centring (LC) and single wavelength standardization (SWS) to more complex direct standardization (DS) and piecewise direct standardization (PDS). The results of these five calibration transfer methods are compared reciprocally, as well as with regard to a full recalibration. Four PLS quantification models, each predicting the concentration of one of the four main ingredients in the studied liquid detergent composition, are aimed at transferring. Accuracy profiles are established from the original and transferred quantification models for validation purposes. A reliable representation of the calibration models performance before and after transfer is thus established, based on β-expectation tolerance intervals. For each transferred model, it is investigated whether every future measurement that will be performed in routine will be close enough to the unknown true value of the sample. From this validation, it is concluded that instrument standardization is successful for three out of four investigated calibration models using multivariate (DS and PDS) transfer approaches. The fourth transferred PLS model could not be validated over the investigated concentration range, due to a lack of precision of the slave instrument. Comparing these transfer results to a full recalibration on the slave instrument allows comparison of the predictive power of both Raman systems and leads to the formulation of guidelines for further standardization projects. It is concluded that it is essential to evaluate the performance of the slave instrument prior to transfer, even when it is theoretically identical to the master apparatus.

Calibration transfer of a Raman spectroscopic quantification method from at-line to in-line assessment of liquid detergent compositions

The industrial production of liquid detergent compositions entails delicate balance of ingredients and process steps. In order to assure high quality and productivity in the manufacturing line, process analytical technology tools such as Raman spectroscopy are to be implemented. Marked chemical specificity, negligible water interference and high robustness are ascribed to this process analytical technique. Previously, at-line calibration models have been developed for determining the concentration levels of the being studied liquid detergents main ingredients from Raman spectra. A strategy is now proposed to transfer such at-line developed regression models to an in-line set-up, allowing real-time dosing control of the liquid detergent composition under production. To mimic in-line manufacturing conditions, liquid detergent compositions are created in a five-liter vessel with an overhead mixer. Raman spectra are continuously acquired by pumping the detergent under production via plastic tubing towards a Raman superhead probe, which is incorporated into a metal frame with a sapphire window facing the detergent fluid. Two at-line developed partial least squares (PLS) models are aimed at transferring, predicting the concentration of surfactant 1 and polymer 2 in the examined liquid detergent composition. A univariate slope/bias correction (SBC) is investigated, next to three well-acknowledged multivariate transformation methods: direct, piecewise and double-window piecewise direct standardization. Transfer is considered successful when the magnitude of the validation sets root mean square error of prediction (RMSEP) is similar to or smaller than the corresponding at-line prediction error. The transferred model offering the most promising outcome is further subjected to an exhaustive statistical evaluation, in order to appraise the applicability of the suggested calibration transfer method. Interval hypothesis tests are thereby performed for method comparison. It is illustrated that the investigated transfer approach yields satisfactory results, provided that the original at-line calibration model is thoroughly validated. Both SBC transfer models return lower RMSEP values than their corresponding original models. The surfactant 1 assay met all relevant evaluation criteria, demonstrating successful transfer to the in-line set-up. The in-line quantification of polymer 2 levels in the liquid detergent composition could not be statistically validated, due to the poorer performance of the at-line model.

Development and validation of an at-line fast and non-destructive Raman spectroscopic method for the quantification of multiple components in liquid detergent compositions

Implementation of process analytical technology (PAT) tools in the manufacturing process of liquid detergent compositions should allow fast and non-destructive evaluation of the product quality. The aim of this study was to develop and validate a rapid method for quantifying the chemical compounds of five washing liquid precursors. Raman spectroscopy was applied in combination with a two-step multivariate modeling procedure. In first instance, a SIMCA (Soft Independent Modeling of Class Analogy) model was developed and validated, allowing the distinction between the different laundry detergents. Once the product was correctly identified, it was aimed at predicting the concentration of its individual components using partial least squares (PLS) models. Raman spectra were collected at-line with a total acquisition time of 20 s, using a non-contact fiber-optic probe.The SIMCA model was perfectly capable of differentiating between the classes of the laundry liquid precursors. Per detergent, the concentration of at least three main ingredients could be predicted with a recovery between 98% and 102% and a standard deviation below 2.5%. Accuracy profiles based on the analysis results of validation samples were then calculated to prove the reliability of the developed regression models. β-expectation tolerance intervals were calculated for each model and for each validated concentration level. The acceptance limits were set at 5% relative bias, indicating that at least 95% of future measurements should not deviate more than 5% from the true value. Furthermore, based on the data of the accuracy profiles, the measurement uncertainty was determined. The developed Raman spectroscopic method demonstrated to be able to rapidly and adequately determine the concentration of the components of interest in the liquid detergent compositions at-line.

5591703 Liquid or granular automatic diswashing detergent compositions containing builder, enzyme and low molecular weight, modified polyacrylate copolymers : Sadlowski Eugene S Cincinnati, OH, United States Assigned to The Procter & Gamble Company

5591703 Liquid or granular automatic diswashing detergent compositions containing builder, enzyme and low molecular weight, modified polyacrylate copolymers : Sadlowski Eugene S Cincinnati, OH, United States Assigned to The Procter & Gamble Company

5281356 Heavy duty liquid detergent compositions containing non-proteolytic enzymes comprising capsules comprising proteolytic enzyme and composite polymer: Liang Tsaur, Michael Aronson, Leslie J Morgan, John Hessel, Jack McCown, John L Gormley assigned to Lever Brothers Company

5281356 Heavy duty liquid detergent compositions containing non-proteolytic enzymes comprising capsules comprising proteolytic enzyme and composite polymer: Liang Tsaur, Michael Aronson, Leslie J Morgan, John Hessel, Jack McCown, John L Gormley assigned to Lever Brothers Company

5281357 Protease containing heavy duty liquid detergent compositions comprising capsules comprising non-proteolytic enzyme and composite polymer: Leslie J Morgan, Michael Aronson, Liang Tsaur, John Hessel, Jack McCown assigned to Lever Brothers Company Division of Conopco Inc

5281357 Protease containing heavy duty liquid detergent compositions comprising capsules comprising non-proteolytic enzyme and composite polymer: Leslie J Morgan, Michael Aronson, Liang Tsaur, John Hessel, Jack McCown assigned to Lever Brothers Company Division of Conopco Inc

5275753 Stabilized alkaline liquid detergent compositions containing enzyme and peroxygen bleach: Buzzaccarini Francesco de, Christiaan A J K Thoen, Jean-Po Boutique, Ixelles, Belgium assigned to The Procter & Gamble Company

5275753 Stabilized alkaline liquid detergent compositions containing enzyme and peroxygen bleach: Buzzaccarini Francesco de, Christiaan A J K Thoen, Jean-Po Boutique, Ixelles, Belgium assigned to The Procter & Gamble Company

5156773 Stabilized enzymatic liquid detergent composition: Danie Kochavi, Vicki Lentner assigned to Novo Nordisk A/S

5156773 Stabilized enzymatic liquid detergent composition: Danie Kochavi, Vicki Lentner assigned to Novo Nordisk A/S

5156773 Stabilized enzymatic liquid detergent composition: Danie Kochavi, Vicki Lentner assigned to Novo Nordisk A/S

5156773 Stabilized enzymatic liquid detergent composition: Danie Kochavi, Vicki Lentner assigned to Novo Nordisk A/S

5156761 Method of stabilizing an enzymatic liquid detergent composition: Dorri Aaslyng, Jadwiga Palicka, DK 4000 Roskilde, Denmark

5156761 Method of stabilizing an enzymatic liquid detergent composition: Dorri Aaslyng, Jadwiga Palicka, DK 4000 Roskilde, Denmark

5156761 Method of stabilizing an enzymatic liquid detergent composition: Dorri Aaslyng, Jadwiga Palicka, DK 4000 Roskilde, Denmark

5156761 Method of stabilizing an enzymatic liquid detergent composition: Dorri Aaslyng, Jadwiga Palicka, DK 4000 Roskilde, Denmark

5089163 Enzymatic liquid detergent composition: Michael P Aronson, Martin Cardinali, Jack T McCown assigned to Lever Brothers Company Division of Conopco Inc

5089163 Enzymatic liquid detergent composition: Michael P Aronson, Martin Cardinali, Jack T McCown assigned to Lever Brothers Company Division of Conopco Inc

4900475 Stabilized built liquid detergent composition containing enzyme: Pallassanna Ramachandran, Jan E Shulman assigned to Colgate-Palmolive Co

4900475 Stabilized built liquid detergent composition containing enzyme: Pallassanna Ramachandran, Jan E Shulman assigned to Colgate-Palmolive Co

4908150 Stabilized lipolytic enzyme-containing liquid detergent composition: John Hessel, Martin S Cardinali, Michael P Aronson assigned to Lever Brothers Company

4908150 Stabilized lipolytic enzyme-containing liquid detergent composition: John Hessel, Martin S Cardinali, Michael P Aronson assigned to Lever Brothers Company

4767562 Enzymatic liquid detergent composition: Alan J Fry, Ellesmere Port, United Kingdom assigned to Lever Brothers Company

4767562 Enzymatic liquid detergent composition: Alan J Fry, Ellesmere Port, United Kingdom assigned to Lever Brothers Company

4842767 Heavy duty built aqueous liquid detergent composition containing stabilized enzymes: Andre Crutzen, H Greenland assigned to Colgate-Palmolive Company

4842767 Heavy duty built aqueous liquid detergent composition containing stabilized enzymes: Andre Crutzen, H Greenland assigned to Colgate-Palmolive Company

Soaps of monohydroxystearic acid in syndet compositions

AbstractSoaps of monohydroxystearic acid, prepared by sulfation of oleic acid followed by hydrolysis, have been evaluated in syndet compositions. They can be used to replace a substantial proportion of the synthetic surfactant in both liquid and solid detergent compositions without loss of detergent efficiency or foaming properties. In contrast, sodium stearate and sodium oleate adversely affect performance. Compatibility is improved when these hydroxystearate soaps are incorporated in built liquid compositions. Since these materials exhibit the usual response of soaps to pH change and to divalent cations, there is complete assurance that they will be inactivated in ground water as well as in sewage treatment plants.