Simultaneous Determination of Sodium Tripolyphosphate, Sodium Sulfate and Linear Alkylbenzensulfonate in Washing Powder by Attenuated Total Reflectance: Fourier Transform Infrared Spectrometry

‘Clean’ hydrolase reactions using commercial washing powder

We report the use of commercial laundry powder as a biocatalyst for a range of lipase-catalysed reactions including (trans)esterification, ester hydrolysis and chemoenzymatic epoxidation reactions. The enzymatic laundry powder exhibited excellent stability and recyclability, making it a readily available and cheap biocatalyst for chemical transformations.

We report the use of commercial laundry powder as a biocatalyst for a range of lipase-catalysed reactions including (trans)esterification, ester hydrolysis and chemoenzymatic epoxidation reactions.

Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy

Concerns about meat authenticity are increasing recently, due to great fraud scandals. This paper analysed real samples (43 adulterated and 12 controls) originated from criminal networks dismantled by the Brazilian Police. This fraud consisted of injecting solutions of non-meat ingredients (NaCl, phosphates, carrageenan, maltodextrin) in bovine meat, aiming to increase its water holding capacity. Five physico-chemical variables were determined, protein, ash, chloride, sodium, phosphate. Additionally, infrared spectra were recorded. Supervised classification PLS-DA models were built with each data set individually, but the best model was obtained with data fusion, correctly detecting 91% of the adulterated samples. From this model, a variable selection based on the highest VIPscores was performed and a new data fusion model was built with only one chemical variable, providing slightly lower predictions, but a good cost/performance ratio. Finally, some of the selected infrared bands were specifically associated to the presence of adulterants NaCl, tripolyphosphate and carrageenan.

Classification of washing powder brands using near-infrared spectroscopy combined with chemometric calibrations

In this study, near-infrared (NIR) spectroscopy is applied for rapid and objective classification of 5 different brands of washing powder. Chemometric calibrations including partial least square discriminant analysis (PLS-DA), back propagation neural network (BP-NN) and least square support vector machine (LS-SVM) are investigated and compared to achieve an optimal result. Firstly, principal component analysis (PCA) is conducted to visualize the difference among washing powder samples of different brands and principal components (PCs) are extracted as inputs of BP-NN and LS-SVM models. The number of PCs and parameters of such models are optimized via cross validation. In experimental studies, a total of 225 spectra of washing powder samples (45 samples for each brand) were used to build models and 75 spectra of washing powder samples (15 samples for each brand) were used as the validation set to evaluate the performance of developed models. As for the comparison of the three investigated models, both BP-NN model and LS-SVM model successfully classified all samples in validation set according to their brands. However, the PLS-DA model failed to achieve 100% of classification accuracy. The results obtained in this investigation demonstrate that NIR spectroscopy combined with chemometric calibrations including BP-NN and LS-SVM can be successfully utilized to classify the brands of washing powder.

Quantitative Determination of Tetraacetylethylenediamine in Laundry Powder by FTIR Spectrometry Using Partial Least Squares Algorithm

Attenuated total reflectance Fourier transform infrared spectrometry was applied for determination of tetraacetylethylenediamine (TAED) in commercial laundry powder samples. Investigating different spectral regions, 1724–1670 cm−1 was selected as the best region. Spectroscopic data were compared with gas chromatography results, demonstrating reliability of the proposed method, higher accuracy and precision. Standard error of estimation (SEE), standard error of prediction (SEP), correlation coefficient (R2) and root mean square error of prediction (RMSEP) was 0.022, 0.031, 0.998 and 0.138 in the selected spectral region, respectively.