Near infrared absorption spectroscopy for the quantification of unsulfated alcohol in sodium lauryl ether sulfate

Compositional variations in the surfactants used to produce personal care products result in significant challenges during large scale manufacturing, for example errors in product viscosity. Characterisation of the surfactant can be completed using chromatographic techniques however these are time consuming and impractical during real-time manufacturing. Near infrared (NIR) absorption spectroscopy with a fibre-optic coupled transmission probe is proposed as an in-line method of determining the levels of unsulfated alcohol in sodium lauryl ether sulfate (SLES). NIR absorption spectra in the region of 4000 – 12000 cm−1 were collected for a range of supplier samples at three temperatures. Gas chromatography - mass spectrometry was used as a reference technique to quantify samples of SLES and quantitative chemometric data analysis was used to produce partial least squares (PLS) calibration models for the prediction of surfactant composition. PLS regression was performed on the data in the spectral regions between 7509 – 5334 cm−1 using a range of data pre-processing techniques to identify the best model. Models were evaluated using root mean square error of cross validation (RMSECV) and residual predictive deviation (RPD) as the primary indicator of model accuracy and robustness. A partial least squares regression model using a generalised least squares weighting data pre-processing approach was found to be the most robust in regards to sample non-homogeneity and temperature, producing a model with an RMSECV = 0.094 w/w% and RPD = 4.03. The model successfully predicted the unsulfated alcohol mass percentage in an external validation of unknown samples with alcohol levels within the model limits of 0.7–2.2 w/w%. Spectra acquired at a resolution of 8 cm−1 with 32 scans take just 16 seconds to obtain, proving that NIR spectroscopy can successfully be applied as an alternative analytical method to gas chromatography for the determination of low level impurities in viscous surfactant systems.

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Nanotechnologies. Characterization of single-wall carbon nanotubes using ultraviolet-visible-near infrared (UV-Vis-NIR) absorption spectroscopy

10.3403/30175309u ◽

2015 ◽

Keyword(s):

Carbon Nanotubes ◽

Absorption Spectroscopy ◽

Near Infrared ◽

Single Wall Carbon Nanotubes ◽

Single Wall Carbon ◽

Nir Absorption

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An Approach of the Madeira Wine Chemistry

Beverages ◽

10.3390/beverages6010012 ◽

2020 ◽

Vol 6 (1) ◽

pp. 12 ◽

Cited By ~ 1

Author(s):

Rosa Perestrelo ◽

Catarina Silva ◽

Carolina Gonçalves ◽

Mariangie Castillo ◽

José S. Câmara

Keyword(s):

Mass Spectrometry ◽

Gas Chromatography ◽

Least Squares ◽

Partial Least Squares ◽

Chemical Reactions ◽

Ageing Process ◽

Gas Chromatography Mass Spectrometry ◽

Wine Aroma ◽

Least Squares Regression ◽

Volatile Composition

Madeira wine is a fortified Portuguese wine, which has a crucial impact on the Madeira Island economy. The particular properties of Madeira wine result from the unique and specific winemaking and ageing processes that promote the occurrence of chemical reactions among acids, sugars, alcohols, and polyphenols, which are important to the extraordinary quality of the wine. These chemical reactions contribute to the appearance of novel compounds and/or the transformation of others, consequently promoting changes in qualitative and quantitative volatile and non-volatile composition. The current review comprises an overview of Madeira wines related to volatile (e.g., terpenes, norisoprenoids, alcohols, esters, fatty acids) and non-volatile composition (e.g., polyphenols, organic acids, amino acids, biogenic amines, and metals). Moreover, types of aroma compounds, the contribution of volatile organic compounds (VOCs) to the overall Madeira wine aroma, the change of their content during the ageing process, as well as the establishment of the potential ageing markers will also be reviewed. The viability of several analytical methods (e.g., gas chromatography-mass spectrometry (GC-MS), two-dimensional gas chromatography and time-of-flight mass spectrometry (GC×GC-ToFMS)) combined with chemometrics tools (e.g., partial least squares regression (PLS-R), partial least squares discriminant analysis (PLS-DA) was investigated to establish potential ageing markers to guarantee the Madeira wine authenticity. Acetals, furanic compounds, and lactones are the chemical families most commonly related with the ageing process.

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Rapid and Cost-Effective Assessment of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy

Agronomy ◽

10.3390/agronomy11040666 ◽

2021 ◽

Vol 11 (4) ◽

pp. 666

Author(s):

Rafael Font ◽

Mercedes del Río-Celestino ◽

Diego Luna ◽

Juan Gil ◽

Antonio de Haro-Bailón

Keyword(s):

Infrared Spectroscopy ◽

Least Squares ◽

Partial Least Squares ◽

Cicer Arietinum ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cross Validation ◽

Neutral Detergent Fiber ◽

Acid Detergent Fiber ◽

Cicer Arietinum L

The near-infrared spectroscopy (NIRS) combined with modified partial least squares (modified PLS) regression was used for determining the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) fractions of the chickpea (Cicer arietinum L.) seed. Fifty chickpea accessions (24 desi and 26 kabuli types) and fifty recombinant inbred lines F5:6 derived from a kabuli × desi cross were evaluated for NDF and ADF, and scanned by NIRS. NDF and ADF values were regressed against different spectral transformations by modified partial least squares regression. The coefficients of determination in the cross-validation and the standard deviation from the standard error of cross-validation ratio were, for NDF, 0.91 and 3.37, and for ADF, 0.98 and 6.73, respectively, showing the high potential of NIRS to assess these components in chickpea for screening (NDF) or quality control (ADF) purposes. The spectral information provided by different chromophores existing in the chickpea seed highly correlated with the NDF and ADF composition of the seed, and, thus, those electronic transitions are highly influenced on model fitting for fiber.

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