Dye, diethylene glycol, water, and surfactant content in ink
Applications | | MetrohmInstrumentation
In the ink production industry, maintaining consistent quality requires monitoring key formulation parameters—dye concentration, solvent levels, surfactant content and moisture. Traditional wet‐chemistry methods for these analyses are laborious, time‐consuming and require skilled personnel. Visible–near infrared (Vis–NIR) spectroscopy offers a rapid, non‐destructive alternative, enabling simultaneous determination of multiple components in a single measurement by untrained operators.
This study aimed to develop and validate quantitative Vis–NIR methods for four critical ball‐pen ink parameters: dye (0.6–4.6 %), diethylene glycol (DEG, 12.5–29.8 %), surfactant (0.0–1.0 %) and water content (63.6–82.1 %). Twenty commercial blue ink samples served as the calibration set and eight independent samples for external validation.
The following instrumentation and configuration were employed:
Spectral data were recorded from 400 to 2350 nm depending on the analyte. Partial least squares (PLS) regression models were built using appropriate spectral pretreatments—second derivative, standard normal variate (SNV) or none—optimizing factors and wavelength ranges for each parameter.
Dye content was modeled over 420–800 nm with no pretreatment and two PLS factors, achieving R²=0.9961, SEC=0.0835 % and SECV=0.0949 %. DEG content between 2220–2300 nm used second‐derivative+SNV pretreatment and two factors (R²=0.9934, SEC=0.5037 %, SECV=0.5888 %). Surfactant content was predicted from 1350–2350 nm with a second‐derivative pretreatment and five factors (R²=0.9774, SEC=0.0368 %, SECV=0.1316 %). Water content was modeled in the 1300–1550 nm region using second‐derivative pretreatment and three factors (R²=0.9909, SEC=0.5571 %, SECV=0.9614 %). External validation on eight independent samples confirmed predictive accuracies within acceptable limits for all four analytes.
Vis–NIR spectroscopy streamlines ink quality control by:
Advances likely include integration of inline Vis–NIR probes for real-time production monitoring, expansion of chemometric models to colored inks and specialty formulations, deployment of handheld NIR devices for field QC and incorporation of machine learning algorithms to further improve prediction robustness and adaptivity.
The developed Vis–NIR methods demonstrate high accuracy and precision for quantifying dye, DEG, surfactant and water in ball‐pen inks. Adoption of this approach can significantly accelerate quality control workflows, reduce costs and improve consistency in ink manufacturing.
No additional literature references were provided in the source document.
NIR Spectroscopy
IndustriesEnergy & Chemicals
ManufacturerMetrohm
Summary
Importance of Topic
In the ink production industry, maintaining consistent quality requires monitoring key formulation parameters—dye concentration, solvent levels, surfactant content and moisture. Traditional wet‐chemistry methods for these analyses are laborious, time‐consuming and require skilled personnel. Visible–near infrared (Vis–NIR) spectroscopy offers a rapid, non‐destructive alternative, enabling simultaneous determination of multiple components in a single measurement by untrained operators.
Objectives and Overview
This study aimed to develop and validate quantitative Vis–NIR methods for four critical ball‐pen ink parameters: dye (0.6–4.6 %), diethylene glycol (DEG, 12.5–29.8 %), surfactant (0.0–1.0 %) and water content (63.6–82.1 %). Twenty commercial blue ink samples served as the calibration set and eight independent samples for external validation.
Methodology and Used Instrumentation
The following instrumentation and configuration were employed:
- NIRS DS2500 Analyzer (Metrohm 2.922.0010) in transflection mode.
- Optically flat quartz transflection vessel, 1 mm path length (Metrohm 6.7401.000).
- Gold diffuse reflector, 1 mm path length (Metrohm 6.7420.000).
- Vision 4.03 chemometric software (Metrohm 6.6069.102).
Spectral data were recorded from 400 to 2350 nm depending on the analyte. Partial least squares (PLS) regression models were built using appropriate spectral pretreatments—second derivative, standard normal variate (SNV) or none—optimizing factors and wavelength ranges for each parameter.
Results and Discussion
Dye content was modeled over 420–800 nm with no pretreatment and two PLS factors, achieving R²=0.9961, SEC=0.0835 % and SECV=0.0949 %. DEG content between 2220–2300 nm used second‐derivative+SNV pretreatment and two factors (R²=0.9934, SEC=0.5037 %, SECV=0.5888 %). Surfactant content was predicted from 1350–2350 nm with a second‐derivative pretreatment and five factors (R²=0.9774, SEC=0.0368 %, SECV=0.1316 %). Water content was modeled in the 1300–1550 nm region using second‐derivative pretreatment and three factors (R²=0.9909, SEC=0.5571 %, SECV=0.9614 %). External validation on eight independent samples confirmed predictive accuracies within acceptable limits for all four analytes.
Benefits and Practical Applications
Vis–NIR spectroscopy streamlines ink quality control by:
- Providing rapid, simultaneous multi‐component analysis in under a minute.
- Eliminating reagents and complex sample preparation.
- Reducing operator training requirements.
- Supporting in‐process monitoring and batch release testing.
Future Trends and Potential Applications
Advances likely include integration of inline Vis–NIR probes for real-time production monitoring, expansion of chemometric models to colored inks and specialty formulations, deployment of handheld NIR devices for field QC and incorporation of machine learning algorithms to further improve prediction robustness and adaptivity.
Conclusion
The developed Vis–NIR methods demonstrate high accuracy and precision for quantifying dye, DEG, surfactant and water in ball‐pen inks. Adoption of this approach can significantly accelerate quality control workflows, reduce costs and improve consistency in ink manufacturing.
References
No additional literature references were provided in the source document.
Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.
Similar PDF
Butyl glycol and propylheptyl alcohol in water-borne paint
|Metrohm|Applications
NIR Application Note NIR-29 Butyl glycol and propylheptyl alcohol in water-borne paint NIR values / % Propylheptyl alcohol Reference values / % This Application Note shows that Vis-NIR spectroscopy is ideally suited to quantify two important additives – butyl glycol…
Key words
propylheptyl, propylheptylalcohol, alcoholpaint, paintglycol, glycolbutyl, butylnir, nirsev, sevgray, grayblack, blacklab, labpress, presspredicted, predictedregression, regressionlight, lightsmartprobe
Purity, degree of substitution (DS), and moisture content of carboxymethyl cellulose (CMC)
|Metrohm|Applications
NIR Application Note NIR-31 Purity, degree of substitution (DS), and moisture content of carboxymethyl cellulose (CMC) NIR values / % Moisture content Reference values / % This Application Note shows that Vis-NIR spectroscopy can be used to quantify three important…
Key words
carboxymethyl, carboxymethylcellulose, cellulosesev, sevcmc, cmcnir, nirmoisture, moisturesep, seppress, presswavelength, wavelengthregression, regressionvalue, valuecentered, centeredcontent, contentsec, secpurity
Determination of amine number and solid content of dipping paint
|Metrohm|Applications
NIR Application Note NIR-30 Determination of amine number and solid content of dipping paint This Application Note shows that Vis-NIR spectroscopy can be used to quantify the amine number and solid content of electrophoretic coating material in the paint industry.…
Key words
paint, paintamine, aminedipping, dippingcontent, contentsolid, solidsev, sevsmartprobe, smartprobenumber, numberelectrophoretic, electrophoreticnirs, nirsdeposition, depositionquantitative, quantitativenir, nirpress, pressbeautification
Determination of cotton linter to wood ratio in cellulose
|Metrohm|Applications
NIR Application Note NIR-32 Determination of cotton linter to wood ratio in cellulose This Application Note shows that Vis-NIR spectroscopy can be used to determine the cotton linter to wood ratio in cellulose samples. This ratio is important in pulp…
Key words
linter, linterwood, woodnir, nircotton, cottonpulp, pulpvis, viscellulose, celluloseviscosity, viscosityvisible, visiblemetrohm, metrohmsev, sevratio, ratiochemometrical, chemometricalspectroscopy, spectroscopyiris