Abstract:Near‐infrared (NIR) spectroscopy is a popular technique for the measurement of chemical and physical properties in‐line using predictive models. The success of these models in industrial settings, in terms of accuracy and precision, often relies on the removal or avoidance of non‐linear spectral changes associated with fluctuating process parameters like temperature. In this work, a NIR calibration model developed to predict the viscosity of micellar liquids in‐line is used to evaluate various methods designed… Show more
“…The spectra were acquired with a Matrix-F Fourier transform near-infrared (FT-NIR; Bruker, Germany) fiber-coupled to a transmission process probe with a pathlength of 2 mm (Excalibur XP 20) where the infrared radiation is transmitted once through the sample. The process fluid was a micellar solution 16 that was circulated at a constant flowrate of 140 kg·hr −1 and temperature of 30 °C ± 2°C. More details related to probe settings, material being measured, and the predictive model used in this work are explained in detail in previous work.…”
Section: Methodsmentioning
confidence: 99%
“…More details related to probe settings, material being measured, and the predictive model used in this work are explained in detail in previous work. 16 Figure 3.Experimental set up used to validate computational fluid dynamics simulations and 50 L recirculation rig with the in-situ NIR probe and a temperature sensor.…”
Process analytical technology (PAT) has developed significantly since its introduction in pharma where many in situ analytical probes and measuring devices are now commercially available, replacing the use of off-line quality control measurements that are typically laborious and time intensive. The use of PAT instrumentation should not interfere with the process itself and subsequently should have no effect on the product whilst measuring representative samples. Implementation of these devices is typically arbitrary using empirical means. Therefore, the objective of this study is to highlight the use of computational fluid dynamics modeling to investigate the effect of interfacing parameters and process parameters of an inline near-infrared (NIR) probe used to determine the viscosity of a non-Newtonian micellar liquid. The parameters investigated for the probe were immersion depth, immersion angle, gap size, and fluid velocity. The results conclude that the immersion angle and depth should both be optimized to prevent stagnant fluid accumulating in the measuring gap ensuring that the NIR measurements are representative of the bulk. The gap size determines the optical pathlength and therefore was also investigated against an existing predictive viscosity model showing no changes in model performance with varying gap size. The use of computational modeling to develop a digital twin prior to PAT implementation at the equipment design stage ensures the technology can perform at its best and will also aid in calibration transfer studies.
“…The spectra were acquired with a Matrix-F Fourier transform near-infrared (FT-NIR; Bruker, Germany) fiber-coupled to a transmission process probe with a pathlength of 2 mm (Excalibur XP 20) where the infrared radiation is transmitted once through the sample. The process fluid was a micellar solution 16 that was circulated at a constant flowrate of 140 kg·hr −1 and temperature of 30 °C ± 2°C. More details related to probe settings, material being measured, and the predictive model used in this work are explained in detail in previous work.…”
Section: Methodsmentioning
confidence: 99%
“…More details related to probe settings, material being measured, and the predictive model used in this work are explained in detail in previous work. 16 Figure 3.Experimental set up used to validate computational fluid dynamics simulations and 50 L recirculation rig with the in-situ NIR probe and a temperature sensor.…”
Process analytical technology (PAT) has developed significantly since its introduction in pharma where many in situ analytical probes and measuring devices are now commercially available, replacing the use of off-line quality control measurements that are typically laborious and time intensive. The use of PAT instrumentation should not interfere with the process itself and subsequently should have no effect on the product whilst measuring representative samples. Implementation of these devices is typically arbitrary using empirical means. Therefore, the objective of this study is to highlight the use of computational fluid dynamics modeling to investigate the effect of interfacing parameters and process parameters of an inline near-infrared (NIR) probe used to determine the viscosity of a non-Newtonian micellar liquid. The parameters investigated for the probe were immersion depth, immersion angle, gap size, and fluid velocity. The results conclude that the immersion angle and depth should both be optimized to prevent stagnant fluid accumulating in the measuring gap ensuring that the NIR measurements are representative of the bulk. The gap size determines the optical pathlength and therefore was also investigated against an existing predictive viscosity model showing no changes in model performance with varying gap size. The use of computational modeling to develop a digital twin prior to PAT implementation at the equipment design stage ensures the technology can perform at its best and will also aid in calibration transfer studies.
The temperature difference of fruit itself will affect its near infrared (NIR) spectrum and the accuracy of its soluble solids content (SSC) prediction model. To eliminate the influence of apple temperature difference on SSC model, the diffuse transmission dynamic online detection device was used to collect the spectral data of apples at different temperatures, and four methods were used to establish partial least squares (PLS) correction models: global correction, orthogonal signal processing (OSC), generalized least squares weighting (GLSW) and external parameter orthogonal (EPO). The results show that the temperature has a strong influence on the diffuse transmission spectrum of apples. The 20℃ model can get a satisfactory prediction result when the temperature is constant, and there will be great errors when detecting samples at other temperatures. The effect of temperature must be corrected to establish a more general model. These methods all improve the accuracy of the model, with the EPO method giving the best results, prediction set correlation coefficient (Rp) is 0.947, Root Mean Square Error of Prediction (RMSEP) is 0.489 %Brix, and the prediction bias (Pred Bias) is 0.009 %Brix. The research results are of great significance to the practical application of SSC prediction of fruits in sorting workshops or orchards.
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