Partial Least Square Analysis of Lysozyme Near-Infrared Spectra

Hu, Shih-Yao B.; Lillquist, Amy; Arnold, Mark A.; Wiencek, John M.

doi:10.1385/abab:87:3:153

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…These bands are centered at 4260, 4365, 4420, and 4600 cm -1 and are consistent with our earlier findings. 12 Although baseline shifts are evident in Figure 2, the sloping nature of the baseline variation is no longer significant. More importantly, there is no apparent shift in the position of the lysozyme absorption bands.…”

Section: Resultsmentioning

confidence: 94%

“…The spectral range for each model represented in Figure 7 was optimized by a previously described method. 12 In the case of the six-factor model, the ideal range is 4705-4475 cm -1 and the RMSEP is 0.090 mg/mL.…”

Section: Resultsmentioning

confidence: 98%

“…[6][7][8][9][10][11] Recently, we measured the limit of detection for lysozyme measurements by NIR spectroscopy under fixed temperature conditions. 12 The well-known temperature sensitivity of NIR spectroscopy is a major issue that bears directly upon the proposed application. This sensitivity originates mostly from the temperature sensitivity…”

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confidence: 99%

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Temperature-Independent Near-Infrared Analysis of Lysozyme Aqueous Solutions

Arnold

Wiencek

2000

Anal. Chem.

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Digital Fourier filtering is used to produce a temperatureinsensitive univariate calibration model for measuring lysozyme in aqueous solutions. Absorbance spectra over the 5000-4000 cm -1 spectral range are collected for lysozyme standards maintained at 14 °C. These spectra are used to compute the calibration model while a set of spectra collected at temperatures ranging from 4 to 24 °C are used to validate the accuracy of this model. The root-mean-square error of prediction (RMSEP) is 0.279 mg/mL over a tested lysozyme concentration range of 0.036-51.6 mg/mL. The detection limit is 0.68 mg/mL. In addition, multivariate calibration models based on partial least-squares regression (PLS) are evaluated and compared to the results from the univariate model. PLS outperforms the univariate model by providing a RMSEP of 0.090 mg/mL. Analysis of variance showed that both calibration methods effectively eliminate the adverse affects created by variations in solution temperature.Optimized protein crystal growth rates are necessary to provide large, high-quality crystals for X-ray diffraction analysis. Temperature can be used to control crystal growth rates by regulating protein saturation levels and, thereby, controlling the extent of supersaturation. The effectiveness of using temperature to control crystal growth rates has been established for selected proteins for which an appropriate temperature-time profile can be developed. 1,2 Such a profile requires detailed knowledge of the temperature-dependent solubility properties of the protein as well as an understanding of the kinetic parameters that govern nucleation and crystal growth for this protein. Such details are not generally known a priori, thereby limiting the utility of this approach.Alternatively, in situ crystal growth rates can be used to drive a temperature-controlled protein crystallization process. In this method, in situ crystal growth rates are obtained by measuring changes in the soluble protein concentration in real time during the crystallization process. Consumption of soluble protein during crystal formation results in lower soluble protein concentrations. Accurate measurement of the soluble protein concentration permits computation of the crystal growth rate from simple mass balance, and this information can be used as a feedback-control parameter for adjusting system temperature. This approach demands a suitable analytical method to provide accurate in situ protein concentrations in a nondestructive, continuous, and temperature-insensitive manner.Near-infrared (NIR) spectroscopy is proposed as a method for monitoring soluble protein levels in situ during crystallization. Conceptually, the measurement is made by passing a selected beam of NIR radiation through the sample of interest and extracting the desired analytical information from the resulting spectral information. Unlike other analytical methods for protein measurements in aqueous solutions, 3,4 NIR spectroscopic measurements are reagentless and nondestructive. Both of these properties are c...

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 98%

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Temperature-Independent Near-Infrared Analysis of Lysozyme Aqueous Solutions

Arnold

Wiencek

2000

Anal. Chem.

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“…Interestingly, there do not appear to be any previous investigations into the application of NIR for the determination of either protein or polysaccharides in white wines although it has been widely applied to other commodities. Previous studies have used NIR for the estimation of protein content in flour (Osborne and Fearn, 1983), milk (Kawamura et al, 2007) and in model solutions of lysozyme (Hu et al, 2000) and albumin (Murayama et al, 1998). Related NIR methods for polysaccharides include those by Benelli et al (2005), Robert and Cadet (1998) and Polesello et al (1990).…”

Section: Introductionmentioning

confidence: 99%

Prediction of colloidal stability in white wines using infrared spectroscopy

Versari

Laghi

Thorngate³

et al. 2011

Journal of Food Engineering

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“…The nondestructive nature of near-infrared spectroscopy is an important advantage for this approach. In addition, near-infrared spectroscopy provides suitable limits of detection and computational methods are available to provide accurate measurements under variable-temperature conditions. , …”

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Multivariate Calibration Models for Lysozyme from Near-Infrared Transmission Spectra in Scattering Solutions of Monodisperse Microspheres

2002

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The ability to quantify lysozyme is demonstrated for a series of aqueous samples with different degrees of scattering. Near-infrared spectra are collected for two sets of lysozyme/scattering solutions. In both sets of samples, the solutions are composed of lysozyme dissolved in acetate buffer with suspended monodisperse latex microspheres of polystyrene. The diameter of the microspheres is 6.4 microm for the first set and 0.6 microm for the second. For each set, the amount of microspheres range from 0.005 to 0.998 wt %, the lysozyme concentrations range from 0.834 to 28.6 mg/mL, and solution compositions are designed to minimize correlations between the concentration of lysozyme and percentage of microspheres. Near-infrared spectra are collected individually for each set of solutions. Single-beam spectra are collected over the combination spectral range (5000-4000 cm(-1), 2.0-2.5 microm) by transmitting the incident radiation through a 1.5-mm-thick sample that is maintained at 21 degrees C. Partial least-squares calibration models are evaluated individually for each data set both with and without wavelength optimization. Results indicate that models from raw, nonmodified, single-beam spectra are incapable of extracting lysozyme concentration from these highly scattering solutions. Accurate concentration measurements are possible, however, by implementing either a multiplicative scatter correction to the single-beam spectra or by taking the ratio of these single-beam spectra to an appropriate reference spectrum. In addition, digital Fourier filtering of these spectra enhances model performance. The best calibration model in the presence of 6.4-microm microspheres is obtained from multiplicative scatter corrected single-beam spectra over the 4550-4190-cm(-1) spectral range. The mean percent error of prediction (MPEP) and standard error of prediction (SEP) for this model are 2.2% and 0.28 mg/mL, respectively. Likewise, the multiplicative scatter corrected spectra with wavelength optimization provided the best calibration model for the 0.6-microm data set. In this case, the MPEP and SEP are 2.3% and 0.44 mg/mL, respectively. In addition, the ability to predict lysozyme concentrations is evaluated for the situation where the degree of scattering is greater in the predication samples compared to the calibration samples. Differences in the prediction ability are noted between the 6.4- and 0.6-microm data sets.

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Partial Least Square Analysis of Lysozyme Near-Infrared Spectra

Cited by 5 publications

References 11 publications

Temperature-Independent Near-Infrared Analysis of Lysozyme Aqueous Solutions

Temperature-Independent Near-Infrared Analysis of Lysozyme Aqueous Solutions

Prediction of colloidal stability in white wines using infrared spectroscopy

Multivariate Calibration Models for Lysozyme from Near-Infrared Transmission Spectra in Scattering Solutions of Monodisperse Microspheres

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