Investigating the Structural Change in Protein Aqueous Solution Using Temperature-Dependent Near-Infrared Spectroscopy and Continuous Wavelet Transform

A Novel Variable Selection Method Based on Binning-Normalized Mutual Information for Multivariate Calibration

Variable (wavelength) selection is essential in the multivariate analysis of near-infrared spectra to improve model performance and provide a more straightforward interpretation. This paper proposed a new variable selection method named binning-normalized mutual information (B-NMI) based on information entropy theory. "Data binning" was applied to reduce the effects of minor measurement errors and increase the features of near-infrared spectra. "Normalized mutual information" was employed to calculate the correlation between each wavelength and the reference values. The performance of B-NMI was evaluated by two experimental datasets (ideal ternary solvent mixture dataset, fluidized bed granulation dataset) and two public datasets (gasoline octane dataset, corn protein dataset). Compared with classic methods of backward and interval PLS (BIPLS), variable importance projection (VIP), correlation coefficient (CC), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS), B-NMI not only selected the most featured wavelengths from the spectra of complex real-world samples but also improved the stability and robustness of variable selection results.

Spectral denoising based on Hilbert–Huang transform combined with F-test

Due to the influence of uncontrollable factors such as the environment and instruments, noise is unavoidable in a spectral signal, which may affect the spectral resolution and analysis result. In the present work, a novel spectral denoising method is developed based on the Hilbert–Huang transform (HHT) and F-test. In this approach, the original spectral signal is first decomposed by empirical mode decomposition (EMD). A series of intrinsic mode functions (IMFs) and a residual (r) are obtained. Then, the Hilbert transform (HT) is performed on each IMF and r to calculate their instantaneous frequencies. The mean and standard deviation of instantaneous frequencies are calculated to further illustrate the IMF frequency information. Third, the F-test is used to determine the cut-off point between noise frequency components and non-noise ones. Finally, the denoising signal is reconstructed by adding the IMF components after the cut-off point. Artificially chemical noised signal, X-ray diffraction (XRD) spectrum, and X-ray photoelectron spectrum (XPS) are used to validate the performance of the method in terms of the signal-to-noise ratio (SNR). The results show that the method provides superior denoising capabilities compared with Savitzky–Golay (SG) smoothing.

Insight into the stability of protein in confined environment through analyzing the structure of water by temperature-dependent near-infrared spectroscopy

To understand the stability of protein in confined environment, the near-infrared (NIR) spectra of aqueous solutions and reverse micelles (RMs) containing bovine serum albumin (BSA), human serum albumin (HSA) and ovalbumin (OVA) were measured at different temperature. With the resolution enhanced spectra calculated by continuous wavelet transform (CWT), the intensity change of the α-helix band at 4617 cm^-1 with temperature shows a clear denaturation of the protein in aqueous solution but not in RMs. The effect of the confined environment on the stability of the proteins is indicated. More importantly, the intensity change of the spectral bands of water around 6956 and 6842 cm^-1 provide an evidence for the denaturation, suggesting that water can be a probe exhibiting the structural change of proteins. Furthermore, comparing the spectral features of different water structures obtained by principal component analysis (PCA) from the spectra of RM with and without BSA, it is demonstrated that the bridging water connecting NH in protein and SO in the inner surface of RM may be the reason for the stabilization.

Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy

Temperature-dependent near-infrared (NIR) spectroscopy has been developed and taken as a powerful technique for analyzing the structure of water and the interactions in aqueous systems. Due to the overlapping of the peaks in NIR spectra, it is difficult to obtain the spectral features showing the structures and interactions. Chemometrics, therefore, is adopted to improve the spectral resolution and extract spectral information from the temperature-dependent NIR spectra for structural and quantitative analysis. In this review, works on chemometric studies for analyzing temperature-dependent NIR spectra were summarized. The temperature-induced spectral features of water structures can be extracted from the spectra with the help of chemometrics. Using the spectral variation of water with the temperature, the structural changes of small molecules, proteins, thermo-responsive polymers, and their interactions with water in aqueous solutions can be demonstrated. Furthermore, quantitative models between the spectra and the temperature or concentration can be established using the spectral variations of water and applied to determine the compositions in aqueous mixtures.

Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology

Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.

Advances in differentiation and identification of foodborne bacteria using near infrared spectroscopy

Rapid and sensitive detection of foodborne bacteria is a growing concern for ensuring safe food supply and preventing human foodborne infections. It is difficult for conventional methods to meet these detection requirements because they are often tedious and time-consuming. In the recent years, near infrared (NIR) spectroscopy has been found to be a promising method for all sorts of analyses in microbiology due to its highly specific absorption signature and non-destructive measurements. In this review, we first briefly introduce the fundamental and basic operational procedure of NIR spectroscopy for foodborne bacteria detection. Then we summarize the main advances and contributions of this technique in the study of foodborne bacteria. Finally, we conclude that much work still remains to be done before NIR spectroscopy really becomes a viable alternative in the field of microbiological characterization.

Research on the Structure of Peanut Allergen Protein Ara h1 Based on Aquaphotomics

Peanut allergy is becoming a life-threatening disease that could induce severe allergic reactions in modern society, especially for children. The most promising method applied for deallergization is heating pretreatment. However, the mechanism from the view of spectroscopy has not been illustrated. In this study, near-infrared spectroscopy (NIRS) combined with aquaphotomics was introduced to help us understand the detailed structural changes information during the heating process. First, near-infrared (NIR) spectra of Ara h1 were acquired from 25 to 80°C. Then, aquaphotomics processing tools including principal component analysis (PCA), continuous wavelet transform (CWT), and two-dimensional correlation spectroscopy (2D-COS) were utilized for better understanding the thermodynamic changes, secondary structure, and the hydrogen bond network of Ara h1. The results indicated that about 55°C could be a key temperature, which was the structural change point. During the heating process, the hydrogen bond network was destroyed, free water was increased, and the content of protein secondary structure was changed. Moreover, it could reveal the interaction between the water structure and Ara h1 from the perspective of water molecules, and explain the effect of temperature on the Ara h1 structure and hydrogen-bonding system. Thus, this study described a new way to explore the thermodynamic properties of Ara h1 from the perspective of spectroscopy and laid a theoretical foundation for the application of temperature-desensitized protein products.

Understanding the effect of urea on the phase transition of poly(N-isopropylacrylamide) in aqueous solution by temperature-dependent near-infrared spectroscopy

Regulating the folding state by denaturants is essential for the structure and function of proteins. Poly(N-isopropyl acrylamide) (PNIPAM) is usually regarded as a model for protein denaturation. The effects of urea, as a denaturant, on the aggregations of PNIPAM was studied by temperature-dependent near-infrared (NIR) spectroscopy, and particularly the variation of water structures was analyzed. The NIR spectra of the polymer-urea solutions containing different polymer concentrations were measured at different temperatures. N-way principal component analysis (NPCA) was performed to observe the spectral information. Three principal components (PCs) containing the spectral information of CH groups were obtained, showing three kinds of CH in the system. Obvious dehydration of the three CH groups occurs at 27.5 °C in solution, but the temperature turns to 27 °C for two kinds of the CH and 26.5 °C for the third one, respectively, in the urea-add solution. The effect of urea on the formation of the intramolecular hydrogen bonds that promotes polymer folding is suggested. The spectral information of NH in urea molecule indicates that the direct interaction of urea and polymer facilitates the stability of the polymer globule state. Furthermore, the spectral information of OH shows that the release of the water molecules with three hydrogen bonds (S₃), which may connect the NH and CO groups in PNIPAM in solution, leads to the phase transition. When urea is added, urea may reduce the content of the S₃ water to facilitate the release, making the phase transition at a low temperature.

Ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics for rapid discrimination of Angelicae Sinensis Radix from its four similar herbs

Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) combined with chemometrics was used for the first time to differentiate Angelicae Sinensis Radix (ASR) from four other similar herbs (either from the same genus or of similar appearance). A total of 191 samples, including 40 ASR, 39 Angelicae Pubescentis Radix (APR), 38 Chuanxiong Rhizoma (CR), 35 Atractylodis Macrocephalae Rhizoma (AMR) and 39 Angelicae Dahuricae Radix (ADR), were collected and divided into the training and prediction sets. Principal component analysis (PCA) was used for observing the sample cluster tendency of the calibration set. Different preprocessing methods were investigated and the optimal preprocessing combination was selected according to spectral signal characteristics and three-dimensional PCA (3D PCA) clustering results. The final discriminant model was built using extreme learning machine (ELM). The exploratory studies on the raw spectra and their 3D PCA scores indicate that the classification of the five herbs cannot be achieved by PCA of the raw spectra. Autoscaling, continuous wavelet transform (CWT) and Savitzky-Golay (SG) smoothing can improve the clustering results to different degrees. Furthermore, their combination in the order of CWT + autoscaling + SG smoothing can enhance the spectral resolution and obtain the best clustering result. These results are also validated using ELM models of raw and different preprocessing methods. By using CWT + autoscaling + SG smoothing + ELM, 100% classification accuracy can be achieved in both the calibration set and the prediction set. Therefore, the developed method could be used as a rapid, economic and effective method for discriminating the five herbs used in this study.

Interaction between tau and water during the induced aggregation revealed by near-infrared spectroscopy

Near-infrared (NIR) spectra are sensitive to the variation of water structure. In this work, NIR spectroscopy was used to investigate the variation of hydration water during the aggregation of R2/wt induced by heparin. The osmolytes, urea and trehalose, were used to slow down and speed up the aggregation. The spectra of R2/wt aqueous solution obtained by NIR spectroscopy at 37 °C were adopted to analyze the structure of water during the aggregation. The spectral features of different water species were observed by principal component analysis (PCA) from the resolution enhanced NIR spectra. The existence of the water molecules with one and two hydrogen bonds around the NH and CH groups of R2/wt, respectively, was suggested, and the variation of the hydrogen-bonded water was found to be an indicator to monitor the process of aggregation. Then, the variation of the water species during the aggregation was analyzed by two-dimensional correlation spectroscopy. The water hydrogen bonded with NH group was found to change earlier than the water around the hydrophobic groups. The results suggest that β-sheet forms though the hydrogen bonds of amide groups in the early stage of the aggregation, and the destruction of the hydrogen bond network of the water around the side chains maybe the main reason for the formation of the ordered amyloid fibers.

Water as a probe for serum-based diagnosis by temperature- dependent near-infrared spectroscopy

Diagnosis based on the compositional variation of biological liquids such as serum has drawn much attention. For exploring the potential diagnostic information from serum samples, temperature-dependent near-infrared (NIR) spectroscopy was developed to obtain the spectral change of water reflecting the interactions in serum solution, and chemometric methods were employed to mine the information from the temperature-dependent NIR spectra. The spectra of 68 healthy controls, 42 patients with the type II diabetes and 16 patients with coronary heart disease were measured and analyzed by chemometric calculations. Continuous wavelet transform (CWT) was used to enhance the resolution of the spectra. From the processed spectra, the information of non-hydrogen-bonded (NHB), weakly hydrogen-bonded (WHB) and strongly hydrogen-bonded (SHB) water species was observed. For explaining the variation of the spectra with temperature, two-dimensional (2D) correlation analysis was adopted. A clear difference in SHB/NHB ratio in the synchronous maps was found between the spectra of the patients and the controls. 86.8% of the controls and 98.3% of the patients can be correctly identified. Furthermore, combining the maps of the synchronous and asynchronous analysis, the correlation between SHB and WHB water species was discovered to have an ability to discriminate the patients of diabetes and heart disease with an accuracy of 83.7% and 75.0%, respectively. Therefore, water may be a probe for providing diagnostic information by temperature-dependent NIR spectroscopy.

Understanding the role of water in the aggregation of poly(N,N-dimethylaminoethyl methacrylate) in aqueous solution using temperature-dependent near-infrared spectroscopy

For understanding the role of water in the aggregation of polymers, the variation of water structures with the structural change of polymers in the process of aggregation was studied by temperature-dependent near-infrared (NIR) spectroscopy. The NIR spectra of the aqueous poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) solutions of different concentrations were measured at different temperatures. The spectral changes of the polymer and water with temperature were analyzed by N-way principal component analysis (NPCA). It was found that, at low concentration, the chains of the polymer tend to form a loose hydrophobic structure below 36 °C and then aggregate into a micelle at a lower critical solution temperature (LCST) of around 39 °C. In the process of the aggregation, the water species with two hydrogen bonds (S2) increases gradually before 36 °C and then a sudden decrease occurs after that temperature. The results clearly indicate that water species S2 plays an important role in the formation of the intermediate, i.e., the loose hydrophobic structure of the polymer chains linked by the two hydrogen bonds of S2 water. When the temperature increases, the dissociation of the hydrogen bonds enables the intermediate to be destroyed to form a micelle structure. For the high concentration solution, however, the spectral information of S2 was not found in the aggregation, suggesting direct formation of the micelle from the dehydrated chains.

Understanding the Interaction Between Oligopeptide and Water in Aqueous Solution Using Temperature-Dependent Near-Infrared Spectroscopy

Investigating the interaction between oligopeptide and water is essential for understanding the structure, dynamics and function of proteins. Temperature-dependent near-infrared (NIR) spectroscopy and independent component analysis (ICA) were employed to study the interaction between oligopeptide and water in aqueous solution. The NIR spectra of two homo-oligopeptides, penta-aspartic acid (D₅) and penta-lysine (K₅), in aqueous solution of different concentration were measured at different temperature (30-90 ℃). Independent component analysis was performed to extract the spectral information that changes with temperature. The independent components (ICs) representing the spectral information of NH and CH₂ groups were obtained. Compared with D₅, the two groups in K₅ change significantly at higher temperature. The result may suggest that K₅ has stronger interaction with water than D₅. Moreover, three ICs that contain the spectral information of the water species with no (S₀), one (S₁), and two (S₂) hydrogen-bonds were obtained. It was shown that the spectral intensity of S₀ and S₁ increases while that of S₂ decreases with the temperature, and the changes of oligopeptide solutions are weaker than those of pure water. The results indicate that water structure is sensitive to temperature and the oligopeptide in aqueous solution improves the thermal stability of the water species. When oligopeptide is added, the spectral intensity of S₀ and S₂ decreases and that of S₁ increases for D₅ solution, but the intensity of all the three species decreases for K₅ solution. Furthermore, the concentration effect of K₅ was found to be stronger than D₅. The result may reveal that D₅ combines with water molecule through forming one hydrogen bond but K₅ interacts with water through a different way.

Understanding the function of water during the gelation of globular proteins by temperature-dependent near infrared spectroscopy

Water plays an indispensable role in the gelation of proteins, but its function still remains unclear. In this work, the variation of water species with the structural changes of globular proteins was investigated using temperature-dependent near infrared (NIR) spectroscopy. Ovalbumin (OVA) was used as a model protein, which forms a gel-like structure as the temperature increases through three phases, i.e., phase I (native), phase II (molten globule state), and phase III (gel state). The structural change and the content variation of different water species in the three phases of gelation were analyzed by two-dimensional correlation NIR spectroscopy and Gaussian fitting. A decrease in the water species with two hydrogen bonds (S2) was found and the change follows the same phases as OVA. In the first two phases, the change occurs after those of other water species but in the third phase, the change is faster than that of free water species. The result indicates that in the native and molten globule states, S2 is located in the hydration shell of OVA to maintain the stability of the protein structure, and then in the gel state, high temperature weakens the hydrogen bonding of S2 and leads to the destruction of the hydration shell, making OVA clusters form a gel structure.

Mutual factor analysis for quantitative analysis by temperature dependent near infrared spectra

Temperature dependent near infrared (NIR) spectroscopy has been developed for analyzing multi-component mixtures and understanding the molecular interactions in solutions. In this work, a chemometric method named as mutual factor analysis (MFA) was proposed for the analysis of temperature dependent NIR spectra. The method extracts the common spectral feature contained in the spectra of different temperature or different concentration. The relative quantity of the extracted spectral feature is proportional to the temperature or concentration. From the spectra of water-glucose mixtures, both the spectral variations induced by temperature and concentration are obtained and the variations are correlated with the inducements, respectively, in a very good linearity. Serum samples were used for validation of the method. An acceptable calibration model with a good correlation coefficient (R² = 0.8639) was obtained for glucose measurement. The relative deviations of the measured concentrations from the calibration model are in the range of -18.7-8.52%, which are in a reasonable level for clinical uses. More importantly, the calculations are based on the spectral information of water that has interactions with the analyte. This provides a new way for quantitative analyses of bio-systems.