Examining water in model membranes by near infrared spectroscopy and multivariate analysis

Near-infrared spectroscopy (NIRS) for monitoring the nutritional composition of black soldier fly larvae (BSFL) and frass

BACKGROUND

The demand for protein obtained from animal sources is growing rapidly, as is the necessity for sustainable animal feeds. The use of black soldier fly larvae (BSFL) reared on organic side streams as sustainable animal feed has been receiving attention lately. This study assessed the ability of near-infrared spectroscopy (NIRS) combined with chemometrics to evaluate the nutritional profile of BSFL instars (fifth and sixth) and frass obtained from two different diets, namely soy waste and customised bread-vegetable diet. Partial least squares (PLS) regression with leave one out cross-validation was used to develop models between the NIR spectral data and the reference analytical methods.

RESULTS

Calibration models with good [coefficient of determination in calibration (Rcal 2 ): 0.90; ratio of performance to deviation (RPD) value: 3.6] and moderate (Rcal 2 : 0.76; RPD value: 2.1) prediction accuracy was observed for acid detergent fibre (ADF) and total carbon (TC), respectively. However, calibration models with moderate accuracy were observed for the prediction of crude protein (CP) (Rcal 2 : 0.63; RPD value: 1.4), crude fat (CF) (Rcal 2 : 0.70; RPD value: 1.6), neutral detergent fibre (NDF) (Rcal 2 : 0.60; RPD value: 1.6), starch (Rcal 2 : 0.52; RPD value: 1.4), and sugars (Rcal 2 : 0.52; RPD value: 1.4) owing to the narrow or uneven distribution of data over the range evaluated.

CONCLUSION

The near-infrared (NIR) calibration models showed a good to moderate prediction accuracy for the prediction of ADF and TC content for two different BSFL instars and frass reared on two different diets. However, calibration models developed for predicting CP, CF, starch, sugars and NDF resulted in models with limited prediction accuracy. © 2023 Society of Chemical Industry.

Investigations of the Photothermal Properties of a Series of Molecular Gold-bis(dithiolene) Complexes Absorbing in the NIR-III Region

The photothermal properties of a series of neutral radical gold-bis(dithiolene) complexes absorbing in the near-infrared-III window (1550-1870 nm) have been investigated. This class of complexes was found to be good photothermal agents (PTAs) in toluene under 1600 nm laser irradiation with photothermal efficiencies around 40 and 60 % depending on the nature of the dithiolene ligand. To the best of our knowledge, these complexes are the first small molecular photothermal agents to absorb so far into the near infrared. To test their applicability in water, these hydrophobic complexes have been encapsulated into nanoparticles constituted by amphiphilic block-copolymers. Stable suspensions of polymeric nanoparticles (NPs) encapsulating the gold-bis(dithiolene) complexes have been prepared which show a diameter around 100 nm. The encapsulation rate was found to be strongly dependent on the nature of the dithiolene ligands. The photothermal properties of the aqueous suspensions containing gold-bis(dithiolene) complexes were then studied under 1600 nm laser irradiation. These studies demonstrate that water has strong photothermal activity in the NIR-III region that, cannot be overcome even with the addition of gold complexes displaying good photothermal properties.

Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

Partial Least Squares, Experimental Design, and Near-Infrared Spectrophotometry for the Remote Quantification of Nitric Acid Concentration and Temperature

Near-infrared spectrophotometry and partial least squares regression (PLSR) were evaluated to create a pleasantly simple yet effective approach for measuring HNO₃ concentration with varying temperature levels. A training set, which covered HNO₃ concentrations (0.1-8 M) and temperature (10-40 °C), was selected using a D-optimal design to minimize the number of samples required in the calibration set for PLSR analysis. The top D-optimal-selected PLSR models had root mean squared error of prediction values of 1.4% for HNO₃ and 4.0% for temperature. The PLSR models built from spectra collected on static samples were validated against flow tests including HNO₃ concentration and temperature gradients to test abnormal conditions (e.g., bubbles) and the model performance between sample points in the factor space. Based on cross-validation and prediction modeling statistics, the designed near-infrared absorption approach can provide remote, quantitative analysis of HNO₃ concentration and temperature for production-oriented applications in facilities where laser safety challenges would inhibit the implementation of other optical techniques (e.g., Raman spectroscopy) and in which space, time, and/or resources are constrained. The experimental design approach effectively minimized the number of samples in the training set and maintained or improved PLSR model performance, which makes the described chemometric approach more amenable to nuclear field applications.

Interaction of triblock copolymers (Pluronic®) with DMPC vesicles: a photophysical and computational study

Pluronic/lipid mix promises stealth liposomes with long circulation time and long-term stability for pharmaceutical applications. However, the influence of Pluronics on several aspects of lipid membranes has not been fully elucidated. Herein it was described the effect of Pluronics on the structured water, alkyl chain conformation, and kinetic stability of dimyristoylphosphatidylcholine (DMPC) liposomes using interfacial and deeper fluorescent probes along with computational molecular modeling data. Interfacial water changed as a function of Pluronics' hydrophobicity with polypropylene oxide (PPO) anchoring the copolymers in the lipid bilayer. Pluronics with more than 30-40 PO units had facilitated penetration at the bilayer while shorter PPO favored a more interfacial interaction. Low Pluronic concentrations provided long-term stability of vesicles by steric effects of polyethylene oxide (PEO), but high amounts destabilized the vesicles as a sum of water-bridge cleavage at the polar head group and the reduced alkyl-alkyl interactions among the lipids. The high kinetic stability of Pluronic/DMPC vesicles is a proof-of-concept of its advantages and applicability in nanotechnology over conventional liposome-based pharmaceutical products for future biomedical applications.

Novel strategy for establishment of an FT-Raman spectroscopy based quantitative model for poplar holocellulose content determination

Raman spectroscopy is effective for studying the ultrastructure, lignin content, and cellulose crystallinity of lignocellulosic materials. However, the quantitative analysis of holocellulose in lignocellulosic materials by this technique is challenging. In this study, based on Fourier-transform Raman (FT-Raman) spectroscopy, a novel strategy for building poplar holocellulose content quantitative model was proposed. Different algorithms were applied, including Principal component regression (PCR), partial least square regression (PLSR), ridge regression (RR), lasso regression (LR), and elastic net regression (ENR). Combined with different algorithms, twelve candidates of internal standard were selected. Sixty models combined by five regression algorithms and twelve internal standards were performed by five-fold cross validation. Consequently, the models constructed through RR, LR, and ENR combined with the internal standard of peak intensity of 2945 cm^-1 were credible (R_p > 0.9, RMSE_p < 1.0, and MAE_p < 0.9). Credible models were obtained, indicating the high potential of FT-Raman spectroscopy for predicting the holocellulose content of lignocellulosic materials.

Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis

Implementing remote, real-time spectroscopic monitoring of radiochemical processing streams in hot cell environments requires efficiency and simplicity. The success of optical spectroscopy for the quantification of species in chemical systems highly depends on representative training sets and suitable validation sets. Selecting a training set (i.e., calibration standards) to build multivariate regression models is both time- and resource-consuming using standard one-factor-at-a-time approaches. This study describes the use of experimental design to generate spectral training sets and a validation set for the quantification of sodium nitrate (0-1 M) and nitric acid (0.1-10 M) using the near-infrared water band centered at 1440 nm. Partial least squares regression models were built from training sets generated by both D- and I-optimal experimental designs and a one-factor-at-a-time approach. The prediction performance of each model was evaluated by comparing the bias and standard error of prediction for statistical significance. D- and I-optimal designs reduced the number of samples required to build regression models compared with one-factor-at-a-time while also improving performance. Models must be confirmed against a validation sample set when minimizing the number of samples in the training set. The D-optimal design performed the best when considering both performance and efficiency by improving predictive capability and reducing number of samples in the training set by 64% compared with the one-factor-at-a-time approach. The experimental design approach objectively selects calibration and validation spectral data sets based on statistical criterion to optimize performance and minimize resources.

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform-Raman Spectrometry

The quick and non-invasive evaluation of lignin from biomass has been the focus of much attention. Several types of spectroscopies, for example, near-infrared (NIR) and Fourier transform-Raman (FT-Raman), have been successfully applied to build quantitative predictive lignin models based on chemometrics. However, due to the effect of sample moisture content and ambient humidity on its signals, NIR spectroscopy requires sophisticated pre-testing preparation. In addition, the current FT-Raman predictive models require large variations in the independent value inputs as restrictions in the corresponding mathematical algorithms prevent the effective biomass screening of suitable genotypes for lignin contents within a narrow range. In order to overcome the limitations associated with the current methods, in this paper, we employed Raman spectra excited using a 1064 nm laser, thus avoiding the impact of water and auto-fluorescence on NIR signals. The optimal baseline correction method, data type, mathematical algorithm, and internal reference were selected in order to build quantitative lignin models based on the data with limited variation. The resulting two predictive models, constructed through lasso and ridge regressions, respectively, proved to be effective in assessing the lignin content of poplar in large-scale breeding and genetic engineering programs.

Unveiling non-linear water effects in near infrared spectroscopy: A study on organic wastes during drying using chemometrics

In the context of organic waste management, near infrared spectroscopy (NIRS) is being used to offer a fast, non-destructive, and cost-effective characterization system. However, cumbersome freeze-drying steps of the samples are required to avoid water's interference on near infrared spectra. In order to better understand these effects, spectral variations induced by dry matter content variations were obtained for a wide variety of organic substrates. This was made possible by the development of a customized near infrared acquisition system with dynamic highly-resolved simultaneous scanning of near infrared spectra and estimation of dry matter content during a drying process at ambient temperature. Using principal components analysis, the complex water effects on near infrared spectra are detailed. Water effects are shown to be a combination of both physical and chemical effects, and depend on both the characteristics of the samples (biochemical type and physical structure) and the moisture content level. This results in a non-linear relationship between the measured signal and the analytical characteristic of interest. A typology of substrates with respect to these water effects is provided and could further be efficiently used as a basis for the development of local quantitative calibration models and correction methods accounting for these water effects.

Influence of steroids on hydrogen bonds in membranes assessed by near infrared spectroscopy

The covalent OH bonds of water vibrate and absorb radiation in the near infrared (NIR) region at wavelengths that vary according to the strength of the bonds which, at the same time, are sensitive to the number and/or strength of hydrogen bonds. By means of multivariate analytical tools, such spectral shift was exploited to study the effect of temperature, 25-hydroxycholesterol and progesterone on the H-bonded network of water in DMPA membranes. Temperature was found as the dominating factor altering the NIR spectra of water and then the H-bonds. Increasing temperatures disrupt the H-bonds network, strengthening the OH covalent bonds. The disruption of the H-bonds along the 13-58 °C range was noticeably greater than that caused by lipids or steroids at 500 μM. The H-bonded network of the interfacial water in DMPA membranes was disrupted by the presence of 25-hydroxycholesterol, but no significant disruption was observed in the presence of progesterone. The reduction of the H-bonds entails a reduction in the aggregation of the interfacial water by a reduction in the number of H-bonded molecules. It is proposed that the number of water molecules bonded with two H-bonds diminishes and the number of molecules with no H-bond increases roughly at similar proportions, with a constant population of molecules with one H-bond. The opposed effects of steroids are discussed in the context of their opposed effects on the phase state of membranes, the membrane water content and the steroid molecular structure.

Identification and Quantitative Determination of Lactate Using Optical Spectroscopy-Towards a Noninvasive Tool for Early Recognition of Sepsis

Uninterrupted monitoring of serum lactate levels is a prerequisite in the critical care of patients prone to sepsis, cardiogenic shock, cardiac arrest, or severe lung disease. Yet there exists no device to continuously measure blood lactate in clinical practice. Optical spectroscopy together with multivariate analysis is proposed as a viable noninvasive tool for estimation of lactate in blood. As an initial step towards this goal, we inspected the plausibility of predicting the concentration of sodium lactate (NaLac) from the UV/visible, near-infrared (NIR), and mid-infrared (MIR) spectra of 37 isotonic phosphate-buffered saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. UV/visible (300–800 nm) and NIR (800–2600 nm) spectra of PBS samples were collected using the PerkinElmer Lambda 1050 dual-beam spectrophotometer, while MIR (4000–500 cm⁻¹) spectra were collected using the Spectrum two FTIR spectrometer. Absorption bands in the spectra of all three regions were identified and functional groups were assigned. The concentration of lactate in samples was predicted using the Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation. The regression analysis showed a correlation coefficient (R²) of 0.926, 0.977, and 0.992 for UV/visible, NIR, and MIR spectra, respectively, between the predicted and reference samples. The RMSECV of UV/visible, NIR, and MIR spectra was 1.59, 0.89, and 0.49 mmol/L, respectively. The results indicate that optical spectroscopy together with multivariate models can achieve a superior technique in assessing lactate concentrations.

Confirmation of brand identification in infant formulas by using near-infrared spectroscopy fingerprints

In this study, a near infrared (NIR) spectroscopy fingerprinting method coupled with principal component analysis (PCA) was developed for the confirmation of brand identification in infant formulas. The NIR spectroscopy fingerprints of the Brand A infant formula were acquired in 12 000-4000 cm-1 at a sample temperature of 20 °C without pressing the sample. The contents of major nutrients of Stage 1, 2, and 3 infant formulas were compared within Brand A. The NIR spectroscopy fingerprints of Brand A Stage 1 samples were compared with those of four other brand-named Stage 1 samples, whereas the fingerprints of Brand A Stage 2 and 3 were compared with those of two of the four brands, to distinguish the differences between brands. The NIR spectroscopy fingerprinting results showed that the Brand A formula can be completely differentiated from the other brands at each stage. The combination of NIR spectroscopy fingerprinting and PCA is an effective method for the purpose of confirmation of brand identification and brand protection in infant formulas.

Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky-Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

Aquaphotomics-From Innovative Knowledge to Integrative Platform in Science and Technology

Aquaphotomics is a young scientific discipline based on innovative knowledge of water molecular network, which as an intrinsic part of every aqueous system is being shaped by all of its components and the properties of the environment. With a high capacity for hydrogen bonding, water molecules are extremely sensitive to any changes the system undergoes. In highly aqueous systems-especially biological-water is the most abundant molecule. Minute changes in system elements or surroundings affect multitude of water molecules, causing rearrangements of water molecular network. Using light of various frequencies as a probe, the specifics of water structure can be extracted from the water spectrum, indirectly providing information about all the internal and external elements influencing the system. The water spectral pattern hence becomes an integrative descriptor of the system state. Aquaphotomics and the new knowledge of water originated from the field of near infrared spectroscopy. This technique resulted in significant findings about water structure-function relationships in various systems contributing to a better understanding of basic life phenomena. From this foundation, aquaphotomics started integration with other disciplines into systematized science from which a variety of applications ensued. This review will present the basics of this emerging science and its technological potential.

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.

Excited State Intramolecular Proton Transfer Plus Aggregation-Induced Emission-Based Diketopyrrolopyrrole Luminogen: Photophysical Properties and Simultaneously Discriminative Detection of Trace Water in Three Organic Solvents

Developing solid state near-IR (NIR) emitters and simultaneously discriminative detection of trace water in organic solvents has long been a significant challenge. In this work, a novel diketopyrrolopyrrole-based luminogen (DPP1) with excited state intramolecular proton transfer (ESIPT) and aggregation-induced emission (AIE) characteristics has been designed and synthesized. Its amorphous and crystal solids show red and NIR-emissive fluorescence at 625 and 675 nm, respectively. When DPP1 reacts with fluoride anion, the resulting system (DPP1·F) can discriminatively detect the water content in aprotic solvents with colorimetric and fluorescent dual modes. Distinct fluorescent responses of "turn-on", "ratiometric turn-off", and "ratiometric turn-on" and low limits of detection of 0.0064, 0.042, and 0. The water-induced sensitive and fast change in THF was applied to the determination of water in foodstuffs in practical solid state indicator paper strips.

Essentials of Aquaphotomics and Its Chemometrics Approaches

Aquaphotomics is a novel scientific discipline involving the study of water and aqueous systems. Using light-water interaction, it aims to extract information about the structure of water, composed of many different water molecular conformations using their absorbance bands. In aquaphotomics analysis, specific water structures (presented as water absorbance patterns) are related to their resulting functions in the aqueous systems studied, thereby building an aquaphotome-a database of water absorbance bands and patterns correlating specific water structures to their specific functions. Light-water interaction spectroscopic methods produce complex multidimensional spectral data, which require data processing and analysis to extract hidden information about the structure of water presented by its absorbance bands. The process of extracting information from water spectra in aquaphotomics requires a field-specific approach. It starts with an appropriate experimental design and execution to ensure high-quality spectral signals, followed by a multitude of spectral analysis, preprocessing and chemometrics methods to remove unwanted influences and extract water absorbance spectral pattern related to the perturbation of interest through the identification of activated water absorbance bands found among the common, consistently repeating and highly influential variables in all analytical models. The objective of this paper is to introduce the field of aquaphotomics and describe aquaphotomics multivariate analysis methodology developed during the last decade. Through a worked-out example of analysis of potassium chloride solutions supported by similar approaches from the existing aquaphotomics literature, the provided instruction should give enough information about aquaphotomics analysis i.e. to design and perform the experiment and data analysis as well as to represent water absorbance spectral pattern using various forms of aquagrams-specifically designed aquaphotomics graphs. The explained methodology is derived from analysis of near infrared spectral data of aqueous systems and will offer a useful and new tool for extracting data from informationally rich water spectra in any region. It is the hope of the authors that with this new tool at the disposal of scientists and chemometricians, pharmaceutical and biomedical spectroscopy will substantially progress beyond its state-of-the-art applications.