Printed Edition of the Special Issue Published in Molecules

Over the past few decades, near-infrared (NIR) spectroscopy has emerged as one of the most rapidly advancing spectroscopic techniques [1]. Rapid determination of nutritional parameters of pasta/sauce mixtures by hand-held near-infrared spectroscopy. Molecules.

Figure 1. Results analysis for Web of Science query “near infrared spectroscopy” for publication years (2009–2018) [6].

Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic

• Introduction
• Results
• Discussion
• Methods and Materials 1. Hyperspectral Measuring System
• Conclusions
• Further Validations and Developments

Although analytical solutions of the light transport equations in the diffusion approximation in tissue systems are available [18,19], the measurement geometries often do not correspond to the use of the HSI-camera in a clinical environment [20,21]. Procedures for solving the inverse problem (calculation of model parameters from the measured spectrum) for more realistic multilayer models are still computationally expensive [20].

Figure 1. (a) Measurements of the hands in an occlusion test; right: hand of the occluded arm, left:

Distinct Difference in Sensitivity of NIR vs. IR Bands of Melamine to Inter-Molecular Interactions with

Results and Discussion

These observations will find good confirmation in the subsequent analysis of the NIR spectrum of melamine. In the calculations based on a single molecule (vs. the crystalline lattice model), the out-of-plane deformation vibrations (e.g. δtwistNH2, δwaggNH2, δooring) are positioned at higher computed wavenumbers, while the in-plane deformation vibrations are positioned at the lower computed wavenumbers.

Figure 1. Molecular structure of melamine. (A) single molecule; (B) the content of a unit cell; (C) 3 × 3

Materials and Methods 1. Experimental

The convergence criterion for the vibrational analysis was successfully achieved, as the sonic modes of the crystal lattice approached close to zero values ​​(no more than -0.5 cm-1). For the determination of the basic electronic properties at the DFT level, B3LYP functional and triple-ζSNST basis set [67] were chosen; This method has been repeatedly proven to produce good results [31,33,65].

Conclusions

A spectroscopic and theoretical study in the near infrared region of low concentration aliphatic alcohols. Phys. Temperature shift of conformational equilibria of butyl alcohols studied by near-infrared spectroscopy and fully anharmonic DFT.J.

Near-Infrared and Anharmonic Computational Study

Experimental and Computational Methods 1. Materials and Spectroscopic Measurements

Variational solution of the problem of anharmonic vibrations of molecules in the central force field.J. Solvent dependence of the absorption intensities and wavenumbers of the fundamental and first overtone NH stretching vibration of pyrrole studied by near-infrared/infrared spectroscopy and DFT calculations. J. Spectroscopic and quantum mechanical calculation study of the isotopic substitution effect in the NIR spectra of methanol.J.

Two-dimensional correlation analysis of the second overtone of the ν(OH) mode of octan-1-ol in the pure liquid phase.Appl. Sample Availability: Samples of the compounds (CH3CH2OD, CH3CD2OH, CD3CH2OH, CD3CD2OH, CD3CD2OD) are available from the authors.

Table 11. Samples used in this study

Comparison of Multivariate Regression Models Based on Water- and Carbohydrate-Related Spectral

Material and Methods 1. Samples

In contrast to the 1 mm and 2 mm cuvettes - which were simply filled with a certain amount of sample solution - the 0.1 mm cuvette can be dismantled and therefore had to be filled differently. All samples were measured nine times, while the cells were refilled with fresh solution for each of the nine repeated measurements. Note that the small band around 4500 cm−1 (marked with an asterisk in Figure 1) is caused by O−H residues in the quartz windows of the 0.1 mm cell (probably due to water impurities [33]) and is assigned to a combination of an O−H stretching vibration and one of the SiO2 fundamental vibrations [8,34].

10.5 (Camo Software AS, Oslo, Norway) was used to pre-process the NIR spectra and to construct and validate multivariate regression models. The performance of the PLS-R models was evaluated using the root mean square error (RMSE), which was calculated according to Equation (1), where jeyi.

Figure 1 shows an exemplary NIR spectrum of water containing glucose with the described division of spectral regions

Results and Discussion

The size of the RMSE is closely related to the number of PLS-R factors (or latent variables), which is a crucial parameter for a well-performing PLS-R model [37,38]. Since the glucose-water system used in this study is quite simple, the number of PLS-R factors used in the PLS-R models should be kept quite low to avoid modeling noise and thus irrelevant spectral information (overfitting). However, using too few PLS-R factors can lead to poor model performance due to the lack of explained variance in the NIR spectra (underfitting).

The optimal number of PLS-R factors was determined by examining the regression coefficients, the loadings and correlation loadings of each PLS-R factor as well as the explained variances. The glucose-related regions G1 and G2 showed a clear pattern in the direction of increasing glucose concentrations (Figure 3e,f,j–l), while such a clear pattern was missing in the NIR spectra of the water-related regions W1 and W2 at first glance (Figure 3a–c,g).

NIR spectra of the calibration set recorded with the 0.1 mm cell are shown here exemplary. The results of the PLS-R calibration and test set validation procedure are shown in Table 2. The explained Y variance of the test set already reached 99.9% in the first PLS-R factor (see Table 3), therefore invalidating the use of more PLS-R factors.

However, compared to the derived spectra of the 1 mm cell in the combination region G2 (Figure 3k), the spectra in Figure 3l appeared somewhat noisy. Measurement of the concentrations of glucose and citric acid in the aqueous solution of a blood anticoagulant using near-infrared spectroscopy.J.

Figure 4. Illustration of the glucose concentration dependent pattern in the water band located at around 6900 cm −1 (region W1)

Investigation of Direct Model Transferability Using Miniature Near-Infrared Spectrometers

Materials and Methods 1. Materials

Handheld near-infrared spectrometers: State-of-the-art instruments and practical applications. NIR news. Review of the use of portable near-infrared spectrometers in the agro-food industry.Appl. Quantification of biodiesel and vegetable oil adulteration in diesel/biodiesel blends using a portable near-infrared spectrometer. Fuel.

Near-infrared spectroscopy calibration transfer for quantitative analysis of fishmeal mixed with soybean meal.J. Transfer of multivariate classification models between laboratory and process near-infrared spectrometers for discrimination of green Arabica and Robusta coffee beans.

Table 6. Polymer materials used for the classification study.

Investigations into the Performance of a Novel Pocket-Sized Near-Infrared Spectrometer for

Materials and Methods

Prediction of moisture, fat and inorganic salts in processed cheese by near-infrared reflection spectroscopy and multivariate data analysis.J. Prediction of dry matter, fat, pH, vitamins, minerals, carotenoids, total antioxidant capacity and color in fresh and freeze-dried cheeses by visible-near-infrared reflectance spectroscopy.J. Prediction of sensory properties of European Emmentaler cheese using near-infrared spectroscopy: a feasibility study. Food Chem.

An overview of the principles and applications of near-infrared spectroscopy to characterize meat, fat and meat products. Non-destructive prediction of enteric coating layer thickness and drug dissolution rate by near-infrared spectroscopy and X-ray computed tomography.Int.

Rapid Determination of Nutritional Parameters of Pasta/Sauce Blends by Handheld

Near-Infrared Spectroscopy

Experimental Section 1. Experimental Set-Up

The choice of the number of latent variables (factors) is a critical point in PLS model development and should be based on the relationship with other statistical parameters such as RMSEC and RMSECV [37]. Figure 4 shows plots of the RMSEC/RMSECV values ​​versus the latent variable number for the individual calibrations of the nutritional parameters. RMSEC (red) and RMSECV (blue) versus the latent variable number for the individual calibrations of the nutritional parameters.

Content Range and statistical parameters obtained for the individual PLS models of the nutritional parameters. A summary of the prediction results for the test set samples is provided in Tables 3 and 4.

Table 1. Nutritional parameter values calculated for 100 g of dry pasta and 100 g of sauce.

Calibration Transfer Based on Affine Invariance for NIR without Transfer Standards

Results and Discussion 1. Analysis of the Corn Dataset

Summary of Root Mean Square Error of test set (RMSEP) and Root Mean Square Error of calibration set (RMSEC) of different methods. Thus, there is a linear relationship between the predicted values ​​of the two instruments for wheat data set. Thus, there is a significant bias between uncorrected predicted values ​​of the slave instrument and predicted values ​​of the master instrument.

We found the same phenomenon for the uncorrected slave instrument prediction values ​​relative to the master instrument actual values. From Table 4 it can be seen that there is a statistically significant difference compared to CCA, SBC and PDS.

Table 1. Summary of the partial least squares (PLS) models and properties.

Materials and Methods 1. Dataset Description

The purpose of the PLS model is to ensure the optimal number of latent variables. We correct the predicted value of the slave tool with the rotation and translation of affine transformation. Based on the PLS model, the score matrices and predicted values ​​are calculated as indicated.

Given calibration set of the master(Xmcal,ymcal), calibration set of the slaveXsca and test set(Xstest,ystest). Then the angles and deviations between the coefficients of the master instrument and the corresponding coefficients of the slave instrument are calculated.

Figure 11. (A) Spectral differences between m5 and mp6 of corn samples; (B) spectral differences between B1 and B2 of wheat samples; (C) spectral differences between B1 and B3 of wheat samples;

Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm

Dragonfly Algorithm

In the next section, we will introduce the principles of the dragonfly algorithm in continuous and discrete fields. This observation inspired the design of the dragonfly algorithm because there are two similar phases (called exploration and exploitation) in traditional swarm optimization methods. In static swarm mode, dragonflies fly in different directions in a small area, which corresponds to the exploration phase; in dynamic swarm mode, dragonflies fly over a larger area along one direction, which is the main objective of the exploitation phase.

As seen in the table above, dragonflies tend to coordinate their flight while maintaining proper separation and cohesion in a dynamic swarm manner. In the continuous domain, the dragonflies can update their positions by adding a step vector ΔX to the position vector X. However, in the discrete domain, the position of the dragonflies cannot be updated in this way because the position vectors X can only be 0 or 1.

Figure 1. Five main strategies by which dragonflies change their position [8].

Wavelength Selection Framework Based on BDA and Ensemble Learning

Similar to traditional swarm optimization methods, the wavelength section algorithm was designed on the basis of the individual BDA. To solve this problem, Figure 3b illustrates a possible solution designed on the basis of multi-BDA. Instead, the idea of ​​traditional ensemble learning was introduced to improve the stability of the wavelength selection results.

Moreover, the computational complexity of the BDA method based on ensemble learning is less than that of the multi-BDA method, which mainly reflects on the cost calculation. The experimental results proved that, although the sample size in the subset became smaller, the performance was close to that of the multi-BDA method.

Figure 2. The flowchart of the binary dragonfly algorithm (BDA)-based wavelength selection method.

Experimental Results and Discussion

The influence of the BDA iteration on the wavelength selection results will also be introduced in the discussion section. Wavelength selection results using the single-BDA method. a) Wavelength selection results by applying a 10 times search; (b) corresponding general performance of the quantitative analysis model. This means that if the number of selected wavelengths is too small, the general performance of the quantitative analysis model may decrease.

Therefore, we want to quantitatively analyze the influence of the values ​​of these parameters on the wavelength selection results. Therefore, improving the generalized performance of the quantitative analysis model is a hot topic in NIR spectroscopy.

Table 2. Values of the BDA parameters.

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify

Materials and Methods 1. Samples Preparation

In the low-level data fusion strategy, the FT-MIR and NIR spectral signals are directly correlated and again form an independent data matrix. The mid-level data fusion strategy, namely feature-level data fusion, consisted of significant variables from individual data sources, including FT-MIR and NIR spectra. A high-level data aggregation strategy, namely decision data aggregation, combined voting results from.

FT-MIR and NIR spectral data fusion: a synergistic strategy for the geographic traceability of Panax notoginseng.Anal. Traceability of wildParis polyphyllaSmith var.yunnanensis based on data fusion strategy of FT-MIR and UV-Vis combined with SVM and random forest.Spectrochim.

Figure 6. Scheme of the low-, mid-, and high-level data fusion approaches used to combine the FT-MIR signals and NIR signals.

PLS Subspace-Based Calibration Transfer for

Near-Infrared Spectroscopy Quantitative Analysis

Results and Discussion 1. The Analysis of the Corn Dataset

In addition, the RMSEP of PLSCT gradually stabilized when the number of samples in the standard set was from 20 to 30. When the number of samples in the standard set was 30, the smallest RMSEP obtained by PLSCT was 0.6604. Differences between the first pseudo-predicted feature of the slave instrument test set before and after transfer to the PLS subspace.

From AppendixATableA1, as the number of samples in the standard set increased, the performance of PLSCT gradually became better. The feature predicted by the standard set of the slave instrument is a pseudo-predicted feature Ts_mstd constructed from the PLS model of the master instrument.

Figure 1. The effect of selecting the number of factors when building T s_m std and ˆ T m std on the cross-validation error

At-Line Monitoring of the Extraction Process of

Rosmarini Folium via Wet Chemical Assays, UHPLC Analysis, and Newly Developed Near-Infrared

Spectroscopic Analysis Methods