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Perspectives in Near Infrared Spectroscopy and Related Techniques

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A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 34874

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Prof. Dr. Christian Huck

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Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: vibrational spectroscopy; separation science; enrichment techniques; mass spectrometry
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Dr. Krzysztof Bec

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Institute of Analytical Chemistry and Radiochemistry, CCB-Center for Chemistry and Biomedicine, Leopold-Franzens University, Innrain 80-82, 6020 Innsbruck, Austria
Interests: vibrational spectroscopy; NIR spectroscopy; analytical chemistry; physical chemistry; chemometrics
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Dear Colleagues,

Near-infrared (NIR) spectroscopy is one of the most rapidly advancing spectroscopic techniques. Mainly known as an analytical tool useful for sample characterization and content quantification, it is a potent tool used in various other fields, e.g. NIR imaging techniques in biophotonics, medical applications or in characterization of food products. Ongoing miniaturization and simplification of instrumentation brings the technique even to the ordinary consumer. NIR spectroscopy contributes to basic science and physical chemistry as well, e.g. by giving insights into the nature of molecular vibrations or intermolecular interactions. The technique has been developing in conjunction with advanced methods of data analysis; recent years have highlighted the potential in applying computational chemistry for improving interpretability of NIR spectra. However, one may also notice some adverse effects of such popularity. The growing diversity of the methods and applications related to NIR spectroscopy has led to a dispersion of the contributions among disparate scientific communities.

Following the success of the former Special Issue published in Molecules ‘Advances in Near Infrared Spectroscopy and Related Computational Methods’ (https://0-www-mdpi-com.brum.beds.ac.uk/journal/molecules/special_issues/infrared_computational) we consider the present Special Issue as the continuation that will be further helpful in bringing together these distinct communities.

The Special Issue aims for reflecting the diversity of methods and applications in NIR spectroscopy. To suit this purpose, it additionally welcomes research topics not directly focused on NIR region, which however remain relevant by employing the methodologies essential for NIR spectroscopy. We believe such scope promotes the exchange of ideas with no artificial limits or strict categorization. We thus hope the Special Issue will create a formidable opportunity for Authors and Readers in pushing the frontier of this discipline of science.

Prof. Dr. Christian Huck
Dr. Krzysztof Bec
Guest Editors

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Keywords

Near-infrared spectroscopy (NIRS)
analytical spectroscopy
NIR imaging/mapping
hyperspectral image processing
hand-held/portable spectrometers
chemometrics and data analysis
theoretical spectroscopy
theoretical methods for spectra interpretation

Related Special Issue

Advances in Near Infrared Spectroscopy and Related Computational Methods in Molecules (31 articles)

Published Papers (11 papers)

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Research

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17 pages, 3382 KiB

Open AccessArticle

Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods

by Ning Ai, Yibo Jiang, Sainab Omar, Jiawei Wang, Luyue Xia and Jie Ren

Molecules 2022, 27(2), 335; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules27020335 - 06 Jan 2022

Cited by 13 | Viewed by 3774

Abstract

Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R²), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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17 pages, 3451 KiB

Open AccessArticle

Anharmonicity and Spectra–Structure Correlations in MIR and NIR Spectra of Crystalline Menadione (Vitamin K₃)

by Krzysztof B. Beć, Justyna Grabska, Christian W. Huck, Sylwester Mazurek and Mirosław A. Czarnecki

Molecules 2021, 26(22), 6779; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26226779 - 10 Nov 2021

Cited by 4 | Viewed by 1877

Abstract

Mid-infrared (MIR) and near-infrared (NIR) spectra of crystalline menadione (vitamin K₃) were measured and analyzed with aid of quantum chemical calculations. The calculations were carried out using the harmonic approach for the periodic model of crystal lattice and the anharmonic DVPT2 calculations applied for the single molecule model. The theoretical spectra accurately reconstructed the experimental ones permitting for reliable assignment of the MIR and NIR bands. For the first time, a detailed analysis of the NIR spectrum of a molecular system based on a naphthoquinone moiety was performed to elucidate the relationship between the chemical structure of menadione and the origin of the overtones and combination bands. In addition, the importance of these bands during interpretation of the MIR spectrum was demonstrated. The overtones and combination bands contribute to 46.4% of the total intensity of menadione in the range of 3600–2600 cm⁻¹. Evidently, these bands play a key role in shaping of the C-H stretching region of MIR spectrum. We have shown also that the spectral regions without fundamentals may provide valuable structural information. For example, the theoretical calculations reliably reconstructed numerous overtones and combination bands in the 4000–3600 and 2800–1800 cm⁻¹ ranges. These results, provide a comprehensive origin of the fundamentals, overtones and combination bands in the NIR and MIR spectra of menadione, and the relationship of these spectral features with the molecular structure. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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15 pages, 3200 KiB

Open AccessArticle

Anharmonic DFT Study of Near-Infrared Spectra of Caffeine: Vibrational Analysis of the Second Overtones and Ternary Combinations

by Justyna Grabska, Krzysztof B. Beć, Yukihiro Ozaki and Christian W. Huck

Molecules 2021, 26(17), 5212; https://doi.org/10.3390/molecules26175212 - 27 Aug 2021

Cited by 10 | Viewed by 2838

Abstract

Anharmonic quantum chemical calculations were employed to simulate and interpret a near-infrared (NIR) spectrum of caffeine. First and second overtones, as well as binary and ternary combination bands, were obtained, accurately reproducing the lineshape of the experimental spectrum in the region of 10,000–4000 cm⁻¹ (1000–2500 nm). The calculations enabled performing a detailed analysis of NIR spectra of caffeine, including weak bands due to the second overtones and ternary combinations. A highly convoluted nature of NIR spectrum of caffeine was unveiled, with numerous overlapping bands found beneath the observed spectral lineshape. To properly reflect that intrinsic complexity, the band assignments were provided in the form of heat maps presenting the contributions to the NIR spectrum from various kinds of vibrational transitions. These contributions were also quantitatively assessed in terms of the integral intensities. It was found that the combination bands provide the decisively dominant contributions to the NIR spectrum of caffeine. The first overtones gain significant importance between 6500–5500 cm⁻¹, while the second overtones are meaningful in the higher wavenumber regions, particularly in the 10,000–7000 cm⁻¹ region. The obtained detailed band assignments enabled deep interpretation of the absorption regions of caffeine identified in the literature as meaningful for analytical applications of NIR spectroscopy focused on quantitative analysis of caffeine content in drugs and natural products. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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11 pages, 1031 KiB

Open AccessArticle

Innovative Combination of Dispersive Solid Phase Extraction Followed by NIR-Detection and Multivariate Data Analysis for Prediction of Total Polyphenolic Content

by Christoph Kappacher, Markus Neurauter, Matthias Rainer, Günther K. Bonn and Christian W. Huck

Molecules 2021, 26(16), 4807; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26164807 - 09 Aug 2021

Cited by 3 | Viewed by 1846

Abstract

Recently polyphenols attracted great interest in the field of food and nutrition as well as in the pharmaceutical and cosmetics industries due to their health benefits through antioxidative behavior in the human body. However, because of the high number of compounds characterized as phenols and their structural diversity, quantification of polyphenols turns out to be a highly complex task. Although, a wide variety of analytical methods are used for the determination of total polyphenolic content, they are all found to be lacking in a variety of different tasks, such as their limits of detection and quantification, repeatability, accuracy and specificity. For this reason, a novel approach combining the advantages of solid phase purification, near infrared analysis and multivariate data analysis was investigated for the prediction of total polyphenolic content, suitable for a wide range of sample matrices. Dispersive solid phase extraction was performed and optimized using polyvinylpyrrolidone as sorbent, known to selectively bind polyphenols. Near-infrared detection of adsorbed polyphenols was carried out subsequently. Furthermore, the method was in-house validated, examining selectivity, repeatability and accuracy, working range, as well as multivariate limit of detection and limit of quantification, comparing it with two routinely used methods—namely, Folin–Ciocalteu photometric assay and Löwenthal titration. The novel established method was applied for the prediction of total polyphenolic content in tea and wine samples. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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18 pages, 4888 KiB

Open AccessArticle

A Novel Tool for Visualization of Water Molecular Structure and Its Changes, Expressed on the Scale of Temperature Influence

by Zoltan Kovacs, Bernhard Pollner, George Bazar, Jelena Muncan and Roumiana Tsenkova

Molecules 2020, 25(9), 2234; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25092234 - 09 May 2020

Cited by 9 | Viewed by 4742

Abstract

Aquaphotomics utilizes water-light interaction for in-depth exploration of water, its structure and role in aqueous and biologic systems. The aquagram, a major analytical tool of aquaphotomics, allows comparison of water molecular structures of different samples by comparing their respective absorbance spectral patterns. Temperature is the strongest perturbation of water changing almost all water species. To better interpret and understand spectral patterns, the objective of this work was to develop a novel, temperature-scaled aquagram that provides standardized information about changes in water molecular structure caused by solutes, with its effects translated to those which would have been caused by respective temperature changes. NIR spectra of Milli-Q water in the temperature range of 20–70 °C and aqueous solutions of potassium chloride in concentration range of 1 to 1000 mM were recorded to demonstrate the applicability of the proposed novel tool. The obtained results presented the influence of salt on the water molecular structure expressed as the equivalent effect of temperature in degrees of Celsius. The temperature-based aquagrams showed the well-known structure breaking and structure making effects of salts on water spectral pattern, for the first time presented in the terms of temperature influence on pure water. This new method enables comparison of spectral patterns providing a universal tool for evaluation of various bio-aqueous systems which can provide better insight into the system’s functionality. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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21 pages, 2817 KiB

Open AccessArticle

Differentiation of South African Game Meat Using Near-Infrared (NIR) Spectroscopy and Hierarchical Modelling

by Kiah Edwards, Marena Manley, Louwrens C. Hoffman, Anel Beganovic, Christian G. Kirchler, Christian W. Huck and Paul J. Williams

Molecules 2020, 25(8), 1845; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25081845 - 16 Apr 2020

Cited by 10 | Viewed by 3318

Abstract

Near-infrared (NIR) spectroscopy, combined with multivariate data analysis techniques, was used to rapidly differentiate between South African game species, irrespective of the treatment (fresh or previously frozen) or the muscle type. These individual classes (fresh; previously frozen; muscle type) were also determined per species, using hierarchical modelling. Spectra were collected with a portable handheld spectrophotometer in the 908–1676-nm range. With partial least squares discriminant analysis models, we could differentiate between the species with accuracies ranging from 89.8%–93.2%. It was also possible to distinguish between fresh and previously frozen meat (90%–100% accuracy). In addition, it was possible to distinguish between ostrich muscles (100%), as well as the forequarters and hindquarters of the zebra (90.3%) and springbok (97.9%) muscles. The results confirm NIR spectroscopy’s potential as a rapid and non-destructive method for species identification, fresh and previously frozen meat differentiation, and muscle type determination. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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13 pages, 1271 KiB

Open AccessArticle

Unified Classification of Bacterial Colonies on Different Agar Media Based on Hyperspectral Imaging and Machine Learning

by Peng Gu, Yao-Ze Feng, Le Zhu, Li-Qin Kong, Xiu-ling Zhang, Sheng Zhang, Shao-Wen Li and Gui-Feng Jia

Molecules 2020, 25(8), 1797; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25081797 - 14 Apr 2020

Cited by 10 | Viewed by 3199

Abstract

A universal method by considering different types of culture media can enable convenient classification of bacterial species. The study combined hyperspectral technology and versatile chemometric algorithms to achieve the rapid and non-destructive classification of three kinds of bacterial colonies (Escherichia coli, Staphylococcus aureus and Salmonella) cultured on three kinds of agar media (Luria–Bertani agar (LA), plate count agar (PA) and tryptone soy agar (TSA)). Based on the extracted spectral data, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were employed to established classification models. The parameters of SVM models were optimized by comparing genetic algorithm (GA), particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA). The best classification model was GOA-SVM, where the overall correct classification rates (OCCRs) for calibration and prediction of the full-wavelength GOA-SVM model were 99.45% and 98.82%, respectively, and the Kappa coefficient for prediction was 0.98. For further investigation, the CARS, SPA and GA wavelength selection methods were used to establish GOA-SVM simplified model, where CARS-GOA-SVM was optimal in model accuracy and stability with the corresponding OCCRs for calibration and prediction and the Kappa coefficients of 99.45%, 98.73% and 0.98, respectively. The above results demonstrated that it was feasible to classify bacterial colonies on different agar media and the unified model provided a continent and accurate way for bacterial classification. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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17 pages, 2025 KiB

Open AccessArticle

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

by Xiulin Bai, Qinlin Xiao, Lei Zhou, Yu Tang and Yong He

Molecules 2020, 25(7), 1651; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25071651 - 03 Apr 2020

Cited by 18 | Viewed by 2526

Abstract

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. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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24 pages, 6122 KiB

Open AccessArticle

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

by Tao Shen, Hong Yu and Yuan-Zhong Wang

Molecules 2020, 25(6), 1442; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25061442 - 23 Mar 2020

Cited by 23 | Viewed by 3374

Abstract

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm⁻¹) and Fourier transform mid-infrared (MIR: 4000–600 cm⁻¹) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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14 pages, 2172 KiB

Open AccessArticle

Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens

by Yuangui Yang, Yanli Zhao, Zhitian Zuo, Ji Zhang, Yao Shi and Yuanzhong Wang

Molecules 2020, 25(5), 1219; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25051219 - 09 Mar 2020

Cited by 7 | Viewed by 2925

Abstract

Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of G. rigescens varied in different geographical origins and parts. In this study, a total of 783 samples collected from different parts of G. rigescens in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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Review

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8 pages, 373 KiB

Open AccessReview

The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities

by Daniel Cozzolino

Molecules 2021, 26(22), 6981; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26226981 - 19 Nov 2021

Cited by 26 | Viewed by 3306

Abstract

Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals. Full article

(This article belongs to the Special Issue Perspectives in Near Infrared Spectroscopy and Related Techniques)

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