Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments

Miniaturized Near-Infrared spectrophotometers in forensic analytical science - a critical review

The advent of miniaturized NIR instruments, also known as compact, portable, or handheld, is revolutionizing how technology can be employed in forensics. In-field analysis becomes feasible and affordable with these new instruments, and a series of methods has been developed to provide the police and official agents with objective, easy-to-use, tailored, and accurate qualitative and quantitative forensic results. This work discusses the main aspects and presents a comprehensive and critical review of compact NIR spectrophotometers associated with analytical protocols to produce information on forensic matters.

Reinvestigation of the internal glycan rearrangement of Lewis a and blood group type H1 epitopes

Protonated ions of fucose-containing oligosaccharides are prone to undergo internal glycan rearrangement which results in chimeric fragments that obfuscate mass-spectrometric analysis. Lack of accessible tools that would facilitate systematic analysis of glycans in the gas phase limits our understanding of this phenomenon. In this work, we use density functional theory modeling to interpret cryogenic IR spectra of Lewis a and blood group type H1 trisaccharides and to establish whether these trisaccharides undergo the rearrangement during gas-phase analysis. Structurally unconstrained search reveals that none of the parent ions constitute a thermodynamic global minimum. In contrast, predicted collision cross sections and anharmonic IR spectra provide a good match to available experimental data which allowed us to conclude that fucose migration does not occur in these antigens. By comparing the predicted structures with those obtained for Lewis x and blood group type H2 epitopes, we demonstrate that the availability of the mobile proton and a large difference in the relative stability of the parent ions and rearrangement products constitute the prerequisites for the rearrangement reaction.

E-eye and FT-NIR combined with multivariate algorithms to rapidly evaluate the dynamic changes in the quality of Gastrodia elata during steaming process

Gastrodia elata (GE) is traditionally subjected to steaming, and steaming duration plays a crucially important role in determining GE quality. This study examined the variations in bioactive components during the steaming process and proposed the utilization of electronic eye and Fourier Transform near-infrared (FT-NIR) spectroscopy for quality assessment. The findings revealed that the levels of parishin E parishin B, parishin A, and gastrodin initially rose and subsequently declined, while 4-Hydroxybenzyl alcohol exhibited a rapid decrease followed by stabilization. With prolonged steaming, the brightness of GE decreased, while the red and yellow tones became more pronounced and the color saturation increased. FT-NIR divided the steaming process into three stages: 0 min (raw GE), 0-9 min (partially steamed GE), and 9-30 min (fully steamed GE), and the partial least squares regression models effectively predicted the levels of five components. Overall, this study provided valuable insights into quality control in food processing.

Harmonic and anharmonic vibrational computations for biomolecular building blocks: Benchmarking DFT and basis sets by theoretical and experimental IR spectrum of glycine conformers

Advanced vibrational spectroscopic experiments have reached a level of sophistication that can only be matched by numerical simulations in order to provide an unequivocal analysis, a crucial step to understand the structure-function relationship of biomolecules. While density functional theory (DFT) has become the standard method when targeting medium-size or larger systems, the problem of its reliability and accuracy are well-known and have been abundantly documented. To establish a reliable computational protocol, especially when accuracy is critical, a tailored benchmark is usually required. This is generally done over a short list of known candidates, with the basis set often fixed a priori. In this work, we present a systematic study of the performance of DFT-based hybrid and double-hybrid functionals in the prediction of vibrational energies and infrared intensities at the harmonic level and beyond, considering anharmonic effects through vibrational perturbation theory at the second order. The study is performed for the six-lowest energy glycine conformers, utilizing available "state-of-the-art" accurate theoretical and experimental data as reference. Focusing on the most intense fundamental vibrations in the mid-infrared range of glycine conformers, the role of the basis sets is also investigated considering the balance between computational cost and accuracy. Targeting larger systems, a broad range of hybrid schemes with different computational costs is also tested.

Scaling-up VPT2: A feasible route to include anharmonic correction on large molecules

Vibrational analysis plays a crucial role in the investigation of molecular systems. Though methodologies like second-order vibrational perturbation theory (VPT2) have paved the way to more accurate simulations, the computational cost remains a difficult barrier to overcome when the molecular size increases. Building upon recent advances in the identification of resonances, we propose an approach making anharmonic simulations possible for large-size systems, typically unreachable by standard means. This relies on the fact that, often, only portions of the whole spectra are of actual interest. Therefore, the anharmonic corrections can be included selectively on subsets of normal modes directly related to the regions of interest. Starting from the VPT2 equations, we evaluate rigorously and systematically the impact of the truncated anharmonic treatment onto simulations. The limit and feasibility of the reduced-dimensionality approach are detailed, starting on a smaller model system. The methodology is then challenged on the IR absorption and vibrational circular dichroism spectra of an organometallic complex in three different spectral ranges.

Near-infrared spectroscopy of H3O+⋯Xn (X = Ar, N2, and CO, n = 1-3)

Near-infrared (NIR) spectra of H3O+⋯Xn (X = Ar, N2, and CO, n = 1-3) in the first overtone region of OH-stretching vibrations (4800-7000 cm-1) were measured. Not only OH-stretching overtones but also several combination bands are major features in this region, and assignments of these observed bands are not obvious at a glance. High-precision anharmonic vibrational simulations based on the discrete variable representation approach were performed. The simulated spectra show good agreement with the observed ones and provide firm assignments of the observed bands, except in the case of X = CO, in which higher order vibrational mode couplings seem significant. This agreement demonstrates that the present system can be a benchmark for high precision anharmonic vibrational computations of NIR spectra. Band broadening in the observed spectra becomes remarkable with an increase of the interaction with the solvent molecule (X). The origin of the band broadening is explored by rare gas tagging experiments and anharmonic vibrational simulations of hot bands.

Non-neotissue constituents as underestimated confounders in the assessment of tissue engineered constructs by near-infrared spectroscopy

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

Advances, limitations, and considerations on the use of vibrational spectroscopy towards the development of management decision tools in food safety

Food safety and food security are two of the main concerns for the modern food manufacturing industry. Disruptions in the food supply and value chains have created the need to develop agile screening tools that will allow the detection of food pathogens, spoilage microorganisms, microbial contaminants, toxins, herbicides, and pesticides in agricultural commodities, natural products, and food ingredients. Most of the current routine analytical methods used to detect and identify microorganisms, herbicides, and pesticides in food ingredients and products are based on the use of reliable and robust immunological, microbiological, and biochemical techniques (e.g. antigen-antibody interactions, extraction and analysis of DNA) and chemical methods (e.g. chromatography). However, the food manufacturing industries are demanding agile and affordable analytical methods. The objective of this review is to highlight the advantages and limitations of the use of vibrational spectroscopy combined with chemometrics as proxy to evaluate and quantify herbicides, pesticides, and toxins in foods.

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

Thermodynamic and Vibrational Aspects of Peptide Bond Hydrolysis and Their Potential Relationship to the Harmfulness of Infrared Radiation

The primary physicochemical effect upon exposure to infrared radiation (IR) is the temperature increase of cells. The degradation of proteins via the hydrolysis of peptide bonds is related to cell malfunction. In this work, the degradation of proteins/peptides under the influence of IR radiation is theoretically studied. It is shown that the low value of enthalpy of peptide bond hydrolysis has two consequences: (a) the enthalpy of hydrolysis is sensitive to small variations in the bond strength, and the hydrolysis of weak peptide bonds is exothermic, while the hydrolysis of stronger bonds is endothermic; (b) the increase in temperature (e.g., due to IR exposure) changes the enthalpy of the reaction of some weak peptide bonds from exothermic to endothermic (that is, their hydrolysis will be favored upon further increase in temperature). Simple calculations reveal that the amount of absorbed energy during the overtone and hot band transitions of the H-O-H and C-N stretching vibrations is comparable to the activation energy of the (uncatalyzed) hydrolysis. A critical discussion is provided regarding the influence of different IR wavelengths on peptide bond hydrolysis.

Time-Resolved Mid-Infrared Photothermal Microscopy for Imaging Water-Embedded Axon Bundles

Few experimental tools exist for performing label-free imaging of biological samples in a water-rich environment due to the high infrared absorption of water, overlapping with major protein and lipid bands. A novel imaging modality based on time-resolved mid-infrared photothermal microscopy is introduced and applied to imaging axon bundles in a saline bath environment. Photothermally induced spatial gradients at the axon bundle membrane interfaces with saline and surrounding biological tissue are observed and temporally characterized by a high-speed boxcar detection system. Localized time profiles with an enhanced signal-to-noise, hyper-temporal image stacks, and two-dimensional mapping of the time decay profiles are acquired without the need for complex post image processing. Axon bundles are found to have a larger distribution of time decay profiles compared to the water background, allowing background differentiation based on these transient dynamics. The quantitative analysis of the signal evolution over time allows characterizing the level of thermal confinement at different regions. When axon bundles are surrounded by complex heterogeneous tissue, which contains smaller features, a stronger thermal confinement is observed compared to a water environment, thus shedding light on the heat transfer dynamics across aqueous biological interfaces.

Structures of water on the surface of anatase TiO2 studied by diffuse reflectance near-infrared spectroscopy

Investigating the structures of water on metal oxides is helpful for understanding the mechanism of the adsorptions in the presence of water. In this work, the structures of adsorbed water molecules on anatase TiO₂ (101) were studied by diffuse reflectance near-infrared spectroscopy (DR-NIRS). With resolution enhanced spectrum by continuous wavelet transform (CWT), the spectral features of adsorbed water at different sites were found. In the spectrum of dried TiO₂ powder, there is only the spectral feature of the water adsorbed at 5-coordinated titanium atoms (Ti_5c). With the increase of the adsorbed water, the spectral feature of the water at 2-coordinated oxygen atoms (O_2c) emerges first, and then that of the water interacting with the adsorbed water can be observed. When adenosine triphosphate (ATP) was adsorbed on TiO₂, the intensity of the peaks related to the adsorbed water decreases, indicating that the adsorbed water is replaced by ATP due to the strong affinity to Ti_5c. Therefore, there is a clear correlation between the peak intensity of the adsorbed water and the adsorbed quantity of ATP. Water can be a NIR spectroscopic probe to detect the quantity of the adsorbed ATP. A partial least squares (PLS) model was established to predict the content of adsorbed ATP by the spectral peaks of water. The recoveries of validation samples are in the range of 92.00-114.96% with the relative standard deviations (RSDs) in a range of 2.13-5.82%.

Experimental demonstration of a new near-infrared spectroscopy technique based on optical dual-comb: DC-NIRS

We present a novel near-infrared spectroscopy technique based on Dual-Comb optical interrogation (DC-NIRS) applied to dispersive media. The technique recovers the frequency response of the medium under investigation by sampling its spectral response in amplitude and phase. The DC-NIRS reference and sample signals are generated using electro-optic modulation which offers a cost-effective, integrable solution while providing high adaptability to the interrogated medium. A careful choice of both line spacing and optical span of the frequency comb ensures that the retrieved information enables the reconstruction of the temporal impulse response of the medium, known as the diffuse-time-of-flight (DTOF), to obtain its optical properties with a 70 µs temporal resolution and 32 ps photon propagation delay resolution. Furthermore, the DC-NIRS technique also offers enhanced penetration due to noiseless optical amplification (interferometric detection). The presented technique was demonstrated on a static bio-mimetic phantom of known optical properties reproducing a typical brain's optical response. The DTOF and optical properties of the phantom were measured, showing the capabilities of this new technique on the estimation of absolute optical properties with a deviation under 3%. Compared to current technologies, our DC-NIRS technique provides enhanced temporal resolution, spatial location capabilities, and penetration depth, with an integrable and configurable cost-effective architecture, paving the way to next-generation, non-invasive and portable systems for functional brain imaging, and brain-computer interfaces, among other. The system is patent pending PCT/ES2022/070176.

Diagnostic and monitoring applications using Near infrared (NIR) Spectroscopy in cancer and other diseases

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.

Prediction of Anthocyanidins Content in Purple Chinese Cabbage Based on Visible/Near Infrared Spectroscopy

Purple Chinese cabbage (PCC) has become a new breeding trend due to its attractive color and high nutritional quality since it contains abundant anthocyanidins. With the aim of rapid evaluation of PCC anthocyanidins contents and screening of breeding materials, a fast quantitative detection method for anthocyanidins in PCC was established using Near Infrared Spectroscopy (NIR). The PCC samples were scanned by NIR, and the spectral data combined with the chemometric results of anthocyanidins contents obtained by high-performance liquid chromatography were processed to establish the prediction models. The content of cyanidin varied from 93.5 mg/kg to 12,802.4 mg/kg in PCC, while the other anthocyanidins were much lower. The developed NIR prediction models on the basis of partial least square regression with the preprocessing of no-scattering mode and the first-order derivative showed the best prediction performance: for cyanidin, the external correlation coefficient (RSQ) and standard error of cross-validation (SECV) of the calibration set were 0.965 and 693.004, respectively; for total anthocyanidins, the RSQ and SECV of the calibration set were 0.966 and 685.994, respectively. The established models were effective, and this NIR method, with the advantages of timesaving and convenience, could be applied in purple vegetable breeding practice.

Development of a CH2-dependent analytical method using near-infrared spectroscopy via the integration of two algorithms: non-dominated sorting genetic-II and competitive adaptive reweighted sampling (NSGAII-CARS)

In most of the near-infrared studies, near-infrared spectra (NIRS) were often mathematically treated. However, these algorithms selected a large number of variables and latent variables, and they caused the over-fitting phenomenon, which became very common. The large number of variables made it impossible to extract the "chemical information" directly from the NIRS. To build robust and interpretable mathematical models, the non-dominated sorting genetic-II-competitive adaptive reweighted sampling (NSGAII-CARS) algorithm was proposed to determine influential functional groups for quantitative analysis. In this research, data on a primary mixture of two amino acids (AAs), namely NH₂(CH₂)₃COOH and HOOC(NH₂)CH(CH₂)₂COOH, was used to illustrate the algorithm. The principle of the algorithm was first to find out the different characteristic spectral regions of two amino acids by extreme points according to Non-dominated Sorting Genetic-II (NSGAII). Second, based on the absolute value of the regression coefficient, we found out [ν(CH₂) + 2δ(CH₂)] and [2ν(CH₂)], where the wavenumber ranged from 6165 to 5683 cm^-1, were the influential functional groups for quantitative analysis. Finally, the CARS (competitive adaptive reweighted sampling) algorithm was combined with NSGAII to find the specific fingerprint points for the determination of two AAs. Compared with the previous results, the NSGAII-CARS algorithm not only pointed out the influential quantitative functional groups but also used only 6 points for HOOC(NH₂)CH(CH₂)₂COOH and 18 points for NH₂(CH₂)₃COOH to achieve the full-spectrum quantitative effect. The results proposed a general algorithm for the quantitative analysis of NIRS obtained in the binary or ternary mixed systems. The MATLAB codes of the NSGAII-CARS algorithm are available on the website: https://github.com/Mark1988NK/NSGAII-CARS-Algorithm.git.

Reflective FT-NIR and SERS studies of HER-II breast cancer biomarker using plasmonic-active nanostructured thin film immobilized oriented antibody

We describe the fabrication of plasmonic-active nanostructured thin film substrate as a label-free surface-enhanced Raman scattering (SERS)-based biosensor immobilized covalently with monoclonal HER-II antibody (mAb) to detect overexpressed HER-II as a biomarker in breast cancer serum (BCS). Oriented conjugation of mAb via hydrazone linkage to provide higher mAb accessibility was characterized by UV-vis and reflective Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The interaction of BCS with mAb was studied by FT-NIR and nonresonant SERS at 637 nm. The results showed detection of glycoprotein content at different laser powers including a rise in amino acid and glycan content with varying results at higher power. With nonresonant SERS we observed nonlinear behavior of peak intensity. Analysis of variance was implemented to determine the effect of laser power which was found not to be a contributing factor. However, at the nanoscale, factors including the heating effect and aggregation of molecules can contribute to the nonlinearity of peak intensity.

Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification

C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Currently, the medicinal cannabis and industrial hemp industries depend on costly chromatographic analysis for cannabinoid quantification, limiting production, research and development. Combined with chemometrics, Near-InfraRed spectroscopy (NIRS) has potential as a rapid, accurate and economical alternative method for cannabinoid analysis. Using chromatographic data on 12 therapeutically relevant cannabinoids together with spectral output from a diffuse reflectance NIRS device, predictive chemometric models were built for major and minor cannabinoids using dried, homogenised C. sativa inflorescences from a diverse panel of 84 accessions. Coefficients of determination (r²) of the validation models for 10 of the 12 cannabinoids ranged from 0.8 to 0.95, with models for major cannabinoids showing best performance. NIRS was able to discriminate between neutral and acidic forms of cannabinoids as well as between C₃-alkyl and C₅-alkyl cannabinoids. The results show that NIRS, when used in conjunction with chemometrics, is a promising method to quantify cannabinoids in raw materials with good predictive results.

Preparation, Characterization of New Antimicrobial Antitumor Hybrid Semi-Organic Single Crystals of Proline Amino Acid Doped by Silver Nanoparticles

Proline is water soluble amino acid extensively used in drug delivery systems. Compounds of cobalt (Co) transition metal have potent antimicrobial and anticancer activities. However, a drug delivery system combining proline cobalt is not reported yet. For the first time, new hybrid semi-organic single crystals of proline cobalt chloride (PCC) are prepared. The novelty of the article is also that single crystal proline cobalt chloride showed potent antimicrobial and antitumor activity. Doping of PCC by Ag⁰NPs significantly increased these biological activities. The anisotropic magnetic properties of single crystals can mitigate the cytotoxicity of Ag⁰NPs on normal cells. Silver nanoparticles (Ag⁰NPs) improved the crystal habits and physicochemical properties. Ag⁰NPs showed the best performance, paramagnetic materials n-type semiconductors due to delocalized excess electrons of Ag⁰NPs incorporated in the crystal lattice interstitially. Crystals have high absorptivity for UV-radiation electromagnetic radiation. Ag⁰NPs enhanced AC electrical conductivity up to 2.3 × 10⁴ Ω cm^-1 due to high electron density. Proline doped crystals are obtained in good purity as triclinic unit cell with having anisotropic magnetism. PCCAg⁰NPs crystal exhibited: high antimicrobial activities to various bacterial and fungal species, inhibition zone (mm): 21, 25, 24, 26, 30, 28, 12, and 46 for S. aureus, E. faecalis, S. typhi, E. coli, P. aerugino, K. pneumoniae, A. braselienses, and C. albicans, respectively, in comparison to ciprofloxacin antibiotic (23, 0, 26, 26, 25, 0, 0, 0) for the same tested species, respectively; higher cytotoxicity against breast cancer cells (IC₅₀ 22.1 μM) than the reference drug cisplatin (IC₅₀ 11.7 μM); and lower cytotoxicity to normal healthy lung cells MRC-5, (IC₅₀ 145.5 μM) than cisplatin (IC₅₀ 30.2 μM). Hence, this crystal is a candidate for chemotherapy of breast cancer.

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology

This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm^-1 with a resolution of 4 cm^-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R² ≥ 0.95) except for the RT variable (RMSE of 0.891% and R² of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R² of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

The Influence of Freeze-Dried Alcohol-Water Extracts from Common Yarrow (Achillea millefolium L.) and German Chamomile (Matricaria chamomilla L.) on the Properties of Elastomer Vulcanizates

This research work aimed to investigate the properties of freeze-dried extracts from Matricaria chamomilla L. and Achillea millefolium L. and to perform a characterization of their impact on the natural rubber-based vulcanizates. First, extracts were prepared in three different solvents at selected volume ratios: water (100), water-methanol (50/50), and water-ethanol (50/50). Next, the freeze-drying of extracts was established and then obtained bio-additives were introduced to the rubber mixtures. Freeze-dried extracts were investigated by UV-VIS diffuse reflectance spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Near-Infrared spectroscopy (NIR) and thermogravimetric analysis (TGA). Antioxidant activity and total phenolic content (TPC) were also defined. Rubber mixtures were examined in a rheometer and after vulcanization they were subjected to accelerated simulated aging by UV radiation and thermo-oxidative aging. To determine the resistance of vulcanizates to the degradation processes, the study of cross-linking density (equilibrium swelling method), mechanical properties (tensile strength, elongation at break) and color change were conducted. Performed studies proved the antioxidant activity of freeze-dried extracts caused by the high content of polyphenols and their beneficial influence on the properties of elastomer vulcanizates.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Anharmonic Vibrational Raman Optical Activity of Methyloxirane: Theory and Experiment Pushed to the Limits

Combining Raman scattering and Raman optical activity (ROA) with computer simulations reveals fine structural and physicochemical properties of chiral molecules. Traditionally, the region of interest comprised fundamental transitions within 200-1800 cm^-1. Only recently, nonfundamental bands could be observed as well. However, theoretical tools able to match the observed spectral features and thus assist their assignment are rather scarce. In this work, we present an accurate and simple protocol based on a three-quanta anharmonic perturbative approach that is fully fit to interpret the observed signals of methyloxirane within 150-4500 cm^-1. An unprecedented agreement even for the low-intensity combination and overtone transitions has been achieved, showing that anharmonic Raman and ROA spectroscopies can be valuable tools to understand vibrations of chiral molecules or to calibrate computational models.

Anharmonic Aspects in Vibrational Circular Dichroism Spectra from 900 to 9000 cm-1 for Methyloxirane and Methylthiirane

Vibrational circular dichroism (VCD) spectra and the corresponding IR spectra of the chiral isomers of methyloxirane and of methylthiirane have been reinvestigated, both experimentally and theoretically, with particular attention to accounting for anharmonic corrections, as calculated by the GVPT2 approach. De novo recorded VCD spectra in the near IR (NIR) range regarding CH-stretching overtone transitions, together with the corresponding NIR absorption spectra, were also considered and accounted for, both with the GVPT2 and with the local mode approaches. Comparison of the two methods has permitted us to better describe the nature of active “anharmonic” modes in the two molecules and the role of mechanical and electrical anharmonicity in determining the intensities of VCD and IR/NIR data. Finally, two nonstandard IR/NIR regions have been investigated: the first one about ≈2000 cm^–1, involving mostly two-quanta bending mode transitions, the second one between 7000 and 7500 cm^–1 involving three-quanta transitions containing CH-stretching overtones and HCC/HCH bending modes.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Comprehensive evaluation of Dendrobium officinale from different geographical origins using near-infrared spectroscopy and chemometrics

Dendrobium officinale, often used as a kind of tea for daily drinks, has drawn increasing attention for its beneficial effects. Quality evaluation of D. officinale is of great significance to ensure its health care value and safeguard consumers' interest. Given that traditional analytical methods for assessing D. officinale quality are generally time-consuming and laborious, this study developed a comprehensive strategy, with the advantages of being rapid and efficient, enabling the quality evaluation of D. officinale from different geographical origins using near-infrared (NIR) spectroscopy and chemometrics. As the quality indicators, polysaccharides, polyphenols, total flavonoids, and total alkaloids were quantified. Three types of wavelength selection methods were used for model optimization and these were synergy interval (SI), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS). From the qualitative perspective, the geographical origins of D. officinale were differentiated by NIR spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) and support vector classification (SVC). The PLS models constructed based on the wavelengths selected by CARS yielded the best performance for prediction of the contents of quality indicators in D. officinale. The root mean square error (RMSEP) and coefficient of determination (R_p²) in the independent test sets were 12.7768 g kg^-1 and 0.9586, 1.1346 g kg^-1 and 0.9670, 0.3938 g kg^-1 and 0.8803, 0.0825 and 0.7031 and for polysaccharides, polyphenols, total flavonoids, and total alkaloids, respectively. As for the origin identification, the nonlinear SVC was superior to the linear PLS-DA, with the correct recognition rates in calibration and prediction sets up to 100% and 100%, respectively. The overall results demonstrated the potential of NIR spectroscopy and chemometrics in the rapid determination of quality parameters and geographical origin. This study could provide a valuable reference for quality evaluation of D. officinale in a more rapid and comprehensive manner.

Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy

The quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples.

Near-Infrared Spectroscopy as a Potential COVID-19 Early Detection Method: A Review and Future Perspective

The coronavirus disease 2019 (COVID-19) pandemic is a worldwide health anxiety. The rapid dispersion of the infection globally results in unparalleled economic, social, and health impacts. The pathogen that causes COVID-19 is known as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A fast and low-cost diagnosis method for COVID-19 disease can play an important role in controlling its proliferation. Near-infrared spectroscopy (NIRS) is a quick, non-destructive, non-invasive, and inexpensive technique for profiling the chemical and physical structures of a wide range of samples. Furthermore, the NIRS has the advantage of incorporating the internet of things (IoT) application for the effective control and treatment of the disease. In recent years, a significant advancement in instrumentation and spectral analysis methods has resulted in a remarkable impact on the NIRS applications, especially in the medical discipline. To date, NIRS has been applied as a technique for detecting various viruses including zika (ZIKV), chikungunya (CHIKV), influenza, hepatitis C, dengue (DENV), and human immunodeficiency (HIV). This review aims to outline some historical and contemporary applications of NIRS in virology and its merit as a novel diagnostic technique for SARS-CoV-2.

Spectral pattern study of citrus black rot caused by Alternaria alternata and selecting optimal wavelengths for decay detection

Fungal decay is one of the most common diseases that affect postharvest operations and sales of citrus. Sometimes, fungal disease develops and spreads inside the fruit and in the advanced stages of the disease, it appears apparent, so the use of efficient and reliable methods for early detection of the disease is very important. In this study, early detection of citrus black rot disease caused by Alternaria genus fungus was examined using spectroscopy. Jaffa oranges were inoculated with Alternaria alternata. The samples were inspected by spectroscopy (200–1100 nm) in the 1st, 2nd, and 3rd weeks after inoculation. The classification of healthy and infected samples and selection of most important wavelengths were conducted by soft independent modeling of class analogy (SIMCA). The most important wavelengths in the detection of healthy and infected samples of the 1st week were 507, 933, 937, and 950 nm with a classification accuracy of 60%. The most important wavelengths of the 2nd week were 522 and 787 nm with a classification accuracy of 60%. Also, wavelengths of 546, 660, 691, and 839 were found to be effective in the 3rd week with a classification accuracy of 100%.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Antioxidant and Anti–Aging Activity of Freeze–Dried Alcohol–Water Extracts from Common Nettle (Urtica dioica L.) and Peppermint (Mentha piperita L.) in Elastomer Vulcanizates

The research article aimed to examine the antioxidant nature of freeze–dried extracts from Urtica dioica L. and Mentha piperita L. and to present a deep characterization of their influence on the properties of natural rubber–based vulcanizates before and after simulated aging processes. Natural extracts were prepared in three solvent systems at selected volume ratios: water (100), methanol–water (50/50), ethanol–water (50/50), which were further lyophilized and used as additive to natural rubber mixtures. Freeze–dried materials were investigated by UV–VIS diffuse reflectance spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), thermal stability by thermogravimetric analysis (TGA). Antioxidant activity and total phenolic content (TPC) were also examined. Prepared samples were subjected to accelerated simulated degradation processes by using ultraviolet and thermo-oxidative aging. Vulcanizates resistance to degradation effects was determined by the study of cross-linking density (equilibrium swelling method), mechanical properties (tensile strength, elongation at break) and color change in comparison with the results of the reference samples. The research showed that analyzed extracts are characterized by a high content of polyphenols and antioxidant activity, thus have a protective influence on elastomer vulcanizates against damaging effects of aging processes, which consequently extends the lifetime of materials.

Analysis of wheat flour-insect powder mixtures based on their near infrared spectra

Insects are gaining more and more space in food and feed sectors, creating an intense scientific interest towards insects as food ingredients. Several papers deal with cereal-based products complemented by insect powder in the past few years. However, adulteration and quality control of such products present some hot topics for researchers, e.g., how can we justify the amounts and/or species of the insects used in the given products? Our paper aims to answer such questions by analysing seven edible insect powders of different species independently. The mixtures with wheat flour were analysed using near infrared spectroscopy and chemometric methods. Not only powders of different species were clearly differentiated, but also mixtures created by different amounts of wheat flour. Prediction of insect content showed 0.65% cross-validated error. The proposed methodology gives an excellent tool for quality control of insect-based cereal food products.

Evaluation of ORAC, IR and NMR metabolomics for predicting ripening stage and variety in melon (Cucumis melo L.)

A combination of FTIR- and NMR-based metabolomics approaches coupled to multivariate data analysis techniques was used for the first time to characterize and explore the metabolic changes along the ripening of two Spanish melon landraces (Jimbee N and Jimbee XL). Furthermore, the fatty acids profile and the antioxidant capacity were investigated by GC-FID and ORAC method, respectively. Despite FTIR was able to identify changes in the sugars content between fruits of different maturity stages, it failed to discriminate between both varieties, being exceeded by NMR in both situations. Increased fruit maturation led to an increase of sucrose, fatty acids and β-carotene, accompanied by a decrease of glucose, fructose, citrate, amino acids, and polyphenols (which were positively correlated with fruits antioxidant activity).

Sensitivity enhanced NIR photoacoustic CO detection with SF6 promoting vibrational to translational relaxation process

A challenge for slowly relaxing carbon monoxide (CO) molecules detection using photoacoustic spectroscopy (PAS) is to promote the vibration-translation (V-T) relaxation process. Addressing this challenge, a sensitivity enhanced photoacoustic CO sensor with sulfur hexafluoride (SF₆) as the promotor is investigated and demonstrated. A 1568 nm near-infrared (NIR) laser diode and a customized optical amplifier are used as the excitation source to generate the photoacoustic signal. A differential photoacoustic cell is simulated and designed to obtain identical laminar flow distribution in the resonant cell to suppress the flow noise. The modulation frequency and added SF₆ volume ratio are optimized experimentally to achieve optimal sensitivity. Feasibility and performance of the CO sensor with a small amount of SF₆ as promotor is discussed and evaluated, obtaining a ~ 2 times improvement of signal value compared to the one with pure N₂ background and resulting in a minimum detection limit of 467.5 ppb for CO detection.

An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants

Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and hardware concomitantly with data analytics, have allowed for the progress in novel applications of these technologies in the field of nutraceutical and bio compound analysis. In recent years, several studies have demonstrated the capability of vibrational spectroscopy to evaluate and/or measure these nutraceuticals in a broad selection of fruit and plants as alternative to classical analytical approaches. This article highlights, as well as discusses, the challenges and opportunities that define the successful application of vibrational spectroscopy techniques, and the advantages that these techniques have to offer to evaluate and quantify nutraceuticals in fruits and plants.

Quantification of Silymarin in Silybi mariani fructus: Challenging the Analytical Performance of Benchtop vs. Handheld NIR Spectrometers on Whole Seeds

The content of the flavonolignan mixture silymarin and its individual components (silichristin, silidianin, silibinin A, silibinin B, isosilibinin A, and isosilibinin B) in whole and milled milk thistle seeds (Silybi mariani fructus) was analyzed with near-infrared spectroscopy. The analytical performance of one benchtop and two handheld near-infrared spectrometers was compared. Reference analysis was performed with HPLC following a Soxhlet extraction (European Pharmacopoeia) and a more resource-efficient ultrasonic extraction. The reliability of near-infrared spectral analysis determined through partial least squares regression models constructed independently for the spectral datasets obtained by the three spectrometers was as follows. The benchtop device NIRFlex N-500 performed the best both for milled and whole seeds with a root mean square error of CV between 0.01 and 0.17%. The handheld spectrometer MicroNIR 2200 as well as the microPHAZIR provided a similar performance (root mean square error of CV between 0.01 and 0.18% and between 0.01 and 0.23%, respectively). We carried out quantum chemical simulation of near-infrared spectra of silichristin, silidianin, silibinin, and isosilibinin for interpretation of the results of spectral analysis. This provided understanding of the absorption regions meaningful for the calibration. Further, it helped to better separate how the chemical and physical properties of the samples affect the analysis. While the study demonstrated that milling of samples slightly improves the performance, it was deemed to be critical only for the analysis carried out with the microPHAZIR. This study evidenced that rapid and nondestructive quantification of silymarin and individual flavonolignans is possible with miniaturized near-infrared spectroscopy in whole milk thistle seeds.

Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis

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The use of vibrational spectroscopy combined with data analytics is discussed.

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The measure of bioactive compounds metabolites in fruit samples is presented.

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Advantages and limitations of these techniques are discussed.

The last three decades have demonstrated the ability of combining data analytics (e.g. big data, machine learning) with modern analytical instrumental techniques such as vibrational spectroscopy (VIBSPEC) (e.g. NIR, Raman, MIR) and sensing technologies (e.g. electronic noses and tongues, colorimetric sensors) to analyse, measure and monitor a wide range of properties and samples. Developments in instrumentation, hardware and software have placed VIBSPEC as a useful tool to quantify several bioactive compounds and metabolites in a wide range of fruit and plant samples. With the incorporation of hand-held and portable instrumentation, these techniques have been valuable for the development of in-field and high throughput applications, opened new frontiers of analysis in fruits and plants. This review will present and discuss some of the current applications on the use of VIBSPEC techniques combined with data analytics on the measurement bioactive compounds and plant metabolites in different fruit samples.

Spectra-structure correlations in NIR region of polymers from quantum chemical calculations. The cases of aromatic ring, C=O, C≡N and C-Cl functionalities

Near-infrared (NIR) spectroscopy is a valued analytical tool in various applications involving polymers. However, complex nature of NIR spectra imposes difficulties in their direct interpretation. Here, anharmonic quantum chemical calculations are used to simulate NIR spectra of nine polymers; acrylonitrile butadiene styrene (ABS), ethylene-vinyl acetate (EVAC), polycarbonate (PC), polyethylene terephthalate (PET), polylactide or polylactic acid (PLA), polymethylmethacrylate (PMMA), polyoxymethylene (POM), polystyrene (PS) and polyvinylchloride (PVC). The generalized spectra-structure correlations are derived for these systems with focus given to the manifestation in NIR spectra of aromatic ring, C=O, C≡N and C-Cl functionalities. It is concluded that the nature of NIR polymer bands is only moderately sensitive to the remote chemical neighborhood. The majority of NIR absorption of polymers originates from binary combination bands, while the first overtones are meaningful only in ca. 6200-5500 cm^-1 region. The contribution of the overtone bands is relatively higher for the polymers bearing aromatic rings because of higher intensity of C-H stretching overtones. Highly characteristic combination bands of the modes localized in aromatic ring (ring deformation and CH stretching) are relatively independent on the remaining structure of the polymer. The combination bands originating from C=O group are more sensitive to the chemical neighborhood in near proximity, forming a useful fingerprint for a specific polymer. In contrast, the vibrational bands of C≡N functionality are far less useful in NIR region than in infrared (IR) region. With aid of the calculated absorption bands, structural specificity of NIR spectroscopy of polymers can be markedly improved.

Prognostic Features of Near-Infrared Spectroscopy Following Primary Radical Prostatectomy

BACKGROUND

Only a few biomarkers have been evaluated for their prognostic value with regard to biochemical recurrence (BCR) following primary radical prostatectomy. We explored the possibilities of using near-infrared (NIR) spectroscopy as a prognostic biomarker for BCR-free survival (BCR-FS).

METHODS

Tissue specimens from 82 prostate cancer patients were obtained. Formalin-fixed paraffin-embedded slides (hematoxylin-eosin-stained) were analyzed using NIR spectroscopy. Prognostic features for BCR-FS were determined following normalization of the spectra.

RESULTS

Several differences were found throughout the NIR spectrum for the patients with or without BCR, for both the first derivative and second derivative of the NIR spectrum. Following categorization and Cox regression analysis, spectral regions at 5236 cm^-1 (first derivative; median BCR-FS not reached versus 3.2 years; HR_high = 0.18 [0.08-0.39]; and p < 0.0001) and at 5956 cm^-1 (second derivative; median BCR-FS not reached versus 3.8 years; HR_low = 0.22 [0.10-0.48]; and p = 0.0002) showed prognostic properties for BCR-FS. The combination of both parameters further increased the prognostic value of NIR (p < 0.0001).

CONCLUSIONS

We demonstrated NIR spectral variations between patients with or without BCR, which have been shown to have prognostic value. This easy-to-use technique could possibly further improve post-primary radical prostatectomy monitoring and swift referral to adjuvant local therapies. Further elaboration is highly recommended to fully elucidate these variations and to gain a deeper insight into the changing chemical and physical compositions of the prostate tumor architecture.

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

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.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

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

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.

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).

Near-infrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model

In this article, we proposed a method to estimate pancreas shrinkage with pancreas β cell insulin secretion. The β cells in the pancreas secrete insulin and digestive enzymes after food consumption. Conventionally, the pancreas structure estimation is done with magnetic resonance imaging (MRI) and ultrasound imaging techniques. However, the structure of the pancreas changes due to islet cell death. The presence of islet cells is detected through near infrared (NIR) spectroscopy signal acquired from the epigastric region (pancreas) of the abdomen. Subsequently, the NIR spectroscopy signal from the pancreas is analyzed with multi synchrosqueezing transform (MSST); whereas, the β cell insulin secretion varies for diabetic and nondiabetic persons. The existence of β cell and insulin secretion correlates with Root Mean Square (RMS) and kurtosis via a multivariate regression model to evaluate pancreas shrinkage. In terms of numerical results, NIR spectroscopy signal from the pancreas was obtained for about 20 nondiabetic and 20 diabetic persons. The pancreas shrinkage was estimated with 88% accuracy. The results are validated with MRI pancreas images for earlier detection of the apoptotic pancreas. The pancreas shrinkage causes lower insulin emission and unpredictable blood glucose in diabetic patients. Analysis of NIR spectroscopy signals of the pancreas with MSST was done to obtain higher-order and lower-order frequency components.

Nondestructive Detection of Weight Loss Rate, Surface Color, Vitamin C Content, and Firmness in Mini-Chinese Cabbage with Nanopackaging by Fourier Transform-Near Infrared Spectroscopy

A nondestructive optical method is described for the quality assessment of mini-Chinese cabbage with nanopackaging during its storage, using Fourier transform-near infrared (FT-NIR) spectroscopy. The sample quality attributes measured included weight loss rate, surface color index, vitamin C content, and firmness. The level of freshness of the mini-Chinese cabbage during storage was divided into three categories. Partial least squares regression (PLSR) and the least squares support vector machine were applied to spectral datasets in order to develop prediction models for each quality attribute. For a comparative analysis of performance, the five preprocessing methods applied were standard normal variable (SNV), first derivative (lst), second derivative (2nd), multiplicative scattering correction (MSC), and auto scale. The SNV-PLSR model exhibited the best prediction performance for weight loss rate (R_p² = 0.96, RMSEP = 1.432%). The 1st-PLSR model showed the best prediction performance for L* value (R_p² = 0.89, RMSEP = 3.25 mg/100 g), but also the lowest accuracy for firmness (R_p² = 0.60, RMSEP = 2.453). The best classification model was able to predict freshness levels with 88.8% accuracy in mini-Chinese cabbage by supported vector classification (SVC). This study illustrates that the spectral profile obtained by FT-NIR spectroscopy could potentially be implemented for integral assessments of the internal and external quality attributes of mini-Chinese cabbage with nanopacking during storage.

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

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.