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Here, we present the first examination of the state of water under a soft confinement in eight aliphatic alcohols including cyclopentanol, 1-pentanol, 1-hexanol, 1-heptanol, 1-octanol, 1-decanol, 2-octanol and 3-octanol. Due to relatively large size of the aliphatic part, water has limited solubility in all studied alcohols. Water content in saturated solutions was determined by Karl Fischer titration and correlated with the spectroscopic data. This way, we determined the molar absorptivity of the ν 2 +ν 3 combination mode. The effect of addition of water and temperature variation was monitored by ATR-IR and NIR spectroscopy. Analysis of the experimental results was guided by DFT calculations, which provided the structures, harmonic MIR spectra and binding energies of selected alcohol-water complexes. Our studies demonstrated that the state of water in alcohols is related to its solubility, which depends on structure of solvent molecules. The solubility of water in 1-alcohols decreases on increasing of the chain length, but for long chain alcohols this effect is less evident. More apparent solubility reduction appears in going from the primary to secondary alcohols. The effective shielding of the OH group in the linear alcohols is achieved when on both sides of the OH group are ethyl or longer substituents, while the shielding by methyl groups is less efficient. Water is much better soluble in the cyclic alcohols as compared with the linear ones due to better accessibility of the OH group. The soft confinement of water in aliphatic alcohols allows for flexible structural arrangements and interactions. Even at low water content, we did not observe free molecules of water. At these conditions, the molecules of water are singly or doubly bonded to the OH groups from the alcohol. Increasing solubility of water reduces the number of the free OH groups and leads to formation of water clusters. Obtained results allow concluding that in alcohols with sizable aliphatic part the molecules of water are confined in the vicinity of the OH groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Contamination by microplastics, a global environmental concern, demands effective monitoring. While current methods focus on characterizing the smallest particles, their low throughput hinders practical assessment. Miniaturized near-infrared (NIR) spectroscopy offers high-throughput capabilities and rapid on-site analysis, potentially filling this gap. However, diverse sensor characteristics result in significant differences among handheld NIR spectrometers. This study characterizes the analytical performance of these instruments for identifying soil microplastics, comparing miniaturized devices MicroNIR 1700ES, NeoSpectra Scanner, microPHAZIR, nanoFTIR-NIR, NIR-S-G1, and SCiO sensor against a reference benchtop instrument, the NIRFlex N-500. Detection of common polymers, ABS, EVAC, HDPE, LDPE, PA6, PMMA, POM, PET, PS, PTFE, and SBR, at low concentrations (0.75 % w/w) was possible without sample preparation. Sensor selection proved crucial; FT instruments N-500 and NeoSpectra Scanner provided the most accurate analysis, while other handheld instruments faced various challenges. Covariance analysis, Principal Component Analysis (PCA), and mid-level data fusion revealed that miniaturized NIR spectrometers can successfully screen microplastics on-site. However, the ability of each sensor to discriminate certain groups of polymers strongly depends on its spectral characteristics. This study demonstrates the importance of sensor selection in the development of portable NIR spectroscopy for environmental monitoring of microplastics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The post-mortem interval estimation for human skeletal remains is critical in forensic medicine. This study used Raman spectroscopy, specifically comparing a handheld device to a Raman microscope for PMI estimations. Analyzing 99 autopsy bone samples and 5 archeological samples, the research categorized them into five PMI classes using conventional methods. Key parameters-like ν 1 PO 4 3- intensity and crystallinity-were measured and analyzed. A principal component analysis effectively distinguished between PMI classes, indicating high classification accuracy for both devices. While both methods proved reliable, the fluorescence interference presented challenges in accurately determining the age of archeological samples. Ultimately, the study highlighted how Raman spectroscopy could enhance PMI estimation accuracy, especially in non-specialized labs, suggesting the potential for improved device optimization in the field.

Neocallimastigomycota are a phylum of anaerobic gut fungi (AGF) that inhabit the gastrointestinal tract of herbivores and play a pivotal role in plant matter degradation. Their identification and characterization with marker gene regions has long been hampered due to the high inter- and intraspecies length variability in the commonly used fungal marker gene region internal transcribed spacer (ITS). While recent research has improved methodology (i.e. switch to LSU D2 as marker region), molecular methods will always introduce bias through nucleic acid extraction or PCR amplification. Here, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are introduced as two nucleic acid sequence-independent tools for the characterization and identification of AGF strains. We present a proof-of-concept for both, achieving an independent prediction accuracy of above 95% for models based on discriminant analysis trained with samples of three different genera. We further demonstrated the robustness of the NIRS model by testing it on cultures of different growth times. Overall, NIRS provides a simple, reliable, and nondestructive approach for AGF classification, independent of molecular approaches. The HSI method provides further advantages by requiring less biomass and adding spatial information, a valuable feature if this method is extended to mixed cultures or environmental samples in the future., Competing Interests: The authors have no conflicts of interest to declare that are relevant to the content of this article., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

The bioactive compounds Acetyl-11-keto-β-boswellic acid (AKBA) and 11-keto-β-boswellic acid (KBA), found in the resin of the Boswellia tree, exhibit anti-inflammatory properties, rendering Boswellia resin an intriguing natural medicinal products. However, the content of boswellic acids varies across different Boswellia species and proper knowledge of its species-dependent nature, as well as alternatives to the resource- and time-intensive HPLC analysis, are lacking. Here we present a comprehensive investigation into the boswellic acid content of seven Boswellia species from ten countries and introduce a novel and non-destructive Near-Infrared spectroscopy method for predicting boswellic acid concentrations in solid resin samples. The HPLC-UV reference analysis revealed AKBA concentrations of up to 7.27 % (w/w) with KBA concentrations reaching up to 1.28 % (w/w). Principal Component Analysis of the HPLC and NIR spectroscopy data unveiled species-specific variations, facilitating differentiation based on boswellic acid content, characteristic chromatograms and NIR spectra. Using the HPLC-UV quantification as reference, we developed a Partial Least Squares regression model based on NIR spectra of the resin samples. This model demonstrated highly satisfactory predictive capabilities for AKBA content, achieving a root mean square error of prediction of 0.74 % (w/w) and an R 2 val of 0.79 in independent test set validation. Although the model was less effective for predicting KBA content, it still offered valuable estimates. The spectroscopic method introduced in this study provides a cost-effective and solvent-free approach for predicting boswellic acid content, demonstrating the potential for application in non-laboratory settings through the use of miniaturized NIR spectrometers. Consequently, this method aligns well with the principles of green chemistry and addresses the growing demand for alternative analytical techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Cystic echinococcosis (CE) is a global health concern caused by cestodes, posing diagnostic challenges due to nonspecific symptoms and inconclusive radiographic results. Diagnosis relies on histopathological evaluation of affected tissue, demanding comprehensive tools. In this retrospective case study, Fourier transform infrared microscopy was explored for detecting and identifying CE through biochemical changes in human tissue sections. Tissue samples from 11 confirmed CE patients were analyzed. Archived FFPE blocks were cut and stained, and then CE-positive unstained sections were examined using Fourier transform infrared microscopy post-deparaffinization. Results revealed the method's ability to distinguish echinococcus elements from human tissue, irrespective of organ type. This research showcases the potential of mid-infrared microscopy as a valuable diagnostic tool for CE, offering promise in enhancing diagnostic precision in the face of the disease's complexities., (© 2024 The Authors. Journal of Biophotonics published by Wiley‐VCH GmbH.)

This comprehensive review paper aims to captivate the applicability of in-sorbent detection, where near-infrared spectroscopy (NIRS) converges with enrichment technologies. For this purpose, we collected and summarized information regarding the combination of several sophisticated analytical enrichment techniques with NIRS to further explore and develop this synergistic approach. Peer-reviewed publications, matching the criteria of in situ NIR measurements prior analyte elution, have been collected, investigated, and concluded within this review. Investigations according to used materials, commercial or self-made, composition, organic or inorganic and applied analytical methodologies have been carried out. Applications extending over a multitude of chemical fields, from environmental to medicinal applications. As this review concludes, the combination of these techniques further expands the applicability of NIRS and moreover tries to solve the long-standing issue of the comparably low sensitivity regarding this vibrational technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

In forensic medicine, estimating human skeletal remains' post-mortem interval (PMI) can be challenging. Following death, bones undergo a series of chemical and physical transformations due to their interactions with the surrounding environment. Post-mortem changes have been assessed using various methods, but estimating the PMI of skeletal remains could still be improved. We propose a new methodology with handheld hyperspectral imaging (HSI) system based on the first results from 104 human skeletal remains with PMIs ranging between 1 day and 2000 years. To differentiate between forensic and archaeological bone material, the Convolutional Neural Network analyzed 65.000 distinct diagnostic spectra: the classification accuracy was 0.58, 0.62, 0.73, 0.81, and 0.98 for PMIs of 0 week-2 weeks, 2 weeks-6 months, 6 months-1 year, 1 year-10 years, and >100 years, respectively. In conclusion, HSI can be used in forensic medicine to distinguish bone materials >100 years old from those <10 years old with an accuracy of 98%. The model has adequate predictive performance, and handheld HSI could serve as a novel approach to objectively and accurately determine the PMI of human skeletal remains., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Biliary complications (BCs) negatively impact the outcome after liver transplantation. We herein tested whether hyperspectral imaging (HSI) generated data from bile ducts (BD) on reperfusion and machine learning techniques for data readout may serve as a novel approach for predicting BC., Methods: Tissue-specific data from 136 HSI liver images were integrated into a convolutional neural network (CNN). Fourteen patients undergoing liver transplantation after normothermic machine preservation served as a validation cohort. Assessment of oxygen saturation, organ hemoglobin, and tissue water levels through HSI was performed after completing the biliary anastomosis. Resected BD segments were analyzed by immunohistochemistry and real-time confocal microscopy., Results: Immunohistochemistry and real-time confocal microscopy revealed mild (grade I: 1%-40%) BD damage in 8 patients and moderate (grade II: 40%-80%) injury in 1 patient. Donor and recipient data alone had no predictive capacity toward BC. Deep learning-based analysis of HSI data resulted in >90% accuracy of automated detection of BD. The CNN-based analysis yielded a correct classification in 72% and 69% for BC/no BC. The combination of HSI with donor and recipient factors showed 94% accuracy in predicting BC., Conclusions: Deep learning-based modeling using CNN of HSI-based tissue property data represents a noninvasive technique for predicting postoperative BC., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Iberian ham is a highly appreciated product and according to Spanish legislation different labels identify different products depending on the genetic purity. Consequently, "100% Iberian" ham from purebred Iberian animals is more expensive than "Iberian" ham from Iberian x Duroc crosses. The hypothesis of this study was that to avoid labelling fraud it is possible to distinguish the breed (Iberian or Iberian x Duroc) of acorn-fed pigs of Iberian ham without any prior preparation of the sample by using spectroscopy that is a rapid and reliable technology. Moreover, portable devices which can be used in situ could provide similar results to those of benchtop equipment. Therefore, the spectra of the 60 samples (24 samples of 100% Iberian ham and 36 samples of Iberian x Duroc crossbreed ham) were recorded only for the fat, only for the muscle, or for the whole slice with two benchtop near-infrared (NIR) spectrometers (Büchi NIRFlex N-500 and Foss NIRSystem 5000) and five portable spectrometers including four portable NIR devices (VIAVI MicroNIR 1700 ES, TellSpec Enterprise Sensor, Thermo Fischer Scientific microPHAZIR, and Consumer Physics SCiO Sensor), and one RAMAN device (BRAVO handheld). The results showed that, in general, the whole slice recording produced the best results for classification purposes. The SCiO device showed the highest percentages of correctly classified samples (97% in calibration and 92% in validation) followed by TellSpec (100% and 81%). The SCiO sensor also showed the highest percentages of success when the analyses were performed only on lean meat (97% in calibration and 83% in validation) followed by microPHAZIR (84% and 81%), while in the case of the fat tissue. Raman technology showed the best discrimination capacity (96% and 78%) followed by microPHAZIR (89% and 81%). Therefore, spectroscopy has proved to be a suitable technology for discriminating ham samples according to breed purity; portable devices have been shown to give even better results than benchtop spectrometers., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)