Search Results (48)

To address issues of food authenticity, such as fraud and origin tracing, it is essential to employ methods in food fingerprinting that are efficient, economical, and easy to use. This review highlights the capabilities of vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, as non-invasive tools for food authentication. These methods offer rapid, cost-effective, and environmentally friendly analysis across diverse food matrices. This review further discusses recent advances such as hyperspectral imaging, portable devices, and data fusion strategies that integrate chemometrics and artificial intelligence. Despite their promise, challenges remain, including limited sensitivity for certain compounds, spectral overlaps, fluorescence interference in Raman spectroscopy, and the need for standardized validation protocols. Looking forward, trends such as the miniaturization of devices, real-time monitoring, and AI-enhanced spectral interpretation are expected to significantly advance the field of food authentication. Full article

It is desirable to find and evaluate innovative sustainable products guaranteeing and increasing the quality and productivity of radish (Raphanus sativus). Compost tea (CT) represents a natural organic preparation providing benefits to the soil–plant system, including a biostimulant action against climate change. Therefore, we evaluated whether CT can influence radish nutraceutical properties and its primary metabolism. In particular, the roots resulting from CT treatment were examined via conventional (total antioxidant and phenol contents) and spectroscopic techniques (high-resolution NMR and NIR) and compared with controls. It was proved that CT exerted a positive effect on the radish quality, which led to a significantly larger size in those treated (TRT), accompanied by higher contents of total antioxidants and phenols. The assignment of ¹H and ¹³C signals in the NMR spectra allowed the delineation of the NMR fingerprint of the radish primary metabolome, which was processed by multivariate statistical analyses (PCA, PLS-DA, and heatmap clusterisation). TRT metabolites exhibited a peculiar profile, characterized by higher levels of glutamine and malic acid, along with lower levels of glucose, fructose, sucrose, lactic acid, and tryptophan. NIR spectroscopy also identified a recognisable profile in TRT, confirming its role as an alternative and accessible technique to appreciate the organic treatment’s effects on radish. Full article

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients. Full article

Optical instruments require extremely high precision, and even minor surface contamination can severely impact their performance. Peelable coatings offer an effective and non-damaging method for removing contaminants from optical surfaces. In this study, an amphiphilic polyacrylate copolymer (PMLEA) was synthesized via solution radical copolymerization using the lipophilic monomer lauryl acrylate (LA) and hydrophilic monomers ER-10, methyl methacrylate (MMA), and butyl acrylate (BA). The structure and molecular weight of the copolymer were characterized using Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), and gel permeation chromatography (GPC). The hydrophilic–lipophilic balance, surface tension, and wettability of the copolymer were analyzed through water titration, the platinum plate method, and liquid contact angle tests. The cleaning performance of the copolymer coating on quartz glass surface contaminants was evaluated using optical microscopy and Ultraviolet-Visible Near-Infrared (UV-Vis-NIR) spectroscopy. The study examined the effect of varying the ratio of LA to ER-10 on the hydrophilicity, lipophilicity, cleaning efficiency, and mechanical properties of the copolymer coating. The results showed that when the mass ratio of LA to ER-10 was 1:2, the synthesized copolymer exhibited optimal performance in removing dust, grease, and fingerprints from quartz glass surfaces. The coating had a tensile strength of 2.57 MPa, an elongation at break of 183%, and a peeling force of 2.07 N m⁻¹. Full article

The distinctive quality of Sichuan south-road dark tea (SSDT) is gradually disappearing with processing innovation. Here, near-infrared (NIR) spectroscopy (NIRS) and spectrofluorometric techniques were utilized to determine the spectral characteristics of dried SSDT and its brew, respectively. Combined with chemical analysis, the multidimensional quality characteristics of SSDT will be presented. Finally, the NIR spectral fingerprint of dried SSDT was observed, with Kangzhuan (KZ) and Jinjian (JJ) showing a very similar NIR spectrum. The SiPLS models effectively predicted the levels of theabrownin, caffeine, and epigallocatechin gallate, based on the NIR spectrum, with root-mean-square errors of calibration of 0.15, 0.12, and 0.02 for each chemical compound, root-mean-square errors of prediction of 0.20, 0.09, and 0.03, and both corrected and predicted correlation coefficients greater than 0.90. Meanwhile, the fluorescence characteristics of the SSDT brew were identified based on the parallel factor analysis for the fluorescence excitation–emission matrix (EEM). The KZ and JJ brews could be classified with 100% accuracy using extreme-gradient-boosting discriminant analysis. The integration of NIRS and fluorometric EEM seems to be a powerful technique for characterizing SSDTs, and the results can greatly benefit the production and quality control of SSDTs. Full article

In small clinical studies, the application of transcranial photobiomodulation (PBM), which typically delivers low-intensity near-infrared (NIR) to treat the brain, has led to some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, despite the extensive literature detailing the mechanisms of action underlying PBM outcomes, the specific mechanisms affecting neurodegenerative diseases are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and thus provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer’s and other neurodegenerative diseases. Thus, we investigated the effects of PBM on these cellular structures in the quest for insights into the underlying therapeutic mechanisms. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, $22.5$ J/cm² dose). Peaks in the Raman fingerprint region (300–1900 cm⁻¹)—in particular, in the amide I band (1600–1700 cm⁻¹)—were used to quantify the percentage of protein secondary structures. Under this band, hidden signals of $\mathrm{C}$=$\mathrm{O}$ stretching, belonging to different structures, are superimposed, producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of polymerized NIR-exposed tubulin, characterized by a notable decrease in $\mathsf{\alpha }$-helix content and a concurrent increase in $\mathsf{\beta }$-sheets compared to the control samples. This PBM-induced $\mathsf{\alpha }$-helix to $\mathsf{\beta }$-sheet transition connects to reduced microtubule stability and the introduction of dynamism to allow for the remodeling and, consequently, refreshing of microtubule structures. This newly discovered mechanism could have implications for reducing the risks associated with brain aging, including neurodegenerative diseases like Alzheimer’s disease, through the introduction of an intervention following this transition. Full article

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements. Full article

The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm⁻¹ was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10⁻⁸ were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis. Full article

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R² values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process. Full article

Due to their appealing colors, gem quality tourmalines, particularly the blue to green Cu- and Mn-bearing Li-tourmalines known as the Paraíba type, have been of significant interest since their discovery at the end of 1980s. At the same time, the demand of other similar colored tourmalines increased. Most Paraíba-type tourmalines belong to the elbaite species; however, liddicoatite gems can also be found. Recognizing and classifying various tourmaline species, especially these valued Paraíba-type tourmalines, are important for geologists, mineralogists, mineral collectors, and gemologists. This study explores the application of Raman spectroscopy in random crystal orientations to distinguish between the elbaite and liddicoatite tourmaline species. Raman spectra were collected from faceted blue to green Li-tourmalines alongside chemical analysis using EDXRF (Energy Dispersive X-ray Fluorescence), UV-Vis-NIR (Ultraviolet-Visible-Near InfraRed Spectroscopy), and PL (Photoluminescence spectroscopy) to provide comprehensive characterization. The results show that Raman spectroscopy, particularly in the OH stretching region, is a useful tool for differentiating elbaite from liddicoatite, and this identification remains consistent regardless of crystal orientation. The fingerprint region in the Raman spectra, on the other hand, is orientation-dependent and can only differentiate the two species when detected in specific orientations. Furthermore, Paraíba-type tourmalines can be identified by visible-near infrared (Vis-NIR) spectroscopy, although not by Raman spectroscopy. Full article

In recent years, there has been renewed interest in the maintenance of food quality and food safety on the basis of metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Nontargeted spectroscopy techniques such as FTIR, NIR and Raman can provide fingerprint information for metabolomic constituents in agricultural products, natural products and foods in a high-throughput, cost-effective and rapid way. In the current review, we tried to explain the capabilities of FTIR, NIR and Raman spectroscopy techniques combined with multivariate analysis for metabolic fingerprinting and profiling. Previous contributions highlighted the considerable potential of these analytical techniques for the detection and quantification of key constituents, such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids and flavonoids in the food matrices. Additionally, promising results were obtained for the identification and characterization of different microorganism species such as fungus, bacterial strains and yeasts using these techniques combined with supervised and unsupervised pattern recognition techniques. In conclusion, this review summarized the cutting-edge applications of FTIR, NIR and Raman spectroscopy techniques equipped with multivariate statistics for food analysis and foodomics in the context of metabolomic fingerprinting and profiling. Full article

Fabric is a commonly found piece of physical evidence at most crime scenes. Forensic analysis of fabric is typically performed via microscopic examination. This subjective approach is primarily based on pattern recognition and, therefore, is often inconclusive. Most of the fabric material found at crime scenes is colored. One may expect that a confirmatory identification of dyes can be used to enhance the reliability of the forensic analysis of fabric. In this study, we investigated the potential of near-infrared Raman spectroscopy (NIRS) in the confirmatory, non-invasive, and non-destructive identification of 15 different dyes on cotton. We found that NIRS was able to resolve the vibrational fingerprints of all 15 colorants. Using partial-squared discriminant analysis (PLS-DA), we showed that NIRS enabled ~100% accurate identification of dyes based on their vibrational signatures. These findings open a new avenue for the robust and reliable forensic analysis of dyes on fabric directly at crime scenes. Main conclusion: a hand-held Raman spectrometer and partial least square discriminant analysis (PLS-DA) approaches enable highly accurate identification of dyes on fabric. Full article

This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky–Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch. Full article

Stand-off detection of latent traces avoids the scene alteration that might occur during close inspection by handheld forensic lights. Here, we describe a novel sensor, named Crime Light Imaging (CLI), designed to perform high-resolution photography of targets at a distance of 2–10 m and to visualize some common latent traces. CLI is based on four high-power illumination LEDs and one color CMOS camera with a motorized objective plus frontal filters; the LEDs and camera could be synchronized to obtain short-exposure images weakly dependent on the ambient light. The sensor is integrated into a motorized platform, providing the target scanning and necessary information for 3D scene reconstruction. The whole system is portable and equipped with a user-friendly interface. The preliminary tests of CLI on fingerprints at distance of 7 m showed an excellent image resolution and drastic contrast enhancement under green LED light. At the same distance, a small (1 µL) blood droplet on black tissue was captured by CLI under NIR LED, while a trace from 15 µL semen on white cotton became visible under UV LED illumination. These results represent the first demonstration of true stand-off photography of latent traces, thus opening the way for a completely new approach in crime scene forensic examination. Full article

This study aimed to develop an analytical method to determine the geographical origin of Moroccan Argan oil through near-infrared (NIR) or mid-infrared (MIR) spectroscopic fingerprints. However, the classification may be problematic due to the spectral similarity of the components in the samples. Therefore, unsupervised and supervised classification methods—including principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modeling of Class Analogy (SIMCA)—were evaluated to distinguish between Argan oils from four regions. The spectra of 93 samples were acquired and preprocessed using both standard preprocessing methods and multivariate filters, such as External Parameter Orthogonalization, Generalized Least Squares Weighting and Orthogonal Signal Correction, to improve the models. Their accuracy, precision, sensitivity, and selectivity were used to evaluate the performance of the models. SIMCA and PLS-DA models generated after standard preprocessing failed to correctly classify all samples. However, successful models were produced after using multivariate filters. The NIR and MIR classification models show an equivalent accuracy. The PLS-DA models outperformed the SIMCA with 100% accuracy, specificity, sensitivity and precision. In conclusion, the studied multivariate filters are applicable on the spectroscopic fingerprints to geographically identify the Argan oils in routine monitoring, significantly reducing analysis costs and time. Full article