Recent research in the fields of forensic and biological anthropology reveals that much can be gleaned from the elemental analysis of skeletal remains. This is a burgeoning area of analysis, however, and foundational studies on the elemental analysis of bones require representative remains, that is, de-fleshed bones from humans or animals. While manual de-fleshing can be straightforward, the precise cleaning of tissues from the bones requires more attention or the use of a maceration aid to break down flesh. Several maceration methods have been established that use detergent, enzymatic detergent, enzyme solution, heat, or invertebrates (specifically, flesh-eating beetles or Dermestes maculatus ) to de-flesh bones. These methods must be evaluated for potential chemical interference and effects on the bone matrix before they can be applied in the elemental analysis of skeletal remains. In this study, these maceration methods, as well as a “control” method that did not include the use of a maceration technique to aid in de-fleshing, were applied to a sample set of 54 porcine ribs. The effects of each maceration method on the bones’ chemical compositions were assessed using X-ray fluorescence (XRF), laser-induced breakdown spectroscopy (LIBS), and inductively coupled plasma mass spectrometry (ICP-MS). The effects of each maceration method on the bones’ physical matrices were assessed using spectrophotometry and digital microscopy. Each maceration aid is found to result in bones that are chemically distinct from the control group, but invertebrate maceration produces bones that most resemble the control group when analyzed via LIBS or ICP-MS, while enzymatic maceration produces bones that most resemble the control group when analyzed via XRF.
Eggshell membrane (ESM) extracts are widely utilized as bioactive biomaterials and nutraceuticals due to their rich content of structural proteins and glycosaminoglycans. Ensuring the compositional purity of these materials—specifically the degree of separation between the organic membrane and the inorganic, mineralized eggshell—is critical for processing efficacy and quality control. In this study, 1064 nm near-infrared Raman spectroscopy was employed for high-fidelity, nondestructive compositional analysis. Characteristic Raman signals for carbonate (1085 cm −1 ) and Amide I (1668 cm −1 ) were utilized to differentiate the inorganic shell and organic membrane phases. A robust linear calibration model (R 2 = 0.99) was established between Raman intensity ratios and known mass ratios using laboratory-prepared reference standards. Application of this quantitative methodology to representative consumer-grade ESM powders successfully estimated residual hard-shell mass percentages, demonstrating that 1064 nm Raman spectroscopy provides a rapid, reliable analytical framework for monitoring material purity in ESM processing and manufacturing.
In this study, a new sensor for the fluorescence detection of guanosine and Mg 2+ ions using a “turn-off–on” mechanism was successfully constructed using 2-mercapto-5-nitrobenzimidazole (MNB) modified bimetallic molybdenum–gold nanoclusters (Mo-AuNCs). The method has minimal cost, excellent sensitivity, good selectivity, simplicity, and speed. After being excited at 390 nm, the MNB-Mo-AuNCs fluorescence emission peak was obtained at 531 nm. The addition of guanosine selectively reduces the fluorescence intensity of MNB-Mo-AuNCs and the turn-on mechanism recovers the quenched fluorescence in the presence of Mg 2+ . The limits of detection (LODs) for the guanosine and Mg 2+ ions using this approach were 1.43 and 0.10 µM, respectively. The fluorescence sensing technique based on MNB-Mo-AuNCs demonstrated exceptional performance for guanosine detection in biological samples. The method showed great reproducibility (relative standard deviation < 2%) and recovery ranging from 96.3% to 99.21% in plasma and serum samples indicating the method considerable potential for real-world applications.
All-optical thermometry using nano- and submicron diamond particles containing negatively charged nitrogen vacancy (NV − ) centers enables high-sensitivity temperature sensing at the microscale. However, its extension to imaging with temporal resolution has been limited by the requirement for picometer-level accuracy in wavelength determination. We developed an optical fiber-bundle-based imaging thermometry system that simultaneously acquires zero-phonon line spectra from multiple spatial channels and resolves their peak wavelength shifts using a high-dispersion Czerny–Turner spectrometer with in situ wavelength calibration. The total wavelength uncertainty was reduced to 4–6 pm, corresponding to a temperature accuracy of 0.4–0.6 °C at 25.0 °C. Imaging thermometry was demonstrated for an ensemble of submicron diamond particles at 34.0 °C and 40.0 °C.
In biopharmaceutical production, ultrafiltration and diafiltration (UF/DF) becomes essential when high concentrations and tight control over the final formulation of drug substance is needed. Currently, the possibilities for direct inline monitoring of this process step are limited. This study evaluates the effectiveness of three spectroscopic techniques, i.e., mid-infrared (MIR), Raman, and variable pathlength ultraviolet (UV), or inline monitoring of protein concentration. Results demonstrate that all three techniques are suitable for monitoring this operation, with root mean squared errors of prediction (RMSEP) of 5.0, 3.2, and 3.2% for MIR, variable pathlength UV, and Raman spectroscopy, respectively. MIR and Raman spectroscopy were also tested for the quantification of excipient concentration in solution, with promising results. These findings indicate that all these advanced spectroscopic methods can enable inline monitoring capabilities, thus reducing the reliance on time-consuming offline analytics and leading to shorter development timelines. This integration of process analytical technology (PAT) tools in biopharmaceutical manufacturing can offer potential improvements in process control and variability reduction.
Robust in-field sensing technologies are essential for advancing precision agriculture and autonomous field robotics toward analysing internal quality attributes of fruits and vegetables. This study demonstrated in-the-field, non-contact near-infrared (NIR) spectroscopy for determining total soluble solids (TSS), a measure of sugar content, in on-the-plant strawberries under daytime conditions. A compact NIR interaction instrument (750–1020 nm), designed for robotic operation, was built and tested in a polytunnel environment under varying day- and night-time conditions. The instrument was calibrated using a partial least squares regression (PLSR) model built on laboratory data collected in 2025 from 200 strawberries of a single variety. It was tested on 100 strawberries of two varieties that were measured in 2024, while still attached to the plant. During night-time operation, TSS was predicted with a standard error of prediction ( SEP ) of 0.73 % TSS and a bias of 0.65 % TSS. Under challenging daytime conditions with strong and fluctuating ambient light, measurements were more affected by additional shot noise from the ambient light, resulting in SEP s up to 1.35 % TSS and biases up to 1.45 % TSS, both of which are acceptable for most applications. The measurement time was 12 s. Robust performance was achieved by implementing rapid and continuous ambient light sampling and correction, combined with outlier rejection of spectra of insufficient quality. These findings confirm the feasibility of in-field, on-the-plant NIR spectroscopy for assessing internal fruit quality and provide practical design guidelines to support further in-field implementations of NIR spectroscopy.
Wavelength calibration of linear array spectrometers is conventionally performed by fitting a polynomial function of pixels to calibration data consisting of spectral lines with known wavelengths and their pixel locations. Alternatively, a multiparameter physical model of the optical system can be constructed and the parameters optimized to reproduce the calibration data. Here, a new spectrometer wavelength calibration method is introduced with two distinguishing features: the reference calibration points are treated as fixed points, and are used as nodes for cubic Hermite spline interpolation between those points. Conveniently, in the Hermite form of cubic splines, the calibration data, i.e., the calibration wavelengths together with the slopes at those calibration points, explicitly appear as the parameters of the interpolating functions. The slopes are derived from the grating equation and the physical model of the optical system. As a result, the calibration curve is fully determined without the need for intermediate calculations such as polynomial regression or model parameter fitting. Zero residual error is ensured at those reference points and interpolation errors in the intervals between reference points are very low, approaching the limit of spectral line peak location uncertainty. The calibration procedure is demonstrated for a compact flat-field spectrometer. This method improves and simplifies the spectrometer wavelength calibration procedure and offers a convenient method of calibration for systems with limited computing resources.
As part of the public health investigation into elevated lead (Pb) levels in an apple cinnamon fruit puree, cinnamon was identified as the source of contamination. Cinnamon samples were tested by inductively coupled plasma mass spectrometry (ICP-MS) at the U.S. Food and Drug Administration's (FDA) Kansas City Human and Animal Food Laboratory and determined to contain elevated levels of Pb (>2000–5000 µg/g) and chromium (Cr) (>500–1200 µg/g). Total Pb and Cr concentrations alone could not determine the source of the contamination. To aid in the investigation of potential sources of the Pb and Cr contamination, e.g., particles from grinding equipment or addition of lead (II) chromate (PbCrO 4 ), further information was needed. The FDA's National Forensic Chemistry Center conducted additional analyses in which suspected lead (II) chromate particles were physically isolated and preconcentrated, then analyzed by Raman microspectroscopy. Once Raman spectra of the preconcentrated particles were obtained, the same particles were removed from the microscope slide and digested with nitric acid for ICP-MS analysis. The combination of the Raman spectral comparison to a lead (II) chromate reference standard and confirmation of Pb and Cr in the particles with 1:1 stoichiometry using ICP-MS identified lead (II) chromate as the contamination source. This confirmed that lead (II) chromate had been added to the cinnamon, suggesting intentional adulteration. Previous reports indicate that lead (II) chromate can be added to spices to enhance the color and/or increase the mass of the product. This paper demonstrates how combining two complimentary instrumental techniques was used to identify and quantify an inorganic compound in a possible case of economically motivated adulteration.
Cost effective utilization of renewable biogas requires that the siloxane content be maintained at low parts-per-billion (ppb v ) levels. Infrared (IR) spectrometric methods offer the potential for near-real-time siloxane quantification but are difficult to implement in the presence of certain spectral interferents such as oxygenated volatile organic compounds (VOCs). In this report, a novel two-step gas-stream modification process is implemented in the quantification of siloxanes in biogas by Fourier transform IR spectrometry. The method is demonstrated for synthetic biogas comprising a mixture of methane in nitrogen, with linear (L3) and cyclic (D4) siloxanes present at 100 ppb v levels, and ethanol, acetone, and acetic acid added as model VOCs at 100 parts-per-million (ppm v ) levels. The first step in the process involves sparging the biogas through water to significantly reduce VOC concentrations. The second step removes residual VOCs and the siloxanes by passage of the gas stream over a low temperature metal oxide catalyst. IR spectra acquired after sparging and after passage over the catalyst serve as near-real-time biogas sample and biogas blank spectra, respectively. The biogas blank spectrum and standard siloxane spectra are next used in a simple least squares reconstruction of the biogas sample spectrum for siloxane quantification. Limits of detection for L3 and D4 are determined to be 9.3 and 6.3 ppb v , respectively, while limits of quantification are 31 and 21 ppb v . This method will facilitate the development of simple, inexpensive IR-based devices for on-line and near-real-time monitoring of siloxanes in industrial biogas streams.
This research on prehistoric obsidian artifacts from archaeological sites in Calabria (Italy) was done for the first time since Ammerman and colleagues’ work in Acconia on 52 artifacts more than three decades ago. Using a portable X-ray fluorescence (pXRF) spectrometer, non-destructive trace element analyses were conducted on nearly 2400 obsidian artifacts from >50 sites (Neolithic–Bronze Age, ca. 6000–2000 BCE. This study identified the specific obsidian sources and subsources which were utilized, and what this infers about the socioeconomic characteristics of both the local population and those near the sources themselves, the capabilities and perhaps regularity of maritime transport, and whether there were changes over time. Overall, for the Calabria sites tested so far, obsidian from the four source islands of Lipari, Palmarola, Pantelleria, and Sardinia (Monte Arci) has been identified, a big surprise from the early results. The vast majority comes from Lipari, with most artifacts from Gabellotto Gorge.
We evaluated the capability of near-infrared (NIR) transflectance spectroscopy coupled with multivariate analysis to develop predictive regression models that quantify taste indicators, i.e., soluble solids, titratable acidity (TA), citric acid, sucrose, glucose, and fructose, in orange juice (sample size = 123). The NIR spectra were collected in transflectance mode using a handheld scanner with a series of reflectors that provide three distinct pathlengths (0.50 mm, 0.80 mm, and 2 mm) across the juice samples. Reference data were collected using automatic titrators for TA, refractometer for Brix, and high-performance liquid chromatography (HPLC) for organic acids and sugars. Orange juice samples represented a diverse genetic pool of Hamlin and Valencia varieties harvested at different maturity stages providing a unique range of concentration for each parameter (Brix, 4.3–12.3%, and TA 0.8–3.5 g citric acid/L). Pattern recognition analysis correlated the spectral data to reference values using partial least square regression. Results showed better performance using 0.50 mm and 0.80 mm pathlengths, R pre ≥ 0.82, root mean square error of prediction (RMSEP) range 0.14–6.87, residual prediction deviation (RPD) range 1.2–8.3, residual error rate (RER) range 2.7–30 compared to the 2 mm pathlength (R pre ≥ 0.72, RMSEP range 0.27–4.47, RPD range 1.2–1.7, RER range 5.2–7.8). We demonstrated that a field deployable NIR scanner can provide reliable prediction using a transflectance approach using algorithms that were synchronized for cloud computing using R programming, providing easy accessibility for analysis. This technology offers orange breeders and growers an affordable, rapid (10 s), and accurate solution for in-field, real-time monitoring of taste indicators in orange juice, which can help expedite critical decision-making processes in the field (harvest timing, process optimization, and aid in breeding research).
A comparison of microextraction sampling methods to directly analyze uranium isotopics on cotton swipes was performed concurrently with traditional bulk-processing methods. For the microextraction sampling approach, two different detection platforms were evaluated, a quadrupole-based inductively coupled plasma mass spectrometer (ICP-MS) and the liquid sampling-atmospheric pressure glow discharge (LS-APGD) coupled to an Orbitrap mass spectrometer. Results presented from this innovative sampling approach (i.e., microextraction) are compared with a more traditional approach employed for analysis of cotton-based environmental swipes, namely bulk ashing/digestion, separation, and subsequent analysis by high-precision multi-collector ICP-MS. Overall, the microextraction approach proved to be a reliable and accurate means to determine isotopic ratios of uranium collected on cotton swipes. The ICP-MS-based detection had relative standard deviations of <0.65% for the major isotopic determinations, whereas the LS-APGD-Orbitrap method had relative standard deviations of <3%. The percent relative difference for the 235 U/ 238 U ratios, in comparison to the expected values, was <1% for ICP-MS and <4% for the microplasma-Orbitrap method. Additionally, the microextraction ICP-MS accurately (<2%) and precisely (<5%) determined the minor isotopic compositions (i.e., 234 U/ 238 U and 236 U/ 238 U).
We present an initial investigation into the performance of a multicollector inductively coupled plasmamass spectrometer equipped with a pre-mass filter (Neoma MC-ICP-MS/MS) for making plutonium (Pu) isotope ratio measurements on solutions containing low level (i.e., pg mL –1 ) Pu concentrations. This assessment was achieved by comparison of the 240 Pu/ 239 Pu, 241 Pu/ 239 Pu, and 242 Pu/ 239 Pu ratios attained over a one month period on the MC-ICP-MS/MS with the long-term (∼1 year) performance observed on the predecessor MC-ICP-MS (Neptune Plus) instrument each equipped with an equipped with an APEXΩ desolvating nebulizer for repeated measurements of certified reference materials from New Brunswick Program Office (NBL PO) CRM 136a and CRM 137. The MC-ICP-MS/MS performance of repeated measurement of CRM 136a (n = 20) resulted in mean values of 240 Pu/ 239 Pu = 0.1448 ± 0.0006, 241 Pu/ 239 Pu = 0.00371 ± 0.00006, and 242 Pu/ 239 Pu = 0.00682 ± 0.00006 ( k = 2). The CRM 137 ( n = 20), analyzed during the same analytical sessions, produced mean values for 240 Pu/ 239 Pu = 0.2414 ± 0.0006, 241 Pu/ 239 Pu = 0.00464 ± 0.00007, and 242 Pu/ 239 Pu = 0.0157 ± 0.0001 ( k = 2). These results closely align with the certificate values for CRM 136a and CRM 137 and are within the k = 2 envelopes defined by the long-term performance of the traditional MC-ICP-MS approach (Neptune Plus). Examination of the performance of the various Pu isotope ratios as a function of total Pu content revealed accurate results (<3% relative difference, or RD) above ∼50 fg total Pu. The results presented here demonstrate the capability of the MC-ICP-MS/MS making accurate and precise low level Pu isotopic measurements. While the intent of this work was not to investigate the functionality of the collision cell, the pre-mass filter was employed. Future studies are warranted to investigate the entire capability of the MC-ICP-MS/MS collision cell and pre-cell mass filter optimization for performing low level Pu isotope measurements, even in mixed matrix samples.
The use of dispersive near-infrared (NIR) absorption spectroscopy in real-time fuel property measurement is a promising approach for enhancing engine performance. Such sensing, coupled with appropriate controls, may broaden the range of viable fuels available for use in compression ignition (CI) engines. An important fuel property for CI engines is the cetane number/derived cetane number (CN/DCN). This study builds upon recent work focusing on employing vibrational spectroscopy for determining CN/DCN for real-time on-board sensing. The NIR region, measured with moderate resolution, has some variability in the combination bands from which models can be generated for the prediction of fuel properties, at least for fuels similar to current jet fuels. Sensing in this region is relatively low-cost and robust compared with other spectral regions and schemes. Machine learning (ML) models to predict DCN from NIR spectral data are described. These models can address some of the challenges associated with on-board deployment, including sensing spectral range/resolution shifts and baseline shifts. The resolution and ranges of measurements on a sensor can be different than those on analytical devices used to collect the training set and generate the models. Two ML model approaches, one based on imputation and the other based on ensemble models, are evaluated to tackle that problem. The results indicate that the ensemble model is capable of accurate predictions achieving a coefficient of determination (R² score) above 0.86 when evaluated across various resolutions, i.e., 12 nm, 10 nm, and 2 cm –1 , and achieved an average R 2 score of 0.857 when evaluated on varying spectral range.
Poly(ethylene terephthalate) (PET) is one of the leading polymers in the packaging industry. It is often copolymerized with isophthalic acid (IPA) to tune its properties, yet its comonomer content is not always known. In this work, we develop simple Raman spectroscopy methods to quantify the IPA content of PET samples in the range most used for bottle production without any pretreatment. The calibration curves allow precise quantification of IPA content for amorphous samples (R 2 = 0.997), and good estimates for semicrystalline samples (R 2 = 0.952) and commercial products exhibiting common spectroscopic challenges. This work leverages the speed and accessibility of Raman spectroscopy for solid-state IPA quantification, making it a practical alternative to established techniques.
Drug overdoses in the United States of America reached over 110 000 in 2023 with a large percentage due to fentanyl and other synthetic opioids. While provisional data from the U.S. Centers for Disease Control and Prevention (CDC) shows a decrease of 26.9% in drug overdoses in the United States during 2024, these numbers are still staggering. Fentanyl is used medicinally to manage pain in dosage forms such as intravenous solutions, oral transmucosal lozenges, and transdermal patches. It has potency ∼100 times that of morphine, so these dosage forms effectively manage delivery of the small amount of fentanyl required to achieve the desired therapeutic effect. In recent years, illicit powder versions of fentanyl have become available. Small amounts are mixed with other drugs and cutting agents. These low-dose-fentanyl mixtures emerged as a threat. It is necessary to identify fentanyl in these mixtures to mitigate risk to illicit-drug users, first responders, and the public at large. Although there are several spectroscopic methods used for the reliable detection and identification of fentanyl as an unadulterated powder, these methods are unable to achieve the limits of fentanyl detection required in low-dose mixtures. The purpose of this study was to determine whether commercial off-the-shelf (COTS) ion mobility spectrometry (IMS) with a dielectric barrier discharge (DBD) non-radioactive ionization source and preprogrammed fentanyl detection algorithms could be used to detect and identify fentanyl at low concentrations in an acetaminophen matrix. Acetaminophen is commonly encountered in illicit drug samples. The use of preprogrammed detection algorithms is consistent with how field users operate, relying on methods that have been optimized by the instrument manufacturer for an established set of target threats. This research shows that COTS IMS which uses a DBD non-radioactive ionization source and preprogrammed fentanyl detection algorithms can detect fentanyl at concentrations as low as 0.1% (w/w) in acetaminophen.
Fentanyl and fentanyl analogs are the drugs most often implicated in opioid overdose fatalities. Law and drug enforcement agencies are actively working to remove fentanyl-containing drugs from circulation; however, detection in the field can be challenging. Portable Raman spectroscopy is a valuable tool for identifying fentanyl, but the traditional method of identifying compounds requires comparison to a reference library on the system, which can limit the ability to detect novel fentanyl analogs. To overcome this challenge, a spectral barcoding technique was validated using fentanyl and a range of fentanyl analogs in addition to non-fentanyl confusants. Compared to traditional Raman techniques using an internal system library for identification, the spectral barcoding technique was better at identifying fentanyl-containing compounds while ruling out benign samples. Ultimately, this spectral barcoding technique allows for better identification in a constantly changing landscape of fentanyl analogs.
Raman spectroscopy is attractive for probing complex mixtures, but in many real samples strong fluorescence overwhelms the Raman bands needed for quantitative analysis. This work asks a practical question: under large and varying fluorescence, is it better to invest in hardware-based shifted excitation Raman difference spectroscopy (SERDS) or in preprocessing of conventional Raman spectra? We construct a simulation framework that generates more than 12 million spectra of benzophenone-alanine mixtures embedded in fluorescent matrices. Six datasets emulate realistic fluorescence behaviors, including constant backgrounds, photobleaching, random intensity fluctuations, and changes in fluorescence shape. For each scenario we form paired libraries of conventional Raman and SERDS spectra and build partial least squares regression models on (i) raw spectra containing fluorescence and (ii) spectra after asymmetric least squares or discrete wavelet transform background removal and normalization. Across most cases with stable or smoothly varying fluorescence, conventional Raman combined with suitable preprocessing matches or modestly exceeds SERDS in predicting mixture composition. SERDS provides a clear advantage only when fluorescence intensity or spectral shape fluctuates strongly and in an uncorrelated fashion between measurements, and even then, depends on closely matched sampling volumes at the two excitation wavelengths. These results show that visually cleaner SERDS spectra do not automatically yield more accurate models. Instead, the optimal strategy depends on fluorescence statistics and the available preprocessing pipeline. The simulation framework and decision rules developed here offer practical guidance for designing Raman measurements in fluorescence-rich environments such as soils and other heterogeneous natural materials.
A symposium at SciX LI sought to demystify commercialization of academically developed technologies, focused on analytical and spectroscopic measurements. Speakers ranged from those with successful commercial products to those in the early stages of product development. Speakers sought to explain the challenges, motivations, excitement, and frustrations of moving beyond technical papers and patents to making saleable products and successful enterprises. Their insights are summarized.
Developing global classification and calibration models for identifying edible oil type and predicting peroxide value, a measure of the degree of oxidation directly related to oil freshness is predicated on capturing variance differences within and between growing seasons across a large set of 19 naturally aged (5–7 years) oil types. Our approach utilizes a larger sample set and longer aging period than reported studies (typically 2–5 years), includes multiple growing seasons, and is based on natural rather than accelerated thermal aging of oil samples. We provide spectroscopic data (Raman scattering, near- and mid-infrared absorption) for 19 edible oil types, which represent the most diverse and only naturally aged sample set reported to date. The data, which consist of three data collection campaigns acquired using different analytical methods at different times during the aging process, provide opportunities to investigate and validate new machine learning approaches for classification, calibration, and data fusion. In the supplemental material, vibrational spectroscopy data from all samples are included along with the R programming language code for preprocessing data and building basic classification and calibration models.
