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Society for Applied Spectroscopy
@sasspectroscopy.bsky.social
150 followers 13 following 350 posts
www.s-a-s.org

The Society for Applied Spectroscopy is a nonprofit organization formed to advance and disseminate knowledge and information concerning the art and science of spectroscopy.
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sasspectroscopy.bsky.social
Happy Presidents' Day to our friends in the US! Just a reminder, our offices are closed today.
February 16, 2026 at 2:13 PM
sasspectroscopy.bsky.social
📢 The SAS offices will be closed Monday, February 16 for the holiday.
Regular operations will resume on Tuesday.

#SAS #Spectroscopy
February 13, 2026 at 3:13 PM
sasspectroscopy.bsky.social
🎉 Welcome to our new Applied Spectroscopy Practica editors! We’re excited to welcome Mustafa Culha and Shawn (Xiaoyun) Chen to the editorial team. Their expertise in spectroscopy, nanotechnology, and industrial applications is a tremendous asset to the journal.
February 12, 2026 at 3:09 PM
sasspectroscopy.bsky.social
🔬 Heading to Pittcon March 7–11? Don’t miss the SAS-sponsored session “Spectroscopy and Sustainability.” 🌱 Learn how spectroscopy is driving innovation and sustainability across science & industry. Details: https://bit.ly/4qF7ZTg
#SAS #Spectroscopy #Pittcon
February 11, 2026 at 3:08 PM
sasspectroscopy.bsky.social
New from Applied Spectroscopy!
Advanced #Hyperspectral Signature Processing for Chemical Stress Detection in #Vegetable Leaves Using #Hierarchical #Feature Extraction and #Enhanced #Ensemble #Model
Read more: https://doi.org/10.1177/00037028251411953
#SAS #Spectroscopy #pesticides #vegetable
Abstract: The increasing concern about the presence of pesticides in vegetable leaves has underscored an urgent need for real-time, nondestructive, and accurate detection methods. Traditional methods are reliable but laboratory-based, costly, and unsuitable for field monitoring. In this study, we propose an efficient learning model pipeline that uses hyperspectral reflectance signatures to detect pesticide residue in plant leaves. We extract a comprehensive set of 39 domain-specific features based on vegetation indices, red-edge metrics, spectral statistics, and derivative profiles. To enhance the performance, use a multilayer perceptron to extract more features. A feature fusion module is used to combine both domain-specific features and features extracted by a multilayer perceptron. Further refinement is achieved through a feed-forward attention scoring module that dynamically weights important features. The efficiency of the system is evaluated using an enhanced extra trees classifier, which shows superior classification performance and stability across different feature formats. With cross-validation, our model achieves an accuracy of 94.69%, significantly outperforming conventional classifiers such as convolutional neural networks, support vector machines, and ensemble models such as random forest and extra trees. This framework not only improves interpretability and performance but also provides a foundation for a real-time, on-site pesticide monitoring solution.
February 11, 2026 at 2:10 PM
sasspectroscopy.bsky.social
🏆 The Peter Griffiths Award honors the best paper published annually in Applied Spectroscopy Practica. Congrats to the first recipients: Sara Fraser-Miller (Flinders Univ.) with Mitchell C. Chalmers, Teemu Tomberg & Keith Gordon for their award-winning Raman spectroscopy paper (Vol. 2, Issue 4). 🎉
February 10, 2026 at 3:48 PM
sasspectroscopy.bsky.social
New from Applied Spectroscopy!
High-Accuracy Age-Range Prediction of Bamboo Using Deep Fusion of Laser-Induced Breakdown Spectroscopy Spectral Data
Read more: https://doi.org/10.1177/00037028261417366
#SAS #Spectroscopy #LIBS #spectral #data #fusion
Abstract: Bamboo craftsmanship is highly valued for its aesthetics and cultural significance. The classification of bamboo craftsmanship plays a key role in preserving its heritage and ensuring its quality. However, the surface characteristics of bamboo exhibit substantial variation due to environmental factors. This study proposes a novel method using laser-induced breakdown spectroscopy (LIBS) combined with spectral data fusion to enhance the identification accuracy of bamboo age ranges. By fusing spectra from different bamboo parts, a broader range of elemental compositions can be captured while minimizing the influence of regional variations. A total of 50 bamboo craftsmanship samples of five different age ranges were prepared, and their internal and external surface LIBS spectra were collected for data analysis. Experimental results demonstrate that the peak selection–linear discriminant analysis model presents the highest classification accuracy of 99.0% before spectral data fusion. After fusion, the accuracy can be further improved to 99.9%. Additionally, a comparison of various data fusion methods reveals that the Concat method, which increases the dimensionality of the feature space and provides richer data representation, exhibits the best compatibility with LIBS spectral characteristics and classification models. In conclusion, the combination of LIBS and data fusion methods proves to be an effective approach for accurately identifying bamboo age ranges.
February 9, 2026 at 4:04 PM
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💡 SAS member perk: Full online access to Applied Spectroscopy—every issue back to the 1940s! If you read multiple articles a year, membership pays for itself. Learn more: https://bit.ly/3LRexPF
February 9, 2026 at 2:07 PM
sasspectroscopy.bsky.social
🏅 The NY/NJ Section of SAS is accepting nominations for the 2026 Gold Medal Award, honoring outstanding contributions to applied spectroscopy. Award presented at EAS 2026 (Nov 16–18, Plainsboro, NJ). ⏰ Deadline: Feb 27, 2026. Apply: https://bit.ly/49WjG0M
#SAS #Spectroscopy #Awards #EAS
February 6, 2026 at 3:06 PM
sasspectroscopy.bsky.social
New from Applied Spectroscopy!
Complex-Valued Chemometrics in Spectroscopy: Inverse Least Squares Regression
Read more: https://doi.org/10.1177/00037028251358392
#SAS #Spectroscopy #Chemometrics #refractive #index #Inverse #Least #Squares #Regression
Abstract: Inverse least squares (ILS) regression is an advancement of classical least squares (CLS) regression, enabling the calculation of concentrations without requiring prior knowledge of the number of components in a mixture. Complex-valued ILS further enhances the performance of ILS by incorporating the complex refractive index function, as demonstrated in the thermodynamically ideal mixtures of benzene–toluene and benzene–cyclohexane. In both systems, the mean absolute error can be reduced by over 50% using the leave-one-out cross-validation (LVOOCV) scheme with complex-valued ILS. Additional error reduction is achievable by leveraging correlations between the errors and the imaginary components of the concentrations or volume fractions. Since the complex refractive index function can be conveniently determined using conventional infrared spectroscopy through the Kramers–Kronig relations, we believe that complex-valued machine learning has the potential to significantly advance analytical applications.
February 6, 2026 at 2:08 PM
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📣 The 2026 SAS Advertising Prospectus is now available! Discover sponsorship and advertising opportunities to reach a global spectroscopy audience. View the prospectus: https://bit.ly/4q5vT9u
#SAS #Spectroscopy #Advertising
February 5, 2026 at 3:04 PM
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🔬 SAS will be at Pittcon, March 7–11, 2026! Connect with experts, explore new technologies, and engage with the spectroscopy community at this premier analytical science conference. Learn more: https://loom.ly/Zkx4CPY
#SAS #Spectroscopy #Pittcon
Laboratory Science Conference & Exposition
Pittcon is a dynamic, international conference and exposition on laboratory science, a venue for presenting the latest advances in analytical research and scientific instrumentation, and a platform for continuing education and science-enhancing opportunity.
pittcon.org
February 4, 2026 at 8:45 PM
sasspectroscopy.bsky.social
New from Applied Spectroscopy!
Morphology-Enhanced Rolling Ball Algorithm for Baseline Removal
Read more: https://doi.org/10.1177/00037028251384654
#SAS #Spectroscopy #Raman #Baseline #Removal #overfitting
Abstract: As a preprocessing step of spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, electrophoresis, etc., the baseline correction is very important for improving the signal quality, thereby ensuring the reliability and accuracy of the data analysis. Methods such as polynomial fitting, wavelet transforms, and frequency-domain filtering are widely used for baseline correction, effectively reducing interference and enhancing the reliability of signal analysis. However, these methods have certain limitations: (i) Polynomial fitting faces challenges in determining the optimal order, which may affect the fitting quality, (ii) wavelet transforms are complex and require fine adjustments, and (iii) frequency-domain filtering may cause signal distortion. These shortcomings affect the implementation of the algorithm in spectral related industries. Therefore, finding an appropriate algorithm to optimize baseline removal is crucial for the development of automated spectral analysis equipment. Here, we propose a rolling ball baseline removal algorithm based on morphological operations. With its simple implementation and excellent baseline removal performance, this method effectively avoids the overfitting problems. It is suitable for baseline correction in not only Raman spectroscopy, but also various other types of spectral data. In all, this approach offers a convenient and efficient general solution for the processing of various spectral data.
February 4, 2026 at 1:53 PM
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New from Applied Spectroscopy!
Stimulated Raman Spectroscopy Using a Tunable Visible Broadband Probe Pulse Generated by Kerr Instability Amplification
Read more: https://doi.org/10.1177/00037028251375438
#SAS #Spectroscopy #Raman #Kerr #tunable #femtosecond
Abstract: Femtosecond, broadband stimulated Raman spectroscopy is a popular approach to measuring molecular dynamics with excellent signal-to-noise and spectral resolution. We present a new method for broadband stimulated Raman spectroscopy that employs Kerr instability amplification to amplify the supercontinuum spectrum from sapphire and create a highly tunable Raman probe spectrum spanning from 530 to 1000  nm (–6000 to 2800 cm–1). Our method, called Kerr instability amplification for broadband-stimulated Raman spectroscopy (KAB-SRS) provides an alternative to optical parametric amplifiers by producing a broader and more tunable spectrum at a significantly reduced cost to OPA implementations. We demonstrate the effectiveness of KAB-SRS by measuring the stimulated Raman loss spectrum of 1-decanol.
February 2, 2026 at 1:54 PM
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New from Applied Spectroscopy!
Analysis of Liquids Using the Submerged Plasma for Isotopic Detection and Elemental Resolution (SPIDER)
Read more: https://doi.org/10.1177/00037028251374696
#SAS #Spectroscopy #submerged #plasma $isotopic #detection
Abstract: We developed a sensor called the Submerged Plasma for Isotopic Detection and Elemental Resolution (SPIDER) probe, which uses an atmospheric pressure glow discharge below the surface of liquids to excite species in the liquid. Through emission spectroscopy of molten salts, liquid metals, and heavy water, we demonstrated the SPIDER probe’s high resolution, accuracy, and versatility. We successfully identified trace concentrations of transition and rare-earth metals in molten salts and detected the isotopic shift of the H_beta --> D_beta emission line. Our analysis revealed unconventional spectral alkali line shapes, indicating two competing excitation modes: film explosion and droplet vaporization. The film explosion mode, characterized by dense plasma, exhibited self-reversal and broadband continuum emission, while the droplet vaporization mode, associated with diffusive plasma, produced narrow-line emissions. By analyzing circuit transients alongside individual plasma events, we observed that film explosions generate higher currents, likely due to a shorter plasma length as the current preferentially flows through the thin liquid layer. Altogether, our results highlight the SPIDER probe’s efficacy and flexibility, making it well-suited for online material quantification of liquids in extreme environments.
January 30, 2026 at 1:59 PM
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👀 Have you registered for Pittcon 2026? Early bird pricing ends today, Jan 29! Don’t miss cutting-edge research & innovations in analytical science, March 7–11. Register now: https://loom.ly/Zkx4CPY
#SAS #Spectroscopy #Pittcon
Laboratory Science Conference & Exposition
Pittcon is a dynamic, international conference and exposition on laboratory science, a venue for presenting the latest advances in analytical research and scientific instrumentation, and a platform for continuing education and science-enhancing opportunity.
pittcon.org
January 29, 2026 at 2:59 PM
sasspectroscopy.bsky.social
Start the new year strong with SAS sponsorship or advertising! Reach engaged professionals and align your brand with innovation in spectroscopy. Learn more: https://bit.ly/44RudbU
#SAS #Spectroscopy #Sponsorship
January 28, 2026 at 3:10 PM
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New from Applied Spectroscopy!
Raman Spectroscopy for Monitoring Polymerization, Quality Control, and Additive Distribution in Styrene–Divinylbenzene-Based Proppants
Read more: https://doi.org/10.1177/00037028251386484
#SAS #Spectroscopy #Raman #Styrene #Divinylbenzene #polymerization
Abstract: Raman spectroscopy was applied to monitor polymerization, verify quality control, and analyze additive distribution in styrene–divinylbenzene (Sty–DVB) based proppants. The method relies on C=C vibrational markers to follow monomer consumption, crosslinking, and additive incorporation. Quality control included quantifying vinyl, cis, and trans C=C in polybutadiene (PB) modifiers and the ethyl-vinyl benzene (EVB) content in DVB crosslinkers. EVB content by Raman showed excellent agreement with independent carbon-13 nuclear magnetic resonance (¹³C-NMR) measurements. Styrene copolymerization with DVB was tracked in real time using a fiber-optic Raman probe in a temperature-controlled microreactor. DVB accelerates styrene consumption due to its higher reactivity and radical stabilization. PB additives do not affect overall polymerization kinetics. In terms of additives, Raman calibration confirms that PB double bonds remain largely unreacted, consistent with limited copolymerization and phase separation. Polyphenylene oxide (PPO) slows down Sty polymerization while Raman mapping demonstrates its homogeneous dispersion within the matrix, validating its incorporation and expected impact on material properties. Overall, Raman spectroscopy provides a direct, non-invasive, and scalable approach to monitor polymerization and verify additive distribution, establishing it as a practical tool for process optimization in Sty–DVB proppant formulations.
January 28, 2026 at 1:58 PM
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🔍 Exploring new career options in spectroscopy or analytical science? Check out the latest opportunities on the SAS Career Postings page—your next role could be waiting. https://bit.ly/3LUQj6R
#SAS #Spectroscopy #Careers
January 27, 2026 at 6:26 PM
sasspectroscopy.bsky.social
New from @applspecpractica.bsky.social!

Spectral Barcoding for the #Detection of #Fentanyl and Fentanyl Analogs Using #Raman Spectroscopy

Read the full open-access article here: https://loom.ly/iVM6Ee4

#SAS #spectroscopy #openaccess
January 27, 2026 at 2:05 PM
sasspectroscopy.bsky.social
New from Applied Spectroscopy!
Re-Examining Tip-Enhanced Raman Signals at High Spatial Resolution Under Ambient Conditions Using Graphene Nanobubbles
Read more: https://doi.org/10.1177/00037028251382936
#SAS #Spectroscopy #Raman #graphene #nanobubbles #defects
Abstract: High-spatial-resolution tip-enhanced Raman spectroscopy (TERS) measurements were carried out under ambient conditions on graphene nanobubbles with various associated structural features. The resulting signals were analyzed with consideration of the characteristic features inherent to high resolution TERS. Compared to flat graphene regions, nanobubbles and their associated nanoconvex pinning sites demonstrated enhanced TERS signals, attributed to the efficient coupling between the strong tip-enhanced electric field and out-of-plane deformations in graphene. Strong coupling with highly confined near-field light activates the D bands even in the absence of defects, with intensity depending on the degree of deformations. While the D band is observed across the nanobubbles, some local regions exhibit a weaker D band intensity compared to the surrounding areas. Given the finite number of hexagonal lattices within the area of highly confined near-field, this reduction in intensity is likely to result from defects that cause missing hexagonal lattices. These findings highlight the capability of near-field induced Raman signals in probing high resolution features of nanomaterials even under ambient conditions, providing deeper insights into their characteristics in situ.
January 26, 2026 at 1:54 PM
sasspectroscopy.bsky.social
Check out SAS President John Wasylyk's contribution to @applspecpractica.bsky.social titled "Spectroscopy and Sustainability: A Perfect Match and a Perfect Fit at @pittcon.bsky.social 2026"!

Link: https://loom.ly/Y5CwR7M

January 23, 2026 at 5:44 PM
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New from Applied Spectroscopy!
Surface-Enhanced Raman Spectroscopy Semi-Quantitative Molecular Profiling with a Convolutional Neural Network
Read more: https://doi.org/10.1177/00037028251377474
#SAS #Spectroscopy #Raman #SERS #CNN #SVR
Abstract: Surface-enhanced Raman scattering (SERS) spectroscopy represents a powerful analytical platform that combines non-destructive, label-free molecular identification with exceptional sensitivity for trace-level detection. Its capacity to generate information-rich spectral fingerprints makes SERS particularly advantageous for simultaneous multi-analyte analysis across diverse sample matrices, including complex biological systems. This study addresses the analytical challenges associated with identifying and quantifying multiple molecular species in complex environments by integrating SERS with advanced machine learning methodologies. We developed a hierarchical analytical framework that leverages the complementary strengths of deep learning and regression techniques: A multi-label convolutional neural network (CNN) for discriminating structurally similar analytes from SERS spectral data, coupled with a support vector regression (SVR) model for semi-quantitative determination of relative concentration ratios among identified species. The methodology was systematically validated using binary mixtures of short-chain fatty acids (SCFAs) as representative biomolecular targets, with performance rigorously benchmarked against established multivariate statistical methods and conventional machine learning approaches. Experimental validation demonstrated robust classification accuracy for both analytes at physiologically relevant concentrations, maintaining consistent performance across simple aqueous media and complex cell culture environments. These results establish the viability of the integrated SERS-CNN-SVR approach for advanced mixture analysis applications where precise identification and quantification of multiple biomarkers is essential.
January 23, 2026 at 1:55 PM
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🤝 New year, new connections! Join SAS and connect with a global community of spectroscopists through technical groups, regional sections, and special interest communities. Your network starts here.
Learn more: https://bit.ly/4q0tDks

#SAS #Spectroscopy #Networking #NewYearNewMembership
January 22, 2026 at 6:35 PM
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New from @applspecpractica.bsky.social!

Automatic Detection of Low Concentrations of #Fentanyl in #Acetaminophen Using Commercial Off-the-Shelf Non-Radioactive #Ionization Ion Mobility Spectrometry #IMS

Read the full article here: https://loom.ly/yJJS68o
January 22, 2026 at 2:05 PM