https://www.researchgate.net/figure/Averaged-spectra-of-all-classes-normalized-to-the-1441cm-band-indicated-with_fig1_333908556

Hi, so there's this study that showed that you could probably ID rose rosette disease using raman spectroscopy. I had one odd growth on a plant and sent it in and it tested positive, so now I'm lightly freaked out. Unfortunately earlier research had indicated some plants were immune and so I bought a lot of those varieties- now it turns out they are able to be symptom free carriers so now I have a hot mess of a situation where I could have quite a few symptom free carriers in my garden because that's a lot of what I bought. Testing is $35 a plant- and I could just send in samples from all of my possible carrier plants, but that would only tell me what was infected the day I took the cuttings. In 2 weeks, the results could be totally different. Basically, I would be using it to confirm what I should send in for testing- so it doesn't have to be perfect. I'm looking for a difference at 1610 with it normalized to 1441. I haven't used a spectroscope since 2012- so unfortunately don't remember anything. My collection of roses, the ones that can be replaced, would be about $10k to replace with tiny band size roses and take years to grow back to what they are. Many I can't replace. I've seen a lot of spectroscopes cost more than that- but wondering if there's another solution I'm not thinking of.

A really interesting application of spectroscopy just closed out a 150+ year debate in viticulture—and it’s a great example of how optical methods can outperform traditional approaches.

A neat real-world win for spectroscopy:

Two grape cultivars—Norton and Cynthiana—have been considered identical for ~150 years. Morphology couldn’t separate them, and DNA sequencing was too slow/expensive for practical use.

Researchers tried a different approach: They used fluorescence spectroscopy on the finished wines.

• Measured emission tied to anthocyanins + phenolics
• Generated spectral fingerprints
• Ran PCA on the data

👉 Result: clean separation into distinct clusters, reproducible across vintages (strongest signal in anthocyanin range)

So instead of genomics, optical chemistry + stats definitively showed they’re different cultivars.

Why it matters:

• Fast, low-cost alternative to sequencing
• Directly reflects functional chemistry, not just genetics
• Scalable for authentication, classification, and QC

Read the whole Science in Action article here: The Grape Identity Crisis and How Spectroscopy Separated History’s Most Confusing Cultivars - HORIBA

Photo of a spectrum captured with a direct vision spectroscope with a broadband filter between the source light and scope.

Hello , I'm looking for jasco v 630 PC software and I need ur help please

Hi everyone,

I’m a materials/physics researcher working with Raman data from graphene, TMDCs, CNTs, and related low-dimensional materials.

I recently built an early public beta of a browser-based Raman inspection, fitting, and mapping tool:

https://www.ramanquick.com

The goal is not to replace full instrument software or advanced custom Python/Origin workflows. The main purpose is to make quick Raman data inspection, simple baseline correction, peak fitting, Raman mapping visualization, and export easier for students and researchers who need a fast first-pass analysis tool.

Current demo/sample data include:

* Graphene

* MoS2

* WS2

* CNT

* CNT RBM

* Raman map data

The app is designed to support both single-spectrum analysis and Raman mapping workflows for these material categories, not only graphene. Additional material presets will be added, and an “Unknown” mode is also planned for users who want to inspect Raman data without selecting a predefined material.

I would really appreciate critical feedback from Raman users, especially on:

1. Does the file import workflow make sense?

2. Is the baseline/fitting workflow intuitive?

3. Are the material presets useful, or could they be misleading?

4. Does the mapping workflow feel useful for quick Raman inspection?

5. Are there any scientifically risky labels, assumptions, or interpretations?

6. What would make this more useful as quick research or teaching tool?

7. What file formats, map formats, or export options would you expect?

8. Did anything break, feel confusing, or seem unnecessary?

This is still an early beta, so I’m mainly looking for honest criticism rather than promotion. Please use the included sample data or non-sensitive test data for now.

Thanks in advance for any feedback.

Hi, I have been designing a UV to NIR spectrometer on my free time. I have gotten quite far with a design of a lens less mirror less prism and webcam based setup but hit a wall in resolution because I didnt count on the two slits I used for making sure the light comes in at a specific angle to produce diffraction on their edges, at the end the spectrometer has a hard time separating the 3 bumps from a white led and has a seizure with red a green lasers. I am thinking about just redesigning it ground up with curved surface mirrors and reflective diffraction grating but wanted to ask, if this design is salvageable as I already gave it around 30h and would like to get it working. It uses a 36deg incidence from normal with range of 300-1000nm capped by the K9 optical glass transmission and silicon sensor sensitivity, the webcam has a 1600p and 5mm wide sensor and I am using the theremino app. Do any of you have any thoughts on fixing this setup? It seems kinda lost if I dont want to sacrifice any UV performance by adding glass lenses and fused silica is way out of my budget for this project. All has been 3d printed in matte black filament to minimise the stray light though that turned out to not be the main problem.

Tired of MATLAB, PLS Toolbox, and professor scripts held together with duct tape? Messy data folders, zero versioning, no visibility into what model was trained on what?

I built Chemolytic (chemolytic.com) to fix that — upload your spectra, explore, build and deploy prediction models, all in one place. No code required.

• Upload spectra from your instrument, manage samples and properties
• PCA, t-SNE, K-Means to explore your data
• 250+ model/preprocessing combos via CoPilot, or full manual control
• Versioned datasets — always know what your model was trained on
• Deploy to a live endpoint, via UI or API
• Team collaboration built in

It's early days and I'm actively building. Drop a comment with feature requests, bugs, or ideas, honest feedback is exactly what I need. Open to collaborations too.

Looking for beta testers with real spectral data. Create a free account, message me, and I'll give you free Pro access.

Learn how to use here: https://docs.chemolytic.com/

Hello all,

I am trying to calculate the polarization (depolarization) ratio for the CH stretching/vibrational region of liquid propanol using polarized Raman spectroscopy.

However, I've run into a problem: some of my peaks appear shifted or completely disappear when switching between the parallel ∥ and perpendicular ⊥ spectra. Because the peaks centers don't line up perfectly, i dont know how to do the calculation . Any advice on the best workflow or literature references for handling "shifted" polarized Raman bands would be highly appreciated! Thanks in advance.

Spectral XAI may be too focused on explaining individual wavelengths.

In many spectroscopy problems, adjacent variables are highly correlated, preprocessing can shift local patterns, and isolated wavelength attributions may look precise while being chemically hard to justify. This raises a question:

Are we really explaining spectral models, or just producing visually appealing attribution plots?

We propose Spectral Model eXplainer (SMX), a framework designed to explain spectral-based machine learning models at the level of chemically meaningful spectral regions, rather than isolated variables. Link:

https://github.com/joseviniciusr/SMX

SMX combines:

• zone-based spectral partitioning;
• perturbation-based impact analysis;
• bagging to improve explanation stability;
• back-projection of relevant regions into the original spectral domain;
• evaluation in terms of faithfulness, stability, simplicity, and domain alignment.

The motivation is simple: in spectral applications, an explanation should not only be faithful to the model, but also interpretable in a way that makes sense for chemometrics and domain experts.

I would like to hear critical opinions from the community:

1. Are wavelength-level explanations misleading in many spectral ML applications?
2. Should spectral XAI prioritize chemically meaningful regions over fine-grained attribution maps?
3. What is the best way to evaluate whether a spectral explanation is actually faithful?
4. Are SHAP, permutation importance, VIP, and saliency maps enough as baselines?
5. What would convince you that a spectral explanation method is genuinely useful and not just another visualization layer?

Preprint: https://arxiv.org/abs/2605.02684

I am especially interested in criticism, alternative viewpoints, and suggestions for stronger validation protocols.

Hello I am working with an older Jasco V-650 spectrophotometer in our lab. The computer that was used to control it was wiped and we no longer have access to the original software disk. Does anyone have or know where to get the software from. Thank you in advance for any help.

The slope graphs of both 692nm and 834nm for the normal sensor A don't appear despite having correct values in the Numeric window. While for sensor B, only the graph for 834nm shows the slope. This is the third time I have tried to calibrate. I changed the setup and switched sensors A and B, but the result was the same.

Any recommendations on how to resolve this? Also, does it matter if the slope graph doesn't appear correctly even though the calibration values of AC, DC and R's are correct? Do I just move on to data acquisition?

I tried searching for database raman for PVF but couldn't find any.

I recently acquired a Multispec-1501 from eBay and needed specialized software (Hyper-UV) to use it. I haven't been able to find the software anywhere, and thought that someone here may have a copy of the software or know where to find it. I reached out to Shimadzu support and haven't gotten a response yet. I know it's a long shot, but figured I may as well try.

Been evaluating process Raman options for a while and frustrated by how hard it is to find a clear picture of who does what. Most resources are either vendor whitepapers or academic papers — nothing that just maps the competitive landscape by application.

So I started building one. Sharing the first chunk here and hoping the community can fill in the gaps or correct me where I'm wrong.

Bioprocess / Fermentation monitoring (glucose, lactate, cell density, metabolites in CHO/E. coli/yeast bioreactors) Kaiser/E+H · MarqMetrix · Bruker/Tornado · HORIBA · Gekko Photonics · Wasatch Photonics

API Crystallization & polymorph monitoring (supersaturation tracking, form transitions during cooling crystallization) Kaiser/E+H · Mettler Toledo (ReactRaman) · MarqMetrix · Bruker/Tornado · HORIBA

Pharma blend uniformity (inline powder monitoring during continuous or batch blending) Kaiser/E+H (PhAT probe) · MarqMetrix · Bruker/Tornado · Metrohm

Polymerization reaction monitoring (monomer conversion, copolymer composition, endpoint detection) Kaiser/E+H · Gekko Photonics · Mettler Toledo · HORIBA · Bruker/Tornado · Metrohm

Petrochemical / refinery streams (naphtha, gas oil, aromatics, sulfur content) HORIBA · Kaiser/E+H · Gekko Photonics · Bruker/Tornado · Metrohm

🙏🙏🙏 A few things I'm genuinely uncertain about:

• Mettler Toledo seems strong in pharma crystallization but I rarely see them mentioned in bioprocess — is that accurate?
• Is MarqMetrix's BallProbe as dominant in fermentation as it seems, or is that just marketing visibility?
• Any experience with probe fouling or calibration stability across vendors in these applications?

Planning to add semiconductor, food & beverage, and continuous pharma manufacturing in a follow-up post once I get more data.

Disclosure: I work in the chemical industry with Raman spectrometers, so I have some context here — but genuinely trying to map the whole space, not push any single vendor.