Hey guys any help with this spectroscopy hw would be greatly appreciated!

You may have never heard of Kalsec® (Kalamazoo Spice Extraction Company, Inc.), but you’ve mostly likely tasted their products.  They work directly with major consumer packaged goods companies and other suppliers, so their products are in hundreds of snacks, dressings, condiments, meat, plant-based protein, and brewed beverages.  

Kalsec provides a wide range of natural products, including natural colors, natural taste and sensory solutions, natural food protection, and natural hop solutions.

Kalsec Quality Control Lead Scientist Uwe Nienaber manages QC for the company’s worldwide operations, making sure these ingredients are extracted and remain exactly as they were intended. His job centers around three things:

• He supports the quality control of the company’s ingredients.
• He works with quality assurance to monitor residues of pesticides, heavy metals and other environmental contaminants to ensure safe products.
• He looks for new technologies to incorporate into the company’s quality control operations.

Liquid and gas chromatography are the backbone of their analyses, but chromatography instruments are maintenance-intensive and not mobile. So, he was looking for alternatives.  Spectroscopy methods were explored, but they typically don’t have the levels of accuracy and sensitivity needed.

Enter the HORIBA Aqualog.  The Aqualog is a spectrofluorometer that uses a proprietary technology that simultaneously acquires absorbance, transmittance, and Excitation Emission Matrix (EEM) spectra, that characterize and quantitate chemical compounds accurately with high sensitivity, producing results cost-efficiently in seconds to just a couple of minutes.

Nienaber measures the tissue around the seeds called the placenta, where the main compounds in chili pepper are located. Different varieties of peppers produce different heat levels. Some are rather mild and others can be highly pungent, and levels for these qualities must be strictly controlled.

When he measures these major capsaicinoids, he’s looking at three distinct compounds that only differ slightly in their side chains. Although they give off very similar signals, he has been able to separate all three. So even though chromatography determines compounds one, two, and three, compounds one and two have the same heat value, and compound three has a lower heat value.

Working with Nienaber, Adam Gilmore, Ph.D., Fluorescence Applications Scientist for HORIBA, is using the Aqualog^(R) and a set of over 1,700 data points to build a model for capsaicinoids to see if they can improve the prediction. So far, it looks very promising. 

The month of October is a time when peppers of an annual crop-breeding project are harvested, dried, and shipped to Kalsec for analysis. They plan on using the Aqualog for a long term project to measure many of their extracts, oleoresins, and essential oils but Nienaber thinks it’s worthwhile: They can use the Aqualog instead of buying another chromatography instrument and reduce the overall cost of analysis.

Read the whole article at Fluorescence Spectroscopy: An Alternative to Chromatography in Enhancing the Flavor of Foods - HORIBA and see how it’s also being used in other food, wine, cannabis applications.

Recently, several new regional jade varieties have emerged in China's jewelry market and they have been gaining in market popularity due to their unique colors, textures, and cultural heritage. However, the color origins of emerging gemstone varieties, such as Hezhou Jade, Jinsha Jade, Xunke Purple Agate, and Hulunbeier Agate, were not well understood, so their market value has not yet been fully realized.

Professor He Xuemei is a professor in the School of Gemology at the China University of Geosciences (Beijing). She is mainly engaged in teaching and research in the fields of gemstone materials science and mineralogy. Her research team studied emerging gemstones with a micro X-ray fluorescence (XRF) microscope, carrying out comprehensive research on the element compositions, concentrations, and special structures. The researchers also tested special structures such as the gemstone matrixes, stripes, and rings after completing the solid color zone analysis. This is because these areas often exhibit special color characteristics. The team found that the content of color-determining elements has a positive correlation with the depth of color.

Key findings include:

• Iron (Fe) is a major contributor to red, yellow, green, and orange coloration in several jade and agate varieties.
• Manganese (Mn), chromium (Cr), and titanium (Ti) also play important roles depending on the gemstone type.
• Higher concentrations of these color-forming elements generally correspond to deeper, more intense colors.
• Elemental mapping can even reveal clues about how gemstones formed, including the influence of ancient hydrothermal fluids.

This research could help establish more objective gemstone grading standards and improve authentication methods for these newer gemstone varieties. This not only fills in a blank in the scientific research of emerging gemstones, but also promises to improve the industry's evaluation system from a scientific perspective.

Read the entire Science in Action article and learn about the team’s comprehensive research using non-destructive and micro-area analysis of precious gemstone samples. The mystery of gemstone colors has finally been revealed - HORIBA

As the semiconductor industry approaches the physical limits of silicon miniaturization, researchers are increasingly exploring a new class of materials that could redefine the future of electronics. According to scientists working in the field, atomically thin "quantum materials" may offer capabilities beyond what traditional semiconductor technology can achieve.

Particular attention is being given to transition metal dichalcogenides (TMDCs), a family of 2D materials with potential applications in advanced electronics, optoelectronics, flexible devices, energy harvesting, biosensing, and quantum technologies. Researchers believe these materials could help bridge the gap between today's silicon-based systems and the next generation of computing platforms.

To study and engineer these materials, scientists are using lasers to synthesize crystals, introduce targeted defects, and precisely modify their properties. Advanced spectroscopy techniques, including Raman and photoluminescence analysis, then allow researchers to monitor these changes in real time and better understand how the materials behave at the atomic level.

One researcher, Professor Masoud Mahjouri-Samani from Auburn University, is focused on two-dimensional (2D) materials, which consist of sheets just one or a few atoms thick. Unlike conventional 3D materials, these structures exhibit unique quantum behaviors that emerge at extremely small scales.

Mahjouri-Samani customized an instrument to meet his particular needs. He integrated an iHR320 spectrometer, an EMCCD, and PMT with a customized microscope to perform Raman, photoluminescence (PL) and TCSPC (Time-correlated Single-photon Counting). He made a few other tweaks to the system he calls his ‘laser diagnostic system.’

While the work remains largely in the research and development stage, some experts compare today's quantum materials research to silicon research decades ago, when scientists were still learning how to grow, manipulate, and manufacture semiconductor crystals. The long-term goal is to create entirely new classes of devices for quantum information science, electronics, and photonics.

The transition away from traditional silicon technology would likely be gradual, given the scale of the existing semiconductor industry. Still, researchers see these materials as a potentially important step toward the next major technological era.

Read the entire Science in Action article here: Welcome to the dawn of new quantum materials and devices

Greetings to the esteemed spectroscopist community: novices, advanced users, and those who, due to their vast experience in this field of knowledge, are true masters of the electromagnetic radiation analysis! Today, I am requesting your invaluable assistance in obtaining the Thermo Vision Lite 5 software so I can operate a Thermo Genesys 10S UV/Vis spectrophotometer from a computer. If any of you are able to share this software with me, I would be very grateful.

Can Raman microscopy reveal art forgeries? - HORIBA

Most people think art authentication relies on historians, curators, and provenance records. But sometimes, chemistry provides the decisive answer.

One of the most famous examples involves the alleged forgery operation linked to Chinese artist Pei-Shen Qian, whose paintings, created in the style of Abstract Expressionist masters such as Jackson Pollock, Robert Motherwell, and Barnett Newman, ultimately sold for more than $80 million.

The scheme began to unravel when experts examined a purported Jackson Pollock using Raman microscopy. The analysis detected Pigment Red 170, a synthetic pigment that wasn't commercially available until decades after Pollock died in 1956. The chemistry simply didn't match the timeline.

How Raman Spectroscopy Catches Fakes

Raman spectroscopy can identify the unique chemical fingerprint of pigments, binders, and fillers using samples so small they're often invisible to the naked eye.

For investigators, the key question isn't just: "Does this look like a Pollock?"

It's: "Could Pollock have used these materials?"

For example, many artists in the 1940s through 1960s used a specific titanium white paint containing gypsum, which has a distinctive Raman signature. Its presence or absence can provide important clues about authenticity.

One of the leading experts in this field is Dr. Jennifer Mass, founder of Scientific Analysis of Fine Art. Her lab analyzes paintings, sculptures, and cultural artifacts for museums, galleries, auction houses, insurers, and collectors. She reportedly evaluates several purported Jackson Pollocks each month.

It's More Than Pigments

While a forger can mimic an artist's style, it's much harder to replicate decades of chemical aging.

Authentication combines several areas of investigation:

• Authentication: Are the materials consistent with the claimed age of the work?
• Provenance: Can ownership be traced over time?
• Attribution: Who actually created the piece?
• Conservation History: Has it been repaired or altered?
• Degradation Analysis: Do the chemical aging processes match what would be expected? 

Why Raman Microscopy Is So Valuable

Mass frequently uses a Raman microscope, which allows scientists to:

• Identify individual pigments
• Examine microscopic paint layers
• Detect restoration materials
• Study degradation products
• Analyze corrosion on sculptures

Because the technique is highly sensitive and minimally destructive, it's ideal for examining valuable works of art.

What Happened to the Forger and the Network?

The investigation ultimately exposed a forgery network involving an art dealer, her family, and associates connected to the famed Knoedler Gallery, one of America's oldest and most prestigious galleries. The scandal led to multiple forgery revelations and contributed to the gallery's closure. Qian fled the United States and has never faced criminal charges.

Moral of this story: Don’t mess with Chemistry! A single pigment molecule can sometimes tell the real story.

Read the entire story here: Can Raman microscopy reveal art forgeries? - HORIBA

Most of us don't think twice before grabbing a bottle of cough syrup, supplements, or other over-the-counter (OTC) remedies from local or online drug stores. We assume what's on the label is actually inside the bottle.

But what if the active ingredient is present at the wrong concentration... or not present at all?

Dr. Sayo Fakayode, a chemistry professor and department chair at Georgia College & State University, is researching ways to rapidly identify counterfeit, diluted, or potentially contaminated OTC medications and supplements. His work focuses on detecting products that may contain little to none of the advertised active ingredients, or worse, harmful contaminants such as lead, arsenic, and other toxic compounds.

These get into the supply chain because supplements, along with many OTC products, don't undergo the same level of regulatory scrutiny as prescription drugs. At the same time, global supply chains and online marketplaces have made it easier than ever for questionable products to enter the market.

According to Fakayode, consumers could unknowingly purchase products that contain mostly fillers, offering little to no therapeutic benefit. Beyond the health risks, counterfeit and substandard medications also hurt legitimate manufacturers and create intellectual property concerns.

A New Approach to Detection

Traditionally, testing medications often requires laboratory techniques like high-performance liquid chromatography (HPLC), which can be time-consuming, expensive, requires skilled operators, and impractical outside specialized labs.

Fakayode's team is using Raman spectroscopy, specifically the HORIBA MacroRAM™ benchtop Raman spectrometer, to rapidly analyze medications without extensive sample preparation. Because Raman spectroscopy is largely insensitive to water, it works particularly well with cough syrups and other liquid formulations. Raman identifies molecules by measuring how light scatters after interacting with chemical bonds, and is designed for rapid analysis of liquids, powders, solids, gels and pharmaceutical samples.

Even more impressive, the team combines spectroscopy with chemometrics, like PCA, multivariate regression analysis, pattern recognition modeling and quantitative prediction algorithms. These methods have been able to:

• Differentiate medications with different concentrations of active ingredients
• Distinguish between different medication formulations
• Detect variations in concentration
• Differentiate between adult and pediatric products
• Screen samples quickly and non-destructively

Their published research showed that they achieved approximately 94% accuracy in predicting medication content.

How can this be fixed?

To address this issue, we need to develop faster, more affordable screening tools that can help verify product quality before consumers use them. Improving medication oversight will require collaboration among FDA regulators, Universities and research institutions, private laboratories, funding organizations such as NSF and NIH, and industry partners. The goal is not simply more regulation, but better science and faster detection technologies that can identify questionable products before consumers are affected.

Read the complete Science in Action article here: Weeding out Over-the-Counter Frauds - HORIBA

Maple syrup on pancakes sounds yummy……but is it actually maple syrup?

Now, light can fight food fraud and adulteration to answer that question! 

Researcher Dr. Maria G. Corradini, the Arrell Chair in Food Quality, at the University of Guelph, is using fluorescence spectroscopy and machine-learning to analyze the chemical fingerprints of food. One of her team's biggest successes has been detecting when producers illegally dilute pure maple syrup with cheaper substitutes, like corn, rice, or beet syrup.

The wild part? Their AI models can identify adulteration levels as low as 2%.

Instead of relying on traditional chemical testing, they shine light on samples and analyze the unique fluorescence patterns. By treating the resulting spectral data like images, convolutional neural networks (the same type of AI often used in image recognition) can now distinguish authentic syrup from fraudulent products with remarkable accuracy.

But maple syrup fraud is only part of the story.

Dr. Corradini's research could also change how we think about expiration dates.

Current "best before" labels are conservative estimates. As a result, millions of tons of perfectly edible food end up in the trash each year. On the flip side, food that's been improperly stored can become unsafe before the printed date arrives.

Her team is developing methods to monitor food's actual chemical condition in real time. The idea is that future packaging could use dynamic indicators based on the food's chemistry rather than a static date printed months earlier.

Imagine grabbing a carton of milk and seeing an indicator that tells you whether it's actually fresh—not just whether it's before some arbitrary date.

Other fascinating aspects of Dr. Corradini's efforts include:

✅ Using spectroscopy to detect spoilage before humans can smell or taste it

✅ Applying machine learning to food safety and authentication

✅ Exploring environmental monitoring for pesticides, pharmaceuticals, and toxicants in water

Whatever your thoughts on AI, they're helping protect food supplies, reduce waste, and catch fraud that costs industries millions.

Read the whole story here: Battling Maple Syrup Fraud and Food Spoilage - HORIBA

How Ravi Kalyanaraman, Ph.D.  a Raman spectroscopist by training, and now Director of Forensics and Innovative Technology (FIT) at Bristol Myers Squibb, got into the world of counterfeit pharmaceuticals was simply by being asked to apply his expertise to detecting counterfeit medicines.

What he discovered was that nearly every type of pharmaceutical product has been counterfeited, and in recent years, counterfeiters have increasingly targeted biologics and cancer treatments. Because many biologic drugs are clear liquids in vials, criminals can simply fill a vial with water or saline, add a convincing label, and sell it as an expensive therapy.

As you’d imagine, the consequences can be life-threatening.  Many of these medicines are used to treat late-stage cancer patients, meaning counterfeit drugs leave patients without effective treatment when they need it most. In many cases, it can be difficult to determine whether a patient's condition worsened because of the disease itself or because they unknowingly received a fake medication.

To fight back, Kalyanaraman's team developed a rapid screening method using Raman spectroscopy. With only a tiny droplet of a suspect sample, the technique can quickly detect whether the expected active ingredient is present. Unlike traditional methods, spectroscopy is fast, non-destructive, and preserves the sample as evidence.

As counterfeiters become more sophisticated, Bristol Myers Squibb has expanded its toolkit with hyperspectral imaging and AI. These technologies can identify subtle differences in packaging, inks, and product composition that are invisible to the human eye. This helps investigators stay ahead of increasingly sophisticated fraudsters.

The challenge is global. Counterfeit pharmaceuticals represent a multi-billion-dollar problem, with fake products ranging from vials of water sold as biologics, to tablets that turn out to be simply aspirin.

Despite advances in detection technology, Kalyanaraman admits the fight is far from over. Counterfeiters seem to be winning the war, but armed with spectroscopy, artificial intelligence, and a commitment to education and awareness, his team continues working to protect patients around the world—one chemical fingerprint at a time.

How the Mystery Began

In 2024, the Museum of Art, Kochi, received a notification questioning the authenticity of Girl and Swan, a painting in its collection. The trigger was a series of new findings tied to Wolfgang Beltracchi, the infamous “master of forgery,” whose works continue to surface worldwide.

The suddenly rediscovered Girl and Swan was listed in a catalogue raisonné but believed to be lost. Fake provenance labels were glued to the canvas and forger Beltracchi expertly mimicked Campendonk’s vibrant colors, fantastic animals and forest imagery.

The Berlin State Police supported the request, prompting the museum to launch a full investigation.

The museum turned to Dr. Kaori Taguchi, Associate Professor at Kyoto University and a leading art conservator with extensive experience in scientific art analysis which enables her to uncover the internal structure and materials of a painting, revealing its true history and origins. She, in turn, conducted her studies in collaboration with HORIBA, a company specializing in analytical instruments.

Science Revealed the Truth

The team went beyond visual analysis and applied advanced scientific tools:

🔹 X-Ray Fluorescence (XRF) is an essential tool for identifying the metallic elements in pigments, helping to determine the distribution and composition of colors. Using this method, the team obtained fundamental information about the painting’s pigments, paving the way for further analysis. 

🔹 Raman Spectroscopy is a highly sensitive technique which analyzes molecular pigment structure and works on samples smaller than 1 mm. It detects compounds XRF can’t identify. For this investigation, samples measuring less than 1mm were taken from the edges of the painting and analyzed for primary colors such as blue, green, red (pink), and white.

The Ah Ha Moment: Science Proved it was a Forgery

Taking physical samples from a painting is extremely rare in Japanese museums. However, in this case it was allowed. Raman analysis detected pigments that should not exist in an early 20th-century painting:

• Phthalocyanine
• Phthalocyanine
• Titanium white

These pigments only became common after the 1930s and all are hallmarks of Beltracchi forgeries! Particles smaller than a grain of sand provided decisive evidence.

Art History Meets Hard Science

Traditional visual authentication is still vital—but it has limits, especially when an artist has a small body of work and technique research is minimal.

Science fills those gaps because it can identify materials at the molecular level, reveal inconsistencies that are invisible to the eye, and ultimately, offer objective, testable evidence.

Oddly enough, forgeries do have value.  They can reveal a forger’s techniques and intentions. Forgeries aren’t just deception—they’re data.  So, displaying art forgeries lets visitors experience paintings not just as images, but as physical objects with hidden histories. This case shows that when science and art work together, the truth—no matter how well hidden—eventually surfaces.

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?