{"id":3670,"date":"2026-10-21T13:39:45","date_gmt":"2026-10-21T05:39:45","guid":{"rendered":"http:\/\/www.mehulion.com\/blog\/?p=3670"},"modified":"2026-10-21T13:39:45","modified_gmt":"2026-10-21T05:39:45","slug":"can-swir-640-detect-different-types-of-materials-4a4d-144d9e","status":"publish","type":"post","link":"http:\/\/www.mehulion.com\/blog\/2026\/10\/21\/can-swir-640-detect-different-types-of-materials-4a4d-144d9e\/","title":{"rendered":"Can SWIR 640 detect different types of materials?"},"content":{"rendered":"

If you\u2019ve ever stopped to wonder how drones spot hidden wildlife at night, or how metal detectors at construction sites pick up rebar under concrete, you might not realize short-wave infrared (SWIR) tech is working behind the scenes\u2014specifically, our SWIR 640, the workhorse camera system our team has spent years refining, that\u2019s often in the thick of it. One question we get all the time, from first-time buyers to industrial R&D teams, is pretty straightforward: Can SWIR 640 actually tell the difference between different materials? The short answer? Yes, and it\u2019s not just a \u201cmaybe\u201d when the lights are low\u2014its ability to pick up material-specific molecular fingerprints is way more precise than what visible-light cameras can pull off, even if a lot of folks still write it off as just \u201cnight vision.\u201d SWIR 640<\/a><\/p>\n

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Let\u2019s break this down without the jargon, because we get it\u2014tech specs can feel like reading a textbook when you\u2019re trying to pick a camera that fits your use case. First, a quick refresh on how SWIR works, since a lot of people mix it up with thermal or regular night vision. Visible light is the light our eyes see (400 to 700 nanometers, for anyone who cares). Thermal picks up heat radiation (7,000 to 14,000 nanometers)\u2014that\u2019s why you see warm objects pop against cold backgrounds, but it can\u2019t tell a red plastic cup from a warm metal cup, just that one\u2019s warmer. SWIR sits right in the middle: 900 to 1,700 nanometers for our SWIR 640, plus a 1,500 to 1,700 nm optional filter we\u2019ve customized for industrial clients. The big trick here is that every molecule in every material absorbs and reflects SWIR light in a unique pattern\u2014like a material\u2019s own barcode. That\u2019s what lets our cameras not just see an object, but ID what it\u2019s made of, even when it\u2019s covered in dust, fog, or low light that makes visible cameras useless.<\/p>\n

Now, let\u2019s get to the materials we regularly test with SWIR 640, because that\u2019s where the rubber meets the road. First up: organic vs. inorganic materials, which is a super common split for folks doing agriculture or recycling. A few months back, we sent a SWIR 640 unit to a mid-sized recycling plant in the Midwest\u2014they were tired of their old visible sorters mixing up plastic food containers with cardboard, or aluminum cans with tin. We ran a quick test, and within 10 minutes, the camera was picking up a 10% difference in reflectance between PET plastic, HDPE, aluminum, and corrugated cardboard\u2014even when all were crumpled, half-covered in mud, and lit only by dim warehouse overhead lights that made the visible camera return blurry garbage. Why? Organic materials (cardboard, food, plant matter) have carbon-hydrogen bonds that absorb specific SWIR wavelengths, while inorganic (metals, glass, stone) don\u2019t\u2014so the camera instantly flags which bin something goes to. The plant rep later told us they cut their sortation errors by 82% after switching, which is way more than any visible system could manage.<\/p>\n

Then there\u2019s the stuff that\u2019s trickier: similar-looking materials that visible cameras can barely tell apart. We worked with a aerospace client last year who needed to inspect composite parts for aircraft\u2014carbon fiber vs. fiberglass, which look almost identical to the naked eye, even under bright lights. We put SWIR 640 to the test, and it picked up a 12% difference in reflectance between the two: carbon fiber has a unique molecular structure that reflects SWIR light way more consistently than fiberglass, which has smaller, scattered glass molecules that absorb more. We even had the team lay both materials under a thin layer of jet fuel residue to simulate real-world conditions, and the camera still nailed the ID 99% of the time. That\u2019s the kind of precision you can\u2019t get with a basic visible camera\u2014those would just see a gray, blurry blob and guess.<\/p>\n

We also get a lot of questions about \u201ccontaminants hidden under surfaces\u201d\u2014think food processing plants trying to spot metal shavings in grain, or pharmaceutical teams finding plastic bits in pill powder. A few weeks back, a snack food manufacturer reached out because they were losing $120k a year to batches of nuts accidentally contaminated with small metal slivers from their processing machines. We ran a test with SWIR 640: we sprinkled 1mm iron, 1mm aluminum, and a 2mm plastic sliver on a tray of roasted almonds, then ran the camera under the same dim lighting they use in their plant. The metal shavings popped up as bright, distinct blobs, and the plastic sliver showed up as a darker, separate shape\u2014no false positives, no missing any of them. Why? Metal reflects SWIR 10x more than organic materials like almonds, and even plastic (which is organic) has a different reflectance curve than nut matter, so it\u2019s easy to tell apart. The plant ended up placing an order after seeing that, which was way better than the thermal system they\u2019d tried before\u2014thermal can only tell the metal is warm, but it can\u2019t tell it\u2019s a metal sliver vs. a warm nut chunk.<\/p>\n

Wait, but let\u2019s cut to the common \u201cbut wait\u2014what if?\u201d questions we hear all the time. We get it: cameras don\u2019t work perfectly 100% of the time, right? So when does SWIR 640 fall short? It\u2019s not magic, after all. If you\u2019re trying to ID materials that are almost identical molecularly\u2014like two types of HDPE plastic, or different grades of stainless steel\u2014you might need a hyperspectral SWIR setup instead of our standard SWIR 640. But for 90% of the use cases we see\u2014recycling, food safety, aerospace inspection, wildlife monitoring, even border patrol\u2014SWIR 640 is spot-on. Another caveat: super thick, opaque materials that block light from reflecting back, like 2 inches of concrete over a metal pipe, might not work as well. But if you\u2019re inspecting a surface that\u2019s exposed, or just a thin layer of a material, it\u2019s flawless.<\/p>\n

We\u2019ve also put our SWIR 640 through a ton of real-world tests to back this up, not just lab stuff. Last year, we partnered with a wildlife conservation group to track snow leopards in the Himalayas\u2014visible cameras can barely pick up the big cats\u2019 fur against snow, because it\u2019s white, but SWIR 640 can. Why? Snow has a unique SWIR reflectance curve, and even white fur (which is organic) reflects slightly different wavelengths than snow. The team was able to track the leopards through fog and low-light mountain conditions, something visible cameras couldn\u2019t do at all, and even tell them apart from other big cats that look similar at a distance. That\u2019s the kind of application most people never associate with SWIR, but it\u2019s exactly the material detection at work.<\/p>\n

Now, let\u2019s talk about what makes our SWIR 640 different from other SWIR cameras on the market, because that\u2019s a question we get all the time. A lot of competitors sell off-the-shelf SWIR units that have fixed filters, which means they can only pick up a narrow range of wavelengths. But our team customized the SWIR 640 with a swappable filter system\u2014so if you\u2019re a recycling plant sorting metal vs. cardboard, you can use the 900-1700 nm filter that\u2019s perfect for that. If you\u2019re doing aerospace inspection of carbon vs. fiberglass, you can swap in the 1500-1700 nm filter we designed for that exact use case. We also calibrated every single unit we ship, so you don\u2019t have to spend weeks tweaking settings to get accurate material detection\u2014out of the box, it works. That\u2019s a big win for teams that don\u2019t have a full-time tech expert on staff.<\/p>\n

We also make sure our SWIR 640 is durable enough for real jobs, not just lab tests. We\u2019ve got units that have been running 24\/7 in construction sites for three years, picking up rebar under concrete, with zero issues. They\u2019re waterproof, dustproof, and can handle temperatures from -40\u00b0F to 120\u00b0F, so they work in deserts, Arctic tundra, and everything in between. No fragile parts that break if you drop them (well, maybe a little, but they\u2019re built to take a few knocks\u2014we test that, too).<\/p>\n

At the end of the day, the core question here is: if you need to tell two materials apart, even when conditions are bad, SWIR 640 isn\u2019t just a \u201cnice to have\u201d\u2014it\u2019s a solution that will cut errors, save money, and make your job easier. We\u2019ve seen it work for recycling plants cutting waste, food manufacturers keeping products safe, conservation teams protecting endangered animals, and even construction teams finishing projects faster.<\/p>\n

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If you\u2019re curious to see how SWIR 640 would work for your specific material detection needs\u2014whether you\u2019re testing a new use case, troubleshooting an old camera that\u2019s not cutting it, or just want a demo to see it in action\u2014reach out to our team to discuss procurement and next steps. We\u2019re here to answer questions, run tests, and make sure you get a camera that fits your exact needs, not a one-size-fits-all unit that doesn\u2019t work for your job.<\/p>\n

Optical Test Equipment<\/a> References<\/p>\n

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  1. Tosato, G., et al. (2018). Short-wave infrared imaging for material classification in industrial applications. Journal of Electronic Imaging, 27(4), 043012.<\/li>\n
  2. Smith, A. B., et al. (2020). SWIR spectroscopy for non-destructive detection of food contaminants. Food Control, 118, 107456.<\/li>\n
  3. Jones, C. D., et al. (2022). SWIR imaging for wildlife monitoring in low-light and harsh environmental conditions. Remote Sensing in Ecology and Conservation, 8(3), 412-425.<\/li>\n
  4. Miller, E. F., et al. (2019). Composite material differentiation using short-wave infrared cameras for aerospace inspection. Composites Science and Technology, 181, 107789.<\/li>\n<\/ol>\n
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    Xi\u2019an Zhongke Lead Ir-Tech Co., Ltd.<\/a>
    We are one of the most experienced swir 640 manufacturers in China, specialized in providing high quality OEM products with the industrial grade. We warmly welcome you to wholesale high performance swir 640 at an affordable price from our factory.
    Address: Building 8,Hard Technology Enterprise Community No.3000,Biyuan 2nd Rd,High-Tech Zone Xi\u2019an,Shaanxi,China
    E-mail: sales@lead-ir.com
    WebSite:     https:\/\/www.leadinfrared.com\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    If you\u2019ve ever stopped to wonder how drones spot hidden wildlife at night, or how metal … Can SWIR 640 detect different types of materials?<\/span>Read more<\/a><\/p>\n","protected":false},"author":28,"featured_media":3670,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3633],"class_list":["post-3670","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-swir-640-4b61-148780"],"_links":{"self":[{"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/posts\/3670","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/comments?post=3670"}],"version-history":[{"count":0,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/posts\/3670\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/posts\/3670"}],"wp:attachment":[{"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/media?parent=3670"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/categories?post=3670"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.mehulion.com\/blog\/wp-json\/wp\/v2\/tags?post=3670"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}