When it comes to food waste, the numbers are staggering, as ImpactVision’s Abi Ramanan and Gustav Nipe point out in this month’s cover story. One-third of all food produced in the world is lost, annually generating about 8 percent of global carbon emissions. According to the U.N. Food and Agriculture Organization (FAO), the food that’s wasted every year would feed more than double the number of undernourished people around the globe. Hyperspectral imaging can help reverse some of this by monitoring food nondestructively and yielding actionable insights about food freshness, ripeness, and remaining shelf life. Technical obstacles to widespread adoption remain, including stronger illumination that doesn’t radiate heat, which could affect the food being assessed. But new solutions are emerging, including neural network machine learning frameworks that can discern the pH of beef or other red meat. Don’t miss the authors’ accounting of the technology and its promise. See “Hyperspectral Imaging Fights Food Waste” (read article). Elsewhere in the magazine • Contributing Editor Hank Hogan writes about the expanding applications of ultrafast lasers for micromachining. The price of such lasers has fallen significantly, opening the way for wider use in the automotive industry and beyond. But their use requires hitting the right combination of shorter pulse widths, throughput, and costs. “Lasers for Micromaching: Shorter Pulses, More Power” (read article). • In a related story, Brennan DeCesar and Mark Boyle of Amada Miyachi America review four types of lasers that are typically used for microwelding. Each laser type — pulsed neodymium-doped yttrium aluminum garnet (Nd:YAG); continuous-wave (CW) fiber; quasi-continuous-wave (QCW) fiber; and nanosecond fiber — has capacities that work best for specific applications. In cases where several options may work, cost of ownership and serviceability can tip the scales. “Navigating Best Laser Choices Crucial for Microwelding” (read article). • Jessica Gehlhar of Edmund Optics gives a concise rundown on the factors to ensure vision systems of 3D measurements remain accurate. What matters, Gehlhar writes, is the combination of thoughtful design, sound manufacturing, and initial and in-the-field calibrations. “Vision Systems for 3D Measurement Require Care to Ensure Alignment” (read article). • For our Picks column, Johannes Hiltner of MVTec Software GmbH outlines a few straightforward criteria for choosing machine vision software. The best software system will meet current requirements and serve as a tool for upcoming tasks. “Machine vision software needs to be future-proof” (read article). Enjoy the issue.