首页|Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning
Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "Commercial cultivation of the microalgae Haematococcus pluvialis to produce nat ural astaxanthin has gained significant traction due to the high antioxidant cap acity of this pigment and its application in foods, feed, cosmetics and nutraceu ticals. "However, monitoring of astaxanthin content in cultures remains challenging and relies on invasive, time consuming and expensive approaches. "In this study, we employed reflectance hyperspectral imaging (HSI) of H. pluvia lis suspensions within the visible spectrum, combined with a1-dimensional convo lutional neural network (CNN) to predict the astaxanthin content (ug mg-1) as qu antified by high-performance liquid chromatography (HPLC). This approach had low average prediction error (5.9%) across a gradient of astaxanthin contents and was only unreliable at very low contents (<0.6 micrograms mg-1).
NETL
NSTL
