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In-situ Detection of Rice Using Laser Induced Breakdown Spectroscopy and Machine Learning

March 20th 2025

This research investigates the application of laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) for detecting elemental composition of food, using rice as an example.

Extreme close-up of heap of raw purple sweet potatoes on wooden background. Generated with AI. Image Credit: © suchetpong - stock.adobe.com.
Discrimination of Geographical Origin for Purple Sweet Potato Using Hyperspectral Imaging Combined with Multivariate Analysis

March 10th 2025

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Geographical Traceability of Millet by Mid-Infrared Spectroscopy and Feature Extraction

February 13th 2025

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Identification of Different Dairy Products Using Raman Spectroscopy Combined with Fused Lasso Distributionally Robust Logistic Regression

January 23rd 2025

Salt in wooden bowl on dark stone table. Sea salt on black background. | Image Credit: © Milan - stock.adobe.com
Vibrational Spectroscopic Analysis of Six New Hexafluorosilicate Salts Containing Different Amino Acids

January 15th 2025

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Developing Portable Raman Spectroscopy Methods for Identification of Raw Materials Used in Pharmaceutical Development and Manufacturing

February 1st 2017

Portable instrumentation for Raman spectroscopy has rapidly evolved over the last decade, where sample testing that once occurred in the laboratory is now executed in the field (e.g. warehouse).   Portable Raman spectroscopy is a powerful technique for the rapid identification of diversely sourced raw materials used in pharmaceutical processing.  In addition to portability; reduced cost, rapid data acquisition and ease of use make this powerful technique attractive and accessible to both expert spectroscopists and non-specialists.  In most cases, the method development can be easily accomplished in the laboratory after which the instrument and methods are transferred to field for sample analysis or warehouse areas for inspection of incoming raw material.  Qualitative Raman methods for identification of raw materials typically utilize spectral libraries for sample to standard comparison.  When developing Raman spectral libraries for raw material identification, great care is required when considering critical factors (e.g. instrument type, Raman capability, container type, container interference, background interference, material variability) that can potentially influence the identity of the material.  This paper discusses portable Raman techniques and approaches for raw material identification, as well as key considerations for developing and validating Raman spectral libraries.