Infrared (IR) Spectroscopy

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Innovative Smart Sensor Monitoring Grapes in Vineyard in Modern IoT Farming Practices © Asraf-chronicles-stock.adobe.com

A team of researchers from the International Iberian Nanotechnology Laboratory (INL) in Braga, Portugal, has developed an autonomous Internet of Things (IoT) spectral sensing system designed to monitor grape ripening in real-time. The study, led by Hugo M. Oliveira, Alessio Tugnolo, Natacha Fontes, Carlos Marques, and Álvaro Geraldes, was published in Computers and Electronics in Agriculture and introduces a novel approach to non-destructive, in-situ optical monitoring of grape maturity.

IoT theme with abstract high speed technology © Tierney-chronicles-stock.adobe.com

Researchers highlight the growing role of Internet of Things (IoT) and sensor technologies in enhancing food security and agricultural sustainability. The study, published in Ain Shams Engineering Journal, explores the applications, benefits, and challenges of smart agriculture, emphasizing the potential of optical sensors in monitoring and optimizing farming practices.

Innovative Semiconductor Materials for Infrared Sensors © ryanking999-chronicles - stock.adobe.com

A recent study provides an in-depth overview of the latest advancements in infrared (IR) semiconductor sensor technology, highlighting new materials, enhanced detection capabilities, and expanding applications across industrial, medical, security, and environmental fields. The research explores how quantum dots, graphene, and novel nanomaterials are revolutionizing IR detection, paving the way for more efficient and versatile sensor systems.

Foxtail millet | Image Credit: © zhengzaishanchu - stock.adobe.com.

The study developed an effective mid-infrared spectroscopic identification model, combining principal component analysis (PCA) and support vector machine (SVM), to accurately determine the geographical origin of five types of millet with a recognition accuracy of up to 99.2% for the training set and 98.3% for the prediction set.

Orchid flower. | Image Credit: © phanthit malisuwan - stock.adobe.com

A new study published in Food Control combines Fourier transform infrared (FT-IR) spectroscopy and deep learning to accurately authenticate the geographical origin of Gastrodia elata f. glauca, offering a reliable method for geographical indication (GI) verification and fraud prevention in the herbal and food industry.

Battling the fentanyl crisis using ATR FT-IR and machine learning © Tahorima - stock.adobe.com

Researchers have successfully demonstrated that human nails can serve as a reliable biological matrix for detecting fentanyl use. By combining attenuated total reflectance-Fourier transform infrared (ATR FT-IR) spectroscopy with machine learning, the study achieved over 80% accuracy in distinguishing fentanyl users from non-users. These findings highlight a promising, noninvasive method for toxicological and forensic analysis.

Woman testing glucose level with traditional glucose monitor © Andrey Popov - stock.adobe.com

A research team is claiming significantly enhanced accuracy of non-invasive blood-glucose testing by upgrading Fourier transform infrared spectroscopy (FT-IR) with multiple-reflections, quantum cascade lasers, two-dimensional correlation spectroscopy, and machine learning. The study, published in Spectrochimica Acta Part A, reports achieving a record-breaking 98.8% accuracy, surpassing previous benchmarks for non-invasive glucose detection.

Visualizing Propane: Molecular Structure in 3D. Generated with AI. | Image Credit: © Samana - stock.adobe.com.

In the fourth installment of our review of infrared spectral interpretation, we will discuss the spectroscopy of hydrocarbons. We will look at the stretching and bending vibrations of methyl (CH3) and methylene (CH2) groups, how to distinguish them, and how to know whether one or both of these functional groups are present in a sample. We will also discuss aromatic hydrocarbons, specifically the C-H stretching and bending peaks of mono- and disubstituted benzene rings, and how to distinguish them.