Our full-length interview with Huck covers more than just NIR spectroscopy in food and bio analysis. Spectroscopy sat down with Huck to also discuss current trends going on in spectroscopy, delving into what challenges spectroscopists face today and how they can solve these concerns.
At Pittcon, Spectroscopy sat down with Christian Huck of the University of Innsbruck to talk about how NIR and imaging spectroscopy are being used in food and bioanalysis, and where this industry is heading in the future.
Near-infrared spectroscopy was recently used to estimate sweetness and total soluble solids content in cherry tomatoes.
Hyperspectral imaging was recently used to characterize chicken breast affected by myopathies, which can affect their texture and quality.
Researchers from Jiangsu University and Jimei University have developed an AI-powered detection system using near-infrared spectroscopy and a convolutional neural network long short-term memory (CNN-LSTM) model to accurately identify petroleum contamination in edible oils for improving food safety and quality control.
A recent study published in the Journal of Food Composition and Analysis explores the potential of fluorescence anisotropy as a tool for quantifying structural anisotropy in food, offering new insights for improving plant-based alternatives and dairy product textures.
Researchers from Jiangsu University review advancements in computer vision and spectroscopy for non-destructive citrus quality assessment, highlighting the role of AI, automation, and portable spectrometers in improving efficiency, accuracy, and accessibility in the citrus industry.
A recent study examined a novel method to detect adulteration in camellia oil.
A recent study out of Ben-Gurion University investigated how Fourier transform infrared-attenuated total reflectance (FT-IR-ATR) spectroscopy can detect fungal contamination in bread.
Last year, we released a content series titled “Advancing Agriculture for Future Generations.” Here, we compile some of the latest studies in this space.
A recent paper published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy argued that near-infrared (NIR) spectroscopy technology is the most sustainable choice for food production.
A recent study published in Meat Science highlighted how visible and near-infrared (vis-NIR) spectroscopy, when combined with chemometrics, can differentiate lamb meat based on pasture-finishing durations.
A recent study examines widespread microplastic contamination in key Oregon seafood species, emphasizing the need for coordinated local and global efforts to reduce plastic pollution and protect ecosystems, public health, and cultural traditions.
A recent study from Shanghai University demonstrated aa novel method for identifying and quantifying animal-origin milk powders.
A recent study examines how vibrational spectroscopic techniques are being used to evaluate the quality of seaweed.
A new study published in Food Control introduces an approach for assessing antioxidant levels in edible oils using artificial intelligence and spectroscopy, offering significant potential for improving food quality control.
A recent study from China explored a new, non-destructive method combining terahertz time-domain spectroscopy (THz-TDS) and machine learning to accurately classify wheat gluten strength.
A recent study used Fourier transform mid-infrared (FT-IR) spectroscopy and machine learning (ML) algorithms to understand the mineral content in camel’s milk.
A recent study explored how polymer-based tea bags contribute to the release of microplastics and nanoplastics (MNPL).
Researchers from Italy have developed a Raman spectroscopy-based method for the rapid detection of Clostridium spores in milk. This technique offers significant advantages over traditional methods, reducing detection time by nearly half while maintaining sensitivity and reliability.
A recent study published in Food Research International demonstrates how visible and near-infrared spectroscopy (Vis-NIRS) combined with machine-learning algorithms can accurately authenticate meat and fat based on livestock feeding systems, offering a sustainable and reliable solution for traceability in the meat industry.
Researchers at Yanshan University have developed a groundbreaking method combining Raman spectroscopy and deep learning models to accurately identify and quantify components in blended vegetable oils.
This study aimed to assess and detect adulteration of Kelulut honey with different percentages of rice syrup using near-infrared (NIR) spectroscopy.
A recent study out of Russia introduced a new method for identifying plant-based oils and adulterated dairy products.
Researchers at Henan Agricultural University have developed a multi-channel magnetic flow device combined with surface-enhanced Raman spectroscopy (SERS) for the rapid and precise isolation, identification, and quantification of lactic acid bacteria and yeast, revolutionizing quality control in fermented food production.
