October 18th 2024
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.
September 11th 2024
An increasing number of antibiotic residue problems in food have emerged around the world. We examine how SERS is used to identify antibiotic residues in chicken, focusing on doxycycline hydrochloride and tylosin.
Exploring the Potential of the Yb(III) (HE)4 Complex for Oncotherapy Using UV-vis Spectroscopy
June 1st 2021Evaluation of the UV-vis spectra of the reaction product of ytterbium (III) with hematoxylin (HE) indicates the formation of a rare earth complex that further reacts with marine mammal DNA, indicating the potential that this complex may have anti-tumor properties.
Fingerprinting of Mineral Medicine Natrii Sulfas by Fourier Transform Infrared Spectroscopy
June 1st 2021We show how FT-IR may be used for quality control analysis of natrii sulfas, a transparent crystalline material used in natural medicine that primarily contains sodium sulfate decahydrate, crystallized from sulfate minerals.
Terahertz Spectral Characterization of Plasma Spray–Deposited Nickel Film on an Alumina Cylinder
April 1st 2021Plasma spray–deposited metal films are used in many industrial applications. This study shows how high resolution terahertz time-domain spectroscopy (THz-TDS) can be used to analyze and characterize such films.
Raman measurements of chromite minerals demonstrated that chromium content could be accurately determined, supporting a possible application of portable Raman devices on Earth or in space for mineral analysis of asteroids and planets.
Investigating a Laser-Induced Titanium Plasma Under an Applied Static Electric Field
We investigate the effect of an applied electric field on the laser-induced titanium plasma for laser induced breakdown spectroscopy (LIBS) for the purpose of assessing electron density with respect to laser energy.
Raman Spectroscopy Analysis of Minerals Based on Feature Visualization
November 1st 2020The advantages of machine-learning methods have been widely explored in Raman spectroscopy analysis. In this study, a lightweight network model for mineral analysis based on Raman spectral feature visualization is proposed. The model, called the fire module convolutional neural network (FMCNN), was based on a convolutional neural network, and a fire-module was introduced to increase the width of the network, while also ensuring fewer trainable parameters in the network and reducing the model’s computational complexity. The visualization process is based on a deconvolution network, which maps the features of the middle layer back to the feature space. While fully exploring the features of the Raman spectral data, it also transparently displays the neural network feature extraction results. Experiments show that the classification accuracy of the model reaches 0.988. This method can accurately classify Raman spectra of minerals with less reliance on human participation. Combined with the analysis of the results of feature visualization, our method has high reliability and good application prospects in mineral classification.
Fungal infections and mycotoxin contamination in food products pose a major threat to the world population. Mycotoxins contaminate approximately 25% of the world’s food products and cause severe health problems through the utilization of affected food products. The major mycotoxins in different foods are aflatoxins, ochratoxins, fumonisins, zearalenone, trichothecenes, and deoxynivalenol. Today, various conventional and nondestructive techniques are available for the detection of mycotoxins across multiple food products. Conventional methods are time-consuming, require chemical reagents, and include many laborious steps. Therefore, nondestructive techniques like near-infrared (NIR) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, hyperspectral imaging, and the electronic nose are a priority for online detection of fungal and mycotoxin problems in different food products. In this article, we discuss recent improvements and utilization of different nondestructive techniques for the early detection of fungal and mycotoxin infections in various food products.