Low-Cost Pocket Fluorometer and Chemometric Tools for Green and Rapid Screening of Deoxynivalenol in Durum Wheat Bran.

Cereal crops are frequently contaminated by deoxynivalenol (DON), a harmful type of mycotoxin produced by several species fungi. The early detection of mycotoxin contamination is crucial for ensuring safety and quality of food and feed products, for preventing health risks and for avoiding economic losses because of product rejection or costly mycotoxin removal. A LED-based pocket-size fluorometer is presented that allows a rapid and low-cost screening of DON-contaminated durum wheat bran samples, without using chemicals or product handling.

Smartphone-Enabled Colorimetry.

A smartphone is used as a colorimeter. The performance characterization for colorimetry is presented using both the built-in camera and a clip-on dispersive grating. Certified colorimetric samples provided by Labsphere are considered as test samples.

Bluetooth-Connected Pocket Spectrometer and Chemometrics for Olive Oil Applications.

Unsaturated fatty acids are renowned for their beneficial effects on the cardiovascular system. The high content of unsaturated fatty acids is a benefit of vegetable fats and an important nutraceutical indicator. The ability to quickly check fat composition of an edible oil could be advantageous for both consumers and retailers.

Novel Probe for Thermally Controlled Raman Spectroscopy Using Online IR Sensing and Emissivity Measurements.

Temperature rise during Raman spectroscopy can induce chemical alterations of the material under analysis and seriously affect its characterization. Thus, such photothermal side effects can represent a serious problem to be carefully controlled in order to safeguard the integrity of the material and its spectral features. In this work, an innovative probe for thermally controlled portable Raman spectroscopy (exc.

Quantitative Compositional Analyses of Calcareous Rocks for Lime Industry Using LIBS.

Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation.