Microencapsulation by a Spray Drying Approach to Produce Innovative Probiotics-Based Products Extending the Shelf-Life in Non-Refrigerated Conditions.

Recently, there has been a growing interest in producing functional foods containing encapsulated probiotic bacteria due to their positive effects on human health. According to their perceived health benefits, probiotics have been incorporated into a range of dairy products, but the current major challenge is to market new, multicomponent probiotic foods and supplements. Nevertheless, only a few products containing encapsulated probiotic cells can be found as non-refrigerated products.

Development of a "single-click" analytical platform for the detection of cannabinoids in hemp seed oil.

In this work, an innovative screening platform is developed and validated for the on site detection of cannabinoids in hemp seed oil, for food safety control of commercial products. The novelty of this completely automated tool consists of a miniaturized NIR spectrometer operating in a wireless mode that permits processing samples in a rapid and accurate way and to obtain in a single click the early detection of a residual amount of cannabinoids in oil, including cannabidiol (CBD), the psychoactive Δ9-tetrahydrocannabinol (THC) and the Δ9-tetrahydrocannabinolic acid (THCA). Simulated samples were realized to instruct the platform and prediction models were developed by chemometric analysis of the NIR spectra using partial least square regression algorithms.

Monitoring of cannabinoids in hemp flours by MicroNIR/Chemometrics.

Cannabinoids in hemp plant are strictly located in the inflorescence, in particular in top side of the plant while a lower amount may be found in the leaves. As a consequence, the lower amount of cannabinoids which can be recovered in the hemp flours is obtained from seeds, thus from contamination during harvesting procedures. To this aim, a screening test for the real time detection of cannabinoids in hemp flour was developed by a miniaturized analytical platform based on the MicroNIR spectrometer.

The detection of cannabinoids in veterinary feeds by microNIR/chemometrics: a new analytical platform.

In this work, the capabilities of a novel miniaturized and portable microNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of cannabinoids in veterinary feeds. In order to develop a predictive model that could identify and simultaneously quantify the residual amounts of cannabinoids, specimens from popular veterinary feeds were considered and spiked with increasing amounts of cannabidiol (CBD), Δ9-tetrahydrocannabinol (THC), and cannabigerol (CBG). Partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSr) were applied for the simultaneous detection and quantification of cannabinoids.

MicroNIR/Chemometrics: A new analytical platform for fast and accurate detection of Δ9-Tetrahydrocannabinol (THC) in oral fluids.

Background: Δ-Tetrahydrocannabinol (THC) is already considered one of the most addictive substances since an increasing number of consumers/abusers of THC and THC based products are observed worldwide. In this work, the capabilities of a novel miniaturized and portable MicroNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of Δ-Tetrahydrocannabinol (THC) oral fluids without any pretreatment.

Methods: Specimens from volunteers were collected in order to consider any sources of variability in the spectral response and spiked with increasing amount of THC in order to realize predictive models to be used in real cases.

"Lab-on-Click" Detection of Illicit Drugs in Oral Fluids by MicroNIR-Chemometrics.

A novel, entirely automated MicroNIR-chemometric platform was developed for the "lab-on-click" detection of illicit drugs in nonpretreated oral fluids, and a novel tool for the first-level test is proposed. Calibration of the method was achieved by collecting oral-fluid specimens from volunteers, and chemometric analysis was considered for the development of models for prediction for cocaine, amphetamine, and Δ9-tetrahydrocannabinol. In addition, a comprehensive model was optimized for the simultaneous prediction of positive-negative samples and the specific illicit drug used by abusers in a single "click".