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. In this work, spray drying technology was investigated in order to produce an innovative nutraceutical formulation based on lactic acid bacteria (LAB), and was able to ensure a good storage stability of probiotics (no less than 109 CFU/cps) in non-refrigerated conditions. Probiotic-loaded microparticles from spray drying experiments were produced under different conditions and compared by thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and the enumeration of the number of viable cells in order to identify the formulation exhibiting the most promising characteristics. Results from the dissolution test revealed that the optimized formulation provides a suitable amount of living cells after digestion of microparticles stored for 12 months at room temperature and confirmed that the microencapsulation process by spray drying ensures a good protection of probiotics for nutraceutical purposes.

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. Chemometrics was used to develop models of prediction to identify the cannabinoids and simultaneously to quantify the residual amounts in order to accomplish specific regulatory legislation according to the country. In particular, Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) were applied and results demonstrated that MicroNIR/Chemometric platform permit to differentiate hemp flours according to the presence of CBD, THC and CBG. In addition, for each cannabinoid, a quantification method was developed in the range 0.001-0.1 %ww, and their performances were evaluated by comparing results to those obtained by the reference procedures. Processing of the real samples provided a suitable correlation and confirmed the capabilities of this innovative platform to be used for the monitoring of the residual content of cannabinoids in hemp flours.

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. The results demonstrated that the microNIR/chemometric platform could statistically identify the presence of CBD, THC and CBG in the simulated samples containing cannabinoids from 0.001 to 0.01%w/w, with the accuracy and sensitivity of the official reference methods actually proposed. The method was checked against false positive and true positive responses, and the results proved to be those required for confirmatory analyses, permitting to provide a fast and accurate method for monitoring cannabinoids in veterinary feeds.

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. Once calibrated, the platform was used to predict samples acquired in the market and on websites. Validation of the system was achieved by comparing results with those obtained from GC-MS analyses and a good correlation was observed.

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

BACKGROUND: Δ9-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 Δ9-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. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) for the simultaneously detection and quantification of THC, were applied to baseline corrected spectra pre-treated by first derivative transform. RESULTS: Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify THC abuse in simulated oral fluid samples containing THC from 10 to 100 ng/ml, with a precision and a sensitivity of about 1.51% and 0.1% respectively. CONCLUSIONS: The coupling MicroNIR/Chemometrics permits to simplify THC abuse monitoring for roadside drug testing or workplace surveillance and provides the rapid interpretation of results, as once the model is assessed, it can be used to process real samples in a "click-on" device.

"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". The detection ability of the method was checked for true-positive and false-positive outcomes, and results were validated by a GC-MS reference official method. The MicroNIR-chemometric platform provided the simultaneous prediction of the three most frequently abused addictive drugs with the sensitivity and accuracy of the confirmatory analyses, offering the advantages of rapidity and simplicity and demonstrating that it is a promising tool for supporting public-health surveillance.