The potential of near infrared spectroscopy to estimate the content of cannabinoids in Cannabis sativa L.: A comparative study.

Cannabis has been one of the oldest source of food, textile fiber and psychotropic substances. Cannabinoids are the main biologically active constituents of the Cannabis genus, with a demonstrated medicinal value. Its production is becoming legalized and regulated in many countries, thus increasing the need for a rapid analysis method to assess the content of cannabinoids. Gas chromatography (GC) is the preferred analytical method for the determination of these compounds, although is a slow and costly technique. Near infrared spectroscopy (NIR) has the potential for the quantitative prediction of quality parameters, and also of pharmacologically active compounds, but no references about cannabinoids prediction has been previously reported. The aim of the present research was to develop a fast, economical, robust and environmentally friendly method based on NIR technology that allow the quantification of the main cannabinoids present in Cannabis sativa L. SAMPLES: A total of 189 grinded and dried samples from different genotypes and registered varieties were used. The content of the cannabinoids CBDV, Δ9-THCV, CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN were determined by gas chromatography. Spectra were collected in a dispersive NIR Systems 6500 instrument, and in a Fourier transform near Infrared (FT-NIR) equipment. The sample group was divided into calibration and validation sets, to develop modified partial lest squares (PLS) regression models with WINISI IV software with the dispersive data, and PLS models using OPUS 7.2 with the FT-NIR ones. Excellent coefficient of determination of cross validation (R2CV from 0.91 to 0.99), were obtained for the prediction of CBD, CBC, Δ8-THC, Δ9-THC, CBG and CBN, with standard error of prediction (SEP) values among 1.5-3 times the standard error of laboratory (SEL); and good for CBDV and Δ9-THCV cannabinoids (R2 values of 0.89 and 0.83, respectively) with the dispersive instrument. Similar calibration and validation statistics have been obtained with the FT-NIR instrument with the same sample sets, using its specific OPUS software. In conclusion, a methodology of quantitative determination of cannabinoids in Cannabis raw materials has been developed for the first time using NIR and FT-NIR instruments, with similar good predictive results. This new analytical method would allow a simpler, more robust and precise estimation than the current standard GC.

Effects of dietary concentrate composition and linseed oil supplementation on the milk fatty acid profile of goats.

Milk fat composition can be modulated by the inclusion of lipid supplements in ruminant diets. An interaction between the lipid supplement and the forage to concentrate ratio or the type of forage in the rations may affect milk fat composition. However, little is known about the effects of the starch-to-non-forage NDF ratio in the concentrate and lipid supplementation of goat diets. The aim of this work was to determine the role of dietary carbohydrates in goats rations supplemented with linseed oil on animal performance and milk fatty acid (FA) profile. A total of 16 dairy goats were allocated to two simultaneous experiments (two treatments each), in a crossover design with four animals per treatment and two experimental periods of 25 days. In both experiments alfalfa hay was the sole forage and the forage to concentrate ratio (33:67) remained constant. The concentrate in experiment 1 consisted of barley, maize and soybean meal (concentrate rich in starch), whereas it included soybean hulls replacing 25% of barley and 25% maize in experiment 2 (concentrate rich in NDF). As a result, the starch-to-non-forage NDF ratio was 3.1 in experiment 1 and it decreased to 0.8 in experiment 2. Both concentrates were administered either alone or in combination with 30 g/day of linseed oil. Animal performance parameters were not affected by experimental treatments. In contrast, major changes were observed in milk FA profile due to lipid supplementation and the type of concentrate. Linseed oil significantly raised vaccenic and rumenic acids as well as α-linolenic acid and its biohydrogenation intermediates while decreased medium-chain saturated FA (12:0 to 16:0) in milk fat. Milk fat contents of odd and branched-chain FA and trans-10 18:1 responded differently to linseed oil supplementation according to the concentrate fed.

Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats.

Milk fatty acid (FA) composition is important for the goat dairy industry because of its influence on cheese properties and human health. The aim of the present work was to evaluate the feasibility of NIRS reflectance (oven-dried milk using the DESIR method) and transflectance (liquid milk) analysis to predict milk FA profile and groups of fats in milk samples from individual goats. NIRS analysis of milk samples allowed to estimate FA contents and their ratios and indexes in fat with high precision and accuracy. In general, transflectance analysis gave better or similar results than reflectance mode. Interestingly, NIRS analysis allowed direct prediction of the Atherogenicity and Thrombogenicity indexes, which are useful for the interpretation of the nutritional value of goat milk. Therefore, the calibrations obtained in the present work confirm the viability of NIRS as a fast, reliable and effective analytical method to provide nutritional information of milk samples.

Short communication: Feeding linseed oil to dairy goats with competent reticular groove reflex greatly increases n-3 fatty acids in milk fat.

A crossover experiment was designed to compare the effects of 2 ways of feeding linseed oil on milk fat fatty acid (FA) composition. Ten lactating goats, trained to keep competent their inborn reticular groove reflex, received a daily dose of linseed oil (38 g/d) either with their solid (concentrate) feed (CON) or emulsified in skim milk and bottle-fed (BOT). Two groups of 5 goats received alternative and successively each of the treatments in two 15-d periods. α-Linolenic acid in milk fat rose up to 13.7% in the BOT versus 1.34% in the CON treatment. The n-6 to n-3 FA ratio was significantly reduced in goats receiving bottle-fed linseed oil (1.49 vs. 0.49). Contents of rumen biohydrogenation intermediates of dietary unsaturated FA were high in milk fat of goats under the CON treatment but low in those in the BOT treatment. These results point to a clear rumen bypass of the bottle-fed linseed oil. This strategy allows obtaining milk fat naturally very rich in n-3 FA and very low in trans FA. Translating this approach into practical farm conditions could enable farmers to produce milk enriched in specific FA.

Direct prediction of bioethanol yield in sugar beet pulp using near infrared spectroscopy.

Sugar beets are a raw material for the production of sugar and ethanol. The decision on which end product to pursue could be facilitated by fast and reliable means of predicting the potential ethanol yield from the beets. A Near Infrared (NIR) Spectroscopy-based approach was tested for the direct prediction of the potential bioethanol production from sugar beets. A modified partial least squares (MPLS) regression model was applied to 125 samples, ranging from 21.9 to 31.0 gL(-1) of bioethanol in sugar beet brei. The samples were analyzed in reflectance mode in a Direct Contact Food Analyser (DCFA) FOSS-NIRSystems 6500 monochromator, with standard error of cross validation (SECV), standard error of prediction (SEP), coefficient of determination (r(2)) and coefficient of variation (CV) of 0.51, 0.49, 0.91 and 1.9 gL(-1), respectively. The NIR technique allowed direct prediction of the ethanol yield from sugar beet brei (i.e. the product obtained after sawing beets with a proper machine) in less than 3 min.