No abstract

Burden associated with patients' psychological symptoms uniquely contributed to caregiver depression, further highlighting the clinical utility and necessity for hospice providers to address the emotional needs of patients and their caregivers alike. Developing clinical procedures to identify and respond to such needs would not only behoove hospice agencies, but it would likely enhance the caregiving experience holistically, which might be particularly imperative for male caregivers.

Commercial tea tree oil (TTO) has to conform chemically to the international standard ISO4730: 2017, which defines acceptable ranges for the 15 main terpenoids therein. Lack of in‐house capacity for the technology typically required for terpenoid quantification (GC‐FID) necessitates that Australian TTO producers outsource the analyses to commercial service providers. This is associated with high cost and slow turnaround times, which have been recognized as limiting factors for TTO process optimization in the areas of quality assurance, distillation and blending. This paper describes the testing of two custom portable Raman devices with different lasers (785 and 1064 nm) for the relative quantification of the 15 main TTO components using model‐based predictions. Initial testing showed that the 1064‐nm device provided superior data for TTO predictions. Machine‐learning algorithms were trained with spectral data from the 1064‐nm device and corresponding GC‐FID data from 214 TTO samples. Hold‐over validation (HV) correlations between actual and predicted values using up to 53 unseen samples showed r2 at or above 0.92 for 10 and at or above 0.95 for 6 of the 15 specified terpenoids. The highly abundant key TTO quality compound terpinen‐4‐ol showed an HV r2 of 0.96 with a root mean square error (RMSE) of 0.37%, whereas the lowly abundant key compound 1,8‐cineole showed an HV r2 of 1.00 with a RMSE of 0.13%. These results indicated a strong potential for Raman spectrometry to provide real‐time product quality data at TTO distillation sites enabling direct feedback control and process optimisations.

C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Currently, the medicinal cannabis and industrial hemp industries depend on costly chromatographic analysis for cannabinoid quantification, limiting production, research and development. Combined with chemometrics, Near-InfraRed spectroscopy (NIRS) has potential as a rapid, accurate and economical alternative method for cannabinoid analysis. Using chromatographic data on 12 therapeutically relevant cannabinoids together with spectral output from a diffuse reflectance NIRS device, predictive chemometric models were built for major and minor cannabinoids using dried, homogenised C. sativa inflorescences from a diverse panel of 84 accessions. Coefficients of determination (r2) of the validation models for 10 of the 12 cannabinoids ranged from 0.8 to 0.95, with models for major cannabinoids showing best performance. NIRS was able to discriminate between neutral and acidic forms of cannabinoids as well as between C3-alkyl and C5-alkyl cannabinoids. The results show that NIRS, when used in conjunction with chemometrics, is a promising method to quantify cannabinoids in raw materials with good predictive results.