Defining the Sensory Profiles of Raw Almond ( Prunus dulcis) Varieties and the Contribution of Key Chemical Compounds and Physical Properties.

This study describes the sensory composition of commercial sweet almond varieties across two California growing seasons. It also discusses the relationship between sensory attributes and chemical and physical measures. Raw, whole almonds (43 samples each of 13 varieties in 2015 and 40 samples each of 10 varieties in 2016) were evaluated for their sensory profiles using descriptive sensory analysis. The 2016 samples were also analyzed for macro- and micronutrients, amygdalin, volatile composition (using gas chromatography-mass spectrometry), and physical properties, and the results were modeled with the sensory data. Independence, Sonora, and Wood Colony were harder, more fracturable, and crunchy, whereas Fritz and Monterey were more moist and chewy, reflecting their moisture contents. Aldrich and Fritz were higher in marzipan/benzaldehyde flavor, which is related to amygdalin, benzaldehyde, phenylethyl alcohol, and benzyl alcohol. New insights are provided into sweet-almond composition and the sensorial contribution of headspace volatiles. This assists almond growers and processors in describing and marketing almond varieties.

Modeling the prevalence and incidence of Alzheimer's disease and mild cognitive impairment.

A number of systems have been proposed for classifying older adults who suffer from cognitive impairment or decline but do not yet meet criteria for Alzheimer's disease (AD). The classification, Mild Cognitive Impairment (MCI), has attracted much attention. It uses relatively specific diagnostic criteria and individuals who meet these criteria appear to be at substantial risk for the development of AD. However, little data is available to define the prevalence of MCI in any age group. We propose a simple mathematical model for the progression of patients from Non-Affected (NA) to MCI to AD. This first-order Markov model defines the likely prevalence of MCI at specific ages. Primary assumptions of the model include an AD prevalence of 1% at age 60 increasing to 25% at age 85 and a conversion rate from MCI to AD of 10% constant across all ages considered. We used the best available information for our model and found (1) that the MCI prevalence increased from 1% at age 60 to 42% at age 85 and (2) that the conversion rate from NA to MCI increased from 1% per year at age 60 to 11% at age 85. In conclusion, this model allows estimation of prevalence of MCI and conversion from NA to MCI based upon known prevalences of AD, conversion rates of MCI to AD, and death rates. Due to its substantial prevalence, MCI may be an important target for screening and possible intervention.