Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for fatty acids of 16.0, 20.5, 22.6 and fat, respectively.

Saffron in the treatment of patients with mild to moderate Alzheimer's disease: a 16-week, randomized and placebo-controlled trial.

WHAT IS KNOWN: Herbal medicines have been used in the treatment of behavioural and psychological symptoms of dementia but with variable response. Crocus sativus (saffron) may inhibit the aggregation and deposition of amyloid ß in the human brain and may therefore be useful in Alzheimer's disease (AD). OBJECTIVE: The goal of this study was to assess the efficacy of saffron in the treatment of mild to moderate AD. METHODS: Forty-six patients with probable AD were screened for a 16-week, double-blind study of parallel groups of patients with mild to moderate AD. The psychometric measures, which included AD assessment scale-cognitive subscale (ADAS-cog), and clinical dementia rating scale-sums of boxes, were performed to monitor the global cognitive and clinical profiles of the patients. Patients were randomly assigned to receive capsule saffron 30 mg/day (15 mg twice per day) (Group A) or capsule placebo (two capsules per day) for a 16-week study. RESULTS: After 16 weeks, saffron produced a significantly better outcome on cognitive function than placebo (ADAS-cog: F=4·12, d.f.=1, P=0·04; CDR: F=4·12, d.f.=1, P=0·04). There were no significant differences in the two groups in terms of observed adverse events. WHAT IS NEW AND CONCLUSION: This double-blind, placebo-controlled study suggests that at least in the short-term, saffron is both safe and effective in mild to moderate AD. Larger confirmatory randomized controlled trials are called for.