Effect of light, food additives and heat on the stability of sorghum 3-deoxyanthocyanins in model beverages.

This work aimed to evaluate the stability of sorghum 3-deoxyanthocyanins (DXA) in model beverages (pH 3.5) elaborated with crude sorghum phenolic extract, containing ascorbic acid and sulphite, under fluorescent light exposure and subjected to heat treatment. There was no significant difference in the DXA degradation during storage under light exposure (24.16%) and absence of light (20.72%). DXA degradation did not differ in the presence of ascorbic acid during storage under light exposure (23.99-25.38%) and absence of light (19.87-21.74%). The addition of sulphite caused an initial bleaching reaction, but as a reversible reaction, the anthocyanin content was higher on the last day of storage compared to the first day. There were no significant differences in total anthocyanin content of all treatments subjected to the heat treatment (80 °C for 5 and 25 min). Thus, crude DXA are very stable under light, additives and heat, and may be useful as natural food colourants

Quantitative phytochemical analysis and their antioxidant activity of Cocculus hirsutus (l.) Diels fruit

Anthocyanins, a large group of red-blue plant pigments, occur in flowers and fruits of higher plants. The fruits of Cocculus hirsutus (L.) Diels was extracted with acidified methanol and used for  phytochemicals and antioxidant activity analysis. The total flavonoid, anthocyanin and phenol content were found to be 260±20 mg/g, 0.788±0.236 mg/g and 326.66±3.05 mg/g respectively. DPPH, ABTS and Nitric oxidescavenging activity exhibited an IC50 value of111.35±1.12, 80.90±0.39 and 79.84±1.48 respectively. The IC50 value of reducing power assay, inhibition of lipid peroxidase in egg yolk and Metal chelating and was identified to be 97.03 ±0.88, 107.6±0.48 and 200.27±1.85µg/ml respectively. The positive control showed an IC50 value of 39.78 ±0.07, 23.68±0.06, 63.62±1.22, 53.74±1.34, and 70.59±2.8 and 51.26±0.39 µg/ml respectively. The total antioxidant activity of the fruit anthocyanin exhibited highest absorbance of 0.382±0.005 for 100µg/ml concentration.  

Cross infection and genetic diversity of Fusariumoxysporum f. sp. cubense, the casual agent of Fusarium wilt in banana.

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is posing a serious threat to banana production in India. Out of 234 Foc isolates collected from different parts of India, 100 representative isolates were subjected to cross reaction as well as diversity analyses. In-vitro cross reaction tests involving different nit-1 testers of Foc race 1 and race 2 indicated that there was a cross reaction between race 1 and race 2 Foc isolates in many cases. Validation carried out under pot culture conditions also confirmed the cross reaction between the members of Foc race 1 and race 2. For diversity analyses, VCG analyses were carried out using the local nit-M testers. The result showed the presence of nine different VCGs in India. The molecular characterisation of Foc isolates by PCR-RFLP analysis of rDNA IGS region, using six restriction enzymes grouped the Indian Foc isolates into 13 IGS genotypes. Among these, group 1 (AAAAAA) was the most common and consisted of 44 isolates of pathogenic Fusarium

Combined General Vector Machine for Single Point Electricity Load Forecast

General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GYM is applied into electricity load fore­cast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model ( ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GYM, BPNN, SVM and ARIMA are proposed and verified. Results show that GYM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast

Combined General Vector Machine for Single Point Electricity Load Forecast

General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast