Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion.

The present work proposes to detect adulteration in rice flour using mineral profiles. Eighty-seven flour samples from two rice kinds (Indica and Japonica) plus thirty adulterated flour samples were analyzed by ICP OES. After obtaining the quantitative elemental fingerprint of the samples, PCA and LDA were applied. Binary and multiclass associations were considered to assess rice flour authenticity through fraud identification. Models based on element predictors showed accuracies ranging from 72 to 88% to distinguish adulterated and unadulterated samples. The fusion of the mineral features with the principal components (PCs) obtained from PCA provided classification rates of 100% in training samples, and 91-100% in test samples. The proposed method proved to be a useful tool for quality control in the rice industry since a perfect success rate was achieved for rice flour fraud detection.

Australia: A Continent Without Native Powdery Mildews? The First Comprehensive Catalog Indicates Recent Introductions and Multiple Host Range Expansion Events, and Leads to the Re-discovery of Salmonomyces as a New Lineage of the Erysiphales.

In contrast to Eurasia and North America, powdery mildews (Ascomycota, Erysiphales) are understudied in Australia. There are over 900 species known globally, with fewer than currently 60 recorded from Australia. Some of the Australian records are doubtful as the identifications were presumptive, being based on host plant-pathogen lists from overseas. The goal of this study was to provide the first comprehensive catalog of all powdery mildew species present in Australia. The project resulted in (i) an up-to-date list of all the taxa that have been identified in Australia based on published DNA barcode sequences prior to this study; (ii) the precise identification of 117 specimens freshly collected from across the country; and (iii) the precise identification of 30 herbarium specimens collected between 1975 and 2013. This study confirmed 42 species representing 10 genera, including two genera and 13 species recorded for the first time in Australia. In Eurasia and North America, the number of powdery mildew species is much higher. Phylogenetic analyses of powdery mildews collected from Acalypha spp. resulted in the transfer of Erysiphe acalyphae to Salmonomyces, a resurrected genus. Salmonomyces acalyphae comb. nov. represents a newly discovered lineage of the Erysiphales. Another taxonomic change is the transfer of Oidium ixodiae to Golovinomyces. Powdery mildew infections have been confirmed on 13 native Australian plant species in the genera Acacia, Acalypha, Cephalotus, Convolvulus, Eucalyptus, Hardenbergia, Ixodia, Jagera, Senecio, and Trema. Most of the causal agents were polyphagous species that infect many other host plants both overseas and in Australia. All powdery mildews infecting native plants in Australia were phylogenetically closely related to species known overseas. The data indicate that Australia is a continent without native powdery mildews, and most, if not all, species have been introduced since the European colonization of the continent.

Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination.

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy.

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.