Pests and diseases affecting potato landraces and bred varieties grown in Peru under indigenous farming system

The major pests and diseases were identified and quantified on thirteen potato landraces and three bred varieties cultivated in Peru. Late blight (Phytophthora infestans) was the primary biotic constraint affecting plants before flowering with an average severity of 24%. No other pathogens caused severe foliar disease, but black scurf (Rhizoctonia solani) was relatively common on tubers of some genotypes with incidence ranging from 4.30 to 33.33%. The viruses most generally considered important in potato seed degeneration, PVY and PLRV, were extremely rare, with 1.11 and 0.12 % incidence, respectively. Other viruses considered mild, such as PVX and PVS, were more common, with incidence of 28.23 and 22.29 %, respectively. Potato flea beetle (Epitrix spp.), potato leaf beetle (Diabrotica spp.) and Andean potato weevil (Premnotrypes spp.) were common, with incidence of 28.14, 18.75 and 13.61%, respectively. Potato landraces known as Ishkupuru, Lengua de vaca, Chaulina, Chaulina Tajacaja and Negro cayash were identified as potentially resistant to P. infestan

A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

[Abstract]: Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.Universidad Nacional Autónoma de México; PIAPI 2042Universidad Nacional Autónoma de México; PAPIIT IT20091