Suchergebnisse

Potato (Solanum tuberosum, L) compounds are generally determined by analytical methods including gasliquid chromatography (GLC), HPLC and UV-VIS spectrophotometry. These methods require a lot of time and are destructive. Therefore, they seem to be not suitable for in-line applications in the food industry. Nearinfrared spectroscopy (NIRS) is a technique that presents some advantages over reference methods for quantitative analysis of agricultural and food products since it is fast, reliable and non-destructive. For this reason, in this study, quantitative analyses were carried out to determine main compounds in potatoes using NIRS. Potato tubers grown in two consecutive years were used for the analyses. NIR spectral acquisition was acquired on lyophilized samples. In year 1, a total of 135 samples were used while 228 samples were used in year 2. Lyophilized samples were also scanned by NIRS, two replicates per samples were acquired and the mean spectrum of each sample was used for the analysis. Different chemical analyses were carried out each year. Thus, in year 1 the following parameters were quantified: reducing sugars (RS) and nitrogen (N), whereas in year 2, total soluble phenolics (TSP) and hydrophilic antioxidant capacity (HAC) were extracted and quantified. Then, chemometric analyses were performed using Unscrambler X (version 10.3, CAMO software AS, Oslo, Norway) to correlate wet chemical analysis with spectral data. Quantitative analyses based on PLS regression models were developed in order to predict the above chemical compounds of tubers in a non-destructive manner. Good PLS regression models were obtained for the prediction of nitrogen and TSP with coefficients of determination (R2) above 0.83. Moreover, PLS models obtained for the estimation of HAC could be used for screening and approximate calibrations. ; This work was financed within the frame of INIA's project RTA2013-00006-C03-01-03, the Basque Government and the Universidad Pública de Navarra through the concession of a predoctoral research grant.

Powdery mildew is a worldwide major fungal disease for grapevine, which adversely affects both crop yield and produce quality. Disease identification is based on visible signs of a pathogen once the plant has already been infected; therefore, techniques that allow objective diagnosis of the disease are currently needed. In this study, the potential of hyperspectral imaging (HSI) technology to assess the presence of powdery mildew in grapevine bunches was evaluated. Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range). Image processing was performed to extract spectral and spatial image features and then, classification models by means of Partial Least Squares Discriminant Analysis (PLS-DA) were carried out for healthy and infected pixels distinction within grape bunches. The best discrimination was achieved for the PLS-DA model with smoothing (SM), Standard Normal Variate (SNV) and mean centering (MC) pre-processing combination, reaching an accuracy of 85.33% in the cross-validation model and a satisfactory classification and spatial location of either healthy or infected pixels in the external validation. The obtained results suggested that HSI technology combined with chemometrics could be used for the detection of powdery mildew in black grapevine bunches. ; This research received funding from the Department of Economic Development of the Navarre Government (Project: DECIVID (Res.104E/2017)), by the Spanish Ministry of Economy and Competitiveness (Project TIN2016-77356-P) and by the research services of the Universidad Pública de Navarra. C.P.-R is a beneficiary of postgraduate scholarships funded by Universidad Pública de Navarra (FPI-UPNA-2017 (Res.654/2017)).