Color measurement using a smartphone applied to Madeira wines

Computer vision is a field that uses techniques to acquire, process, analyze and understand images from the real world in order to produce numeric or symbolic information in the form of decisions [1]. This project aims to use computer vision to prepare an app to analyze a Madeira Wine and characterize it (identify its variety) by its color. Dry or sweet wines, young or old wines have a specific color. It uses techniques to compare histograms in order to analyze the images taken from a test sample inside a special container designed for this purpose. The color analysis from a wine sample using an image captured by a smartphone can be difficult. Many factors affect the captured image such as, light conditions, the background of the sample container due to the many positions the photo can be taken (different to capture facing a white wall or facing the floor for example). Using new technologies such as 3D printing it was possible to create a prototype that aims to control the effect of those external factors on the captured image. The results for this experiment are good indicators for future works. Although it’s necessary to do more tests, the first tests had a success rate of 80% to 90% of correct results. This report documents the development of this project and all the techniques and steps required to execute the tests.

An improved and fast UHPLC-PDA methodology for determination of L-ascorbic and dehydroascorbic acids in fruits and vegetables: evaluation of degradation rate during storage

This study provides a versatile validated method to determine the total vitamin C content, as the sum of the contents of L-ascorbic acid (L-AA) and dehydroascorbic acid (DHAA), in several fruits and vegetables and its degradability with storage time. Seven horticultural crops from two different origins were analyzed using an ultrahigh-performance liquid chromatographic–photodiode array (UHPLC-PDA) system, equipped with a new trifunctional high strength silica (100% silica particle) analytical column (100 mm×2.1 mm, 1.7 μm particle size) using 0.1% (v/v) formic acid as mobile phase, in isocratic mode. This new stationary phase, specially designed for polar compounds, overcomes the problems normally encountered in HPLC and is suitable for the analysis of large batches of samples without L-AA degradation. In addition, it proves to be an excellent alternative to conventional C18 columns for the determination of L-AA in fruits and vegetables. The method was fully validated in terms of linearity, detection (LOD) and quantification (LOQ) limits, accuracy, and inter/intraday precision. Validation experiments revealed very good recovery rate of 96.6±4.4% for L-AA and 103.1±4.8 % for total vitamin C, good linearity with r2-values >0.999 within the established concentration range, excellent repeatability (0.5%), and reproducibility (1.6%) values. The LOD of the method was 22 ng/mL whereas the LOQ was 67 ng/mL. It was possible to demonstrate that L-AA and DHAA concentrations in the different horticulture products varied oppositely with time of storage not always affecting the total amount of vitamin C during shelf-life. Locally produced fruits have higher concentrations of vitamin C, compared with imported ones, but vegetables showed the opposite trend. Moreover, this UHPLC-PDA methodology proves to be an improved, simple, and fast approach for determining the total content of vitamin C in various food commodities, with high sensitivity, selectivity, and resolving power within 3 min of run analysis.