The aim of this work was to improve the classification of green asparagus in an agro-export company by way of a Computer Vision System (CVS). Thus, an image analysis application was developed in the MATLAB® environment to classify green asparagus according to the absence of white spots and the width of the product. The CVS performance was compared with a manual classification using the error in the classification as the quality indicator; the yield from the raw material (%) and line productivity (kg/h) as the production indicators; and the net present value (USD) and internal rate of return (%) as the economic indicators. The CVS classified the green asparagus with 2% error; improved the yield from the raw material from 43% to 45%, and line productivity from 5 to 10 kg/h; and increased the net present value by 102,609.00 USD, yielding an Internal Rate of Return of 156.3%, much higher than the Opportunity Cost of the Capital (8.6%). Hence the classification of green asparagus by a CVS is an efficient and profitable alternative to manual classification.

Johonathan Salazar-Campos, Orlando Salazar-Campos, Osmar Gálvez-Ruiz, Herlita Gavidia-Chávez, Mery Gavidia-Chávez, Lorena Irigoin-Guevara, and Jesús Obregón-Domínguez. Prev Nutr Food Sci 2023;28:444-52. https://doi.org/10.3746/pnf.2023.28.4.444

Golden gooseberry (Physalis peruviana) and tree tomato (Solanum betaceum) have bioactive components, which open up the possibility of finding new ways to industrialize them. The objective of the study was to report the chemical and sensory characterization of a new alcoholic beverage produced by mixtures of Physalis peruviana and Solanum betaceum fruits, through fermentation with Saccharomyces cerevisiae (Ale) and Saccharomyces pastorianus (Lager). The physicochemical characterization of the beverages obtained was determined by basic parameters such as alcoholic degree, pH, density, total soluble solids, which were analyzed using: the AOAC "Official Methods of Analysis". Total carotenoids were analyzed according to: mg b-carotene eq/100g; the total phenolic compounds were determined under the Folin Ciocalteus protocol; the color characteristics (L*, a* and b*) using computer vision and Python data analysis. The sensory characterization was established with the help of 60 panelists, who evaluated 8 characteristics (flower aroma, nut aroma, wood aroma, astringency, body, flower odor, nut odor, and wood odor). Through a principal component analysis (PCA) and clustering analysis, it was shown that the fermented Ale-25AG-75BE and Ale-50AG-50BE, presented greater preference with 60 and 80%, being represented by Clusters 3 and 4. Finally, this study shows the feasibility of using mixtures of goldenberry and tree tomato to produce fermented alcoholic beverages, which could be inserted into the market as a new product.