Beet sugar contains an off-aroma, which was hypothesized to generate expectations on the acceptability of a product made with beet sugar. Thus, the objective of this study was to assess the impact of information about the sugar source (beet vs. cane) on the overall liking of an orange-flavored beverage. One hundred panelists evaluated an orange-flavored powdered beverage mix and beverage made with beet and cane sugars using a 5-phase testing protocol involving a tetrad test and hedonic ratings performed under blind and informed conditions. Tetrad test results indicated that there was a significant difference (P < 0.05) between the beverage mix made with beet sugar and cane sugar; however, no difference was found between the beverage made with beet sugar and cane sugar. Hedonic ratings revealed the significance of information conditions on the panelists evaluation of sugar (F = 24.67, P < 0.001); however, no difference in the liking was identified for the beverage mix or beverage. Average hedonic scores were higher under informed condition compared to blind condition for all products, possibly because labels tend to reduce uncertainty about a product. Results from this study are representative of the responses from the general population and suggest that they are not affected by sugar source information in a beverage product. Based on concerns with the use of beet sugar expressed in the popular press, there may be a subgroup of the population that has a preconceived bias about sugar sources due to their prior experiences and knowledge and, thus, would be influenced by labels indicating the sugar source used in a product.
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http://dx.doi.org/10.1111/1750-3841.12668 | DOI Listing |
Front Plant Sci
January 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Horticulture and Landscape Architecture and Center for Rhizosphere Biology, Colorado State University, Fort Collins, Colorado, United States of America.
Root and rhizosphere studies often focus on analyzing single-plant microbiomes, with the literature containing minimum empirical information about the shared rhizosphere microbiome of multiple plants. Here, the rhizosphere of individual plants was analyzed in a microcosm study containing different combinations and densities (1-3 plants, 24 plants, and 48 plants) of cover crops: Medicago sativa, Brassica sp., and Fescue sp.
View Article and Find Full Text PDFPlant Methods
January 2025
Institute of Sugar Beet Research, Holtenser Landstraße 77, 37079, Göttingen, Germany.
Insects
January 2025
College of Life Science and Technology, Xinjiang University, Urumqi 830017, China.
Beet crops are highly vulnerable to pest infestations throughout their growth cycle, which significantly affects crop development and yield. Timely and accurate pest identification is crucial for implementing effective control measures. Current pest detection tasks face two primary challenges: first, pests frequently blend into their environment due to similar colors, making it difficult to capture distinguishing features in the field; second, pest images exhibit scale variations under different viewing angles, lighting conditions, and distances, which complicates the detection process.
View Article and Find Full Text PDFMicroorganisms
December 2024
Departamento de Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala S/N, Col. Santo Tomás, Mexico City 11340, Mexico.
Carbendazim (CBZ) is a fungicide widely used on different crops, including soybeans, cereals, cotton, tobacco, peanuts, and sugar beet. Excessive use of this xenobiotic causes environmental deterioration and affects human health. Microbial metabolism is one of the most efficient ways of carbendazim elimination.
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