ABSTRACT
The Sugar Milling Research Institute NPC (SMRI) has provided the South and southern African sugar industry with analytical services for more than 50 years. The need for an internationally-recognised quality assurance system to provide SMRI members and their customers with the necessary confidence in the results that they were receiving was recognised more than 25 years ago, and it culminated in the SMRI Analytical Services Division achieving ISO/IEC Guide 25:1990 accreditation in 1998. The SMRI analytical laboratory has since successfully progressed through the ISO/IEC 17025:1999 and ISO/IEC 17025:2005 iterations to the current ISO/IEC 17025:2017 standard. The SMRI has therefore had to continually expand the scope of its systems over the past 25 years, in order to match the updated requirements. The system is used for the analysis of weekly composite mixed juice and final molasses samples from all the SMRI South African member mills and some southern African member mills. Similarly, raw and white sugar analyses are conducted, with the reporting of results to the members' specifications. Critical to the accuracy of these results is the continual auditing and monitoring of the methods, equipment and chemical solutions that are used in the analysis methods. This is achieved by using Certified Reference Materials and control samples, as well as participating in internationally-recognised analytical proficiency schemes. Critical to the success of the laboratory in maintaining its accreditation is the competence of the laboratory staff who undergo continual training and assessments. This was demonstrated in 2020 and 2021, when they were able to continue providing the necessary services, despite the challenges faced during the Covid pandemic.
ABSTRACT
The paper proposes a construct for sweeteners (SMH-sugar, molasses, and honey class) consumer behavior, focusing on the mountain Apis Mellifera healing effects and its market. The paper develops three research dimensions, respectively, the importance of the healing properties of SMH products, the consumer behavior of SMH clients, and the world trade of SMH. Apis Mellifera product is considered one of the primary natural prevention and treatment for COVID-19. Presented empirical and experimental studies, respectively, qualitative analysis for Apis Mellifera product, reveal that honey, especially dark honey, presents healing effects. People understand the healing effects of honey in the COVID-19 context, and consequently, honey consumption increased. The forecasting model of the export value, for the 2021-2040 period, takes into consideration the descriptive statistics analysis based on 2001-2020 data. The paper contains relevant data about the SMH class related to statistics of the World Bank, United Nations, Eurostat, International Trade Center, and other sources presented in the paper. Data have been processed into SPSS and Excel, according to ANOVA (descriptive statistics with a focus on frequency analysis) and forecasting analysis. Supplementary Information: The online version contains supplementary material available at 10.1007/s12355-023-01243-6.
ABSTRACT
Introduction: Most areas under spring sugar beet cultivation face severe water restrictions and increasing the area under cultivation of this crop in most of these areas is contrary to the principle of conservation of water and soil resources. The use of new areas for winter sugar beet cultivation should be the area under cultivation of this crop in hot and dry areas. Therefore, winter sowing (pending) of sugar beet with emphasis on the limitations of the country's water resources has been proposed as a solution. Materials and Methods: In this study, the quantitative and qualitative yield of 16 sugar beet genotypes in winter planting were studied as a randomized complete block design with four replications in the Torbat-e-Jam region in the two cropping years (2020-2021 and 2021-2022). The studied genotypes included F-20739, F-20837, F-21083, SBSI-5, SBSI-15, SVZA 2019-JD389, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, FDIR 19 B 3021, FDIR 19 B 4028, F-20591, SBSI-6, SBSI-16, SBSI-7 and SBSI-17 are the breeding populations obtained from the gene bank of the Sugar Beet Seed Breeding Research Institute. In this research, traits such as root yield, sugar content, sugar yield, white sugar yield, Na, K, N, alkalinity, molasses sugar, white sugar content, and extraction coefficient of sugar were measured. Data were analyzed using SAS 9.1 software. The analysis of variance on test data and comparison to the middle of the Duncan test was performed at the 5% level. Factor analysis was calculated to identify the main factors using MINITAB software. Cluster analysis of the studied genotypes was obtained after standardizing the data by the Ward method and using Euclidean distance criterion with the help of SPSS software. Results and Discussion: The results of the combined analysis of variance showed that there was a significant difference between different genotypes of sugar beet at the level of 1% probability for all studied traits except for nitrogen content. The mean comparison showed that the SBSI-15 genotype had the highest root yield (60.66 ton.ha). It should be noted that this genotype in terms of yield index traits did not show significantly different from genotypes F-20739, SBSI-15, SVZA 2019-JD389, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, and FDIR 19 B 4028. Also, the F-20739 genotype had the highest amounts of sugar content (19.5%), white sugar content (16.3%) and extraction coefficient of sugar (83.2%) and the lowest amount of potassium (4.24 meq .100 g-1 of root weight) and Molasses sugar (2.7%). In addition, the highest sugar yield (10.69 t/ha) and white sugar yield (8.68 t/ha) were in FDIR 19 B 3021 genotype. Investigating the correlation of traits showed the highest positive and significant correlation was between sugar yield and white sugar yield (0.99**) and the highest negative and significant correlation was between extraction coefficient of sugar and molasses sugar (-0.95**). Principal factor analysis based on the mean of the traits identified three factors that accounted for a total of 91% of the variability between the data. SBSI-15, SVZA 2019-JD0398, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, FDIR 19 B 3021, and FDIR 19 B 4028 genotypes are distinguished different from other genotypes and they were as superior genotypes in terms of yield index traits. The dendrogram generated from the cluster analysis for white sugar yield classified genotypes into three main groups.