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The objective of the work was to develop, test and evaluate a color and mealiness classification model based on images of sweetpotato roots. A total of 3018 images were collected from 950 samples from October 2021 to November 2022. The captured image data samples were harvested from several sites, including Namulonge, Arua, Bulindi, Nassari, Serere, Rwebitaba, Iganga, Kabarole, Mbale, Mpigi, Busia, Kamuli, Hoima, Kabale and Kenya. Calibrations were done using reference data collected by a sensory panel. Up to twelve cooked roots per genotype were used for sensory evaluation of traits per session. Calibrations used various linear and non-linear models. Using linear regression, high performances were observed of the calibration for orange color intensity (R2 = 0.92, Mean Squared Error (MSE) =0.58), suggesting that the model is sufficient for field application. For mealiness-by-hand and positive area, the best model has a Mean Absolute Error (MAE) of 2.16 and 9.01 respectively.

The objective of the work was to develop, test and evaluate a color and mealiness classification model based on images of sweetpotato roots. A total of 3018 images were collected from 950 samples from October 2021 to November 2022. The captured image data samples were harvested from several sites, including Namulonge, Arua, Bulindi, Nassari, Serere, Rwebitaba, Iganga, Kabarole, Mbale, Mpigi, Busia, Kamuli, Hoima, Kabale and Kenya. Calibrations were done using reference data collected by a sensory panel. Up to twelve cooked roots per genotype were used for sensory evaluation of traits per session. Calibrations used various linear and non-linear models. Using linear regression, high performances were observed of the calibration for orange color intensity (R2 = 0.92, Mean Squared Error (MSE) =0.58), suggesting that the model is sufficient for field application. For mealiness-by-hand and positive area, the best model has a Mean Absolute Error (MAE) of 2.16 and 9.01 respectively.

A variety of imaging sensors are currently used in research and commercial practices to quantify complex crop traits for breeders. Imaging technology is a fast, non-destructive high-throughput phenotyping tool that has been used widely for accurate acquisition of crop traits on a large scale. The DigiEye is a computer-controlled digital camera system for measuring colour and capturing high-quality repeatable images. The SOP highlights the procedure of calibrating the DigiEye, for accuracy and consistance of results.

BACKGROUND: The assessment of user acceptability in relation to crop quality traits should be a full part of breeding selection programs. Our methodology is based on a combination of sensory approaches aiming to evaluate the sensory characteristics and user acceptability of root, tuber and banana (RTB) varieties. RESULTS: The four-stepped approach links sensory characteristics to physicochemical properties and end-user acceptance. It starts with the development of key quality traits using qualitative approaches (surveys and ranking) and it applies a range of sensory tests such as Quantitative Descriptive Analysis with a trained panel, Check-All-That-apply, nine-point hedonic scale and Just-About-Right with consumers. Results obtained on the same samples from the consumer acceptance, sensory testing and physicochemical testing are combined to explore correlations and develop acceptability thresholds. CONCLUSION: A combined qualitative and quantitative approach involving different sensory techniques is necessary to capture sensory acceptance of products from new RTB clones. Some sensory traits can be correlated with physicochemical characteristics and could be evaluated using laboratory instruments (e.g. texture). Other traits (e.g. aroma and mealiness) are more difficult to predict, and the use of a sensory panel is still necessary. For these latter traits, more advanced physicochemical methods that could accelerate the breeding selection through high throughput phenotyping are still to be developed.

A variety of imaging sensors are currently used in research and commercial practices to quantify complex crop traits for breeders. Imaging technology is a fast, non-destructive high-throughput phenotyping tool that has been used widely for accurate acquisition of crop traits on a large scale. The DigiEye is a computer-controlled digital camera system for measuring colour and capturing high-quality repeatable images. The SOP highlights the procedure of calibrating the DigiEye, for accuracy and consistance of results.

This report is part of the RTBfoods project, Work Package (WP) 1. The main objective of RTBfoods is to deploy RTB varieties that meet user-preferred quality traits to increase the adoption and impact of improved RTB varieties in sub-Saharan Africa (SSA). To do so, the project is working to (1) Define what are the key user-preferred quality traits for a range of RTB food products (cassava, yam, potato, sweetpotato, banana) through surveys with end-users (product profiles); (2) Link these product profiles with biophysical and functional properties of RTB food products, and develop laboratory-based methods to assess these properties in a quantitative manner; (3) Develop high-throughput phenotyping protocols (HTPP) for rapid screening of user-preferred quality traits in new RTB varieties; (4) Integrate key user traits into breeding and variety deployment programs.

The purpose of this activity was to understand the important quality characteristics of boiled/steamed sweetpotato from the perspective of processors, through participatory processing demonstrations from raw material to finished product. The activity was conducted in Northern (Lira) and Western Uganda (Kamwenge). Preparation of sweetpotato in Lira was by boiling whereas steaming was used in Kamwenge. Good processing characteristics, in both regions, included easy to peel (firm peel, less pulp is lost), firm root, smooth peel and flesh surface while the undesirable ones were difficult to peel (soft peel, more pulp is lost in the process of peeling) and soft root. Average processing yield from peeling to boiled product for Lira varieties was 74.4% while for Kamwenge (steaming) it was 81.5%. In Lira, preferred boiled sweetpotato characteristics were absence of sap, mealiness, sweet taste and good smell of sweetpotato. Processors here disliked boiled sweetpotatoes which were sappy, fibrous, and not sweet and did not have a characteristic smell. In Kamwenge, preferred steamed sweetpotato had a nice colour (yellow, white, whitish on the inside), was mealy, firm, had a sweet taste and good sweetpotato smell. Least preferred characteristics for steamed sweetpotatoes were; pale colour, fibrousness, not sweet and off odour. Overall, in Lira, Otada (local) was the most preferred variety followed by Okonynedo (local) with NASPOT 8 (improved) and Arakaraka (local) jointly ranked third. In Kamwenge, NASPOT 8 (improved) was most preferred, followed by Kiribwamukwe (local), Otandibata (local) and lastly Ndererabaana (local).

The purpose of this activity was to understand the important quality characteristics of boiled/steamed sweetpotato from the perspective of processors, through participatory processing demonstrations from raw material to finished product. The activity was conducted in Northern (Lira) and Western Uganda (Kamwenge). Preparation of sweetpotato in Lira was by boiling whereas steaming was used in Kamwenge. Good processing characteristics, in both regions, included easy to peel (firm peel, less pulp is lost), firm root, smooth peel and flesh surface while the undesirable ones were difficult to peel (soft peel, more pulp is lost in the process of peeling) and soft root. Average processing yield from peeling to boiled product for Lira varieties was 74.4% while for Kamwenge (steaming) it was 81.5%. In Lira, preferred boiled sweetpotato characteristics were absence of sap, mealiness, sweet taste and good smell of sweetpotato. Processors here disliked boiled sweetpotatoes which were sappy, fibrous, and not sweet and did not have a characteristic smell. In Kamwenge, preferred steamed sweetpotato had a nice colour (yellow, white, whitish on the inside), was mealy, firm, had a sweet taste and good sweetpotato smell. Least preferred characteristics for steamed sweetpotatoes were; pale colour, fibrousness, not sweet and off odour. Overall, in Lira, Otada (local) was the most preferred variety followed by Okonynedo (local) with NASPOT 8 (improved) and Arakaraka (local) jointly ranked third. In Kamwenge, NASPOT 8 (improved) was most preferred, followed by Kiribwamukwe (local), Otandibata (local) and lastly Ndererabaana (local).

The aim of this activity was to establish the essential quality characteristics of boiled potato through participatory processing demonstrations with processors at every step from raw material to final boiled product. The activity was conducted in Central (Rakai) and South Western Uganda (Kabale). Boiled potatoes were prepared similarly by boiling in both Kabale and Rakai. When processing, in both regions, ease of peeling (associated with ease of removing 'eyes'), firmness and tubers which were not watery (low water content) were preferred. Potatoes which were not good for processing were; difficult to peel (deep eyes), watery and soft. Average processing yield from peeling to boiled product for Kabale varieties was 79% while for Rakai it was 68%. In Kabale, the most preferred boiled potato characteristics were firmness, mealiness, good potato taste and smell whereas the least preferred were white colour, too soft, watery, not mealy, lacking good potato taste and smell. Processors in Rakai liked mealiness, firmness, softness and smoothness (in the mouth), good potato taste and smell. The poor characteristics were; not mealy, too soft, watery, hard, no potato taste and smell. Generally, in Kabale, the most preferred variety was Rwangume (NAROPOT 4, improved), followed by Kinigi (local), Kachpot 1 (improved) and Victoria (improved). In Rakai, varietal preference for boiled potato in descending order was; Kasumali (local), Deodeo (NAROPOT 4, improved), Kabale (local) and Victoria (improved).