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BACKGROUND: Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter content (DMC) and the starch fraction of amylose content (AC). Accordingly, in the present study, near-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda. RESULTS: Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45 g 100g−1, whereas, for amylose content, it ranged from 14 to 33 g 100g−1. In the validation set, average DMC was 29.5 g 100g−1, whereas, for amylose content, it was 24.64 g 100g−1. For DMC, a modified partial least square regression model had regression coefficients (R2) of 0.98 and 0.96, respectively, in the calibration and validation set. These were also associated with low bias (−0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9 g 100g−1 to 1.06 g 100g−1. For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to −0.03 and a ratio of performance deviation of 4.23 were observed. CONCLUSION: These findings confirm the robustness of NIRS in the estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations.

The 5-year project 'Breeding roots, tubers and banana products for end user preferences' (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials.

BACKGROUND: Consumers of boiled cassava in Africa, Latin America and Asia use specific preference criteria to evaluate its cooking quality, in terms of texture, colour and taste. To improve adoption rates of improved cassava varieties intended for consumption after boiling, these preference criteria need to be determined, quantified and integrated as post-harvest quality traits in the target product profile of boiled cassava, so that breeding programs may screen candidate varieties based on both agronomic traits and consumer preference traits. RESULTS: Surveys of various end-user groups identified seven priority quality attributes of boiled cassava covering root preparation, visual aspect, taste and texture. Three populations of contrasted cassava genotypes, from good-cooking to bad-cooking, in three countries (Uganda, Benin, Colombia) were then characterized according to these quality attributes by sensory quantitative descriptive analysis (QDA) and by standard instrumental methods. Consumers' preferences of the texture attributes mealiness and hardness were also determined. By analysis of correlations, the consumers' preferences scores were translated into thresholds of acceptability in terms of QDA scores, then in terms of instrumental measurements (water absorption during boiling and texture analysis). The thresholds of acceptability were used to identify among the Colombian and Benin populations promising genotypes for boiled cassava quality. CONCLUSION: This work demonstrates the steps of determining priority quality attributes for boiled cassava and establishing their corresponding quantitative thresholds of acceptability. The information can then be included in boiled cassava target product profiles used by cassava breeders, for better selection and adoption rates of new varieties.

All cassava varieties are known to contain some level of cyanogenic glucosides (CGs). Linamarine and lotaustrine are the commonest CGs in cassava but linamarine is the most abundant. The linamarine is readily hydrolyzed to glucose and acetone cyanohydrin in the presence of linamarase enzyme, which is also inherently produced by the plant. Acetone cyanohydrin decomposes rapidly in neutral or alkaline solution, liberating cyanide gas. Production of free HCN in cassava roots is prevented by compartmentalization of linamerase in cell walls and linamarine and lotaustraline in the vacuole. Therefore, damage/rupture of root tissues is desired for this reaction to occur. This Standard Operating Procedure (SOP) documents steps taken during determination of cassava root HCN content using the picrate method at the National Crops Resources Research Institute (NaCRRI), under the auspices of the RTBfoods project. Furthermore, the RTBfoods project seeks to develop high-throughput phenotyping protocols to provide RTB breeders with fast, accurate, and reproducible procedures for rapid phenotyping of their populations. NIRS is one such technologies that has been widely deployed in plant phenomics. In order to build NIRS prediction models, reference data must be matched with NIRS spectra. This document provides an SOP for generating HCN reference data using the picrate method and matching this data to NIRS spectra. Calibration of HCN reference data and NIRS data is the object of a separate document.

The analyses concern 291 cassava genotypes harvested. in 2019 in Namulonge, Uganda. Genotypes came from NaCRRI Cassava breeding program. For this study 291 cassava roots were analysed; 3 uniformly sized non-necrotic roots per clone were sampled. Among the 281 genotypes, 282 were analysed one time and 9 genotypes were analysed 2 times, the total number of spectra is 300. Dry matter (DM) quantification was achieved in NaCRRI physico-chemical laboratory in Namulonge. Near infrared spectra were scanned in NIR laboratory of NaCRRI in Namulonge. The protocol measurement follows the SOP protocol described for fresh grated cassava: https://doi.org/10.18167/agritrop/00669. DM content, expressed as % of total weight, vary between 10,74% and 45,00% with an average value of 29,42%. On the basis of the 300 samples 2 data sets were constituted using the Kennard- Stone algorithm: one training set (n = 210) and one test set (n =90). DM content was calibrated using Modified Partial Least Squares Regression, for the spectral range: 400 nm - 2500nm, that is to say Visible and NIR regions. Calibrations were done on non-pretreated spectra and pretreated spectra using different pre-treatments. The best model was obtained with no pre-treatment applied to the spectra, the R² was 0,973 with an SECV equal to 0,894%. This model was applied to predict samples from the test set, the standard error of prediction (SEP) is equal to SEP = 0,815% and the R² for prediction is 0,953. The ratio performance to deviation RPD is equal to 4,63. The developed NIRs model for quantification of DM content of cassava root presents an accuracy good enough to enable cassava selection based on DM content. The error of the model is SEP = 0,815% which means that a predicted value could be defined with a confidence interval of +/- 1,63 % associated with 95 % of confidence. Furthermore, the database refers to 291 different genotypes representative of the variability of the DM content, thus the model is robust enough f

The analyses concern 94 cassava genotypes harvested in 2022 in Namulonge, Uganda. Genotypes came from NaCRRI Cassava breeding program. For this study 247 cassava roots were analysed; 3 uniformly sized non-necrotic roots per clone were sampled. Amylose quantification was achieved in NaCRRI physico-chemical laboratory in Namulonge. Near infrared spectra were scanned in NIR laboratory of NaCRRI in Namulonge. The protocol measurement follows the SOP protocol described for fresh grated cassava: https://doi.org/10.18167/agritrop/00669. Amylose content, expressed as % of starch, vary between 15,2% and 33%. On the basis of the 247 samples 2 data sets were constituted: one training set (n = 173) and one test set (n =74) for this 70% of the samples were picked randomly for the training set while the remaining 30% were kept as test set. Amylose content was calibrated using Modified Partial Least Squares Regression, for the spectral range: 400 nm - 2500nm, that is to say Visible and NIR regions. Calibrations were done on non-pretreated spectra and pretreated spectra using different pre-treatments. The best model was obtained with no pre-treatment applied to the spectra, the R² was 0,937 with an SECV equal to 0,801%. This model was applied to predict samples from the test set, the standard error of prediction (SEP) is equal to SEP = 1,023% and the R² for prediction is 0,915. The ratio performance to deviation RPD is equal to 2,947.The developed NIRs model for quantification of amylose content of cassava root presents an accuracy good enough to enable cassava selection based on amylose content expressed as % of dried starch. The error of the model is SEP = 1,02% which means that a predicted value could be defined with a confidence interval of +/- 2,04 % associated with 95 % of confidence. Furthermore, the database refers to 94 different genotypes representative of the variability of the starch properties, in particular for amylose content, thus the model is robust enough for amyl

Recent developments with the CGIAR have led cassava variety development to increasingly focus on assessing clones based on end-user acceptance of the crop and of finished product(s). Consumer testing of boiled cassava was carried out in rural areas (Luweero and Apac) and urban areas (Kampala) of Uganda. Consumers were invited to test boiled products from several cassava varieties and give their opinion about the products using various methods including overall liking (9-point hedonic scale), Just-About-Right (JAR) and Check-All-That-Apply (CATA) tests. Using these methods, consumers indicated their preferred sensory attributes for boiled cassava. Results showed that consumers preferred local varieties Bao, Bwanjule and Nabwangu, which possessed good quality attributes namely; 'mealiness', 'sweetness', 'softness', 'nice aroma', 'whiteness', 'firmness' and 'sticky'. Newly improved varieties and Alanyo Deri- a local variety - were least preferred because boiled cassava product resulting had poor qualities such as; 'dry', 'bitterness', 'watery', 'hard', 'tastelessness', 'yellow', 'fibrous' and 'odourless'. This information gathered in this activity will be key for WP2 to determine appropriate physicochemical evaluations to assess cassava clones' qualities.

The Standard Operating Procedures (SOPs) detailed herein are applicable in the acquisition of spectra from largely intact cassava roots. The procedures allow for scanning and spectral acquisition after a scanning surface has been provided on the intact root. Cassava samples are harvested using approved harvesting tools and labelled after which preparation commences by removing the tail ends of the root. This is followed by sectioning of the root into four equal portions and surface trimming of each section immediately before spectral acquisition. Thereafter, labelling and scanning of the trimmed surface using the ASD-QUALITY SPEC produces four scans representative of one particular root. The procedure is repeated for the rest of the roots producing four subsets of scans from one particular accession. Spectral data produced from these scans is downloaded and further processed for use in calibration development and root chemical composition determination. Processed spectral data is also uploaded as an additional file to the cassava base. Reference data generation is carried out by approved reference methods in repeatability analyses carried out at NaCRRI and NARL. Critical points of consideration include the development of a sample flow and spectra acquisition matrix coupled to correct labelling since sample numbers involved are usually many.

The sensory evaluation by trained panelists was conducted to establish a standard operating procedure (SOP) for routine undertaking of sensory evaluation to determine the quality of boiled cassava roots by by breeders to improve consumer acceptability of new varieties. Accordingly, we worked with the trained panellists to come up with a list of descriptors that could be used to assess the quality of boiled cassava roots. In total, 21 descriptors associated with appearance, texture, taste and aroma of boiled roots were determined. Having learnt from consumer-testing and surveys, there was need to establish standard descriptors that can be routinely used by sensory panelists to assess quality attributes during varietal development process by breeders. To this end, roots from different clones were harvested and prepared for cooking in a food science laboratory. After cooking, one clone was served to the panelist at a time, and requested to evaluate the sample based on their prior knowledge regarding appearance and texture both in mouth and touch; taste and aroma were also assessed. The panelists were given plain paper to write descriptors, after which a discussion was held with all panelists and the team facilitating the activity. The panelists were requested to highlight the key terminologies they had used to evaluate the attributes of cooked cassava. The highlighted descriptors were written on flip charts that were pinned on the wall of the training room so that everybody could easily follow the discussion. The highlighted descriptors were carefully grouped into categories such as texture by hand, aroma, appearance, taste, and texture by mouth. In the end, after a series of discussions and evaluating a number of cassava clones, a catalogue of 21 descriptors for boiled cassava was developed.

Developing the right metric for dry matter is vital in defining measurable parameters related to biochemical properties that define root yield and root quality. In this SOP, the procedure for production of definitive spectra that defines the connection between the spectra acquired from grated cassava fresh root and the dry matter content is described. The procedures describe the scanning and spectral acquisition of fresh root cassava spectra using the FOSS DS2500 NIRS equipment. Cassava samples are harvested from labelled fields using appropriate harvesting tools and prepared by peeling and grating using a laboratory grater. The grated material is placed on a sample plate with accompanying barcode and moved to NIRS equipment. This is followed loading the grates into a sample cup and scanning of the grated material filled in the sample cup. The procedure is repeated by loading fresh material from the main sample into the sample cup producing two subsets of scans from one particular accession. Spectral data produced from these scans is downloaded and further processed for use in calibration development while it is also uploaded on cassava base as an additional file. Reference data generation is carried out by approved reference methods in repeatability analyses carried out at NaCRRI. Critical points of consideration include the development and maintenance of correct labelling.

At NaCRRI, activity four, a component of WP1, involved participatory processing of boiled cassava; this hinged on Step 2 that focused on gendered food mapping. Accordingly, key processing operations important for quality boiled cassava were identified by processors resident in Apac and Luweero districts in Uganda. Survey results indicated that the main processing steps were: peeling, trimming and steaming. However, there were variations in processing roots after trimming. For example, while sliced cassava roots in Luweero were wrapped in banana leaves and steamed, in Apac, sliced roots were immersed in water and boiled. Some consumers in Apac pounded boiled cassava slices into a mash. No significant differences in yield were observed amongst cassava genotypes. Nonetheless, some varieties like “Alanyo dyer” and “Nase 13” which were susceptible to CBSD, had low yield after processing. End-user attributes preferred during processing cassava roots in Apac were: white flesh colour, ease of peeling and a firm root; least preferred attributes included hard to peel, spongy in the middle, and roots which do not cook easily. On the other hand, preferred attributes in Luweero included: easy to peel, white flesh colour, no fibres on the surface of peeled root, a non-lignified/woody ('telina ekitti'), no bitter taste, smooth root surface, and a firm flesh; undesirable attributes included: lignified/woody root and spongy in the middle. This information was consequently incorporated into the consumer questionnaire that was used for Step 4.

The Standard Operating Procedures (SOPs) detailed herein are applicable in the acquisition of spectra from largely intact cassava roots. The procedures allow for scanning and spectral acquisition after a scanning surface has been provided on the intact root. Cassava samples are harvested using approved harvesting tools and labelled after which preparation commences by removing the tail ends of the root. This is followed by sectioning of the root into four equal portions and surface trimming of each section immediately before spectral acquisition. Thereafter, labelling and scanning of the trimmed surface using the ASD-QUALITY SPEC produces four scans representative of one particular root. The procedure is repeated for the rest of the roots producing four subsets of scans from one particular accession. Spectral data produced from these scans is downloaded and further processed for use in calibration development and root chemical composition determination. Processed spectral data is also uploaded as an additional file to the cassava base. Reference data generation is carried out by approved reference methods in repeatability analyses carried out at NaCRRI and NARL. Critical points of consideration include the development of a sample flow and spectra acquisition matrix coupled to correct labelling since sample numbers involved are usually many.

Developing the right metric for dry matter is vital in defining measurable parameters related to biochemical properties that define root yield and root quality. In this SOP, the procedure for production of definitive spectra that defines the connection between the spectra acquired from grated cassava fresh root and the dry matter content is described. The procedures describe the scanning and spectral acquisition of fresh root cassava spectra using the FOSS DS2500 NIRS equipment. Cassava samples are harvested from labelled fields using appropriate harvesting tools and prepared by peeling and grating using a laboratory grater. The grated material is placed on a sample plate with accompanying barcode and moved to NIRS equipment. This is followed loading the grates into a sample cup and scanning of the grated material filled in the sample cup. The procedure is repeated by loading fresh material from the main sample into the sample cup producing two subsets of scans from one particular accession. Spectral data produced from these scans is downloaded and further processed for use in calibration development while it is also uploaded on cassava base as an additional file. Reference data generation is carried out by approved reference methods in repeatability analyses carried out at NaCRRI. Critical points of consideration include the development and maintenance of correct labelling.

Recent developments with the CGIAR have led cassava variety development to increasingly focus on assessing clones based on end-user acceptance of the crop and of finished product(s). Consumer testing of boiled cassava was carried out in rural areas (Luweero and Apac) and urban areas (Kampala) of Uganda. Consumers were invited to test boiled products from several cassava varieties and give their opinion about the products using various methods including overall liking (9-point hedonic scale), Just-About-Right (JAR) and Check-All-That-Apply (CATA) tests. Using these methods, consumers indicated their preferred sensory attributes for boiled cassava. Results showed that consumers preferred local varieties Bao, Bwanjule and Nabwangu, which possessed good quality attributes namely; 'mealiness', 'sweetness', 'softness', 'nice aroma', 'whiteness', 'firmness' and 'sticky'. Newly improved varieties and Alanyo Deri- a local variety - were least preferred because boiled cassava product resulting had poor qualities such as; 'dry', 'bitterness', 'watery', 'hard', 'tastelessness', 'yellow', 'fibrous' and 'odourless'. This information gathered in this activity will be key for WP2 to determine appropriate physicochemical evaluations to assess cassava clones' qualities.

The sensory evaluation by trained panelists was conducted to establish a standard operating procedure (SOP) for routine undertaking of sensory evaluation to determine the quality of boiled cassava roots by by breeders to improve consumer acceptability of new varieties. Accordingly, we worked with the trained panellists to come up with a list of descriptors that could be used to assess the quality of boiled cassava roots. In total, 21 descriptors associated with appearance, texture, taste and aroma of boiled roots were determined. Having learnt from consumer-testing and surveys, there was need to establish standard descriptors that can be routinely used by sensory panelists to assess quality attributes during varietal development process by breeders. To this end, roots from different clones were harvested and prepared for cooking in a food science laboratory. After cooking, one clone was served to the panelist at a time, and requested to evaluate the sample based on their prior knowledge regarding appearance and texture both in mouth and touch; taste and aroma were also assessed. The panelists were given plain paper to write descriptors, after which a discussion was held with all panelists and the team facilitating the activity. The panelists were requested to highlight the key terminologies they had used to evaluate the attributes of cooked cassava. The highlighted descriptors were written on flip charts that were pinned on the wall of the training room so that everybody could easily follow the discussion. The highlighted descriptors were carefully grouped into categories such as texture by hand, aroma, appearance, taste, and texture by mouth. In the end, after a series of discussions and evaluating a number of cassava clones, a catalogue of 21 descriptors for boiled cassava was developed.

At NaCRRI, activity four, a component of WP1, involved participatory processing of boiled cassava; this hinged on Step 2 that focused on gendered food mapping. Accordingly, key processing operations important for quality boiled cassava were identified by processors resident in Apac and Luweero districts in Uganda. Survey results indicated that the main processing steps were: peeling, trimming and steaming. However, there were variations in processing roots after trimming. For example, while sliced cassava roots in Luweero were wrapped in banana leaves and steamed, in Apac, sliced roots were immersed in water and boiled. Some consumers in Apac pounded boiled cassava slices into a mash. No significant differences in yield were observed amongst cassava genotypes. Nonetheless, some varieties like “Alanyo dyer” and “Nase 13” which were susceptible to CBSD, had low yield after processing. End-user attributes preferred during processing cassava roots in Apac were: white flesh colour, ease of peeling and a firm root; least preferred attributes included hard to peel, spongy in the middle, and roots which do not cook easily. On the other hand, preferred attributes in Luweero included: easy to peel, white flesh colour, no fibres on the surface of peeled root, a non-lignified/woody ('telina ekitti'), no bitter taste, smooth root surface, and a firm flesh; undesirable attributes included: lignified/woody root and spongy in the middle. This information was consequently incorporated into the consumer questionnaire that was used for Step 4.