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Background: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product., Results: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R 2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R 2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model)., Conclusion: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R 2  < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

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 (R 2 ) 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. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Background: Assessment of the key preferred quality traits in pounded yam, a popularly consumed yam food product in West Africa, is often done through sensory evaluation. Such assessment is time-consuming and results may be biased. Therefore, there is a need to develop objective, high-throughput methods to predict the quality of consumer-preferred traits in pounded yam. This study focused on how key quality traits in pounded yam proposed to yam breeders were determined, measured by biophysical and biochemical methods, in order to shorten the breeding selection cycle through adoption of these methods by breeders., Results: Consumer tests and sensory quantitative descriptive analysis (QDA) validated that preferred priority quality traits in pounded yam were related to textural quality (smooth, stretchable, moldable, slightly sticky and moderately hard) and color (white, cream or light yellow). There were significant correlations between sensory textural quality attributes cohesiveness/moldability, hardness, and adhesiveness/stickiness, with textural quality measurements from instrumental texture profile analysis (TPA). Color measurement parameters (L*, a*, and b*) with chromameter agreed with that of sensory evaluation and can replace the sensory panel approach. The smoothness (R 2  = 1.00), stickiness (R 2  = 1.00), stretchability (R 2  = 1.00), hardness (R 2  = 0.99), and moldability (R 2  = 0.53) of pounded yam samples can be predicted by the starch, amylose, and protein contents of yam tubers estimated by near-infrared spectroscopy., Conclusion: TPA and Hunter colorimeter can be used as medium-high throughput methods to evaluate the textural quality and color of pounded yam in place of the sensory panelists. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Roots, tubers and bananas (RTBs) contribute immensely to food security and livelihoods in sub-Saharan Africa, Asia and Latin America. The adoption of RTB genotypes in these regions relies on the interplay among agronomic traits, ease of processing and consumer preference. In breeding RTBs, until recently little attention was accorded key textural traits preferred by consumers. Moreover, a lack of standard, discriminant, repeatable protocols that can be used to measure the textural traits deter linkages between breeding better RTB genotypes and end user/consumer preferences. RTB products texture - that is, behaviour of RTB food products under unique deformations, such as disintegration and the flow of a food under force - is a critical component of these preferences. The preferences consumers have for certain product texture can be evaluated from expert sensory panel and consumer surveys, which are useful tools in setting thresholds for textural traits, and inform breeders on what to improve in the quality of RTBs. Textural characterization of RTBs under standard operating procedures (SOPs) is important in ensuring the standardization of texture measurement conditions, predictability of textural quality of RTBs, and ultimately definition of RTB food product profiles. This paper reviews current SOPs for the textural characterization of RTBs, including their various associated methods, parameters, challenges and merits. Case studies of texture characterized during development of SOPs and evaluation of texture of RTB populations are discussed, together with insights into key textural attributes and correlations between instrumental, sensory and consumer assessment of texture unique to various RTB food products. Hardness was considered a universal key textural attribute to discriminate RTBs. The review should provide adequate insight into texture of RTB food products and critical factors in their measurement. It aims to promote inclusion of texture in breeding pipelines by investigating which textural traits are prioritized by consumers, particularly since the inclusion of textural traits has recently gained prominence by breeders in improving RTBs. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)