Search

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: 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.)