Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

Autor: Houngbo ME; CIRAD, UMR AGAP Institut, Montpellier, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France., Desfontaines L; INRAE, UR 1321 ASTRO Agrosystèmes tropicaux, Centre de recherche Antilles-Guyane, Petit-Bourg, France., Diman JL; INRAE, UE 0805 PEYI, Centre de recherche Antilles-Guyane, Petit-Bourg, France., Arnau G; CIRAD, UMR AGAP Institut, Montpellier, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France., Mestres C; CIRAD, UMR Qualisud, Montpellier, France., Davrieux F; CIRAD, UMR Qualisud, Univ Montpellier, Institut Agro, Avignon Université, Université de La Réunion, Montpellier, France., Rouan L; CIRAD, UMR AGAP Institut, Montpellier, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France., Beurier G; CIRAD, UMR AGAP Institut, Montpellier, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France., Marie-Magdeleine C; INRAE, UR 0143 URZ Unité de Recherches Zootechniques, Centre de recherche Antilles-Guyane, Petit-Bourg, France., Meghar K; CIRAD, UMR Qualisud, Montpellier, France., Alamu EO; IITA, Food and Nutrition Sciences Laboratory, Lusaka, Zambia.; IITA, Food and Nutrition Sciences Laboratory, Ibadan, Nigeria., Otegbayo BO; DFST, Bowen University, Iwo, Nigeria., Cornet D; CIRAD, UMR AGAP Institut, Montpellier, France.; UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
Jazyk: angličtina
Zdroj: Journal of the science of food and agriculture [J Sci Food Agric] 2024 Jun; Vol. 104 (8), pp. 4915-4921. Date of Electronic Publication: 2023 Jul 31.
DOI: 10.1002/jsfa.12825
Abstrakt: 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.)
Databáze: MEDLINE
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