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Houngbo ME, Desfontaines L, Diman JL, Arnau G, Mestres C, Davrieux F, Rouan L, Beurier G, Marie-Magdeleine C, Meghar K, Alamu EO, Otegbayo BO, Cornet D. Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4915-4921. [PMID: 37400424 DOI: 10.1002/jsfa.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/07/2023] [Accepted: 07/04/2023] [Indexed: 07/05/2023]
Abstract
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 (R2), 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., R2 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 R2 < 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.
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Affiliation(s)
- Mahugnon Ezékiel Houngbo
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Lucienne Desfontaines
- INRAE, UR 1321 ASTRO Agrosystèmes tropicaux, Centre de recherche Antilles-Guyane, Petit-Bourg, France
| | - Jean-Louis Diman
- INRAE, UE 0805 PEYI, Centre de recherche Antilles-Guyane, Petit-Bourg, France
| | - Gemma Arnau
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | | | - Fabrice Davrieux
- CIRAD, UMR Qualisud, Univ Montpellier, Institut Agro, Avignon Université, Université de La Réunion, Montpellier, France
| | - Lauriane Rouan
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Grégory Beurier
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Carine Marie-Magdeleine
- INRAE, UR 0143 URZ Unité de Recherches Zootechniques, Centre de recherche Antilles-Guyane, Petit-Bourg, France
| | | | - Emmanuel Oladeji Alamu
- IITA, Food and Nutrition Sciences Laboratory, Lusaka, Zambia
- IITA, Food and Nutrition Sciences Laboratory, Ibadan, Nigeria
| | | | - Denis Cornet
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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Discrimination of Minced Mutton Adulteration Based on Sized-Adaptive Online NIRS Information and 2D Conventional Neural Network. Foods 2022; 11:foods11192977. [PMID: 36230054 PMCID: PMC9563429 DOI: 10.3390/foods11192977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/17/2022] Open
Abstract
Single-probe near-infrared spectroscopy (NIRS) usually uses different spectral information for modelling, but there are few reports about its influence on model performance. Based on sized-adaptive online NIRS information and the 2D conventional neural network (CNN), minced samples of pure mutton, pork, duck, and adulterated mutton with pork/duck were classified in this study. The influence of spectral information, convolution kernel sizes, and classifiers on model performance was separately explored. The results showed that spectral information had a great influence on model accuracy, of which the maximum difference could reach up to 12.06% for the same validation set. The convolution kernel sizes and classifiers had little effect on model accuracy but had significant influence on classification speed. For all datasets, the accuracy of the CNN model with mean spectral information per direction, extreme learning machine (ELM) classifier, and 7 × 7 convolution kernel was higher than 99.56%. Considering the rapidity and practicality, this study provides a fast and accurate method for online classification of adulterated mutton.
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