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

Mahugnon Ezékiel Houngbo, Lucienne Desfontaines, Jean-louis Diman, Gemma Arnau, Christian Mestres, Fabrice Davrieux, Lauriane Rouan, Grégory Beurier, Carine Marie-Magdeleine, Karima Meghar, et al.

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## To cite this version:

Mahugnon Ezékiel Houngbo, Lucienne Desfontaines, Jean-louis Diman, Gemma Arnau, Christian Mestres, et al. 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, inPress, 10.1002/jsfa.12825. hal-04218500

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(wileyonlinelibrary.com) DOI 10.1002/jsfa.12825

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

Mahugnon Ezékiel Houngbo,<sup>a,b</sup> Lucienne Desfontaines,<sup>c</sup> Jean-Louis Diman,<sup>d</sup>

Gemma Arnau,<sup>a,b</sup> Christian Mestres,<sup>e</sup> Fabrice Davrieux,<sup>f</sup>

Lauriane Rouan,<sup>a,b</sup> Grégory Beurier,<sup>a,b</sup> Carine Marie-Magdeleine,<sup>g</sup>

Karima Meghar,<sup>e</sup> Emmanuel Oladeji Alamu,<sup>h,i</sup> Bolanle O Otegbayoj<sup>i</sup> and
Denis Cornet<sup>a,b*</sup>

## 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.

**Keywords:** consumer acceptability; tuber quality; amylose content; near infrared spectrometry; convolutional neural network; high throughput phenotyping

<footnote>* Correspondence to: D Cornet, CIRAD Montpellier Centre, UMR Agap Institut, F-34398 Montpellier, France, E-mail: denis.cornet@cirad.fr</footnote>

<footnote>a CIRAD, UMR AGAP Institut, Montpellier, France</footnote>

<footnote>b UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote>c INRAE, UR 1321 ASTRO Agrosystèmes tropicaux, Centre de recherche Antilles-Guyane, Petit-Bourg, France</footnote>

<footnote>d INRAE, UE 0805 PEYI, Centre de recherche Antilles-Guyane, Petit-Bourg, France</footnote>

<footnote>e CIRAD, UMR Qualisud, Montpellier, France</footnote>

<footnote>f CIRAD, UMR Qualisud, Univ Montpellier, Institut Agro, Avignon Université, Université de La Réunion, Montpellier, France</footnote>

<footnote>g INRAE, UR 0143 URZ Unité de Recherches Zootechniques, Centre de recherche Antilles-Guyane, Petit-Bourg, France</footnote>

<footnote>h IITA, Food and Nutrition Sciences Laboratory, Lusaka, Zambia</footnote>

<footnote>i IITA, Food and Nutrition Sciences Laboratory, Ibadan, Nigeria</footnote>

<footnote>j DFST, Bowen University, Iwo, Nigeria</footnote>
# INTRODUCTION

Yam (*Dioscorea* spp.) is the staple food of many populations in the intertropical zone where it is grown. With an annual production of 72 million tons in 2019, it is the second most cultivated root and tuber crop in Africa, after cassava.¹ Yam is a starchy plant, which contains 20–40% dry matter,² of which about 80% is starch.³,⁴ It also provides other nutritional benefits such as proteins, lipids, vitamins, and minerals.⁵

Yam contributes to the food security of more than 60 million people,⁶,⁷ in west Africa,¹ where it is preferably consumed pounded, boiled, as a thick paste prepared from dried yam flour (*amala*), or fried.⁸⁻¹¹ Each type of yam food product requires specific quality attributes but texture attributes are important for any products. For boiled yam, friability is the most important criteria,¹² while for pounded yam, it must be stretchable, mold-able, smooth, and friable for the fingers and mouth,⁸⁻¹¹,¹³,¹⁴ and for *amala*, it should be elastic, soft, and non-sticky.¹⁵ Several studies have shown a link between starch structure and composition and the texture of the product. For *amala*, stickiness was associated with soluble amylose, starch gelatinization temperature and enthalpy changes.¹⁶ For pounded yam, its firmness could be explained by the dry matter, soluble starch, and amylose content.¹⁷

However, although we are beginning to have a clear idea of the traits involved in yam product quality,¹²,¹⁴,¹⁵ their measurement is often tedious and costly.¹⁸ It is critical for breeding programs to integrate quality traits into their selection scheme, so there is an urgent need to develop high-throughput phenotyping methods. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of roots and tubers.¹⁹ It has been used to calibrate the content of starch, sugar, protein, cellulose, ash and minerals in their flour.¹⁹⁻²² However, these studies could not establish a successful predictive model for amylose content in root and tuber plants.²³ Only Masithoh *et al.* (2020)²⁴ established a multi-product model to predict amylose content in canna, cassava, sweet potato, taro, and arrowroot, but not for yam.

The difficulty in predicting amylose content in starchy plants may arise because the vibrational spectrum of saccharides is significantly less specific than that of other biomolecules and their identification is therefore more complex.²⁵ The reference method used in NIRS chemometrics (the science of extracting information from chemical systems by data-driven means) is called the partial least squares (PLS) regression. This method relies on dimension reduction and focuses on a linear relation between spectral data and the variable of interest (e.g., amylose), which implies the loss of part of the information carried by the spectrum. Moreover, this linear method fails to model non-linear relationships. Recently, the convolutional neural network (CNN) has been introduced as a new method for spectral calibration.²⁶ A CNN is a type of deep-learning algorithm that has been used widely for image classification tasks but can also be applied to signal-processing problems.²⁷ The architecture of a CNN consists of multiple layers of artificial neurons that perform convolution operations on the input signal to extract and learn relevant features. The final layer of a CNN typically makes a prediction based on the learned features. Originally designed for image classification, CNNs have also been successful in various signal-processing tasks such as speech recognition, time series analysis, audio classification, and NIRS model calibration.²⁸ Convolutional neural networks allow all dimensions (i.e., wavelength absorbance) to be retained; it has

been demonstrated to be robust to noise, and it exhibits a performance gain in comparison with PLS.²⁶,²⁸⁻³⁰ Thus, this study aims to develop a NIRS model to predict yam amylose content by comparing respective PLS and CNN performance.

# MATERIALS AND METHODS

## Plant material and sample preparation

The diversity panel used in this study consisted of 186 yam flour samples from 21 different *Dioscorea alata* genotypes grown in Guadeloupe, France, in the 2016–2017 cropping season. After harvest, 2–5 tubers from each genotype were peeled, washed, and coarsely diced into 2 cm pieces. The fresh material was placed in an oven maintained at 60 °C for 5 days before being used to prepare the flours. The dry material was ground to 0.25 mm using an SM100 knife grinder (Retsch GmbH, Haan, Germany). A total of 93 tubers of 21 genotypes were harvested. The determination of amylose content by chemical analysis was carried out on three subsamples of flour from each yam tuber. The mean value was then taken as the amylose content per tuber. The NIRS measurement (collection of raw spectra) was carried out on two independent subsamples of flour per tuber in order to be representative of its heterogeneity, giving 186 (93 × 2) near infrared spectra.

## Chemical analysis

The amylose content was determined by the ISO 6647 reference method adapted by Mestres *et al.* (1997)³¹ without defatting the flour. A test portion of the flour was dispersed in a sodium hydroxide solution to an aliquot portion, to which an iodine solution was added. The absorbance of amylose and amylopectin from the colored complex formed was then determined using a UV-visible spectrophotometer (Cary 100, Agilent Technologies, Santa Clara, CA, USA) at 620 nm and 545 nm respectively. The mass fraction of amylose in the sample was then read from a calibration graph, prepared using a simple solution of potato amylose and amylopectin to account for the effect of amylopectin on the color of the amylose-iodine complex in the test solution.

The standard error of the laboratory (SEL) was defined as the standard error of variance between duplicates analyzed by the reference method:

$$SEL = \sqrt{\frac{1}{n} \sum_{i} (x_{1} - x_{2})^{2}} \quad (1)$$

where $x_1 - x_2$ is the difference between duplicate measurements by the reference method on sample i.

## Near infrared spectroscopy measurement

The NIR spectroscopy measures were carried out in the food processing laboratory of INRAE's Tropical Animal Research Unit, UR143, in Guadeloupe. Two replicates of yam flour samples were scanned with a FOSS-NIRSystems model 6500 scanning monochromator (FOSS-NIRSystems, Silver Spring, MD, USA) equipped with an auto cup. The spectroscopic procedures and data recording were conducted with ISIscan (TM) software (FOSS, Hillerød, Denmark). Each flour was placed in a small ring cup 36 mm in diameter and 9.7 mm in height, and reflectance spectra from 400 to 2500 nm were recorded at 2 nm intervals. Each spectrum represented the average of 32 scans.
## Statistical analysis

Before modeling, the yam tuber flour samples were separated into a calibration set (75%, i.e., 140 samples) and a validation set (25%, i.e., 46 samples) using the Kennard-Stone algorithm.³² The calibration set was used to train the models and the validation set to evaluate their accuracy and robustness. Prior to calibration, the raw spectra (output of the NIRS instruments) underwent some preprocessing, which involved mathematical transformations aiming to reduce noise associated with the spectrometer (e.g., model, age), environmental conditions (e.g., temperature), and sample preparation (e.g., particle size, humidity). Preprocessing of near-infrared (NIR) spectra is an essential part of improving the prediction performance or interpretation of multivariate calibrations. The main preprocessing methods,³³ were then applied to the raw spectra: the standard deviation for Gaussian kernel of orders 1-4 (Gaussian), the Savitzky-Golay transformation of orders 1-4 (SavGol), the multiplicative scatter correction (MSC), the standard normal variate (SNV), and the discrete forward and inverse wavelet transform of orders 1 and 2 (Haar). These transformations and their pairwise combinations allowed the generation of 157 different data sets.

For PLS, cross-validation was first used on the calibration set to identify the best combination of preprocessing and the optimal number of components that minimize the root mean square error (RMSE). The best model architecture was then calibrated again on the whole calibration set. This procedure was implemented using Python language (v3.8.5, https://www.python.org) with the scikit-learn 0.23.2 library.³⁴

The CNN model was implemented using Python language (v3.6, https://www.python.org) with a Keras framework (v2.1.5, https://keras.io/) and a TensorFlow backend (v1.6.0, https://www.tensorflow.org). For this model, the raw spectra, the

12 datasets from individual preprocessing, and those from their second-order combination were all merged and kept together for calibration as input data (i.e., 157 datasets). The convolutional neural network was composed of three convolutional layers followed by two dense layers. Mean squared error was used as the loss function. A 20% feature dropout was used between layers to prevent overfitting. Threefold cross-validation was used to calibrate the model.³⁵

The coefficient of determination ($R^2$), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were computed to assess the performances of both models using the following equations:

$$R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \quad (2)$$

where $y_i$ and $\hat{y}_i$ are respectively the observed and the predicted amylose content of the i-th sample, and $\bar{y}$ is the average observed amylose content

$$RMSE = \sqrt{\frac{\sum_{i=1}^{n} (y - \hat{y})^2}{n}} \quad (3)$$

$$RPD = \frac{SD}{RMSE} \quad (4)$$

where SD is the standard deviation of the observed amylose content in the calibration set.

According to Sinnaeve et al. (1994)³⁶ and Williams (2001),³⁷ RPD ≥3 was considered to be successful for analytical purposes in NIRS applications for agricultural products.

<table><caption>**Table 1.** Amylose content of the 21 *Dioscorea alata* L. genotypes</caption><thead><tr><th rowspan="2">Genotypes</th><th rowspan="2">Number of tuber used</th><th rowspan="2">Number of samples</th><th colspan="4">Amylose content (%)</th></tr><tr><th>Average</th><th>Standard deviation</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td>Divin</td><td>5</td><td>15</td><td>21.21</td><td>1.78</td><td>16.45</td><td>24.11</td></tr><tr><td>Dou</td><td>4</td><td>12</td><td>21.76</td><td>1.99</td><td>18.57</td><td>25.65</td></tr><tr><td>Florido</td><td>5</td><td>15</td><td>28.47</td><td>1.86</td><td>26</td><td>32.52</td></tr><tr><td>H2X14M</td><td>5</td><td>15</td><td>27.6</td><td>1.53</td><td>24.16</td><td>30.34</td></tr><tr><td>H2X74F</td><td>2</td><td>6</td><td>23.54</td><td>0.66</td><td>22.49</td><td>24.24</td></tr><tr><td>H4X172</td><td>5</td><td>15</td><td>25.47</td><td>1.19</td><td>22.75</td><td>26.56</td></tr><tr><td>Kabusa</td><td>5</td><td>15</td><td>19.33</td><td>2.22</td><td>12.62</td><td>21.85</td></tr><tr><td>Kinabayo</td><td>3</td><td>9</td><td>19.12</td><td>0.75</td><td>18.21</td><td>20.61</td></tr><tr><td>Marchande</td><td>5</td><td>15</td><td>22.28</td><td>2.03</td><td>20.29</td><td>26.74</td></tr><tr><td>Noulelcae</td><td>5</td><td>15</td><td>23.11</td><td>3.11</td><td>19.18</td><td>29.91</td></tr><tr><td>Nureangdan</td><td>3</td><td>9</td><td>22.6</td><td>0.32</td><td>21.95</td><td>23.13</td></tr><tr><td>Pacala</td><td>5</td><td>15</td><td>24.56</td><td>0.82</td><td>23.06</td><td>25.56</td></tr><tr><td>Peter</td><td>4</td><td>12</td><td>24.76</td><td>0.75</td><td>23.97</td><td>26.76</td></tr><tr><td>Plimbite</td><td>5</td><td>15</td><td>25.21</td><td>2.04</td><td>23.01</td><td>30.65</td></tr><tr><td>Ptris</td><td>5</td><td>15</td><td>24.11</td><td>0.98</td><td>22.78</td><td>26.6</td></tr><tr><td>Roujol</td><td>5</td><td>15</td><td>22.69</td><td>1.16</td><td>20.46</td><td>24.91</td></tr><tr><td>Sinoua</td><td>5</td><td>15</td><td>21.68</td><td>0.81</td><td>20.02</td><td>22.95</td></tr><tr><td>St Vincent</td><td>2</td><td>6</td><td>20.4</td><td>1.31</td><td>19.18</td><td>22.5</td></tr><tr><td>Tagabe</td><td>5</td><td>15</td><td>26.29</td><td>4.52</td><td>21.29</td><td>39.75</td></tr><tr><td>Tiviolet</td><td>5</td><td>15</td><td>24.01</td><td>1.4</td><td>20.85</td><td>25.95</td></tr><tr><td>ToufiTetea</td><td>5</td><td>15</td><td>26.81</td><td>0.86</td><td>25.35</td><td>27.8</td></tr></tbody></table>
## RESULTS

Table 1 presents the variation in the amylose content in the 21 *D. alata* L. genotypes. The amylose content of our samples varied from 12.62 to 39.75% for Kabusa and Tagabe respectively. The SEL of the amylose content reference method was 1.05%. The mean standard deviation within genotypes was 1.52% while the standard deviation of the mean amylose content between genotypes was 2.6%.

The descriptive statistics of the calibration and validation data are presented in Table 2. The calibration and validation datasets exhibited similar ranges, means, and standard deviations (Table 2).

Figure 1 illustrates the performance of each preprocessing combination after the optimal number of components had been identified. The best combination of preprocessing includes a first-order Gaussian (Gaussian 1) followed by a fourth-order Sav-Gol (SavGol 4) and retains 24 components. Globally the standard normal variate (SNV) filter seems to give good stable results as a first pretreatment.

Table 3 shows the performance of the two model types during the calibration and validation steps. The results show a coefficient of determination of 0.89 for the CNN model, which is better than those of the PLS approach (i.e., 0.72). The RMSE of validation of CNN and PLS are respectively 1.33 and 0.81. Their ratio of performance to deviation (RPD) are 2.13 and 3.49, respectively.

Figure 2 presents the measured and predicted values of the two modeling methods during the validation step. Sample scattering and regression line slope deviation from 1 (i.e., bias) is higher for the PLS model than the CNN model.

## DISCUSSION

The amylose content varies considerably from one genotype to another (Table 1) and the variation between genotype is higher than the mean variation within genotype. This suggests the existence of genetic variation, which is essential for the success of breeding programs. In contrast with the variation in amylose content found by Lebot and Malapa (2013)³⁸ (37.3–68.9%), this study

<table><caption>**Table 2.** Descriptive statistics of calibration and validation data</caption><thead><tr><th>Model step</th><th>Standard error of the laboratory</th><th>Mean</th><th>Standard deviation</th><th>Minimum</th><th>Maximum</th><th>Sample size</th></tr></thead><tbody><tr><td>Calibration</td><td>1.23</td><td>23.8</td><td>2.8</td><td>17.4</td><td>31.29</td><td>140</td></tr><tr><td>Validation</td><td>0.85</td><td>23.9</td><td>2.5</td><td>19.5</td><td>31.27</td><td>46</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Root mean square error (RMSE) of the partial least square (PLS) models calibrated over the 157 preprocessing combinations.</figcaption></figure>
<table><caption>**Table 3.** Performance metrics of the partial least square (PLS) and convolutional neural network (CNN) models during the calibration and validation steps</caption><thead><tr><th>Modeling step</th><th>Performance metric</th><th>PLS</th><th>CNN</th></tr></thead><tbody><tr><td rowspan="2">Calibration</td><td>R<sup>2</sup></td><td>0.85</td><td>0.99</td></tr><tr><td>RMSE</td><td>1.09</td><td>0.18</td></tr><tr><td rowspan="3">Validation</td><td>R<sup>2</sup></td><td>0.72</td><td>0.89</td></tr><tr><td>RMSE</td><td>1.33</td><td>0.81</td></tr><tr><td>RPD</td><td>2.13</td><td>3.49</td></tr></tbody></table>

Abbreviations: R², coefficient of determination; RMSE, root mean square error; RPD, ratio of performance to deviation.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Regression analyses comparing the observed and predicted amylose values by partial least square (PLS) and convolutional neural network (CNN) models during the independent validation step.</figcaption></figure>

(12.62–39.75%) exhibited values corresponding to the range commonly found in the literature for yam.⁴,¹⁸,³⁹⁻⁴²

The calibration results show that the preprocessing combination had a significant effect on the performance of the PLS (Fig. 1). Identification of the best preprocessing and the optimal number of components is thus essential for the performance of the PLS model. The use of CNNs makes it possible to overcome the limitations of PLS, namely the reduction in dimension, which often results in a loss of information, and the linearity of the model, which does not allow non-linear relationships between variables to be captured.

This study allowed us to predict the amylose content of yam starch with much higher accuracy than was obtained by Lebot and Malapa (2013)³⁸ (R² = 0.18) and Alamu et al. (2019)¹⁹ (R² = 0.27). The results also show better performance for the CNN (R² = 0.89) than the PLS model (R² of 0.72). The CNN model allowed the prediction of yam amylose content with great accuracy. Indeed, the RMSE of the CNN model was 0.81%.

The ratio of performance to deviation (RPD) can be used to qualify the usefulness of NIRS predictive models in breeding programs: a RPD above 3 is considered suitable for analytical purposes in NIRS applications for agricultural products.³⁶,³⁷ With an RPD of 3.49, the deep learning model provides a reliable and

efficient method for predicting amylose in yam. Near-infrared spectroscopy modeling with CNN offers a high-throughput phenotyping method for yam amylose content, which is considered as a key compositional trait linked to textural quality. However, external validation is still recommended to increase the developed models' accuracy and robustness. It should also be possible to apply the CNNs to other starch flours (cereals and root and tuber crops), either individually or within a meta-model such as that proposed by Masithot et al. (2020).²⁴ The complex architecture of the CNN, with multiple layers of non-linear transformations, allows it to capture complex relationships between inputs and outputs in heterogeneous data, such as multi-species models.

## AUTHOR CONTRIBUTIONS

Conceptualization: Denis Cornet and Lucienne Desfontaines. Data curation: Karima Meghar, Jean-Louis Diman, Gemma Arnau, and Denis Cornet. Formal analysis: Denis Cornet, Mahugnon Ezékiel Houngbo, Grégory Beurier, and Lauriane Rouan. Funding acquisition: Denis Cornet. Investigation: Mahugnon Ezékiel Houngbo, Jean-Louis Diman, Lucienne Desfontaines, Gemma Arnau, and Denis Cornet. Methodology: Denis Cornet, Fabrice Davrieux, Emmanuel Oladeji Alamu, and Christian Mestres. Project
administration: Denis Cornet and Fabrice Davrieux. Resources: Denis Cornet, Gemma Arnau, Carine Marie-Magdeleine. Supervision: Denis Cornet, Jean-Louis Diman, Bolanle O Otegbayo, Emmanuel Oladeji Alamu, and Fabrice Davrieux. Writing and original draft: Mahugnon Ezékiel Houngbo and Denis Cornet.

## ACKNOWLEDGEMENTS

The authors are grateful to Elie Nudol (CIRAD), Christophe Perrot (CIRAD), David Lange (INRAE) and Jocelyne Leinster (INRAE) for field work and sample preparation. The authors would also like to thank the Bill & Melinda Gates Foundation (BMGF) for grant opportunity OPP1178942: Breeding RTB Products for End User Preferences (RTBfoods).

## CONFLICT OF INTEREST

The authors declare no competing interests.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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