Starch granule size and shape characterization of yam (Dioscorea alata L.) flour using automated image analysis.

BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light-scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open-source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 µm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open-source pipeline offers an accurate, robust and discriminating solution for medium-throughput phenotyping of yam starch granule size distribution and shape classification. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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

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.

Effects of NPK and biochar fertilized soil on the proximate composition and mineral evaluation of maize flour.

Series of farming practice methods have been employed to increase maize production but there is no adequate information on the effect of these methods on the nutritional and mineral content of organically grown maize. This study investigated the effects of inorganic and biochar fertilized soils on the proximate composition and mineral content of maize. Maize seeds were planted on organically fertilized soil (sawdust and gliricidia biochar), chemically fertilized soil Nitrogen Phosphorus and Potassium (NPK fertilizer), and soil without any amendment as control. The proximate compositions (protein, ash, crude fat, carbohydrate, and moisture) and mineral contents (Na, Mg, K, Ca, Fe, and Zn) of the maize flour samples were determined using standard methods. The results showed that protein content ranged from 4.58% to 7.24% (protein), ash 0.82% to 1.09%, crude fat 3.84% to 4.61%, moisture 9.76% to 10.60%, and carbohydrate 76.85% to 80.31%. There was no significant (p ≤ 0.05) difference among the proximate compositions except for protein and carbohydrate. Maize planted on NPK fertilized soil had the highest crude protein content of 7.24%. Other results obtained included sodium (55.65 mg/100 g), magnesium (35.87 mg/100 g), and iron (6.78 mg/100 g). Maize from soil without amendments was significantly higher than maize from NPK fertilized and biochar fertilized soils. Also, maize from control plot had the highest calcium content value of 48.95 mg/100 g. We concluded that maize planted with NPK fertilizer had higher nutrient than those planted with biochar application. Also, the mineral content of maize planted in control plot was higher than those on the amended soil.