Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: a review

Open Access Article; Published online: 12 Aug 2020The review aimed to identify the different high‐throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high‐throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid‐infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping

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 hinders the adoption of new genotypes from the 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 Square (PLS) and Convolutional Neural Network (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. 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, while the CNN proved reliable and efficient method. With the application of deep learning method, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, could be predicted accurately using NIRS as a high throughput phenotyping method. This article is protected by copyright. All rights reserved

Contrasting effects of polysaccharide components on the cooking properties of Roots, Tubers and Bananas (RTBs)

Background: Consumer preferences for boiled or fried pieces of roots, tubers and bananas (RTBs) foodstuffs are mainly related to their texture. Different raw and cooked RTBs were physiochemically characterised to determine the effect of biochemical components on their cooking properties. Results: Firmness in boiled sweetpotato increases with sugar and amylose contents but no significant correlation was observed between other physicochemical characteristics and cooking behaviour. Hardness of boiled yam can be predicted by dry matter (DM) and galacturonic acid (GalA) levels. For cassava, no significant correlation was found between textural properties of boiled roots and DM, but amylose and Ca2+ content were correlated with firmness, negatively and positively, respectively. Water absorption of cassava root pieces boiled in calcium chloride solutions was much lower, providing indirect evidence that pectins are involved in determining cooking quality. A highly positive correlation between textural attributes and DM was observed for fried plantain, but no significant correlation was found with GalA, although frying slightly reduced GalA. Conclusion: The effect of main components on texture after cooking differs for the various RTBs. The effect of global DM and major components (i.e., starch, amylose) is prominent for yam, plantain and sweetpotato. Pectins also play an important role on the texture of boiled yam and play a prominent role for cassava through interaction with Ca2+

Characterising quality traits of boiled yam: texture and taste for enhanced breeding efficiency and impact

Open Access ArticleBackground: Boiled yam key quality attributes typical for West African consumers are: crumbly, easy to break, and sweet taste. New yam varieties are being developed but high or medium throughput tools to assess the required quality traits and their range of acceptance are limited. This study assessed the acceptance thresholds of these quality attributes and established the predictive models for screening yam varieties that meet the required consumers' preferences. Results: Overall liking was associated with sweet taste, crumbly and easy to break (r values 0.502, 0.291 and -0.087, respectively). These parameters and selected biophysical parameters highly discriminated the boiled yam varieties. Crumbly texture and easy to break were well-predicted by penetration force and dry matter, whereas sweet taste by dry matter and sugar intensity. A high crumbliness and sweet taste are preferred (sensory scores above 6.19 and 6.22 for crumbly and sweet taste, respectively, on a 10 cm unstructured line scale), while a too high easiness to break is disliked (sensory scores ranging from 4.72 to 7.62). Desirable biophysical targets were between 5.1 and 7.1 N for penetration force, dry matter around 39% and sugar intensity below 3.62 g/100g. Some improved varieties fulfilled the acceptable thresholds, and the screening was improved through the deviation from optimum. Conclusion: The acceptance thresholds and the deviation from optimum for boiled yam assessed through the instrumental measurements are promising tools for yam breeders. This article is protected by copyright. All rights reserved

Connecting Data for Consumer Preferences, Food Quality and Breeding in support of Market-oriented Breeding of Root, Tuber, and Banana Crops

The 5-year project ‘Breeding Roots, Tubers and Banana products for end user preferences’ (RTBfoods) focused on collecting consumers’ preferences on twelve food products to guide breeding programs. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based Target Product Profiles (TPP) were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the RTB breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymized, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology, along with the various methods of measurement used by the project, for labelling data in the databases. The development and application of Standard Operating Procedures, data templates and adapted trait ontologies improved the data quality and its format, enabling to link it to the studied plant material when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials