Quantification and artificial intelligence-based clustering of phenolic compounds in pearl millet genotypes

Introduction

Millets are annual grasses having small grains, belongs to various genera, mostly grown for food, feed and fodder in developing countries of Asia and Africa. Moreover, millets are able to grow in harsh environments where other crops have limited scope to grow. These have shorter growing season and good productivity under poor, dry soil and higher temperature. Millets have low carbon and water footprint, can be grown with minimal inputs on relatively poor soil (Das & Rakshit 2016) [6] . Considering these facts, United nations General Assembly accepted India's proposal to declare 2023 as "International year of millets" to increase awareness of millets contribution for nutrition and food security and, to improve quality of millets and its sustainable production. Sorghum and pearl millet are two most grown crops in millets (Crawford & Lee 2003; Poole & Kane-Potaka 2020) [5,15] . Nonetheless, pearl millet is an under-utilized, dryland coarse cereal crop that provides food and nutritional security in arid and semi-arid regions. Being one of the best crop for climateresilient agriculture, pearl millet is free from major insect attack and diseases, and it can be grown in harsh areas with drought, high salinity, low fertility, low pH and high temperature where maize and wheat are uncultivable (Goswami et al. 2020) [9] . It is also adaptable to harsh temperature conditions caused due to current climate change scenario. In India, it is mainly grown in western Rajasthan, Gujarat and Haryana. Livelihood of millions of third world people still depend on pearl millet. Being rich source of iron and zinc, it can be a lowcost solution for reduction of mineral deficiencies in millet consuming areas (Djanaguiraman et al. 2018; Krishnan and Meera 2018) [7,13] . Pearl millet grains have good functional properties because of its relatively higher fiber content, phytochemical and fatty acid composition. It has anticarcinogenic, antihypertensive, anti-inflammatory and antioxidant characteristics and reduces the risk of atherosclerosis and inflammatory bowel diseases (Vinutha et al. 2022) [20] . Phenolic compounds are one of the secondary plant metabolites and their type and content in grain depends on several factors such as the type of cereal, variety, part of the grain, climatic conditions and cultivation practices (Naczk and Shahidi, 2004) [14] . Phenolic compounds are natural plant secondary metabolites with a wide range of properties that have attracted a lot of attention in recent years. These compounds have several potential health benefits, including their antioxidant properties that protect cells from oxidative damage, and their ability to prevent chronic diseases such as cancer. They also play a role in promoting plant growth and tolerance to environmental stressors (Ali et al. 2022) [2] . The bran portion of cereal grains contains phenolic compounds, particularly phenolic acids, in free, soluble conjugated, and insoluble bound forms (Hemery et al. 2007) [12] . Polyphenols plays many bioactive roles in human body like it acts as free radical scavengers and also acts as inhibitors of low density lipoprotein but it also has some negative impact like it reduce food digestibility and bioavailability by precipitating digestive enzymes, dietary proteins and carbohydrates (Ram et al. 2020) [17] . Therefore, in current study quantification of six different phenolic compounds were performed using UPLC in 13 diverse pearl millet genotypes. In addition, artificial intelligence based analysis was carried out in the presented study for the following tasks: 1. Clustering of the genotypes to identify the genotypes for similar traits.

Materials and Methods

Plant materials: Pearl millets grains of 13 different genotypes were collected from Division of Genetics, Indian Agricultural Research Institute (New Delhi, India). Grains were milled sieved using 0.3 mm mesh sieve and obtained flour was stored in airtight container. Standards of quercetin, epicatechin, cinnamic acid, syringic acid, gallic acid and sinapic acid were purchased from Sigma Aldrich. All other chemicals, solvents and reagents were either HPLC grade or analytical grade.

Sample preparation

Extraction of phenolic compounds were carried out using method of Emmons et al., (1999) [8] . 1 gm fine pearl millet flour was defatted using n-hexane (1:5 w/v, 1 hour) at ambient temperature. After that 0.5 gram dry flour was mixed with 70% methanol and kept for overnight shaking at 300 rpm at 37 °C. Next day sonication of samples was carried out at high frequency (No. 6) for a minute and after that centrifuged at 10,000 rpm for 5 minutes, collected supernatant was filtered through 0.22 micron syringe filter.

Quantification of Phenolic compounds

The quantification of phenolic compounds was carried out using Acquity Ultra Performance Liquid Chromatograph (UPLC) with C 18 column. Mobile phase consisted of two phases, A-acetonitrile: water (20:80), B-acetonitrile: water (80:20) with 0.1% formic acid in each phase. A gradient programme with 0.2 mL/min flow rate, with 0-1 min/ 80% A, 1-6.0 min/ 40% A, 6-12 min/ 20%A, 12-15 min/ 80% A was used. Standard curves of standard compounds were prepared using 5, 10, 50, 100 and 200 ppm and phenolics compounds are quantified using the standard curves.

AI-based analysis

In order to find out the optimal number of clusters that can be formed with the target genotypes, first, the silhouette scores were captured with clusters 2-9. Later, K-means clustering was used with the obtained values of k (Number of clusters) using the phenolic contents of genotypes. For the denodogram generation, the Euclidean distance between the genotypes was taken into consideration to find the distances between genotypes and the graph was formulated.

For the PCA analysis, the first three PCAs were obtained and the variation captured by them and the largest components of the PCAs were noted. For the genotype ranking, the TOPSIS method was used with three weightage settings, to obtain different ranks for different scenarios.

Results and Discussion

Quantification of Phenolic compounds

Pearl millet genotypes showed variability in phenolic compounds composition and quantity. Based on the commercial availability, six phenolic compounds, namely cinnamic acid, epicatechin, gallic acid, quercetin, sinapic acid, and syringic acid, were quantified in pearl millet genotypes (Table 1). Mass spectra of these six compounds are shown in Fig. 1. Out of six phenolic compounds, three compounds i.e., quercetin, epicatechin and cinnamic acid were present in all 13 pearl millet genotypes. In GKD 3 quercetin (6.34 ppm), sinapic acid (53.05 ppm) and syringic acid (23.84 ppm) were reported in highest content.

Epicatechin was found highest in Sulkhaniya Bajra (424.39 ppm). Cinnamic was reported highest in Damodar Bajra (93.92 ppm). Jafrabadi showed maximum amount of gallic acid (741.05 ppm). All these phenolic acids has potential nutraceutical value exhibit antioxidant, antimicrobial property (Abd El-Raouf et al. 2015) [1] , anticancer (Wang et al. 2019) [21] , neuroprotective, anti-inflammatory and antidiabetic properties (Guo et al., 2019) [10] . Cinnamic acid prevent radical chain reactions by giving electrons to radicals that combine with each other and create stable products (Ugazio et al. 2008) [18] . Additionally, it serves as a fragrant component in detergents, flavourings, cosmetics, and toiletries (Yilmaz et al. 2018) [22] .

Dendrogram

Dendrogram shows the closeness of different genotypes with respect to phenolic content (Fig. 3). It showed that Dedha Bajra and Dhodasar Local are most closely related, it was due to similar content of quercetin, epicatechin, cinnamic acid, syringic acid and due to absence of gallic acid in both the genotypes. Chadhi Bajri showed relatedness with both Dedha Bajra and Dhodasar Local due to similar content quercetin, epicatechin, syringic acid and similar amount of sum of all phenolic compounds. After above genotypes Dhanshakti and Chanana Bajra 2 were closely related due to similar content of quercetin, epicatechin and both showed absence of syringic acid and gallic acid.

Feature wise plot

Feature wise plot shows the feature wise variation, here scatteredness of plot shows range of chemical content of each phenolic compounds with respect to various genotypes. Feature wise variation observed maximum in case epicatechin and gallic acid because both compounds had shown wide range in occurrence, epicatechin was observed from 13.097 to 424.393 ppm whereas gallic acid was observed from nil in some genotypes to 741.052 ppm.

Acknowledgement

AcknowledgmentsThe authors duly acknowledge the financial assistance received from ICAR-Indian Agricultural Research Institute, New Delhi.

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