Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation

Abstract

Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed species require a thorough investigation of biochemical characteristics prior to their valorisation. Abundantly available Australian seaweed species lack such comprehensive investigations within the global seaweed industrial value chains. Aiming to bridge this gap, this study characterises Phyllospora comosa thallus segments (blades, stipes, and vesicles) and unsegmented samples collected from separate locations in Victoria, Australia using high throughput characterisation techniques and machine learning classification models. Carbohydrate (64–68 %), ash (27–31 %), potassium (31.01 – 65.01 mg/g), sodium (20.36 – 30.59 mg/g), calcium (15.10 – 18.40 mg/g), magnesium (7.71 – 11.81 mg/g) and iodine (1.57 – 2.74 mg/g) were the most abundant nutrients of the P. comosa biomasses, on a dry weight basis. Variations between segments showed that stipes were rich in carbohydrate, blades in glutamic acid, calcium, magnesium, and iodine and vesicles in potassium, suggesting differing valorisation paths. The “rpart” classification separated the collection sites based on cadmium: Bancoora $<$ 84.9 x 10^-6 mg/g (dw) $\le$ Port Fairy with a 88 % accuracy and segments, initially based on glutamic acid $:$ blades $\ge$ 10.61 mg/g (dw) or protein 45.25 mg/g (dw) $>$ stipes and vesicles and then by potassium $:$ vesicles $\ge$ 44.88 mg/g (dw) $>$ stipes with a 100 % accuracy. These highly accurate characterisation and classification methods, when applied to larger sample sizes will assist in the diversification and expansions of authentic and sustainable Australian seaweed value chains.