Lutein, violaxanthin, and zeaxanthin spectrophotometric quantification: A machine learning approach

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

“… [ 64 ] Optimization of total antioxidants from cashew apple bagasse ANN-GA ANN multilayer perceptron (MLP) based on feed-forward backpropagation was adopted, 70% data for training, 15% data for testing and remaining 15% for validation, 10 hidden neurons were selected which best determine the best results, trained using Levenberg-Marquardt backpropagation RSM parameters optimization (population type of double vector, size of 200, crossover function of 0.8, migration of forward migration, creation function of feasible population, fitness scaling function of rank, selection function of roulette wheel function, crossover function of scattered and mutation function of adaptive feasibility -Traditional “one-factor-at-a-time” approach is laborious and time consuming for the optimization process. -ANN model can solve non-linear multivariate tasks with better computational and mathematical techniques along with GA for the non-linear optimization formalism that further optimise the input variables of ANN models [ 65 ] Optimisation of total phenolic content from jujube ANN ANN MLP based on feed-forward backpropagation was adopted, 70% data for training, 15% data for testing and remaining 15% for validation, trained using Levenberg-Marquardt backpropagation, 5-fold cross-validation -ANN outperformed RSM in terms of superior properties and increased the workable suitability of the dataset [ 66 ] Spectrophotometric quantification of lutein, violaxanthin, and zeaxanthin from Chlorella vulgaris & Scenedesmus almeriensis ML model based on particle swarm optimiser-assisted partial least square regression (PSO-assisted PLS) PSO with swarm social parameter of 0.6, particle cognitive parameter of 0.6, inertia of the best value was modelled using random chaotic function, 80% data for training and remaining 20% for validation, best performance achieved after 50 iterations -PSO improved the flexibility for high dimensionality configurations, limiting cost, significantly reduce the delay in obtaining samples of carotenoids concentrations compared to liquid chromatography while maintaining adequate accuracy [ 67 ] Smartphone-based quantification of chlorophyll & carotenoids contents in olive and avocado oils MLR & LS-SVM (least squares support vector machine) MLR & LS-SVM where dataset divided into calibration set (75%, 104 samples) and test set (25%, 34 samples) using Kennard-Stone algorithm, performed cross validation using leave-one-out method LS-SVM Leveraged estimation type and radial basis function kernel, fit parameters were adjusted automatically, and additional parameters were standard ...…”

“…The data of various pigment concentrations along with their associated visible spectra obtained from HPLC were collected and were used to train the ML model based on particle swarm optimizer-assisted partial least square regression (PSO-assisted PLS). In total, seven feature models including one absorbance and six absorbance derivatives were obtained which leads to less time consumption for the quantification of carotenoid concentrations while retaining adequate accuracy [ 67 ]. In addition, de Carvalho and Nunes [ 68 ] proposed a calibration transfer approach based on digital images taken by smartphones to predict the levels of chlorophyll and carotenoids in olive and avocado oils.…”