A GA-BP method of detecting carbamate pesticide mixture based on three-dimensional fluorescence spectroscopy

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Determination of monophenolase activity based on backpropagation neural network analysis of three-dimensional fluorescence spectroscopy

Tyrosinase is pivotal for melanin formation. Measuring monophenolase activity is of great importance for both fundamental research and industrial applications. For the first time, a backpropagation (BP) artificial neural network with three-dimensional fluorescence spectroscopy was applied for the real-time determination of tyrosinase monophenolase activity. Principal component analysis (PCA) was utilized for the dimension reduction of three-dimensional fluorescence data. The four principal components served as inputs for the neural network. Network parameters were optimized using a genetic algorithm (GA). BP learning algorithm was applied to train the network model to determine tyrosine levels in a binary mixture containing tyrosine and L-DOPA without any chemical separation. The time course of tyrosine consumption by monophenolase was determined to calculate the initial velocity of the enzymatic reaction. The limit of detection of the monophenolase assay was 0.0615 U·mL^-1. This combined strategy of PCA, GAs, and BP artificial neural networks for three-dimensional fluorescence spectroscopy was efficient for the real-time and in-situ determination of monophenolase activity in a cascade reaction.

Assessment of kiwifruit firmness by using airflow and laser technique

Firmness is a valid and widely acknowledged indication of fruit quality that is directly connected to physical structure and mechanical qualities. The deformation signals of kiwifruit for firmness assessment were acquired using an assessment system based on airflow and laser technology in this investigation. Using partial least squares regression (PLSR), genetic algorithm optimization of bp neural network (GA-BP), and an extreme learning machine (ELM), deformation data from kiwifruit was used to create models of Magness-Taylor penetration firmness prediction. The ELM model outperformed the PLSR model, and GA-BP model in the prediction set, with a correlation coefficient of 0.876 and a root mean squared error of 3.576 N in the prediction set. These findings showed that an assessment system based on airflow and laser techniques can be utilized to assess the firmness of kiwifruit quickly and nondestructively.

A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Sufficient steps must be undertaken at various levels to monitor and ensure judicious use of pesticides. Development of prediction models for optimum use of pesticides, pesticide management, and their impact would be of great help in monitoring and controlling the ill effects of excessive use of pesticides. This paper aims to present an exhaustive review of the prediction models developed and modeling strategies used to optimize the use of pesticides.