Photocatalytic degradation of drugs and dyes using a maching learning approach

RSC Advances, Journal Year: 2024, Volume and Issue: 14(13), P. 9003 - 9019

Published: Jan. 1, 2024

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior organic pollutants during catalytic degradation.

Language: Английский

---

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Foods, Journal Year: 2023, Volume and Issue: 12(24), P. 4461 - 4461

Published: Dec. 13, 2023

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms its microbiological quality. Predictive microbiology is field science that focuses on using mathematical models and computational techniques predict growth, survival, behaviour microorganisms other environments. This approach allows researchers, producers, regulatory bodies assess potential risks associated with microbial contamination spoilage, enabling informed decisions be made regarding safety, quality, life. Two-step one-step modelling approaches are primary secondary being used, while machine learning does not require describing quantitative microorganisms, leading spoilage products. comprehensive review delves into various have found applications predictive estimating By examining strengths, limitations, implications different approaches, this provides an invaluable resource researchers practitioners seeking enhance accuracy reliability predictions. Ultimately, deeper understanding these promises advance domain microbiology, fostering improved safety practices, reduced waste, heightened consumer confidence.

Language: Английский

---

A comprehensive survey on weed and crop classification using machine learning and deep learning

Artificial Intelligence in Agriculture, Journal Year: 2024, Volume and Issue: 13, P. 45 - 63

Published: June 26, 2024

Machine learning and deep are subsets of Artificial Intelligence that have revolutionized object detection classification in images or videos. This technology plays a crucial role facilitating the transition from conventional to precision agriculture, particularly context weed control. Precision which previously relied on manual efforts, has now embraced use smart devices for more efficient detection. However, several challenges associated with detection, including visual similarity between crop, occlusion lighting effects, as well need early-stage Therefore, this study aimed provide comprehensive review application both traditional machine learning, combination two methods, across different crop fields. The results show advantages disadvantages using learning. Generally, produced superior accuracy compared under various conditions. required selection right features achieve high classifying conditions consisting early growth effects. Moreover, precise segmentation stage would be cases occlusion. had advantage achieving real-time processing by producing smaller models than thereby eliminating additional GPUs. development GPU is currently rapid, so researchers often accurate identification.

Language: Английский

---

Effects of storage conditions and packaging materials on the postharvest quality of fresh Chinese tomatoes and the optimization of the tomatoes' physiochemical properties using machine learning techniques

LWT, Journal Year: 2024, Volume and Issue: 201, P. 116280 - 116280

Published: May 30, 2024

This study evaluated the impacts of varying storage temperatures and packaging materials on colour, enzymatic activity, phytochemical content, antioxidant properties Chinese tomatoes during storage. More so, machine learning (ML) optimization models were employed to predict optimize effects period, temperatures, tomatoes' physicochemical properties. According two-way ANOVA analysis, temperature impacted all parameters except L anthocyanin. Furthermore, demonstrated a substantial effect factors. The combined also measurements for ΔE. It was possible obtain optimized conditions storing using four constructed two different algorithms. findings from ML models, product at 4 °C with 85 % relative humidity (RH) results in higher-quality end than 25 °C. Additionally, majority that NPHDP packing material will typically produce are higher quality. is vital maintaining quality nutritional value throughout their postharvest.

Language: Английский

---

A comprehensive review to evaluate the synergy of intelligent food packaging with modern food technology and artificial intelligence field

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: July 22, 2024

Abstract This study reviews recent advancements in food science and technology, analyzing their impact on the development of intelligent packaging within complex supply chain. Modern technology has brought about packaging, which includes sensors, indicators, data carriers, artificial intelligence. innovative helps monitor quality safety. These innovations collectively aim to establish an unbroken chain safety, freshness, traceability, from production consumption. research explores components technologies focusing key indicators like time–temperature gas freshness pathogen ensure optimal product quality. It further incorporates various types including chemical biosensors, printed electronics, electronic noses. integrates carriers such as barcodes radio-frequency identification enhance complexity functionality this system. The review emphasizes growing influence looks at new advances intelligence that are driving making it better preserving how modern technologies, especially integration, revolutionizing for quality, reduced waste, enhanced traceability.

Language: Английский

---

Cracking the code of acrylamide and Nε-(carboxymethyl)lysine: Fish oil use and predictive strategies in potato chips during thermal processing

Food Chemistry, Journal Year: 2025, Volume and Issue: 473, P. 143034 - 143034

Published: Jan. 22, 2025

Language: Английский

---

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109382 - 109382

Published: Aug. 27, 2024

Language: Английский

---

Application of Machine Learning to Assess the Quality of Food Products—Case Study: Coffee Bean

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(19), P. 10786 - 10786

Published: Sept. 28, 2023

Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure preparing algorithm depends on correct preparation preprocessing set. set contained coded information (i.e., selected coefficients) based digital photos (input data) specific class bean (output data). Because training data tuning, adequate convolutional neural network (CNN) was obtained, which characterized by high recognition rate these beans at level 0.81 test Statistical analysis performed color RGB space model, made it possible accurately distinguish three distinct categories However, using Lab* became apparent that distinguishing between under-roasted properly roasted major challenge. Nevertheless, model successfully distinguished category over-roasted

Language: Английский

---

Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: March 25, 2024

Abstract Background Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms predicting under-five Nigeria and identify most relevant predictors. Methods The used nationally representative data from 2018 Demographic Health Survey. evaluated performance models such as artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, Logistic Regression using true positive rate, false accuracy, precision, F-measure, Matthew’s correlation coefficient, Area Under Receiver Operating Characteristics. Results found that can accurately predict mortality, with Forest Artificial Neural Network emerging best models, both achieving an accuracy 89.47% AUROC 96%. results show rates vary significantly across different characteristics, wealth index, maternal education, antenatal visits, place delivery, employment status woman, number children ever born, region be top determinants Nigeria. Conclusions findings suggest useful U5M high accuracy. emphasizes importance addressing social, economic, demographic disparities among population study’s inform policymakers workers about targeted interventions reduce

Language: Английский

---

Biosynthesis of biomolecules from saffron as an industrial crop and their regulation, with emphasis on the chemistry, extraction methods, identification techniques, and potential applications in human health and food: A critical comprehensive review

Biocatalysis and Agricultural Biotechnology, Journal Year: 2024, Volume and Issue: 59, P. 103260 - 103260

Published: May 29, 2024

Language: Английский

---