Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning.

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R values above 0.

Factors Associated With Quality of Life Among Colorectal Cancer Patients: Cross-Sectional Study.

Introduction: Colorectal cancer is a chronic condition that affects a substantial proportion of the global population. Ensuring a satisfactory quality of life (QoL) for these patients is, therefore, of critical importance.

Objective: To examine the relationship between sociodemographic, economic, lifestyle, and health-related variables and quality of life in patients with colorectal cancer receiving treatment at a leading health institution in Medellín, Colombia.

Diagnosis and management of primary hyperoxalurias: best practices.

The primary hyperoxalurias (PH 1, 2, and 3) are rare autosomal recessive disorders of glyoxylate metabolism resulting in hepatic overproduction of oxalate. Clinical presentations that should prompt consideration of PH include kidney stones, nephrocalcinosis, and kidney failure of unknown etiology, especially with echogenic kidneys on ultrasound. PH1 is the most common and severe of the primary hyperoxalurias with a high incidence of kidney failure as early as infancy.

Development of a Novel HS-GC/MS Method Using the Total Ion Spectra Combined with Machine Learning for the Intelligent and Automatic Evaluation of Food-Grade Paraffin Wax Odor Level.

The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box-Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%.

Detection and quantification of cocoa powder adulteration using Vis-NIR spectroscopy with chemometrics approach.

The rising demand for cocoa powder has resulted in an upsurge in market prices, leading to the emergence of adulteration practices aimed at achieving economic benefits. This study aimed to detect and quantify cocoa powder adulteration using visible and near-infrared spectroscopy (Vis-NIRS). The adulterants used in this study were powdered carob, cocoa shell, foxtail millet, soybean, and whole wheat.