AIMC Topic: Quality Control

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Screening of bioactive compounds and deep learning-driven quality control of Angong Niuhuang pills.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Angong Niuhuang Pills (AGNHP), a famous Chinese medicine compound preparation, is widely used to treat stroke and other brain disorders. However, owing to the complexity of herbal components and diversity of production...

A comparative analysis of machine learning approaches for predicting maturity in watermelon using acoustic and quality features.

Food chemistry
This study investigated the integration of acoustic and destructive quality features for machine learning-based classification of watermelon maturity into three categories: immature, mature, and over-mature. Nine machine learning algorithms were eval...

Integrating prior knowledge with deep learning for optimized quality control in corneal images: A multicenter study.

Computer methods and programs in biomedicine
OBJECTIVE: Artificial intelligence (AI) models are effective for analyzing high-quality slit-lamp images but often face challenges in real-world clinical settings due to image variability. This study aims to develop and evaluate a hybrid AI-based ima...

New insights in preanalytical quality.

Clinical chemistry and laboratory medicine
The negative impact of preanalytical errors on the quality of laboratory testing is now universally recognized. Nonetheless, recent technological advancements and organizational transformations in healthcare - catalyzed by the still ongoing coronavir...

Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review.

Journal of agricultural and food chemistry
Quality inspection of fruits and vegetables linked to food safety monitoring and quality control. Traditional chemical analysis and physical measurement techniques are reliable, they are also time-consuming, costly, and susceptible to environmental a...

Synergizing meat science and interpretable AI: Quantifying crispness gradients for quality authentication of Tilapia fillet processing.

Food chemistry
Crispy tilapia has become a popular aquatic product due to its unique texture and high market demand. However, fillets at different stages of crispness vary significantly in nutritional value and taste, directly affecting product quality and consumer...

Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies.

Food chemistry
Curcuma, a key ingredient in curry and a popular health supplement, has been subject to adulteration and fraudulent origin labeling. In this study, E-eye, Flash GC e-nose, and FT-NIR, combined with machine learning and multivariate algorithms, were e...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

Clinica chimica acta; international journal of clinical chemistry
A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of consistency on which details to present or to emphasise, and the ultimate report often reflects an indi...

Improving traceability and quality control in the red-meat industry through computer vision-driven physical meat feature tracking.

Food chemistry
Current traceability systems rely heavily on external markers which can be altered or tampered with. We hypothesized that the unique intramuscular fat patterns in beef cuts could serve as natural physical identifiers for traceability, while simultane...

Improvement of near-infrared spectroscopic assessment methods for the quality of Keemun black tea: Utilizing transfer learning.

Food research international (Ottawa, Ont.)
Keemun black tea, a renowned Chinese black tea, presents challenges in quality assessment due to variability in data across different years. To address this, we developed transfer learning algorithms using near-infrared spectral data. The qualitative...