Fishy Forensics: FT-NIR and Machine Learning based authentication of Mediterranean Anchovies (Engraulis encrasicolus) DOI Creative Commons
Nidhi Dalal, María Saiz, Antonio Giandonato Caporale

и другие.

Journal of Food Composition and Analysis, Год журнала: 2024, Номер 136, С. 106847 - 106847

Опубликована: Окт. 11, 2024

Язык: Английский

What the fish? Tracing the geographical origin of fish using NIR spectroscopy DOI Creative Commons
Nidhi Dalal,

Raffaela Ofano,

Luigi Ruggiero

и другие.

Current Research in Food Science, Год журнала: 2024, Номер 9, С. 100789 - 100789

Опубликована: Янв. 1, 2024

Food authentication is a growing concern with rising complexities of the food supply network, fish being an easy target fraud. In this regard, NIR spectroscopy has been used as efficient tool for authentication. This article reviews latest research advances on based The process from sampling/sample preparation to data analysis covered. Special attention was given spectra pre-processing and its unsupervised supervised analysis. Sampling important aspect traceability study samples chosen ought be true representative population. acquired often laden overlapping bands, scattering highly multicollinear. It needs adequate remove all undesirable features. technique can make or break model thus need trial-and-error approach find best fit. As spectral modelling, multicollinear nature demands (PCA) compact features before application multivariate techniques such LDA, PLS-DA, QDA etc. Machine learning modelling shown promising result in negates modelling.

Язык: Английский

Процитировано

8

Food fraud prevention strategies: Building an effective verification ecosystem DOI Creative Commons
Louise Manning, Andrew Macleod, Christian James

и другие.

Comprehensive Reviews in Food Science and Food Safety, Год журнала: 2024, Номер 23(6)

Опубликована: Окт. 8, 2024

Food fraud is an ever-present threat that regulators, food business operators (FBOs), and consumers need to be aware of, prevent where possible, address by developing mitigation strategies detect reduce its negative consequences. While extant literature focuses on detection, there less attention given prevention strategies, a knowledge gap this review seeks address. The aim of was consider food-related initiatives, understand what has worked well, develop series recommendations preventing fraud, both policy related for future research. Reactive (including intelligence based) detection dominates over especially financial, knowledge, time resources are scarce. First-generation tools have been developed vulnerability assessment, risk analysis, development strategies. However, examples integrated control management systems at FBO, supply chain, regulatory levels limited. lack hybrid (public/private) integration as well effective verification ecosystem, weakens existing plans. several emergent practice models prevention, they strengthened focus more specifically capable guardians target hardening. This work implications policymakers, Official Controls bodies, the industry, ultimately who seek consistently purchase safe, legal, authentic.

Язык: Английский

Процитировано

3

Integrating criminology, criminal justice, and crime science in conservation science and practice DOI
William D. Moreto, Richard L. Elligson

Conservation Biology, Год журнала: 2025, Номер 39(2)

Опубликована: Апрель 1, 2025

Abstract It has been argued that the integration of social sciences is crucial in understanding human dimensions conservation. Because conservation can include violation law and policies, criminology, criminal justice, crime science may prove useful for broader community by providing insight into factors influence behavior, how justice system functions, occurs be prevented. Fortunately, criminologists, scholars, scientists are increasingly conducting conceptual empirical research on conservation‐related topics. We devised a theory change demonstrates these fields integrated along with other sciences, enabling conditions foster practice.

Язык: Английский

Процитировано

0

FISH-FIT: A web-based tool to improve European seafood authenticity control DOI Creative Commons

Ute Schröder,

Cármen G. Sotelo,

Regina Klapper

и другие.

Food Control, Год журнала: 2025, Номер unknown, С. 111335 - 111335

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Fishy Forensics: FT-NIR and Machine Learning based authentication of Mediterranean Anchovies (Engraulis encrasicolus) DOI Creative Commons
Nidhi Dalal, María Saiz, Antonio Giandonato Caporale

и другие.

Journal of Food Composition and Analysis, Год журнала: 2024, Номер 136, С. 106847 - 106847

Опубликована: Окт. 11, 2024

Язык: Английский

Процитировано

1