A robust set of qPCR methods to evaluate adulteration in major spices and herbs DOI Creative Commons
Marc Behr, Linda Garlant, Danilo Pietretti

et al.

Food Control, Journal Year: 2024, Volume and Issue: 165, P. 110623 - 110623

Published: June 3, 2024

Consumers and companies associated with food or pharmaceuticals rely on spices herbs in various forms. Their intricate supply chains, elevated prices, low-volume production render them vulnerable to fraudulent practices. However, comprehensive methodologies detect adulterants remain scarce, impeding national control laboratories from enforcing European legislation. In this study, we present quantitative real-time PCR (qPCR) methods designed identify the top five of each six commonly consumed herbs: paprika/chili, turmeric, saffron, cumin, oregano black pepper. The specificity method was confirmed by qPCR analysis a large collection relevant plant species. Each authentic sample combined its respective as identified Union-wide coordinated plan 2021 existing literature. These binary mixtures were used evaluate method's performance respect sensitivity, linearity trueness at four levels concentration. Detection also investigated multi-adulterated samples. SYBR™ Green-based enable specific detection adulterants, their sensitivity allows for distinction between inadvertent contamination deliberate adulteration. Altogether, these contribute safeguard authenticity high-value commodities.

Language: Английский

Recent trends of machine learning applied to multi-source data of medicinal plants DOI Creative Commons
Yanying Zhang, Yuanzhong Wang

Journal of Pharmaceutical Analysis, Journal Year: 2023, Volume and Issue: 13(12), P. 1388 - 1407

Published: July 25, 2023

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. particular, remarkable curative effect of Chinese during Corona Virus Disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal have, therefore, become increasingly popular among public. However, with increasing demand profit plants, commercial fraudulent events such adulteration or counterfeits sometimes occur, which poses a serious threat to clinical outcomes interests consumers. With rapid advances artificial intelligence, machine learning can be used mine information on various establish an ideal resource database. We herein present review that mainly introduces common algorithms discusses their application multi-source data analysis plants. The combination facilitates comprehensive aids effective evaluation quality findings this provide new possibilities promoting development utilization

Language: Английский

Citations

42
Spice authentication by near-infrared spectroscopy: Current advances, limitations, and future perspectives DOI
Eman Shawky, Lutfun Nahar,

Sarah M. Nassief

et al.

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 148, P. 104522 - 104522

Published: April 30, 2024

Language: Английский

Citations

11
A fast method for predicting adenosine content in porcini mushrooms using Fourier transform near-infrared spectroscopy combined with regression model DOI Creative Commons

Guangmei Deng,

Jieqing Li, Honggao Liu

et al.

LWT, Journal Year: 2024, Volume and Issue: 201, P. 116243 - 116243

Published: May 22, 2024

Adenosine is an endogenous neuroprotective agent. It of great importance to research the porcini mushrooms' adenosine for developing products. However, problems, such as old new and traditional methods detecting content are complicated time-consuming, seriously restrict industrial development. The present study aimed achieve a rapid quantification in mushrooms on market using Fourier transform near-infrared (FT-NIR) spectroscopy combined with partial least squares regression (PLSR) model. Herein, nucleoside spectral characteristics large-scale dataset (n=242) were analyzed, which was used calibration set constructing PLSR model had R2 C 0.907 residual predictive deviation (RPD) 2.726. For random samples different origins, P 0.768 RPD 1.326, storage period, 0.952 3.069, various collection years, 0.927 2.548. demonstrated that established method offers reliable prediction strategy samples, has potential be applied market.

Language: Английский

Citations

9
Green Identification of the Source of Pharmaceuticals by Near-Infrared (NIR) Spectroscopy and Chemometrics DOI
Hui Chen,

Cheng Tan,

Bin Cheng

et al.

Analytical Letters, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: Feb. 4, 2025

Language: Английский

Citations

1
Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis DOI
Qianyi Wu, Marciano M. Oliveira,

Eva M. Achata

et al.

Journal of Food Composition and Analysis, Journal Year: 2023, Volume and Issue: 120, P. 105324 - 105324

Published: April 6, 2023

Language: Английский

Citations

21
Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art DOI Creative Commons
Rocío Velázquez, Alicia Rodríguez, Alejandro Hernández

et al.

Foods, Journal Year: 2023, Volume and Issue: 12(18), P. 3373 - 3373

Published: Sept. 8, 2023

There is a necessity to protect the quality and authenticity of herbs spices because increase in fraud adulteration incidence during last 30 years. are several aspects that make quite vulnerable adulteration, including their positive desirable sensorial health-related properties, form which they sold, mostly powdered, economic relevance around world, even developing countries. For these reasons, sensitive, rapid, reliable techniques needed verify agri-food products implement effective prevention measures. This review highlights why highly valued ingredients, importance, official schemes authenticity. In addition this, type frauds can take place with have been disclosed, an overview scientific articles related based on Rapid Alert System Feed Food (RASFF) Web Science databases, respectively, years, carried out here. Next, methods used detect adulterants reviewed, DNA-based mainly spectroscopy image analysis being most recommended. Finally, available measurements for presented, future perspectives also discussed.

Language: Английский

Citations

17
Ability of visible imaging and machine learning in detection of chickpea flour adulterant in original cinnamon and pepper powders DOI Creative Commons

Mohammad Hossein Nargesi,

Kamran Kheiralipour

Heliyon, Journal Year: 2024, Volume and Issue: 10(16), P. e35944 - e35944

Published: Aug. 1, 2024

Adulteration detection in plant-based medicinal powders is necessary to provide high quality products due the economic and health importance of them. According advantages imaging technology as non-destructive tool with low cost time, present research aims evaluate ability visible combined machine learning for distinguish original adulterated samples different levels chickpea flour. The were black pepper, red cinnamon, adulterant was chick pea, adulteration 0, 5, 15, 30, 50 %. results showed that accuracies classifier based on artificial neural networks method classification cinnamon 97.8, 98.9, 95.6 %, respectively. support vector one-to-one strategy 93.33, 97.78 92.22 Visible are reliable technologies detect so can be applied develop industrial systems improving performance reducing operation costs.

Language: Английский

Citations

7
Data fusion based on near-infrared spectroscopy and hyperspectral imaging technology for rapid adulteration detection of Ganoderma lucidum spore powder DOI
Zhiwei Jiang,

Lingjiao Zhong,

Jiejie Xue

et al.

Microchemical Journal, Journal Year: 2023, Volume and Issue: 193, P. 109190 - 109190

Published: Aug. 11, 2023

Language: Английский

Citations

16
Comparing data driven soft independent class analogy (DD-SIMCA) and one class partial least square (OC-PLS) to authenticate sacha inchi (Plukenetia volubilis L.) oil using portable NIR spectrometer DOI Open Access

J.P. Cruz-Tirado,

Daniela Muñoz-Pastor,

Ingrid Alves de Moraes

et al.

Chemometrics and Intelligent Laboratory Systems, Journal Year: 2023, Volume and Issue: 242, P. 105004 - 105004

Published: Oct. 5, 2023

Language: Английский

Citations

13
Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights DOI
Che Shen, Ran Wang,

Hira Nawazish

et al.

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2024, Volume and Issue: 23(6)

Published: Nov. 1, 2024

Food fraud undermines consumer trust, creates economic risk, and jeopardizes human health. Therefore, it is essential to develop efficient technologies for rapid reliable analysis of food quality safety authentication. Machine vision-based methods have emerged as promising solutions the nondestructive authenticity quality. The Industry 4.0 revolution has introduced new trends in this field, including use deep learning (DL), a subset artificial intelligence, which demonstrates robust performance generalization capabilities, effectively extracting features, processing extensive data. This paper reviews recent advances machine vision various DL-based algorithms authentication, DL lightweight DL, used such adulteration identification, variety freshness detection, identification by combining them with system or smartphones portable devices. review explores limitations challenges include overfitting, interpretability, accessibility, data privacy, algorithmic bias, design deployment DLs, miniaturization sensing Finally, future developments field are discussed, development real-time detection systems that incorporate combination expansion databases. Overall, techniques expected enable faster, more affordable, accurate authentication methods.

Language: Английский

Citations

5