Method optimization and validation for multi-class residue analysis in turmeric

Maestroni, Britt; Besil, Natalia; Rezende, Sofía; Liang, Ying; Gérez, Natalia; Karunarathna, Nuwan; Islam, Marivil; Heinzen, Horácio; Cannavan, Andrew; Cesio, María Verónica

doi:10.1016/j.foodcont.2020.107579

Cited by 14 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis of pesticides and antibiotics, the use of which is forbidden in organic agriculture, is a way to differentiate organic and conventional food, and has been recently applied to different matrices such as honey ( Lazarus et al, 2021 ) and spices ( Maestroni et al, 2021 ). However, this approach requires different analytical methods for different food commodities because the list of forbidden substances varies from one commodity to another.…”

Section: Introductionmentioning

confidence: 99%

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Fiamegos

Papoci

Dumitrascu

et al. 2021

Journal of Food Composition and Analysis

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Are the elemental fingerprints of organic and conventional food different? ED-XRF as screening technique

Fiamegos

Papoci

Dumitrascu

et al. 2021

Journal of Food Composition and Analysis

View full text Add to dashboard Cite

show abstract

“…The detection of variants of Sudan dye adulteration in different spices using high‐performance liquid chromatography (HPLC) and ODS III column system along with in‐line micro‐matrix solid‐phase dispersion (MMSPD) method has been presented in reference [3] and has shown about 99% recovery of analytes under optimum condition. A headspace gas chromatography coupled to ion mobility spectrometry‐based method has been explored by Maestroni 4 and also has shown significant performance in terms of recovery values. Application of Raman spectroscopy has also been reported in this context 5 where linear discriminant analysis (LDA) and partial least squares regression (PLSR) techniques have been used for classification and prediction purpose, respectively, and has shown significant performance.…”

Section: Introductionmentioning

confidence: 99%

A color channel based on multiple Random Forest coupled with a computer vision technique for the detection and prediction of Sudan dye‐I adulteration in turmeric powder

Mandal¹,

Chatterjee

Tudu

2021

Color Research & Application

View full text Add to dashboard Cite

Adulterants can cause different health hazards upon prolonged consumption, but it is difficult to detect with human eyes. Non‐destructive turmeric adulteration detection is a challenging research area. The existing adulteration detection processes are largely instrumental and analytical with high accuracy but include limitations like long testing time, expensiveness, and lack of mobility. This work reports a new computer vision framework, which can simultaneously detect the presence of adulteration and predict the possible percentage of adulteration addressing the stated limitations. The scope has been remained to screening of Sudan dye‐I adulteration in turmeric powder. An in‐house database prepared with images of pure and adulterated turmeric powder samples has been used for experimentation. Random Forest algorithm has been employed for both classification and prediction. The model has been validated with standard internal and external validation methods to assess the stability and generalization potential of the model to avoid over‐ and under‐fitting problems. The results of classification show that the presented framework can provide more than 99% accuracy in detection while high correlation coefficient (R2) value in the tune of .99 for prediction. The novelty of the work is its simple histogram‐based color feature extraction, development of ensemble Random Forest prediction model that resulted in high accuracy and development of a faster, non‐invasive, less‐expensive, and validated screening method for adulterated turmeric powder that can be considered as a potential immediate screening method in the supply chain of powdered spices prior to confirmatory testing methods following two‐tiered food fraud testing approach.

show abstract