Multiple adulterants detection in turmeric powder using Vis-SWNIR hyperspectral imaging followed by multivariate curve resolution and classification techniques

Hashemi-Nasab, Fatemeh Sadat; Talebian, Shakiba; Parastar, Hadi

doi:10.1016/j.microc.2022.108203

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…Florián-Huamán [ 151 ] used NIR-HSI (900–1710 nm) to detect nut shells in cumin, classifying pure and adulterated samples using SIMCA with an accuracy of 95% for test samples, while the PLSR model was able to successfully predict adulterant concentration. Visible and short wavelengths of near-infrared hyperspectral imaging (Vis-SWNIR-HIS; 400–1000 nm) have been proposed as a novel technique for turmeric authentication and multiple adulterants (corn flour, rice flour, starch, wheat flour, and zedoary) detection [ 152 ]. In this study, two multivariate resolution techniques of multivariate curve resolution–alternating least squares (MCR-ALS) and mean-field independent component analysis (MF-ICA) were used to extract pure spatial and spectral profiles of the components.DD-SIMCA and PLS-DA were used for the classification of authentic and adulterated samples.…”

Section: Methods For Detecting Spice and Herb Frauds And Adulterationsmentioning

confidence: 99%

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

Velázquez,

Rodríguez,

Hernández

et al. 2023

Foods

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

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Section: Methods For Detecting Spice and Herb Frauds And Adulterationsmentioning

confidence: 99%

Spice and Herb Frauds: Types, Incidence, and Detection: The State of the Art

Velázquez,

Rodríguez,

Hernández

et al. 2023

Foods

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“…NIRS combined with chemometrics can predict the adulteration level of ginger powder, Ganoderma lucidum spore powder, and honeysuckle [9][10][11]. And HSI combined with chemometrics has also been reported to successfully predict the adulteration concentration of oregano, turmeric powder, and saffron [12][13][14]. Additionally, the concepts of data fusion and image feature fusion have been proposed in the fields of food and medicine.…”

Section: Introductionmentioning

confidence: 99%

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Jiang,

Lv,

Zhong

et al. 2023

Foods

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Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

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