Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection

Amirvaresi, Arian; Nikounezhad, Nastaran; Amirahmadi, Maryam; Daraei, Bahram; Parastar, Hadi

doi:10.1016/j.foodchem.2020.128647

Cited by 91 publications

(32 citation statements)

References 35 publications

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“…Recently, Amirvaresi et al [ 135 ] compared NIR and MIR spectroscopic techniques along with multivariate analyses for the detection of adulterants (i.e., C. sativus styles, safflower, calendula, and Rubia genus) in saffron samples. The authors examined the spectra of unadulterated samples obtained from each spectroscopic technique by classifying them with PCA according to their origin (warm and cold climate); they obtained a better prediction with NIR spectra.…”

Section: Ft-mir Spectroscopy Applications In Coffee Cocoa and High-pr...mentioning

confidence: 99%

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Castillejos-Mijangos

Acosta-Caudillo

Gallardo-Velázquez

et al. 2022

Foods

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Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

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Section: Ft-mir Spectroscopy Applications In Coffee Cocoa and High-pr...mentioning

confidence: 99%

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Castillejos-Mijangos

Acosta-Caudillo

Gallardo-Velázquez

et al. 2022

Foods

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“…Near‐infrared reflection (NIR) spectroscopy is an effective tool in the agricultural quality detection domain (Amirvaresi, Nikounezhad, Amirahmadi, Daraei, & Parastar, 2021; Sun, Xu, & Yin, 2009). It has the advantages of fast, non‐destructive, high precision and simple operation.…”

Section: Introductionmentioning

confidence: 99%

Detection of apple varieties by near‐infrared reflectance spectroscopy coupled with SPSO‐PFCM

et al. 2022

J Food Process Engineering

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To accurately identify apple varieties on the assembly line and to improve the development of the apple quality detection, we proposed a possibilistic fuzzy c‐means (PFCM) algorithm based on similar particle swarm optimization (SPSO) for classifying near‐infrared spectroscopy (NIR) spectra. We took the objective function of PFCM as the fitness function of SPSO (SPSO‐PFCM), which reduced the dependence on fuzzy c‐means algorithm (FCM). The similarity between particles maintained the diversity of particles and then avoided premature convergence of particle swarm optimization (PSO). In the experiment, we classified four kinds of apples. SPSO‐PFCM was utilized to develop the classification model for the NIR spectra of apples. The results showed that SPSO‐PFCM had the highest clustering accuracy, which reached 96%. We also used two datasets to verify the algorithm, and the results proved that SPSO‐PFCM had the highest accuracy. Therefore, SPSO‐PFCM combined with NIR was an effective method to identify apple varieties. Practical Applications Different kinds of apples have various nutritional components and prices. Based on this, to identify apple varieties faster, correctly and nondestructively, SPSO‐PFCM and NIR reflectance spectroscopy were used to build a powerful classification model. The experimental results showed that compared with the other four algorithms, SPSO‐PFCM has apparent advantages. A novel method for apple classification of apple varieties using SPSO‐PFCM and NIR spectroscopy was introduced in this paper. With this method, the factory can classify apples quickly and accurately.

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“…The high price and scarcity of Luwak coffee trigger producers and retailers to fraud the product by mixing Luwak coffee with another cheaper coffee affecting its purity level; this also affects the coffee taste, quality, pH value, and antioxidant content. It often occurs in Indonesia as the third largest coffee producer after Brazil and Vietnam, and the case gradually increases in the food industry and becomes a serious problem to overcome to assure the quality for consumers' satisfaction (Amirvaresi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

JST

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Luwak coffee (palm civet coffee) is known as one of the most expensive coffee in the world.In order to lower production costs, Indonesian producers and retailers often mix highpriced Luwak coffee with regular coffee green beans. However, the absence of tools and methods to classify Luwak coffee counterfeiting makes the sensing method's development urgent. The research aimed to detect and classify Luwak coffee green beans purity into the following purity categories, very low (0-25%), low (25-50%), medium (50-75%), and high (75-100%). The classifying method relied on a low-cost commercial visible light camera and the deep learning model method. Then, the research also compared the performance of four pre-trained convolutional neural network (CNN) models consisting of SqueezeNet, GoogLeNet, and AlexNet. At the same time, the sensitivity analysis was performed by setting the CNN parameters such as optimization technique (SGDm, Adam, RMSProp) and the initial learning rate (0.00005 and 0.0001). The training and validation result obtained the GoogLeNet as the best CNN model with optimizer type Adam and learning rate 0.0001, which resulted in 89.65% accuracy. Furthermore, the testing process using confusion matrix from different sample data obtained the best

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Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection

Cited by 91 publications

References 35 publications

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Detection of apple varieties by near‐infrared reflectance spectroscopy coupled with SPSO‐PFCM

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