NIR spectroscopy-multivariate analysis for rapid authentication, detection and quantification of common plant adulterants in saffron (Crocus sativus L.) stigmas

doi:10.1016/j.lwt.2020.109032

LWT

Volume 122, March 2020, 109032

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Highlights

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Class analogy models effectively allowed authentication of saffron.
•
Partial least squares discriminant analysis discriminated adulterated samples at low levels of 1 g/100 g.
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Partial least squares regression could quantify adulteration with LOD as low as 0.5 g/100 g.
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Variable importance to projection method revealed most important variables for correct prediction.

Abstract

The presented work discusses the development of a rapid and precise analytical protocol using near infrared spectroscopy combined with multivariate data analysis to authenticate, detect and quantify most of the commonly encountered plant adulterants used in fraud of saffron stigmas including safflower, pomegranate fruit peel, calendula flower, paprika, curcuma, hibiscus, saffron stamens and exhaustively-extracted saffron stigmas. A Soft Independent Modelling of Class Analogies (SIMCA) model was constructed for authentication of saffron stigmas with 100% sensitivity and a Partial Least Squares-Discriminant Analysis (PLS-DA) model was successfully utilized for correct discrimination of unadulterated and intentionally adulterated saffron samples as it showed 100% sensitivity and 99% specificity. Quantitation of the amount of each individual adulterant was achieved through construction of partial least squares regression (PLSR) models accompanied by variable importance to projection (VIP) method for variable selection which revealed that bands in the spectral ranges 6000-5800 cm⁻¹ followed by 4600–4200 cm⁻¹ and 5400-5000 cm⁻¹ were the most important for correct prediction with detection limits as low as 1%. The models performance was tested using internal and external validation sets indicating their reliability in providing a useful quality assessment tool for saffron in an attempt to prevent its fraud.

Introduction

Among 85 known spices in the world, saffron, the dried stigmas of Crocus Sativus L. family. Iridaceae, is the most high-priced one. It also appears as a top member on the list of the most precious agricultural products, for which reasons, saffron is non-surprisingly known as Red Gold (Husaini et al., 2009). Saffron flowers, with only three stigmas each, are used as food additive due to their aroma, color, and bitter taste (Singh, Singh, & Verma, 2010). Saffron has a long history of use in folk medicine and Ayurvedic health system as a sedative, expectorant, anti-asthma, emmenagogue, adaptogenic agent, and in various preparations for pain relief (Schmidt, Betti, & Hensel, 2007).

Being highly expensive, opened the window for cheaters to adulterate saffron with cheaper adulterants, constituting danger for customers (Petrakis, Cagliani, Tarantilis, Polissiou, & Consonni, 2017). Historically, saffron stigmas have been commonly adulterated with other plant materials possessing similar appearance. There have been several reports demonstrating that dried Crocus sativus stamens, Carthamus tinctorius L. petals (safflower), Calendula officinalis L. petals (calendula), Curcuma longa L. rhizomes, Hibiscus sabdariffa calyces (hibiscus), Capsicum annum fruit (paprika), and Punica granatum fruit peel (pomegranate) in addition to exhaustively extracted saffron stigmas are habitually experienced to be detected as incorporated materials used to fraud saffron (Dowlatabadi et al., 2017; Guijarro-Díez, Castro-Puyana, Crego, & Marina, 2017; Heidarbeigi et al., 2015; Mohamad & Shakeel, 2015; Petrakis, Cagliani, Polissiou, & Consonni, 2015; Petrakis et al., 2017; Schumacher, Mayer, Sproll, Lachenmeier, & Kuballa, 2016, p. 13; Varliklioz Er, Eksi-Kocak, Yetim, & Boyaci, 2017).

The ISO 3632–2 for grading saffron reports a standard UV–vis spectrophotometric method which has been used to detect adulteration of saffron with some of its common adulterants like safflower, turmeric, or calendula, but with considerably very high adulteration levels of about 200 mg/g (Petrakis & Polissiou, 2017; Sabatino et al., 2011).

Molecular methods are now the most widely used techniques for detection of saffron adulteration by materials of plant origin (Babaei, Talebi, & Bahar, 2014; Gul, Nasrullah, Nissar, Saifi, & Abdin, 2018). The main pitfalls of such methods include the high cost of sample preparation and inability to detect adulteration practices with plant extracts or dried powders, most likely due to the lack of DNA material that can be recovered (Soffritti et al., 2016). Several other methods are reported for saffron adulteration detection including High performance liquid chromatography (HPLC) hyphenated to photodiode array (PDA) and electrospray ionization mass spectrometry (ESI-MS) (Sabatino et al., 2011), electronic nose system coupled to multivariate statistical analysis (Heidarbeigi et al., 2015) and finally ¹H nuclear magnetic resonance (¹H NMR) metabolomics (Petrakis et al., 2015). Infrared (IR) spectroscopy has emerged as a lucrative time saving and non-destructive technique for quality assessment of foods and herbal products (Chen et al., 2016; Wilde, Haughey, Galvin-King, & Elliott, 2019). Indeed, Fourier transform mid infrared spectroscopy was implemented for the characterization of crocins and apocarotenoids, (Biancolillo & Marini, 2018; Tarantilis, Beljebbar, Manfait, & Polissiou, 1998). Adulteration of saffron with six plant materials; Crocus sativus stamens, calendula, safflower, turmeric, buddleja, and gardenia could distinctly be identified and evaluated in another study by mid-range infrared spectroscopy (4000–600 cm⁻¹) (Petrakis & Polissiou, 2017).

Meanwhile, the application of near infrared spectroscopy (NIRS) for quality control and detection of saffron adulteration is not yet well investigated. Previous reports include the application of NIRS for the determining of geographical origin as well as the chemical composition of saffron samples from the main producers' countries (Anastasaki et al., 2010; Zalacain et al., 2005). In another report, NIRS combined with multivariate analytical was utilized for quantitative determination of crocin I and II in saffron (Li et al., 2018). Previous utilization of two-dimensional infrared spectroscopy in conjunction with PCA was reported. However, near infrared (NIR) extracted data, together with micro-spectroscopic imaging and FT-IR were used only for evaluation of one common adulterant (i.e. safflower) (Chen, Zhou, & Sun, 2016).

In view of the above mentioned points, the aim of the presented work is to investigate the feasibility of applying near infrared spectroscopy combined to multivariate statistical analysis for rapid, and precise authentication of saffron as well as detection and quantification of adulteration by plant materials at low levels which is considered a challenging task. The study aims at designing a regularity quality control protocol that can be applied for the routine analysis of saffron stigmas.

Section snippets

Plant materials

A total of 50 saffron stigma samples were purchased from reputable companies and their botanical identity were confirmed by the Department of Industrial crops, Faculty of Agriculture, Alexandria University, Egypt. Details of the samples are listed in Table S1. The samples were identified through inspection of their macroscopical and microscopical features. Taxonomical validation through http://mpns.kew.org/mpns-portal/are available as supplementary material.

Exhaustively-extracted saffron

Results and discussion

A workflow of the establishment of the different multivariate models used in this work including SIMCA and PLS-DA classification models, PLSR models, pre-processing, variable selection, internal and external validation is given in Fig. 1.

Conclusion

The main useful target of this work is the development of a practical strategy for the authentication and detection of commercial adulteration of the expensive saffron stigmas by other cheaper plant material using NIR spectroscopy combined with multivariate data analysis. SIMCA class modeling technique was successfully applied for authentication of saffron while PLS-discriminant analysis could effectively discriminate authentic pure samples and intentionally adulterated ones as low as 10 mg/g

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Eman Shawky: Conceptualization, Validation, Formal analysis, Data curation, Investigation, Writing - review & editing, Visualization. Rasha M. Abu El-Khair: Validation, Data curation, Investigation, Writing - review & editing, Visualization. Dina A. Selim: Validation, Data curation, Investigation, Writing - review & editing, Visualization.

Declaration of competing interest

All authors of the manuscript entitled “NIR spectroscopy-multivariate analysis for rapid authentication, detection and quantification of common plant adulterants in saffron (Crocus sativus L.) stigmas “, declare that they have no conflict of interests.

References (40)

S. Babaei et al.
Developing an SCAR and ITS reliable multiplex PCR-based assay forsafflower adulterant detection in saffron samples
Food Control
(2014)
A. Biancolillo et al.
Chemometrics applied to plant spectral analysis
Comprehensive Analytical Chemistry
(2018)
J. bo Chen et al.
Adulteration screening of botanical materials by a sensitive and model-free approach using infrared spectroscopic imaging and two-dimensional correlation infrared spectroscopy
Journal of Molecular Structure
(2016)
I.G. Chong et al.
Performance of some variable selection methods when multicollinearity is present
Chemometrics and Intelligent Laboratory Systems
(2005)
R. De Maesschalck et al.
Decision criteria for soft independent modelling of class analogy applied to near infrared data
Chemometrics and Intelligent Laboratory Systems
(1999)
I.E. Frank et al.
Classification models: Discriminant analysis, SIMCA, CART
Chemometrics and Intelligent Laboratory Systems
(1989)
M. Guijarro-Díez et al.
Detection of saffron adulteration with gardenia extracts through the determination of geniposide by liquid chromatography–mass spectrometry
Journal of Food Composition and Analysis
(2017)
O.M. Kvalheim et al.
Simca - classification by means of Disjoint cross validated principal components models
Data Handling in Science and Technology
(1992)
C. Márquez et al.
FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud
Talanta
(2016)
C.S.W. Miaw et al.
Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars
Talanta
(2018)

E.A. Petrakis et al.

Evaluation of saffron (Crocus sativus L.) adulteration with plant adulterants by1H NMR metabolite fingerprinting

Food Chemistry

(2015)

E.A. Petrakis et al.

Sudan dyes in adulterated saffron (Crocus sativus L.): Identification and quantification by 1H NMR

Food Chemistry

(2017)

E.A. Petrakis et al.

Assessing saffron (Crocus sativus L.) adulteration with plant-derived adulterants by diffuse reflectance infrared Fourier transform spectroscopy coupled with chemometrics

Talanta

(2017)

P.A. Tarantilis et al.

FT-IR, FT-Raman spectroscopic study of carotenoids from saffron (Crocus sativus L.) and some derivatives

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

(1998)

A.S. Wilde et al.

The feasibility of applying NIR and FT-IR fingerprinting to detect adulteration in black pepper

Food Control

(2019)

H. Abdi et al.

Partial least squares methods: Partial least squares correlation and partial least square regression

Methods in Molecular Biology

(2013)

E. Anastasaki et al.

Differentiation of saffron from four countries by mid-infrared spectroscopy and multivariate analysis

European Food Research and Technology

(2010)

D. Ballabio et al.

Classification tools in chemistry. Part 1: Linear models. PLS-DA

Analytical Methods

(2013)

L. Bokobza

Origin of near-infrared absorption bands

L. Chen et al.

A novel strategy of profiling the mechanism of herbal medicines by combining network pharmacology with plasma concentration determination and affinity constant measurement

Molecular BioSystems

(2016)

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NIR spectroscopy-multivariate analysis for rapid authentication, detection and quantification of common plant adulterants in saffron (Crocus sativus L.) stigmas

Highlights

Abstract

Introduction

Section snippets

Plant materials

Results and discussion

Conclusion

Funding

CRediT authorship contribution statement

Declaration of competing interest

Food Control

Comprehensive Analytical Chemistry

Journal of Molecular Structure

Chemometrics and Intelligent Laboratory Systems

Chemometrics and Intelligent Laboratory Systems

Chemometrics and Intelligent Laboratory Systems

Journal of Food Composition and Analysis

Data Handling in Science and Technology

Talanta

Talanta

Food Chemistry

Food Chemistry

Talanta

Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

Food Control

Partial least squares methods: Partial least squares correlation and partial least square regression

Methods in Molecular Biology

Differentiation of saffron from four countries by mid-infrared spectroscopy and multivariate analysis

European Food Research and Technology

Classification tools in chemistry. Part 1: Linear models. PLS-DA

Analytical Methods

Origin of near-infrared absorption bands

A novel strategy of profiling the mechanism of herbal medicines by combining network pharmacology with plasma concentration determination and affinity constant measurement

Molecular BioSystems